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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210804T100000
DTEND;TZID=America/New_York:20210804T110000
DTSTAMP:20260422T230731
CREATED:20210727T225426Z
LAST-MODIFIED:20210727T225426Z
UID:5079-1628071200-1628074800@ece.northeastern.edu
SUMMARY:ECE PhD Dissertation Defense: Amirreza Farnoosh
DESCRIPTION:PhD Dissertation Defense: Unsupervised Learning of Low-Dimensional Dynamical Representations from Spatiotemporal Data \nAmirreza Farnoosh \nLocation: Zoom Link \nAbstract: Ever-improving sensing technologies offer a fast and accurate collection of large-scale spatiotemporal data\, recorded from multimodal sensors of heterogeneous natures\, in various application domains\, ranging from medicine and biology to robotics and traffic control. In this dissertation\, we propose frameworks for learning the underlying representation of these data in an unsupervised manner\, tailored towards several emerging applications\, namely indoor navigation and mapping\, neuroscience hypothesis testing\, time series forecasting\, 3D motion segmentation\, and human action recognition.\nAs such\, (1) we developed an unsupervised framework for real-time depth and view-angle estimation from an inertially augmented video recorded from an indoor scene by employing geometric-based machine learning and deep learning models. (2) We introduced a hierarchical deep generative factor analysis framework for temporal modeling of neuroimaging datasets. Our model approximates high dimensional data by a product between time-dependent weights and spatially-dependent factors which are in turn represented in terms of lower dimensional latents. This framework can be extended to perform clustering in the low dimensional temporal latent or perform factor analysis in the presence of a control signal. (3) We developed a deep switching dynamical system for dynamical modeling of multidimensional time-series data. Specifically\, we employ a deep vector auto-regressive latent model switched by a chain of discrete latents to capture higher-order multimodal latent dependencies. This results in a flexible model that (i) provides a collection of potentially interpretable states abstracted from the process dynamics\, and (ii) performs short- and long-term vector time series prediction in a complex multi-relational setting. (4) We developed a dynamical deep generative latent model for segmentation of 3D pose data over time that parses the meaningful intrinsic states in the dynamics of these data and enables a low-level dynamical generation and segmentation of skeletal movements. Our model encodes highly correlated skeletal data into a set of few spatial basis of switching temporal processes in a low-dimensional latent framework. We extended this model for human action recognition by decoding from these low-dimensional latents to the motion data and their associated action labels.
URL:https://ece.northeastern.edu/event/ece-phd-dissertation-defense-amirreza-farnoosh/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210802T140000
DTEND;TZID=America/New_York:20210802T150000
DTSTAMP:20260422T230731
CREATED:20210729T185822Z
LAST-MODIFIED:20210729T185822Z
UID:5087-1627912800-1627916400@ece.northeastern.edu
SUMMARY:ECE PhD Dissertation Defense: Yulun Zhang
DESCRIPTION:PhD Dissertation Defense: Deep Convolutional Neural Network for Image Restoration and Synthesis \nYulun Zhang \nLocation: Zoom Link \nAbstract: Image restoration and synthesis with deep learning play a fundamental role in the computer vision community. They are widely used on mobile devices (e.g.\, smartphones) or lead to billion-dollar startups. However\, how to design efficient deep convolutional neural networks (CNNs) to extract higher-quality deep CNN features for better image restoration and synthesis is still challenging. In this dissertation talk\, I will describe my recent works to enhance CNN features in the channel dimension or/and the spatial dimensions. First\, for image restoration\, I will briefly introduce our proposed residual dense network. Then\, I will introduce the residual in residual (RIR) structure to train very deep super-resolution networks. Such an RIR structure could also make the network learn more high-frequency information\, being critical for high-resolution output. Attention mechanism (e.g.\, channel attention and spatial attention) is further explored to highlight the features. Second\, for image synthesis\, I will introduce multimodal style transfer via graph cuts. I visualize the deep features and find the multimodal style representation. I then formulate the style matching problem as an energy minimization one\, which could be solved via graph cuts. As a result\, the transferred features contain spatially semantic information\, providing more visually pleasing stylized results. Besides\, we investigate image synthesis about texture hallucination with large scaling factors. We propose an efficient high-resolution hallucination network for very large scaling factors.
URL:https://ece.northeastern.edu/event/ece-phd-dissertation-defense-yulun-zhang/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210729T150000
DTEND;TZID=America/New_York:20210729T160000
DTSTAMP:20260422T230731
CREATED:20210727T191933Z
LAST-MODIFIED:20210727T191933Z
UID:5075-1627570800-1627574400@ece.northeastern.edu
SUMMARY:ECE PhD Proposal Review: Weite Zhang
DESCRIPTION:PhD Proposal Review: High Sensing-capacity Multi-dimensional-coded Millimeter-wave MIMO Imaging System \nWeite Zhang \nLocation: Microsoft Teams Link \nAbstract: Millimeter-wave (mm-wave) MIMO imaging systems have been explored to use more and more complicated radar waveforms to achieve advanced multiplexing and high-performance imaging. As the complexity of the radar waveform increases\, conventional systems inevitably suffer from higher design difficulty and cost. In spite of the radar waveform design\, existing mm-wave imaging systems are still suboptimal due to the fact that the sensing matrix is not tailored properly to achieve its maximum capacity\, which often results in large mutual information between successive measurements\, and limited imaging performance.\nAs the first contribution of this proposal\, high sensing-capacity mm-wave MIMO imaging systems with multi-dimensional-coding are built. In the first prototype\, a 70-77 GHz frequency-modulated continuous wave (FMCW) MIMO imaging system with massive channels is studied. To enhance the sensing-capacity\, a compressive reflector antenna (CRA) is added to perform randomized spatial wavefront coding to increase the measurement diversity. Both static and on-the-move experiments are carried out to show the functionality of the imaging system. In the second prototype\, an 81-86 GHz software-defined mm-wave MIMO imaging system is designed\, which makes use of cost-effective software-defined radios (SDRs) with mm-wave mixers. Due to the baseband flexibility of SDRs\, efficient orthogonal frequency-division multiplexing (OFDM) with binary phase coding is designed as the radar waveform to achieve simultaneous MIMO transmission\, where high receiving signal-to-noise ratio and spectrum efficiency are achieved. Again\, a CRA is designed and applied to increase the measurement diversity. Primary simulation and experimental results show good imaging performance with reduced side lobe effect.\nAs the second contribution of this proposal\, a material characterization method is developed\, which is vital in some important mm-wave imaging applications\, such as security screening\, where both object profile and material information are required for potential threats prediction. Specifically\, a Geometrical Optics (GO) forward model based on a reflectarray imaging system is developed. The GO forward model can be adapted to any other imaging systems as long as their geometrical configurations are known. Both simulations and experiments are performed to show the effectiveness and efficiency of the proposed material characterization method\, where the complex relative permittivity as well as a more accurate shape of the object is retrieved.
URL:https://ece.northeastern.edu/event/ece-phd-proposal-review-weite-zhang/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210728T140000
DTEND;TZID=America/New_York:20210728T150000
DTSTAMP:20260422T230731
CREATED:20210727T191651Z
LAST-MODIFIED:20210727T191651Z
UID:5073-1627480800-1627484400@ece.northeastern.edu
SUMMARY:ECE PhD Proposal Review: Bahare Azari
DESCRIPTION:PhD Proposal Review: Circular-Symmetric Correlation Layer based on FFT \nBahare Azari \nLocation: Zoom Link \nAbstract: Planar convolutional neural networks\, widely known as CNNs\, have been exceptionally successful in many computer vision and machine learning tasks\, such as object detection\, tracking\, and classification. The convolutional layers in CNN are characterized by pattern-matching filters that can identify motifs in the signal residing on a 2D plane. However\, there exists various applications in which we have signals lying on a curved manifold or an arbitrary collection of coordinates\, e.g.\, temperature and climate data on the surface of the (spherical) earth\, and 360-panoramic images acquired from LiDAR. In these applications\, we usually need our network to be equivariant/invariant to various transformations of the input\, i.e.\, as we transform the input according to a certain action of a group\, the output is respectively transformed (equivariance)\, or remains unchanged (invariance). The convolution layers are empirically known to be invariant to small translations of their input image\, but they are not completely immune to relatively large translations Hence\, they may fail on the tasks that requires invariance to a specific transformation\, and and on the data that includes a wide range of that transformation. \nIn this work we consider equivariant/invariant tasks on 360-panoramic data. For a systematic treatment of analyzing the 360-panoramic data\, we propose a circular-symmetric correlation Layer (CCL) based on the formalism of roto-translation equivariant correlation on the continuous group constructed of the unit circle and the real line. We implement this layer efficiently using the well-known Fast Fourier Transform (FFT) and discrete cosine transform (DCT) algorithm. We discuss how the FFT yields the exact calculation of the correlation along the panoramic direction due to the circular symmetry and guarantees the invariance with respect to circular shift. The DCT provides an improved approximation with respect to transnational symmetry compared to what we observe in CNNs. We demonstrated the invariance analysis of networks built with CCL on two benchmark datasets comparing the equivariance of neural networks adopting CCL layers and regular CNN. Then\, we showcase the performance analysis of a general network equipped with CCL on recognition and classification tasks\, such as panoramic scene change detection\, 3D object classification\, LIDAR Semantic Segmentation.
URL:https://ece.northeastern.edu/event/ece-phd-proposal-review-bahare-azari/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210723T100000
DTEND;TZID=America/New_York:20210723T110000
DTSTAMP:20260422T230731
CREATED:20210707T005406Z
LAST-MODIFIED:20210707T005406Z
UID:5032-1627034400-1627038000@ece.northeastern.edu
SUMMARY:ECE PhD Dissertation Defense: Mahmoud Ibrahim
DESCRIPTION:PhD Dissertation Defense: Low-Power Integrated Circuit Design for Wireless Devices in the Internet of Things \nMahmoud Ibrahim \nLocation:  Zoom \nAbstract: Numerous integrated sensing devices are under development for wireless medical diagnostic and monitoring applications. However\, the data rates of wireless devices connected to the Internet of Things are limited and strongly depend on the available power. This research addresses the need for circuit-level design methods to enable higher data rates with lower power consumption in order to facilitate the proliferation of wireless devices that can overcome the speed-power conundrum. The potential applications include continuous-time monitoring of physiological signals\, where increased data rates imply the ability to exchange more information during the same time\, more accurate data\, and/or data from a greater number of sites associated with each wireless node.\nAn energy-efficient binary frequency shift keying (BFSK) transmitter architecture for biomedical applications is introduced as the first part of this dissertation research. To achieve low power consumption with higher data rates\, the novel transmitter architecture leverages image rejection techniques to generate each of the two tones of the transmitted BFSK signal while keeping the phase-locked loop (PLL) oscillator frequency unchanged\, and thus maintaining low PLL power and overall transmitter power. A fabricated prototype chip in 130nm complementary metal-oxide-semiconductor (CMOS) technology achieves data rates up to 10 Mbps while consuming 180 µW with up to -20 dBm output power according to Medical Implant Communication System (MICS) band requirements. The measurement results confirm state-of-the-art energy-efficient performance with 18 pJ/bit.\nAs a natural continuation of the first part of this research\, a complementary receiver architecture is described in the second part of this dissertation to provide full transceiver capabilities. The new receiver design approach takes advantage of the transmitted signal characteristics by using both the frequency information and phase information to demodulate the received digital bits. This design method results in improved sensitivity with reduced power consumption through relaxed receiver block specification requirements. The custom-designed receiver circuits include a new low-noise amplifier (LNA) topology for energy-efficient antenna impedance matching\, and a single mixer circuit that realizes the signal down-conversion with differential in-phase and quadrature-phase baseband output signals to circumvent the complexity associated with two mixers and to save power. Measurement results of the fabricated receiver in 65nm CMOS technology show a sensitivity of -82 dBm with an input signal at 10 Mbps centered around 416 MHz. With a power consumption of 610 µW and an energy efficiency of 61 pJ/bit\, this receiver architecture displays state-of-the-art performance with respect to data rate\, power and sensitivity compared to other receivers in the same frequency range.\nIn addition to the new transmitter and receiver architectures\, a large-signal transconductance linearization technique is presented as part of this dissertation research to extend the dynamic range of analog baseband filters. Furthermore\, a low-power sinusoidal signal generation technique is introduced and analyzed\, which is a versatile and essential component of the transmitter design approach.
URL:https://ece.northeastern.edu/event/ece-phd-dissertation-defense-mahmoud-ibrahim/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210719T113000
DTEND;TZID=America/New_York:20210719T123000
DTSTAMP:20260422T230731
CREATED:20210713T212719Z
LAST-MODIFIED:20210713T212719Z
UID:5046-1626694200-1626697800@ece.northeastern.edu
SUMMARY:ECE PhD Dissertation Defense: Berkan Kadioglu
DESCRIPTION:PhD Dissertation Defense: An Analysis of Algorithms with Discrete Choice Models \nBerkan Kadioglu \nLocation: Zoom Link \nAbstract: In the first half of our work\, we consider a rank regression setting\, in which a dataset of $N$ samples with features in $\mathbb{R}^d$ is ranked by an oracle via $M$ pairwise comparisons.\nSpecifically\, there exists a latent total ordering of the samples; when presented with a pair of samples\, a noisy oracle identifies the one ranked higher w.r.t. the underlying total ordering.\nA learner observes a dataset of such comparisons\, and wishes to regress sample ranks from their features.\nWe show that to learn the model parameters with $\epsilon > 0$ accuracy\, it suffices to conduct $M \in \Omega(dN\log^3 N/\epsilon^2)$ comparisons uniformly at random when $N$ is $\Omega(d/\epsilon^2)$.\nCompared to learning from class labels\, learning from comparison labels has two advantages: First\, comparison labels reveal both inter and intra-class information\, where class labels only contain the former.\nSecond\, comparison labels also exhibit lower variability across different labelers.\nThis has been observed experimentally in multiple domains\, including medicine \citep{campbell2016plus\,kalpathy2016plus\, stewart2005absolute} and recommendation systems \citep{schultz2004learning\,zheng2009mining\,brun2010towards\, koren2011ordrec}\, and is due to the fact that humans often find it easier to make relative\, rather than absolute\, judgements.\nMany works focusing on empirically learning comparison labels show excellent performance in practice \citep{tian2019severity\,yildiz2019classification}.\nOur work provides a theoretical foundation for analyzing and understanding this empirical performance.\nMoreover\, we extend the problem we initially study to a harder setting.\nWe do this by moving from pairwise comparisons to multi-way comparisons.\nFurthermore\, we study an online variant of the previous problem where the goal is to maintain high user engagement throughout the learning period.\nThis of course\, indirectly leads to the goal of learning parameters of the discrete choice model as accurately as possible\, fast.\nThis new problem is directly related to a setting in which a retailer recommends products to customers.\nA common problem in many recommendation tasks is to simultaneously learn the utilities of items to be recommended and maintain high user engagement.\nWe are generally constrained by a limit on the total number of items to be recommended at a time for an unknown time horizon.\nRecently\, bandit algorithms have been proposed for this setting where the multinomial logit model is assumed.\nBounds on error metrics are provided for upper confidence and Thompson sampling based algorithms.\nIn our paper\, we propose a variational inference based Thompson sampling algorithm and identify the required properties to achieve $\tilde O(D^{3/2}\sqrt T)$ worst-case regret.\nThrough extensive experiments we show that our method performs much better than the recently proposed \emph{TSMNL} algorithm in many error metrics.\nWe further accelerate our algorithm to be used in practical settings.
URL:https://ece.northeastern.edu/event/ece-phd-dissertation-defense-berkan-kadioglu/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210713T100000
DTEND;TZID=America/New_York:20210713T110000
DTSTAMP:20260422T230731
CREATED:20210706T174832Z
LAST-MODIFIED:20210706T174832Z
UID:5023-1626170400-1626174000@ece.northeastern.edu
SUMMARY:ECE PhD Dissertation Defense: Maher Kachmar
DESCRIPTION:PhD Dissertation Defense: Active Resource Partitioning and Planning for Storage Systems using Time Series Forecasting and Machine Learning Techniques \nMaher Kachmar \nLocation: Zoom \nAbstract: In today’s enterprise storage systems\, supported data services such as snapshot delete or drive rebuild can result in tremendous performance overhead if executed inline along with heavy foreground IO\, often leading to missing Service Level Objectives (SLOs). Moreover\, new classes of data services\, such as thin provisioning\, instant volume snapshots\, and data reduction features make capacity planning and drive wear-out prediction quiet challenging. Having enough free storage pool capacity available ensures that the storage system operates in favorable conditions during heavy foreground IO cycles. This enables the storage system to defer background work to a future idle cycle. Static partitioning of storage systems resources such as CPU cores or memory caches may lead to missing data reduction rate (DRR) guarantees. However\, typical storage system applications such as Virtual Desktop Infrastructure (VDI) or web services follow a repetitive workload pattern that can be learned and/or forecasted. Learning these workload pattern allows us to address several storage system resource partitioning and planning challenges that may not be overcome with traditional manual tuning and primitive feedback mechanism.\nFirst\, we propose a priority-based background scheduler that learns this pattern and allows storage systems to maintain peak performance and meet service level objectives (SLOs) while supporting a number of data services. When foreground IO demand intensifies\, system resources are dedicated to service foreground IO requests. Any background processing that can be deferred is recorded to be processed in future idle cycles\, as long as our forecaster predicts that the storage pool has remaining capacity. A smart background scheduler can adopt a resource partitioning model that allows both foreground and background IO to execute together\, as long as foreground IOs are not impacted\, harnessing any free cycles to clear background debt. Using traces from VDI and web services applications\, we show how our technique can out-perform a static policy that sets fixed limits on the deferred background debt and reduces SLO violations from 54.6% (when using a fixed background debt watermark)\, to only 6.2% when employing our dynamic smart background scheduler.\nSecond\, we propose a smart capacity planning and recommendation tool that ensures the right number of drives are available in the storage pool in order to meet both capacity and performance constraints\, without over-provisioning storage. Equipped with forecasting models that characterize workload patterns\, we can predict future storage pool utilization and drive wear-outs. Similarly\, to meet SLOs\, the tool recommends expanding pool space in order to defer more background work through larger debt bins. Overall\, our capacity planning tool provides a day/hour countdown for the next Data Unavailability/Data Loss (DU/DL) event\, accurately predicting DU/DL events to cover a future 12-hour time window.\nMoreover\, supported services such as data deduplication are becoming a common feature adopted in the data center\, especially as new storage technologies mature. Static partitioning of storage system resources\, memory caches\, may lead to missing SLOs\, such as the Data Reduction Rate (DRR) or IO latency. Lastly\, we propose a Content-Aware Learning Cache (CALC) that uses online reinforcement learning models (Q-Learning\, SARSA and Actor-Critic) to actively partition the storage system cache between a deduplicated data digest cache\, content cache\, and address-based data cache to improve cache hit performance\, while maximizing data reduction rates. Using traces from popular storage applications\, we show how our machine learning approach is robust and can out-perform an iterative search method for various data-sets and cache sizes. Our content-aware learning cache improves hit rates by 7.1% when compared to iterative search methods\, and 18.2\% when compared to traditional LRU-based data cache implementation.
URL:https://ece.northeastern.edu/event/ece-phd-dissertation-defense-maher-kachmar/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210707T170000
DTEND;TZID=America/New_York:20210707T180000
DTSTAMP:20260422T230731
CREATED:20210706T175131Z
LAST-MODIFIED:20210706T175131Z
UID:5027-1625677200-1625680800@ece.northeastern.edu
SUMMARY:ECE PhD Dissertation Defense: Kaidi Xu
DESCRIPTION:PhD Dissertation Defense: Can We Trust AI? Towards Practical Implementation and Theoretical Analysis in Trustworthy Machine Learning \nKaidi Xu \nLocation: Zoom Link \nAbstract: Deep learning has achieved extraordinary performance in many application domains recently. It has been well accepted that DNNs are vulnerable to adversarial attacks\, which raises concerns of DNNs in security-critical applications and may result in disastrous consequences. Adversarial attacks are usually implemented by generating adversarial examples\, i.e.\, adding sophisticated perturbations onto benign examples\, such that adversarial examples are classified by the DNN as target (wrong) labels instead of the correct labels of the benign examples. The adversarial machine learning aims to study this phenomenon and leverage it to build robust machine learning systems and explain DNNs.\nIn this talk\, I will present the mechanism of adversarial machine learning in both empirical and theoretical ways. Specifically\, a uniform adversarial attack generation framework\, structured attack (StrAttack) is introduced\, which explores group sparsity in adversarial perturbations by sliding a mask through images aiming for extracting key spatial structures. Second\, we discuss the feasibility of adversarial attacks in the physical world and introduce a convincing framework\, Expectation over Transformation (EoT). Utilize EoT with Thin Plate Spline (TPS) transformation\, we can generate Adversarial T-shirts\, a powerful physical adversarial patch for evading person detectors even if it could undergo non-rigid deformation due to a moving person’s pose changes. Third\, we stand on the defense side and design the first adversarial training method based on Graph Neural Network. Finally\, we introduce Linear relaxation-based perturbation analysis (LiRPA) for neural networks\, which computes provable linear bounds of output neurons given a certain amount of input perturbation. LiRPA studies the adversarial example in a theoretical way and can guarantee the test accuracy of a model by given perturbation constraints. The generality\, flexibility\, efficiency and ease-of-use of our proposed framework facilitate the adoption of LiRPA based provable methods for other machine learning problems beyond robustness verification
URL:https://ece.northeastern.edu/event/ece-phd-dissertation-defense-kaidi-xu/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210707T140000
DTEND;TZID=America/New_York:20210707T150000
DTSTAMP:20260422T230731
CREATED:20210706T175010Z
LAST-MODIFIED:20210706T175010Z
UID:5025-1625666400-1625670000@ece.northeastern.edu
SUMMARY:ECE PhD Proposal Review: Xiaolong Ma
DESCRIPTION:PhD Proposal Review: Towards Efficient Deep Neural Network Execution with Model Compression and Platform-specific Optimization \nXiaolong Ma \nLocation: Zoom \nAbstract: Deep learning or deep neural networks (DNNs) have become the fundamental element and core enabler of ubiquitous artificial intelligence. Recently\, with the emergence of a spectrum of high-end mobile devices\, many deep learning applications that formerly required desktop-level computation capability are being transferred to these devices. However\, executing DNN inference is still challenging considering the high computation and storage demands\, specifically\, if real-time performance with high accuracy is needed. Weight pruning of DNNs is proposed\, but existing schemes represent two extremes in the design space: non-structured pruning is fine-grained\, accurate\, but not hardware friendly; structured pruning is coarse-grained\, hardware-efficient\, but with higher accuracy loss. To solve the problem\, we propose a compression-compilation co-optimization framework\, which includes 1) a new dimension\, fine-grained pruning patterns inside the coarse-grained structures that achieves accuracy enhancement and preserve the structural regularity that can be leveraged for hardware acceleration\, 2) a pattern-aware pruning framework that achieves pattern library extraction\, pattern selection\, pattern and connectivity pruning and weight training simultaneously\, and 3) a set of thorough architecture-aware compiler/code generation-based optimizations\, i.e.\, filter kernel reordering\, compressed weight storage\, register load redundancy elimination\, and parameter auto-tuning for real-time execution of the mainstream DNN applications on the mobile platforms. Evaluation results demonstrate that our framework outperforms three state-of-the-art end-to-end DNN frameworks\, TensorFlow Lite\, TVM\, and Alibaba Mobile Neural Network with speedup up to 44.5x\, 11.4x\, and 7.1x\, respectively\, with no accuracy compromise. Real-time inference of representative large-scale DNNs (e.g.\, VGG-16\, ResNet-50) can be achieved using mobile devices.
URL:https://ece.northeastern.edu/event/ece-phd-proposal-review-xiaolong-ma/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210701T110000
DTEND;TZID=America/New_York:20210701T120000
DTSTAMP:20260422T230731
CREATED:20210623T211449Z
LAST-MODIFIED:20210623T211449Z
UID:5007-1625137200-1625140800@ece.northeastern.edu
SUMMARY:ECE PhD Dissertation Defense: Xianfeng Liang
DESCRIPTION:PhD Dissertation Defense: RF Magnetoelectric Microsystems \nXianfeng Liang \nLocation: Zoom Link \nAbstract: Multiferroic materials are the materials that inherently exhibit two or more ferroic properties\, such as ferroelectricity\, ferromagnetism and ferroelasticity\, etc. Magnetoelectric (ME) materials with coupled magnetization and electric polarization have attracted intense interests recently due to the realization of strong ME coupling and their key roles inME applications. Since the revival of thin-film ME heterostructures with giant ME coefficients\, a variety of multifunctional ME devices\, such as sensors\, inductors\, filters\, antennas etc. have been developed. Exciting progress has been made on novel ME materials and devices because of their high-performance ME coupling.\nIn this dissertation\, we will first show the properties of magnetostrictive (FeGaC and SmFe) and piezoelectric (ZnO)thin-film materials that are necessary for realizing strong ME coupling. A systematic investigation of the soft magnetism\, the change of modulus of elasticity with magnetization (delta-E effect)\, and microwave properties was carried out on FeGaC and SmFe thin films. We successfully developed the magnetostrictive FeGaC thin films with low coercive field of less than 1 Oe\, high saturation magnetization\, narrow ferromagnetic resonance (FMR) linewidth\, and an ultra-low Gilbert damping constant of 0.0027. A record high piezomagnetic coefficient of 9.71 ppm/Oe\, high saturation magnetostriction constant of 81.2 ppm\, and large delta-E effect of -120 GPa at 500 nm were achieved. ZnO films with high c-axis crystal orientation was also achieved by carefully optimizing the sputtering process parameters. These properties make them attractive materials for magnetoelectric and other voltage tunable RF/microwave device applications.\nAfter presenting the magnetostrictive and piezoelectric thin films and their static and dynamic properties\, we introduce the radio frequency (RF) ME microsystems. Mechanically driven antennas have been demonstrated to be the most effective method to miniaturize antennas compared to state-of-the-art compact antennas.The ME antennas based on a released magnetostrictive/piezoelectric heterostructure rely on electromechanical resonance instead of electromagnetic wave resonance\, which results in an antenna size as small as one-thousandth of an electromagnetic wavelength. Due to the strong ME coupling in thin-film ME heterostructures\, we proposed the ultra-compact MEMS ME antennas and improved their performance by using anchor designs\, array structure\, and SMR structure. These miniaturized robustME antennas can be implemented in numerous real-world applications such as internet of things\, wearable and bio-implantable devices\, smart phones\, wireless communication systems\, etc. The ME antennas\, with an overall dimension of 700 m×700 m (L×W)\, were designed to operate at a resonant frequency of 2 GHz and experimentally demonstrated a gain of -18.85 dBi. Furthermore\, we demonstrated highly sensitive integrated RF giant magnetoimpedance (GMI)sensors based on amplitude and phase sensitive mechanisms. The amplitude and phase magnetic noise levels were demonstrated to be 810pT /√Hz at 1000 Hz and 100pT /√Hz\, respectively.
URL:https://ece.northeastern.edu/event/ece-phd-dissertation-defense-xianfeng-liang/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210629T110000
DTEND;TZID=America/New_York:20210629T120000
DTSTAMP:20260422T230731
CREATED:20210623T211312Z
LAST-MODIFIED:20210623T211312Z
UID:5005-1624964400-1624968000@ece.northeastern.edu
SUMMARY:ECE PhD Dissertation Defense: Yaoshen Yuan
DESCRIPTION:PhD Dissertation Defense: Enhancing Monte Carlo Light Modeling Methods for the Development of Near-infrared Based Brain Techniques \nYaoshen Yuan \nLocation: Zoom Link \nAbstract: Studying light propagation in biological tissues is critical for developing biophotonics techniques and its applications. Monte Carlo (MC) method\, a stochastic solver for radiative transfer equation\, has been recognized as the gold standard for modeling light propagation in turbid media. However\, due to the stochastic nature of MC method\, millions even billions of photons are usually required to achieve accurate results using MC method\, leading to a long computational time even with the acceleration using graphical processing units (GPU).\nFurthermore\, due to the rapid advances in multi-scale optical imaging techniques such as optical coherence tomography (OCT) and multiphoton microscopy (MPM)\, there is an increasing need to model light propagation in extremely complex tissues such as vessel networks. The mesh-based Monte Carlo (MMC) is usually superior than the voxel-based MC method for such modeling since unlike grid-like voxels\, tetrahedral meshes can represent arbitrary shapes with curved boundaries. However\, the mesh density can be excessively high when the tissue structure is extremely complex\, resulting in high computational costs and memory demand.\nThe goal of this proposal is to focus on solving the challenges mentioned above. To tackle the first challenge\, we came up with a filtering approach with GPU acceleration to improve the signal-to-noise ratio (SNR) of the results while keeping the simulated photons low. The adaptive non-local means (ANLM) filter is selected to suppress the stochastic noise in MC results because 1) the filtering process on each voxel is mutually independent\, making it possible for parallel computing; 2) it has high performance in denoising and a strong capacity in edge-preserving.\nFor the second problem\, a novel method\, implicit mesh-based Monte Carlo (iMMC)\, was proposed to significantly reduce the mesh density. The iMMC utilizes the edge\, node and face of the tetrahedral mesh to model tissue structures with shapes of cylinder\, sphere and thin layer. The typical applications for edge\, node and face-based iMMC are vessel networks\, porous media and membranes\, respectively.\nLastly\, we applied MC simulations and aforementioned filter on segmented brain models derived from MRI neurodevelopmental atlas to estimate the light dosage for transcranial photobiomodulation (t-PBM)\, a technique for treating major depressive disorder using near infrared\, across lifespan. The iMMC simulation was also applied to evaluate the impact of human hair on the brain sensitivity for functional near-infrared spectroscopy (fNIRS). Furthermore\, a new approach that can improve the penetration depth in optical brain imaging as well as PBM is proposed. In this approach\, the possibility of placing light sources in head cavities is investigated using MC simulations. The preliminary results demonstrate a better performance in deep brain monitoring compared to the standard transcranial approach using 10-20 EEG positioning system.
URL:https://ece.northeastern.edu/event/ece-phd-dissertation-defense-yaoshen-yuan-2/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210622T140000
DTEND;TZID=America/New_York:20210622T150000
DTSTAMP:20260422T230731
CREATED:20210614T211638Z
LAST-MODIFIED:20210614T211638Z
UID:4986-1624370400-1624374000@ece.northeastern.edu
SUMMARY:PhD Dissertation Defense: Ala Tokhmpash
DESCRIPTION:PhD Dissertation Defense: Fractional Order Derivative in Circuits\, Systems\, and Signal Processing with Specific Application to Seizure Detection \nAla Tokhmpash \nLocation: Zoom Link \nAbstract: Epilepsy is a chronic brain disease that affects around 50 million people worldwide. This disease is characterized by recurrent seizures\, which are brief episodes of involuntary movement that may involve a part or the entire body and are sometimes accompanied by loss of consciousness. It is the third most common neurological disorder in the United States\, only after Alzheimer’s disease and stroke. Patients suffering from epilepsy\, a brain disorder\, can have more than one type of seizure. Seizure detection systems can be life-changing for patients with epileptic seizures. By accurately identifying the periods in which seizure occurrence has a higher chance of happening we can help epileptic patients live a more normal life. Prior works on automated seizure detection overwhelmingly either rely solely entirely on domain knowledge\, or instead use a black box deep learning model. This thesis aims to integrate machine learning techniques with available seizure detection methods to improve detection performance. In this process\, we take advantage of mathematical tools provided by fractional-order derivatives as well as fuzzy entropy concepts. Specifically\, 1) we show the effectiveness of fractional order methods (FOM) in representing signals with long-range dependencies 2) using case studies in control and power systems\, we further examine the performance of FOM in the presence of parameter uncertainty. 3) using two publicly available data sets of brain signals from multiple patients\, we develop a cohesive framework to leverage FOM for extracting features that can be then used by statistical learning methods. 4) following recent works in this field\, we generalize the notion of entropy to include the fractional-order case. Combined with the fuzzy sets describing the uncertainty in data\, we leverage fractional fuzzy entropy as a robust descriptor of the state of brain signals. Through these case studies\, we demonstrate a significant increase in performance accuracy compared to models that do not consider FOM.
URL:https://ece.northeastern.edu/event/phd-dissertation-defense-ala-tokhmpash/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210622T130000
DTEND;TZID=America/New_York:20210622T140000
DTSTAMP:20260422T230731
CREATED:20210617T174637Z
LAST-MODIFIED:20210617T174637Z
UID:4990-1624366800-1624370400@ece.northeastern.edu
SUMMARY:ECE PhD Dissertation Defense: Majid Sabbagh
DESCRIPTION:PhD Dissertation Defense: The Perils of Shared Computing: A Hardware Security Perspective \nMajid Sabbagh \nLocation: Microsoft Teams Link \nAbstract: Modern processors and hardware accelerators\, in the cloud or on the edge\, are capable of running multiple workloads from different users concurrently. Despite software techniques for security such as virtualization and containers\, a new attack surface is emerging that pertains to the hardware vulnerabilities of shared computing resources\, posing serious threats to shared computing. Fault attacks (FAs)\, Side-Channel Attacks (SCAs)\, and Transient-Execution Attacks (TEA) are three hardware-oriented attacks that target the system implementations. FAs aim to tamper the integrity of application execution through different fault injection methods\, to compromise the data or disrupt computation at run-time. SCAs exploit the information leakage of sensitive applications in physical parameters\, such as power consumption\, electromagnetic emanations\, and timing\, to breach the confidentiality of the application. TEAs exploit transient hardware operations such as speculative execution in Central Processing Units (CPUs) to tap on sensitive data temporarily and retrieve them from implicative microarchitectural states.\nIn this dissertation\, we investigate the three kinds of attacks that all exploit vulnerabilities due to shared computing. We first introduce a new non-invasive FA against Graphics Processing Units (GPUs)\, called overdrive fault attacks. We discover the security vulnerability of GPU’s voltage-frequency scaling (VFS) mechanism\, a common feature to balance power consumption and performance. An out-of-specification configuration of GPU voltage and frequency can be set by an adversary on the host CPU\, through the software interfaces to GPU’s power management units. This setting will cause timing violations for the computation and result in silent data corruptions (SDCs). We apply the overdrive fault attacks on two common victim applications. One is cryptographic applications accelerated by GPU. We launch a differential fault analysis (DFA) attack on an AES kernel running on an AMD RX 580 GPU and successfully recover the secret key. The other victim is convolutional neural network (CNN) inference. We thoroughly characterize fault injections and propagation in a CNN on a GPU and analyze the controllability of the attack. We successfully launch an end-to-end misclassification attack during CNN inferences with careful timing control.\nWe then evaluate a timing side-channel attack called Prime+Probe attack on CPUs and propose a Side-Channel Attack DEtection Tool (SCADET). SCADET is a methodology and a tool that operates on an x86 program’s binary. It records and analyzes the program’s memory accesses using dynamic binary instrumentation by running the program in a controlled environment to accurately identify the malicious access patterns demonstrated by the Prime+Probe attack.\nFinally\, we introduce an efficient hardware-level taint-tracking defense against the most prominent TEAs\, the speculative execution attacks. We take a secure-by-design approach and propose a mechanism called Secure Speculative Execution via RISC-V Open Hardware Design (SSE-RV)\, based on the latest Berkeley Out-of-Order Machine (SonicBOOM). We prototype our SSE-RV processor on an FPGA running a Linux operating system. Our results show that we can protect against Spectre-v1\, v2\, and v5. Our defense scheme is general and can be extended to protect against other transient execution attacks.
URL:https://ece.northeastern.edu/event/ece-phd-dissertation-defense-majid-sabbagh/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210618T090000
DTEND;TZID=America/New_York:20210618T100000
DTSTAMP:20260422T230731
CREATED:20210614T181136Z
LAST-MODIFIED:20210614T181136Z
UID:4983-1624006800-1624010400@ece.northeastern.edu
SUMMARY:PhD Dissertation Defense: Trinayan Baruah
DESCRIPTION:PhD Dissertation Defense: Improving the Virtual Memory Efficiency of GPUs \nTrinayan Baruah \nLocation: Zoom Link \nAbstract: GPUs have been adopted widely based their ability to exploit data-level parallelism found in modern-day applications\, ranging from high performance computing to machine learning. This widespread adoption has\, in part\, been accelerated by the development of more intuitive high-level programming languages\, efficient runtimes and drivers\, and easier mechanisms to manage data movement. Modern day GPUs and multi-GPU systems utilize virtual memory systems\, enabling programmers to access large address spaces that are beyond the physical memory limits a GPU. There mechanisms have built in mechanisms for memory translation\, sparing the programmer from having to reason about complex data-movement operations. Virtual memory support on a GPU includes both hardware and software support. At the hardware level\, translation lookaside buffers~(TLBs) are used to cache translations close to the compute units. At the software level\, the programming model supports a unified memory model which automates the movement of pages across multiple devices in a a system. Despite the improvements in programmability\, due to inefficiencies existing virtual memory management mechanisms\, including TLB management and page migration policies\, the performance obtained on today’s GPUs is sub-optimal.\nIn this dissertation\, we first identify the key challenges in virtual memory support for GPUs today. We then propose mechanisms to reduce the bottlenecks arising from virtual memory support at both a hardware level and at the runtime level. This allows GPUs to fully enjoy the benefits of virtual memory\, while ensuring high performance. We also develop simulation tools that enable researchers to explore new and novel virtual memory features in future single GPU and multi-GPU systems.\nTo enhance hardware support for virtual memory on a GPU\, we explore a mechanism that enables prefetching of page-table entries into the GPUs TLBs\, thereby reducing the number of TLB misses and improving performance. We also leverage the fact that many page-table entries can be shared across different GPU cores. We design a low-cost interconnect that enables sharing of page-table entries across the GPU cores. To improve the performance of unified memory on multi-GPU systems\, we propose a hardware/software mechanism that monitors accesses to each page\, and uses this information when making page-migration decisions. We also propose mechanisms to reduce the cost of TLB shootdowns on the GPU during page-migration in NUMA multi-GPU systems.
URL:https://ece.northeastern.edu/event/phd-dissertation-defense-trinayan-baruah/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210614T090000
DTEND;TZID=America/New_York:20210614T100000
DTSTAMP:20260422T230731
CREATED:20210602T181602Z
LAST-MODIFIED:20210602T181602Z
UID:4966-1623661200-1623664800@ece.northeastern.edu
SUMMARY:Robotics at Northeastern: A personal view of Autonomy Underwater\, On Land and with Aerial Systems
DESCRIPTION:This session will cover interconnected nature of autonomous vehicles on land\, underwater\, and in the air. Using examples from research at Northeastern\, it explores the interconnections of systems design\, application-driven robotics\, and the fundamental algorithms associated with simultaneous localization and mapping (SLAM)\, machine learning (ML)\, and computer vision and imaging as it applies to a variety of scientific and commercial settings. \nThis webinar will be lead by ECE Faculty Member Dr. Hanumant Singh. Event logistics are below: \nDate: June 14\, 2021 \nTime: 9:00am EST \nRegistration \n 
URL:https://ece.northeastern.edu/event/robotics-at-northeastern-a-personal-view-of-autonomy-underwater-on-land-and-with-aerial-systems/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210611T103000
DTEND;TZID=America/New_York:20210611T113000
DTSTAMP:20260422T230731
CREATED:20210609T174749Z
LAST-MODIFIED:20210609T174749Z
UID:4976-1623407400-1623411000@ece.northeastern.edu
SUMMARY:PhD Proposal Review: Rashida Nayeem
DESCRIPTION:PhD Proposal Review: Human Strategies in the Control of Complex Objects: A Task-Dynamic Approach with Clinical Applications \nRashida Nayeem \nLocation: Zoom Link \nPasscode: 247537 \nAbstract: Functional interaction with objects – tool use – is essential in daily living and is regarded as the foundation of our evolutionary advantage. Humans effortlessly interact with a variety of objects\, including those with complex internal dynamics. Even the simple action of picking up a cup of coffee to drink is a mechanically intricate process: the hand applies a force to the cup\, and indirectly to the liquid\, which exerts forces back on the hand. Reacting to and mitigating these dynamics in real time is difficult due to long sensorimotor delays and ubiquitous noise in the human sensorimotor system. Hence\, prediction is necessary to preempt undesired ‘sloshing’. But prediction of this nonlinear and potentially chaotic object is extremely difficult. Hence\, this research tests the hypothesis that humans learn to control the object to make dynamics simpler––or more predictable. Inspired by the task of transporting a ‘cup of coffee\,’ a series of experiments use a virtual and a real testbed that model the task as a cup-and-ball system. In all experiments\, human subjects move the cup with a rolling ball inside. Aim-1 investigates the effect of linearization on human control strategies in a 2D virtual task. Aim-2 examines if humans exploit initial conditions to facilitate predictable dynamics in the 2D virtual task. This is tested in subjects that are provided with either full sensory information\, or when deprived of visual or haptic information. Aim-3 examines how subjects explore and prepare the cup-and-ball in the same 2D task and a novel 3D virtual task introducing planar cup movements. The question is how subjects explore and transport objects that have different dynamic properties\, either unknown or indicated by the visual cues. The analysis adopts a task-dynamic approach that affords principled hypothesis-testing by parsing the complex dynamics into execution and result variables\, with minimal assumptions about the human controller. Aim-4 takes the insights from this basic research to a clinical context\, testing patients with stroke in this functional task. A real version of the cup-and-ball task was created to quantitatively assess severity and recovery of motor impairment in patients after stroke. Using the same analysis methods\, the objective is to sensitively assess impairment in the context of a functional skill.
URL:https://ece.northeastern.edu/event/phd-proposal-review-rashida-nayeem/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210610T153000
DTEND;TZID=America/New_York:20210610T163000
DTSTAMP:20260422T230731
CREATED:20210609T174644Z
LAST-MODIFIED:20210609T174644Z
UID:4974-1623339000-1623342600@ece.northeastern.edu
SUMMARY:PhD Proposal Review: Bernard Herrera
DESCRIPTION:PhD Proposal Review: Ferroelectric Micromachined Ultrasonic Transducers for Intra-body and In-memory Sensing Applications \nBernard Herrera \nLocation: Teams \nAbstract: Piezoelectric Micromachined Ultrasonic Transducers (PMUTs) are Micro Electro-Mechanical Systems (MEMS) devices that have become an established technology in applications such as range-finding\, fingerprint sensing and imaging due to their capability of ultrasonic transduction in a miniaturized footprint\, easily amenable to create large arrays. However\, their application space still remains quite open. PMUTs are well fitted to applications in liquid media\, such as implantable and underwater devices\, due to their inherent acoustic matching and wide bandwidth. Thus\, in the first part of this proposal\, we explore novel applications such as PMUT-based intra-body networking\, power transfer\, source localization\, wide-band matching and duplexing.\nAluminum Nitride (AlN) has been the material of choice for our PMUTs due to its biocompatibility and possibility of single-chip integration with supporting CMOS circuitry. Scandium doping of AlN thin films has recently been demonstrated to increase piezoelectric coupling coefficients while introducing ferroelectric properties in the material. However\, a simultaneous use of both capabilities has not been demonstrated in the state-of-the-art. The ability of having distinct ferroelectric states\, that alter the mechanical performance of the devices\, allows for in-memory sensing and actuation features and provides the building blocks for neuromorphic signal processing capabilities. The second part of the proposal explores the AlScN material integration into novel Ferroelectric Micromachined Ultrasonic Transducers (FMUTs) and their emerging application space.
URL:https://ece.northeastern.edu/event/phd-proposal-review-bernard-herrera/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210610T130000
DTEND;TZID=America/New_York:20210610T140000
DTSTAMP:20260422T230731
CREATED:20210510T185954Z
LAST-MODIFIED:20210510T185954Z
UID:4931-1623330000-1623333600@ece.northeastern.edu
SUMMARY:ECE PhD Proposal Review: Andac Demir
DESCRIPTION:PhD Proposal Review: Automated Bayesian Network Exploration for Nuisance-Robust Inference \nAndac Demir \nLocation: Zoom Link \nAbstract: A fundamental challenge in the analysis of physiological signals is learning useful features that are robust to nuisance factors e.g.\, inter-subject & inter-session variability\, and achieve the highest nuisance-invariant classification performance. Towards resolving this problem\, we introduce 2 frameworks: AutoBayes\, which is an AutoML approach to conduct neural architecture search for research prototyping\, and a GNN based framework: EEG-GNN.\nThe ultimate goal of the AutoBayes framework is to identify the conditional relationship between a physiological dataset\, associated task labels\, nuisance variations and potential latent variables in order to robustly infer the task labels invariant of nuisance factors. Nuisance factors in the case of physiological datasets could be variations in subjects or sessions\, but we only focus on subject variations in the experiments. AutoBayes enumerates all plausible Bayesian networks between data\, labels\, nuisance variations and potential latent variables\, detects and prunes the unnecessary edges according to Bayes-Ball Algorithm\, and then trains the resulting DNN architectures for different hyperparameter configurations in an adversarial/non-adversarial or a variational/non-variational setting to achieve the highest validation performance. Instead of hyperparameter tuning for model optimization\, AutoBayes concentrates on the architecture search of plausible Bayesian networks\, and achieves state-of-the-art performance across several physiological datasets. Furthermore\, we ensemble several Bayesian networks by stacking their posterior probability vectors in a higher level learning space\, train a shallow MLP as a meta learner\, and measure the task and nuisance classification performance on a hold-out dataset. We observe that exploration of different inference Bayesian networks has a significant benefit in improving the robustness of the machine learning pipeline\, and the parallel activity of vast assemblies of different Bayesian network models significantly reduces variation across subjects in the cross-validation setting.\nIn the second part of the proposal\, we benchmark the performance of EEG-GNN against the AutoBayes framework. CNN’s have been frequently used to extract subject-invariant features from EEG for classification tasks\, but this approach holds the underlying assumption that electrodes are equidistant analogous to pixels of an image and hence fails to explore/exploit the complex functional neural connectivity between different electrode sites. We overcome this limitation by tailoring the concepts of convolution and pooling applied to 2D grid-like inputs for the functional network of electrode sites. Furthermore\, we develop various GNN models that project electrodes onto the nodes of a graph\, where the node features are represented as EEG channel samples collected over a trial\, and nodes can be connected by weighted/unweighted edges according to a flexible policy formulated by a neuroscientist. The empirical evaluations show that our proposed GNN-based framework outperforms standard CNN classifiers across ErrP and RSVP datasets\, as well as allowing neuroscientific interpretability and explainability to deep learning methods tailored to EEG related classification problems. Another practical advantage of our GNN-based framework is that it can also be used in EEG channel selection\, which is critical for reducing computational cost\, and designing portable EEG headsets.
URL:https://ece.northeastern.edu/event/ece-phd-proposal-review-andac-demir/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210608T100000
DTEND;TZID=America/New_York:20210608T110000
DTSTAMP:20260422T230731
CREATED:20210602T213048Z
LAST-MODIFIED:20210602T213048Z
UID:4968-1623146400-1623150000@ece.northeastern.edu
SUMMARY:ECE PhD Dissertation Defense: Lorenzo Bertizzolo
DESCRIPTION:PhD Dissertation Defense: Software-Defined Wireless Networking for 5G and Beyond: From Indoor Cells to Non-Terrestrial UAV Networks \nLorenzo Bertizzolo \nLocation: Microsoft Teams \nAbstract: While Software-defined Networking (SDN) is a consolidated and widely adopted concept in fixed infrastructure\, its adoption to the wireless domain has been limited by some fundamental challenges. Unlike traditional fixed infrastructure\, which relies entirely on fiber communications\, wireless networks suffer from unpredictable access and backhaul operations. Therefore\, they ask for new architectural solutions to implement SDN. A prerequisite to implementing SDN for wireless is to adopt programmable radio hardware and software-based implementation of the wireless protocol stack. Then\, it is necessary to adopt control APIs that expose the wireless protocols’ internal operations to external control. Finally\, to bridge the gap between SDN theory and implementation\, this thesis proposes a series of architectural solutions that provide the communication infrastructure and the architectural control innovations necessary to implement SDN control and optimization on real wireless systems. Specifically\, this thesis proposes two different architectural solutions. For fixed Radio Access Networks (RANs) that benefit from a low-latency and reliable backhaul infrastructure\, we propose to implement a centralized control approach similar to SDN for fixed infrastructure. Here a central controller collects network state information of a distributed RAN\, solves a network control problem\, and distributes the solutions to the individual wireless nodes. On the other hand\, for infrastrcuture-less RANs\, which implement both the access and the backhaul in wireless\, we propose a new architectural solution that moves the control logic to the edge of the networks\, to the very wireless nodes.
URL:https://ece.northeastern.edu/event/ece-phd-dissertation-defense-lorenzo-bertizzolo/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210603T150000
DTEND;TZID=America/New_York:20210603T160000
DTSTAMP:20260422T230731
CREATED:20210602T213220Z
LAST-MODIFIED:20210602T213220Z
UID:4970-1622732400-1622736000@ece.northeastern.edu
SUMMARY:ECE PhD Dissertation Defense: Yumin Liu
DESCRIPTION:PhD Dissertation Defense: Learning from Spatio-Temporal Data with Applications in Climate Science \nYumin Liu \nLocation: Zoom Link \nAbstract: Climate change is one of the major challenges to human beings and many other species in our time. In the recent decade\, the number of disasters related to climate change such as wildfires\, storms\, floods and droughts are increasing\, and the casualty and economic losses caused by them are larger compared to those of decades ago. This calls for better and efficient ways to predict climate change in order to better prepare and reduce losses. Predicting climate change involves using historical observational data and model simulated data\, both of which usually involve multiple locations and timestamps and are spatio-temporal. With the rapid development and progress of machine learning\, these methods have achieved several impactful contributions in many domains; we would like to translate its impact to climate science.\nIn this thesis we address several problems in climate science. This challenging complex domain enable us to develop\, innovate\, adapt\, and advance machine learning in the following ways. 1) We develop a multi-task learning method to estimate relationships between tasks and learn the basis tasks in different locations especially for nearby locations which may share similar climate patterns. This method assumes that the weights of an observed task is a linear combination of several latent basis tasks and that the task relationships can be learnt by imposing a regularized precision matrix. 2) We propose a nonparameteric mixture of sparse linear regression models to cluster and identify important climate models for prediction. This model incorporates Dirichlet Process (DP) to automatically determine the number of clusters\, imposes Markov Random Field (MRF) constraints to guarantee spatio-temporal smoothness\, and selects a subset of global climate models (GCMs) that are useful for prediction within each spatio-temporal cluster with a spike-and-slab prior. We derive an effective Gibbs sampling method for this model. 3) We adapt image super resolution methods to climate downscaling — increasing spatial resolution for climate variables for local impact analysis. The proposed method is called YNet which is a novel deep convolutional neural network (CNN) with skip connections and fusion capabilities to perform downscaling for climate variables on multiple GCMs directly rather than on reanalysis data. 4) We use saliency map method to discover dependencies among climate variables. We propose the concept of cyclical saliency map (Cyclic-SM) which are meaningful in climate context and more robust to noise as compared to ordinary saliency maps. We show that Cyclic-SMs can reveal relevant spatial regions for prediction. We demonstrate the effectiveness of this method in climate downscaling\, ENSO index prediction and river flow prediction.
URL:https://ece.northeastern.edu/event/ece-phd-dissertation-defense-yumin-liu/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210603T110000
DTEND;TZID=America/New_York:20210603T120000
DTSTAMP:20260422T230731
CREATED:20210526T201737Z
LAST-MODIFIED:20210526T201737Z
UID:4960-1622718000-1622721600@ece.northeastern.edu
SUMMARY:ECE PhD Proposal Review: Mehdi Nasrollahpourmotlaghzanjani
DESCRIPTION:PhD Proposal Review: RFICs for Biomedical Magnetic and Magnetoelectric Microsystems \nMehdi Nasrollahpourmotlaghzanjani \nLocation: Zoom Links \nAbstract: Design and analysis of the advanced biomedical circuit and systems in wide variety of applications has emerged a significant interest. Not only in different engineering disciplines\, but also in a variety of applications such as neuroscience\, COVID-19\, etc. In this study\, we are proposing an implantable device\, handheld device for detecting different diseases and the RFIC design for the ME antenna and sensor evaluations.\nFirst\, we show and miniaturized implantable device for deep brain implantation that provides wireless power transfer efficiency (PTE) of 1 to 2 orders of magnitude higher than the reported micro-coils for brain stimulation. The proposed device will simultaneously measure the as magnetic field activity when neurons are firing. In the second part we will go over the RFIC design for the bio-implant devices\, evaluation of the ME antennas for communication purposes and the circuit interface to measure the ME and GMI sensors. For final part\, we will discuss the handheld device design for early diagnosis of different diseases such as\, lung cancer\, Alzheimer\, Covid-19\, etc through exhaled breath on the molecularly imprinted polymer (MIP) gas sensors.
URL:https://ece.northeastern.edu/event/ece-phd-proposal-review-mehdi-nasrollahpourmotlaghzanjani/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210526T130000
DTEND;TZID=America/New_York:20210526T140000
DTSTAMP:20260422T230731
CREATED:20210524T222653Z
LAST-MODIFIED:20210524T222653Z
UID:4958-1622034000-1622037600@ece.northeastern.edu
SUMMARY:PhD Dissertation Defense: Kunpeng Li
DESCRIPTION:PhD Dissertation Defense: Visual Learning with Limited Supervision \nKunpeng Li \nLocation: Zoom Link \nAbstract: Deep learning models have achieved remarkable success in many computer vision tasks. However\, they typically rely on large amounts of carefully labeled training data whose annotating process is usually expensive\, time-consuming and even infeasible when considering the task complexity and scarcity of expert knowledge.\nIn this dissertation talk\, I will discuss several explorations along the direction of visual learning with limited supervision. They are mainly about learning from data with weak forms of annotations and learning from multi-modal data pairs. Specifically\, I will first present a guided attention learning framework to conduct semantic segmentation by mainly using image-level labels\, as such weak form of annotation can be collected much more efficiently than pixel-level labels. Under mild assumptions\, our framework can also be used as a plug-in to existing convolutional neural networks to improve their generalization performance. This is achieved by guiding the network to focus on correct things when learning concepts from a limited set of training samples. Besides\, I will also introduce models that can effectively learn from multi-modal data pairs without relying on dense annotations of visual semantic concepts. Our models incorporate relational reasoning ability into the visual representation learning process so that it can be better aligned with the supervision from corresponding text descriptions.
URL:https://ece.northeastern.edu/event/phd-dissertation-defense-kunpeng-li/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210525T100000
DTEND;TZID=America/New_York:20210525T110000
DTSTAMP:20260422T230731
CREATED:20210517T174657Z
LAST-MODIFIED:20210517T174657Z
UID:4945-1621936800-1621940400@ece.northeastern.edu
SUMMARY:ECE PhD Dissertation Defense: Mohammad Hossein Hajkazemi
DESCRIPTION:PhD Dissertation Defense: High-performance Translation Layers for Cloud Immutable Storage \nMohammad Hossein Hajkazemi \nLocation: Zoom Link \nAbstract: Most storage interfaces support in-place updates: blocks can be rewritten\, files can be modified at byte granularity\, fields may be updated in database table rows. Yet internally these layers often rely on out-of-place (immutable) writes. In some cases\, this may be necessary to use media\, such as flash\, SMR (shingled magnetic recording) and IMR (interlaced magnetic recording) disk\, which do not allow overwrites. In others\, it is used to simplify the implementation of transactions and/or crash consistency\, in the form of journaling\, write-ahead logging\, shadow paging\, etc. \nIn a storage system\, translation layers perform out-of-place writes\, and they are implemented in different layers of storage stack from the file system to the storage device firmware depending on the application. In this dissertation I focus on translation layers for cloud immutable storage technologies to improve the cloud I/O performance. As a part of my thesis\, I focus on translation layers for state-of-the-art immutable storage media such as SMR and IMR used in cloud environments\, proposing several novel algorithms to improve their efficiency. I also introduce FSTL\, a framework to design and implement SMR translation layer. Finally\, I describe Collage\, a virtual disk I developed over S3 (could be implemented over a similar object storage) using a translation layer which performs large\, sequential\, out-of-place writes for high performance. It optionally uses fast local storage for write logging and as a write-back cache\, guaranteeing prefix consistency under all failure conditions and recovery of all acknowledged writes if the local cache is not lost. Collage supports snapshots and cloned volumes\, performs well over erasure-coded storage\, and allows consistent asynchronous volume replication over geographic distances. I show that Collage can achieve massive performance improvements (e.g.\, over 100x for microbenchmarks and 10x for macro-benchmarks) over CEPH RBD\, a popular open-source scale-out virtual disk implementation.
URL:https://ece.northeastern.edu/event/ece-phd-dissertation-defense-mohammad-hossein-hajkazemi/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210521T110000
DTEND;TZID=America/New_York:20210521T120000
DTSTAMP:20260422T230731
CREATED:20210503T175740Z
LAST-MODIFIED:20210503T175740Z
UID:4881-1621594800-1621598400@ece.northeastern.edu
SUMMARY:ECE MS Thesis Defense: Daniel Uvaydov
DESCRIPTION:MS Thesis Defense titled DeepSense: Fast Wideband Spectrum Sensing Through Real-Time In-the-Loop Deep Learning \nDaniel Uvaydov \nLocation: Microsoft Teams \nAbstract: Spectrum sharing will be a key technology to tackle spectrum scarcity in the sub-6 GHz bands. To fairly access the shared bandwidth\, wireless users will necessarily need to quickly sense large portions of spectrum and opportunistically access unutilized bands. The key unaddressed challenges of spectrum sensing are that (i) it has to be performed with extremely low latency over large bandwidths to detect tiny spectrum holes and to guarantee strict real-time digital signal processing (DSP) constraints; (ii) its underlying algorithms need to be extremely accurate\, and flexible enough to work with different wireless bands and protocols to find application in real-world settings. To the best of our knowledge\, the literature lacks spectrum sensing techniques able to accomplish both requirements. In this paper\, we propose DeepSense\, a software/hardware framework for real-time wideband spectrum sensing that relies on real-time deep learning tightly integrated into the transceiver’s baseband processing logic to detect and exploit unutilized spectrum bands. DeepSense uses a convolutional neural network (CNN) implemented in the wireless platform’s hardware fabric to analyze a small portion of the unprocessed baseband waveform to automatically extract the maximum amount of information with the least amount of I/Q samples. We extensively validate the accuracy\, latency and generality performance of DeepSense with (i) a 400 GB dataset containing hundreds of thousands of WiFi transmissions collected “in the wild” with different Signal-to-Noise-Ratio (SNR) conditions and over different days; (ii) a dataset of transmissions collected using our own software-defined radio testbed; and (iii) a synthetic dataset of LTE transmissions under controlled SNR conditions. We also measure the real-time latency of the CNNs trained on the three datasets with an FPGA implementation\, and compare our approach with a fixed energy threshold mechanism. Results show that our learning-based approach can deliver a precision and recall of 98% and 97% respectively and a latency as low as 0.61ms.
URL:https://ece.northeastern.edu/event/ece-ms-thesis-defense-daniel-uvaydov/
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210513T140000
DTEND;TZID=America/New_York:20210513T150000
DTSTAMP:20260422T230731
CREATED:20210503T175624Z
LAST-MODIFIED:20210510T175607Z
UID:4880-1620914400-1620918000@ece.northeastern.edu
SUMMARY:ECE PhD Proposal Review: Siyue Wang
DESCRIPTION:PhD Proposal Review: Towards Robust and Secure Deep Learning Models and Beyond \nSiyue Wang \nLocation: Zoom Link \nAbstract: Modern science and technology witness the breakthroughs made by deep learning during the past decades. Fueled by rapid improvements of computational resources\, learning algorithms\, and massive amount of data\, deep neural networks (DNNs) have played a dominant role in more and more real-world applications. Nonetheless\, there is a spring of bitterness mingling with this remarkable success – recent studies reveals that there are two main security threats of DNNs which limit its widespread usage: 1) the robustness of DNN models under adversarial attacks\, and 2) the protection and verification of intellectual properties of well-trained DNN models. \nIn this dissertation\, we fist focus on the security problems of how to build robust DNNs under adversarial attacks\, where deliberately crafted small perturbations added to the clean input can lead to wrong prediction results with high confidence. We approach the solution by incorporating stochasticity into DNN models. We propose multiple schemes to harden the DNN models when facing adversarial threats\, including Defensive Dropout (DD)\, Hierarchical Random Switching (HRS)\, and Adversarially Trained Model Switching (AdvMS). \nThe second part of this dissertation focuses on how to effectively protect the intellectual property for DNNs and reliably identify their ownership. We propose Characteristic Examples (C-examples) for effectively fingerprinting DNN models\, featuring high-robustness to the well-trained DNN and its derived versions (e.g. pruned models) as well as low-transferability to unassociated models. The generation process of our fingerprints does not intervene with the training phase and no additional data are required from the training/testing set.
URL:https://ece.northeastern.edu/event/ece-phd-proposal-review-siyue-wang/
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210507T110000
DTEND;TZID=America/New_York:20210507T120000
DTSTAMP:20260422T230731
CREATED:20210506T233919Z
LAST-MODIFIED:20210506T233919Z
UID:4928-1620385200-1620388800@ece.northeastern.edu
SUMMARY:ECE Faculty Seminar: Sumientra Rampersad
DESCRIPTION:Faculty Seminar: Is temporal interference the key to noninvasive deep brain stimulation? Answers from simulation studies in mice and humans. \nSumientra Rampersad \nLocation: Zoom Link \nAbstract: Transcranial current stimulation (tCS) has been used for two decades to noninvasively investigate and influence brain function in both healthy volunteers and clinical populations. While many positive effects have been found\, the goals of high focality\, accurate targeting and deep stimulation are yet to be achieved. Transcranial temporal interference stimulation (tTIS) is a new form of tCS that might improve the method on all three fronts. tTIS uses two alternating currents to create an amplitude-modulated electric field that can peak deep in the brain. A recent murine study showed promising effects of tTIS and concluded that the technique may be used as a noninvasive form of deep brain stimulation in humans\, but results from human experiments have not yet been published. In this talk I will present results of finite element simulations with realistic head models to investigate the electric fields induced by tTIS in the brain\, comparing results in murine and human head models for tTIS and conventional tCS. Due to the nonlinear nature of tTIS\, conventional methods to optimize tCS fields for a specific brain target cannot be used. I will present two nonconvex optimization methods for tTIS and compare their efficiency and results. Finally\, I will discuss the implications of the results of these simulation and optimization studies for potential applications of tTIS in humans. \nBio: Sumientra Rampersad is an Assistant Research Professor in the Department of Electrical and Computer Engineering at Northeastern University in Boston\, where she leads the Brain Stimulation & Simulation Lab. Dr. Rampersad’s research aims to improve understanding of the working mechanisms behind neuromodulation and improve its application using computational methods and experiments with human subjects. She investigates invasive (ECoG\, sEEG) and noninvasive (tCS\, TMS) brain stimulation\, as well as peripheral stimulation\, and is especially interested in bridging the gap between modeling and experiments through model-based experimentation. Her research in collaboration with various academic and clinical partners has been awarded funding by NIA\, NINDS and NIMH. Dr. Rampersad was previously a research scientist in Northeastern’s Cognitive Systems Lab and obtained her PhD at the Radboud University Donders Institute in Nijmegen\, the Netherlands.
URL:https://ece.northeastern.edu/event/ece-faculty-seminar-sumientra-rampersad/
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210429T120000
DTEND;TZID=America/New_York:20210429T133000
DTSTAMP:20260422T230731
CREATED:20210426T175332Z
LAST-MODIFIED:20210426T175332Z
UID:4870-1619697600-1619703000@ece.northeastern.edu
SUMMARY:Distinguished Speaker Series in Robotics
DESCRIPTION:We cordially invite you to join the\nDISTINGUISHED SPEAKER SERIES IN ROBOTICS\nThursday\, April 29\, 12:00 – 1:30pm \n\nVirtual Meeting – Zoom Link | Meeting ID: 928 6786 9946 | Passcode: 103234 \nhttps://northeastern.zoom.us/j/92867869946?pwd=VTA5R1EwRmZKUjdSeHRpYXpVM09Kdz09 \n\nManual Skills and Dexterity in Robots and Humans \nAude Billard \nProfessor of Robotics\, Swiss Federal Institute of Technology (EPFL)\, Switzerland \n\nPart 1: Robots have moved from imitating humans to exceeding humans’ capabilities – sometimes: The design of robots’ manipulation capabilities is driven by our admiration for humans’ exquisite dexterity and motor agility. Yet\, robots are far from reproducing the complexity of human cognition\, for some skills robots do better than humans. Thanks to their powerful motors and the speed of computation of their computer-driven circuits\, robots can beat humans in precision and reactivity. This talk will give an overview of several approaches developed at LASA to endow robots with the ability to react extremely rapidly in the face of unexpected changes in the environment\, combining control with dynamical systems and machine learning. We use human demonstrations to guide the design of the controller’s parameters to modulate the compliance and to determine the range of feasible paths. A review of these algorithms will be accompanied with illustrations of their implementation for controlling uni-manual and bi-manual manipulation. I will conclude by showing some examples of super-human capabilities for catching objects with a dexterity that exceeds that of human beings. \nPart 2: Understanding bimanual skill – a case study in watchmaking: Human dexterity still eludes largely robotics. In an effort to better understand and model this dexterity\, we took on an adventure and decided to follow a cohort of apprentices at watchmaking\, a craft unique in its requirement for precise control of finger movements. Precise control of force is also of essence to prevent breakage of the tiny\, and often highly valuable\, pieces. In a two-year long training\, apprentice acquire the ability to go beyond their natural perception of touch\, so as to sense when the piece clicks and the screw in. Most impressive is the ability with which they acquire unusual but efficient hand postures. Our study unveils how the two hands work in coordination to distribute control variables and achieve better precision than when using a single hand. \nBio: Aude Billard is professor in robotics at the School of Engineering at the Swiss Federal Institute of Technology in Lausanne (EPFL). Trained in physics and robotics\, she has been a pioneer in the application of machine learning for robotic control and human-robot interactions. Billard’s research focuses on manual control and dexterity\, inspired by human skill. Her work on robotics and human-robot interactions has been recognized numerous times by the Institute of Electrical and Electronics Engineers (IEEE) and she currently holds a leadership position on the executive committee of the IEEE Robotics and Automation Society (RAS) as the vice president of publication activities. \n\nPresented by the Institute for Experiential Robotics and Action Club
URL:https://ece.northeastern.edu/event/distinguished-speaker-series-in-robotics/
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210427T150000
DTEND;TZID=America/New_York:20210427T160000
DTSTAMP:20260422T230731
CREATED:20210421T193929Z
LAST-MODIFIED:20210421T193929Z
UID:4865-1619535600-1619539200@ece.northeastern.edu
SUMMARY:ECE PhD Dissertation Defense: Yue Zheng
DESCRIPTION:PhD Dissertation Defense: Modular Plug-and-Play Photovoltaic Subpanel System \nYue Zheng \nLocation: Zoom Link \nAbstract: This thesis designs\, builds and tests plug-and-play photovoltaic (PV) panels. A prototype modular PV system is built consisting of a dozen small PV units that can slide in and out of a mechanical frame without impacting other units. Each unit contains one PV subpanel and a DC-DC converter with a distributed maximum power point tracking (dMPPT) control board. Each PV unit works at its maximum power\, while every output of the converter is connected in parallel to a DC bus. A new combined control strategy is proposed in which the decision to use centralized or distributed control depends on the system efficiency at the varying load operating points. A disadvantage of this dMPPT structure is that in each PV unit\, the DC-DC converter must convert the entire power from its PV subpanel. Therefore\, this research also explores the use of Differential Power Processing (DPP) system\, which harvests maximum power while only processing a small amount of power due to the mismatches between PV panels. Thus\, DPP structure reduces power loss compared to traditional dMPPT structure. Since it processes only a small amount of power\, differential power processing structure has the potential to further be integrated on a chip and become installed in the junction box during the assembling process. Finally\, the research proposes to implement the plug-and-play features of the solar PV system using wireless power transfer (WPT) instead of hard wire connectors. A series-to-series topology of WPT system (L-R-C series circuit) for one PV unit is proposed. In this system\, the DC-DC converter on the PV side is used to perform MPPT\, while the DC-AC inverter simultaneously perturbs its switching frequency to match possible variations in resonance frequencies. Wireless communication is used between transmitter and receiver. Thus\, the maximum efficiency point on the constant output voltage trajectory can be tracked dynamically under wide and varying operating conditions.
URL:https://ece.northeastern.edu/event/ece-phd-dissertation-defense-yue-zheng/
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210427T110000
DTEND;TZID=America/New_York:20210427T120000
DTSTAMP:20260422T230731
CREATED:20210415T211755Z
LAST-MODIFIED:20210415T211755Z
UID:4847-1619521200-1619524800@ece.northeastern.edu
SUMMARY:Rare Earth Element-Based Magnets: Science\, Supply and Sustainability in 2021 and Beyond
DESCRIPTION:University Distinguished Professor Vincent Harris is presenting “Rare Earth Element-Based Magnets: Science\, Supply and Sustainability in 2021 and Beyond” as part of the Jefferson Science Fellowship Program of the National Academies of Sciences and Engineering. \nRegistration is required in advance of the lecture: Register here \nRare earth elements (REEs) and their supply chain have become topics of great interest to the U.S. diplomatic and national security communities. Presently\, China dominates REE markets in all facets of processing from earth extraction to metals as well as value and commercialization verticals. Beijing has shown no hesitancy in using its position of market dominance to advance its broader political goals and agendas. \nIn this presentation\, we focus on REE-based magnets and associated challenges faced in 2021. We explore REE science and applications\, supply and policy\, and sustainability and environmental impact. We examine what the future holds in terms of alternative sources\, recycling\, and the practice of designing components around the need to employ REEs. Finally\, we report on steps taken by the global community to offset China’s monopoly on rare earths.
URL:https://ece.northeastern.edu/event/rare-earth-element-based-magnets-science-supply-and-sustainability-in-2021-and-beyond/
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210426T100000
DTEND;TZID=America/New_York:20210426T110000
DTSTAMP:20260422T230731
CREATED:20210420T181019Z
LAST-MODIFIED:20210420T181019Z
UID:4858-1619431200-1619434800@ece.northeastern.edu
SUMMARY:ECE MS Thesis Defense: Anran Wei
DESCRIPTION:MS Thesis Defense: A soft-switching non-inverting buck-boost converter \nAnran Wei \nLocation: Zoom Link \nAbstract: There are numerous applications in which DC-DC converters with wide range of voltage gain are required. Non-inverting buck-boost converter is a classical topology that can provide wide range of voltage conversion and bidirectional power transfer; thus\, it is frequently used in industrial applications. However\, the conventional hard-switching configuration\, which transfers power through a link inductor\, can only reach a high voltage conversion ratio at the expense of low efficiency due to switching loss. This thesis proposes a soft switching non-inverting buck-boost converter. This converter uses a small film capacitor in parallel with the link inductor to provide zero voltage switching (ZVS) by allowing the link capacitor and link inductor resonate between power transfer states. Principles of the operation of this converter are presented in this thesis and its performance is evaluated through simulations and experiments.
URL:https://ece.northeastern.edu/event/ece-ms-thesis-defense-anran-wei/
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