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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210701T110000
DTEND;TZID=America/New_York:20210701T120000
DTSTAMP:20260426T173101
CREATED:20210623T171449Z
LAST-MODIFIED:20210623T171449Z
UID:26361-1625137200-1625140800@coe.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://coe.northeastern.edu/event/ece-phd-dissertation-defense-xianfeng-liang/
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210707T140000
DTEND;TZID=America/New_York:20210707T150000
DTSTAMP:20260426T173101
CREATED:20210706T135010Z
LAST-MODIFIED:20210706T135010Z
UID:26505-1625666400-1625670000@coe.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://coe.northeastern.edu/event/ece-phd-proposal-review-xiaolong-ma/
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210707T170000
DTEND;TZID=America/New_York:20210707T180000
DTSTAMP:20260426T173101
CREATED:20210706T135131Z
LAST-MODIFIED:20210706T135131Z
UID:26484-1625677200-1625680800@coe.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://coe.northeastern.edu/event/ece-phd-dissertation-defense-kaidi-xu/
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DTSTART;TZID=America/New_York:20210713T100000
DTEND;TZID=America/New_York:20210713T110000
DTSTAMP:20260426T173101
CREATED:20210706T134832Z
LAST-MODIFIED:20210706T134832Z
UID:26507-1626170400-1626174000@coe.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://coe.northeastern.edu/event/ece-phd-dissertation-defense-maher-kachmar/
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210719T113000
DTEND;TZID=America/New_York:20210719T123000
DTSTAMP:20260426T173101
CREATED:20210713T172719Z
LAST-MODIFIED:20210713T172719Z
UID:26599-1626694200-1626697800@coe.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://coe.northeastern.edu/event/ece-phd-dissertation-defense-berkan-kadioglu/
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210720T080000
DTEND;TZID=America/New_York:20210722T130000
DTSTAMP:20260426T173101
CREATED:20210621T200006Z
LAST-MODIFIED:20210621T200006Z
UID:26347-1626768000-1626958800@coe.northeastern.edu
SUMMARY:COE CommLab/Khoury College Writing Retreat
DESCRIPTION:College of Engineering PhD students are invited to join us for a writing retreat July 20 – 22.  The aim of this retreat is to create sustained writing time for researchers to work in a calm\, supportive environment on a longer project.  Studies have shown that an academic writing retreat supports productivity and progress while also encouraging helpful guidance from peers. \nOur virtual retreat is organized around alternating periods of quiet work on individual projects with collective sessions on topics related to research writing. Each of the three days begins with a welcome message and group gathering. On the last day\, we’ll wrap up the retreat with a virtual lunch to share concluding thoughts. \nRegister here for this event by June 24.
URL:https://coe.northeastern.edu/event/coe-commlab-khoury-college-writing-retreat/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210723T100000
DTEND;TZID=America/New_York:20210723T110000
DTSTAMP:20260426T173101
CREATED:20210706T205406Z
LAST-MODIFIED:20210706T205406Z
UID:26516-1627034400-1627038000@coe.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://coe.northeastern.edu/event/ece-phd-dissertation-defense-mahmoud-ibrahim/
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210728T140000
DTEND;TZID=America/New_York:20210728T150000
DTSTAMP:20260426T173101
CREATED:20210727T151651Z
LAST-MODIFIED:20210727T151651Z
UID:26729-1627480800-1627484400@coe.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://coe.northeastern.edu/event/ece-phd-proposal-review-bahare-azari/
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210728T160000
DTEND;TZID=America/New_York:20210728T170000
DTSTAMP:20260426T173101
CREATED:20210726T142333Z
LAST-MODIFIED:20210726T142333Z
UID:26717-1627488000-1627491600@coe.northeastern.edu
SUMMARY:COE CommLab Research Dissemination Series:  Preprints Workshop
DESCRIPTION:Do you have a fully drafted journal article ready to share with other researchers that you want to share before the traditional peer review process is done? \nPreprinting is one way to disseminate your research almost immediately.  Join the Northeastern COE Communication Lab for a workshop discussing the importance of preprints and open science.  We will cover the basics of preprints and address common concerns about preprinting. \nRegister via Zoom
URL:https://coe.northeastern.edu/event/coe-commlab-research-dissemination-series-preprints-workshop/
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210729T150000
DTEND;TZID=America/New_York:20210729T160000
DTSTAMP:20260426T173101
CREATED:20210727T151933Z
LAST-MODIFIED:20210727T151933Z
UID:26735-1627570800-1627574400@coe.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://coe.northeastern.edu/event/ece-phd-proposal-review-weite-zhang/
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