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Robust Device-Free Mmwave Sensing with Specular Reflection Interference Mitigation

IEEE Transactions on Mobile Computing 2025年第07期24卷 5749-5764页

作者： Liu, Yulin Wang, Jie Gao, Qinghua Pan, Miao Fang, Yuguang Dalian Maritime University School of Information Science and Technology Dalian116026 China Dalian University of Technology Faculty of Electronic Information and Electrical Engineering Dalian China University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States City University of Hong Kong Department of Computer Science Hong Kong Hong Kong

Device-Free mmWave Sensing (DFWS) could sense target state by analyzing how target activities influence the surrounding mmWave signals. It has emerged as a promising sensing technology. However, when employing DFWS indoors, specular reflection interference arises due to the specular reflectors. This interference often induces ghost targets, impacting the accurate estimation of the number and position of targets, resulting in degradation in sensing performance. To tackle this issue, we delve into the generation mechanism of specular reflection interference and analyze its multi-domain characteristics. Through exploration, we discern its temporal sparsity, spatial symmetry or collinearity, and frequency correlation characteristics, and propose four metrics to measure them, accordingly. Specifically, we propose a temporal characteristic quantitative evaluation metric based on identity matching, spatial symmetry and collinearity quantitative evaluation metrics based on geometric analysis, and a frequency correlation quantitative evaluation metric based on Doppler velocity correction, respectively. Based on these metrics, we design a novel Specular Reflection Interference Mitigation (SRIM) method and develop a robust SRIM-DFWS prototype system based on a 60 GHz mmWave radar to validate our proposed method. Experimental results demonstrate that our proposed method could achieve accurate and effective mitigation of specular reflection interference in device-free target tracking. © 2002-2012 IEEE.

关键词： Radar interference

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FeFET Based Logic-in-Memory Pipeline-Style Circuits 17

FeFET Based Logic-in-Memory Pipeline-Style Circuits

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17th IEEE International Conference on Solid-State and Integrated Circuit Technology, ICSICT 2024

作者： Li, Yang Yang, Zhaohui Xia, Yinshui Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo315211 China

ISBN: (纸本)9798350361834

Emerging non-volatile memory technologies provides a solution to break through the limitations of the vonNeumann architecture and using these technologies to develop logic-in-memory circuits. In this paper, we propose a novel circuit design style based on hafnium oxide ferroelectric memory technology, using a accumulator as an example. This design method offers a new direction for the development of logic-in-memory circuits. Additionally, the method significantly increases data processing capability and provides greater flexibility for circuit design. © 2024 IEEE.

关键词： Ferroelectric RAM

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GaN HEMTs based Quad-Active-Bridge DC-DC Converter for PV-Battery-EV-Grid Systems 50

GaN HEMTs based Quad-Active-Bridge DC-DC Converter for PV-Ba...

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Gao, Xin Ilka, Reza Luckett, Benjamin He, JiangBiao Department of Electrical Engineering and Computer Science University of Tennessee KnoxvilleTN United States Department of Electrical and Computer Engineering University of Kentucky LexingtonKY United States

ISBN: (纸本)9781665464543

This paper aims to develop a flexible power management approach to interconnect multiple energy resources based on an isolated, monolithic multiport DC-DC power converter. Specifically, a high efficiency, ultra-compact Quad-Active-Bridge (QAB) DC-DC converter is proposed to interconnect photovoltaic (PV), battery energy storage (BES), electric vehicle (EV) charging/discharging, and a distribution grid. Fast-switching Gallium Nitride (GaN) high-electron-mobility transistors (HEMTs) are employed to achieve higher energy efficiency and power density. To meet the high voltage requirements on the DC grid port, a three-level active-neutral-point-clamped (ANPC) bridge based on 650V GaN HEMTs is developed. To provide galvanic isolation among different power ports, a high-frequency, multi-winding planar magnetic transformer is utilized to couple different power ports for flexible power management. The control and efficiency at four operating modes of the QAB converter are investigated for various weather conditions, and electro-thermal simulations for the QAB converter, including the planar transformer, are carried out in PLECS and Ansys software. Moreover, a 15-kW GaN HEMT based QAB DC-DC converter prototype is implemented for experimental verification. © 2024 IEEE.

关键词： Gallium nitride

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ChebyRegNet: An Unsupervised Deep Learning Technique for Deformable Medical Image Registration 50

ChebyRegNet: An Unsupervised Deep Learning Technique for Def...

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Danish, Muhammad Umair Noorchenarboo, Mohammad Narayan, Apurva Grolinger, Katarina Department of Electrical and Computer Engineering Western University LondonON Canada Department of Computer Science Western University LondonON Canada

ISBN: (纸本)9781665464543

Image registration supports a variety of clinical applications such as treatment planning, disease monitoring, and interventions. Deep learning-based deformable image registration techniques, including VoxelMorph and HyperMorph, are achieving state-of-the-art results but still exhibit considerable errors in multi-modality registrations, such as structural to functional image registration. To address these challenges, this paper proposes ChebyRegNet, a deep learning framework that leverages Chebyshev polynomials and hypernetworks, to enhance registration accuracy. Moreover, a Feature Extraction Network is added that produces deep features for the Chebyshev HyperNetwork, which is responsible for generating parameters for the Image Registration Network. This structure allows ChebyRegNet to successfully handle challenging registration tasks. Experiments show that ChebyRegNet greatly improves registration in terms of target registration errors compared to VoxelMorph and HyperMorph, achieving 0.1 versus 0.2 and 0.19 for HyperMorph and VoxelMorph, respectively. Moreover, ChebyRegNet outperforms the other two techniques in terms of the Dice Score and achieves competitive results in terms of the Jaccard Coefficient. The publicly available code for our work can be accessed at https://***/udanish50/ChebyRegNet/tree/main. © 2024 IEEE.

关键词： Chebyshev polynomials

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Optimistic Online Non-stochastic Control via FTRL 63

Optimistic Online Non-stochastic Control via FTRL

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Mhaisen, Naram Iosifidis, George Delft University of Technology Faculty of Electrical Engineering Mathematics and Computer Science Netherlands

ISBN: (纸本)9798350316339

This paper brings the concept of 'optimism' to the new and promising framework of online Non-stochastic Control (NSC). Namely, we study how NSC can benefit from a prediction oracle of unknown quality responsible for forecasting future costs. The posed problem is first reduced to an optimistic learning with delayed feedback problem, which is handled through the Optimistic Follow the Regularized Leader (OFTRL) algorithmic family. This reduction enables the design of OptFTRL-C, the first Disturbance Action Controller (DAC) with optimistic policy regret bounds. These new bounds are commensurate with the oracle's accuracy, ranging from O (1) for perfect predictions to the order-optimal O(ST) even when all predictions fail. By addressing the challenge of incorporating untrusted predictions into online control, this work contributes to the advancement of the NSC framework and paves the way toward effective and robust learning-based controllers. © 2024 IEEE.

关键词： Stochastic control systems

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The BRAM is the Limit: Shattering Myths, Shaping Standards, and Building Scalable PIM Accelerators 32

The BRAM is the Limit: Shattering Myths, Shaping Standards, ...

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32nd IEEE Annual International Symposium on Field-Programmable Custom Computing Machines, FCCM 2024

作者： Kabir, Md Arafat Kamucheka, Tendayi Fredricks, Nathaniel Mandebi, Joel Bakos, Jason Huang, Miaoqing Andrews, David University of Arkansas Department of Electrical Engineering and Computer Science United States University of South Carolina Department of Computer Science and Engineering United States United States

ISBN: (纸本)9798350372434

Many recent FPGA-based Processor-in-Memory (PIM) architectures have appeared with promises of impressive levels of parallelism but with performance that falls short of expectations due to reduced maximum clock frequencies, an inability to scale processing elements up to the maximum BRAM capacity, and minimal hardware support for large reduction operations. In this paper, we propose a 'Standard' set of design objectives for PIM array-based FPGA designs. We then propose a PIM array-based GEMV accelerator architecture as a case study to show the proposed Standard can be realized in practice. The GEMV accelerator serves as existence proof that dispels several myths surrounding what is normally accepted as clocking and scaling FPGA performance limitations. Specifically, the proposed accelerator clocks at the maximum frequency of the BRAM and scales to 100% of the available BRAMs. Comparative analyses show execution speeds over existing PIM-based GEMV engines on FPGAs and achieving a 2.65×-3.2× faster clock. An AMD Alveo U55 implementation achieves a system clock speed of 737 MHz, providing 64K bit serial multiply-accumulate (MAC) units for GEMV operation. © 2024 IEEE.

关键词： Memory architecture

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A Photovoltaic Interleaved Boost Integrated CL Resonant Converter for Magnetron Application

A Photovoltaic Interleaved Boost Integrated CL Resonant Conv...

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2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Molaei, Aref Kanathipan, Kajanan Lam, John York University Dept. of Electrical Engineering and Computer Science Toronto Canada

ISBN: (纸本)9798350376067

A new single-stage high-gain interleaved boost modular CL resonant converter is proposed in this paper for photovoltaic-powered magnetron application. The proposed converter is able to provide high voltage gain for the magnetron which is a device for microwave applications. Perturb and Observe (P&O) method was used to achieve Maximum Power Point Tracking (MPPT) in the integrated interleaved boost stage by controlling the duty-cycle of the two primary leg switches. The operational principles of the converter are briefly discussed in this paper. Results on a 1.7kW, 230V/3.8kV design are presented to verify the proposed converter performance. © 2024 IEEE.

关键词： Magnetrons

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Enhancing Distribution System Resilience: A Comparative Study of Batteries and Hydrogen Energy Storage Systems 9

Enhancing Distribution System Resilience: A Comparative Stud...

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9th International Conference on Technology and Energy Management, ICTEM 2024

作者： Kordrajabi, Moein Zakariazadeh, Alireza University of Science and Technology of Mazandaran Department of Electrical and Computer Engineering Behshahr Iran

ISBN: (纸本)9798350329797

Power distribution systems need to be more resilient to handle natural disasters and maintain a steady supply of loads. To enhance power distribution systems' resilience, microgrids based on energy storage systems (ESSs) are the most desirable option. Photovoltaic resources (PVs) are the most common sources used in microgrids, but due to their intermittent production, an energystorage system is essential alongside them. The study proposes the use of hydrogen energy storage systems (HESS) over batteries and analyzes the system's performance with HESS. This study examines the impact of PV and HESS on resilience metrics. To evaluate network resilience, three scenarios in two cases, moderate and heavy damage, are explored. Simulations are conducted on an IEEE 33-bus test system that includes three microgrids. The results evidence that HESS, owing to its long-term nature, has the potential toenhance the resilience of distribution networks more effectively than conventional batteries. © 2024 IEEE.

关键词： DC distribution systems

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Multiple Relays Assisted MEC System for Dynamic Offloading and Resource Scheduling with Energy Harvesting 18th

Multiple Relays Assisted MEC System for Dynamic Offloading ...

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18th International Conference on Green, Pervasive, and Cloud Computing, GPC 2023

作者： Wang, Jiming Qu, Long Faculty of Electrical Engineering and Computer Science Ningbo University Zhejiang Ningbo China

ISBN: (纸本)9789819998951

Mobile Edge Computing (MEC) has become an indispensable way to reduce the execution delay of devices. However, for some devices located far away from the MEC server, the transmission delay of communication with MEC is still large. In this case, we consider using multiple relay devices to assist Internet of Things (IoT) devices to communicate with MEC servers. To enhance the energy efficiency of the system, we introduce Energy Harvesting (EH) devices to provide energy for the IoT devices. Our objective is to maximize the utilization of EH devices while minimizing the overall delay in task offloading for the IoT devices. We tackle the problem by formulating it as a Markov Decision Problem (MDP). However, due to the significant expansion of the state space, traditional methods such as relative value iteration and linear iterative reconstruction are ineffective in solving this problem. Hence, we propose an approach called Multi-Relay Assisted Dynamic Computation Offloading (MRADCO) algorithm, which leverages the Lyapunov optimization technique. It is important to note that our proposed algorithm makes decisions solely based on the current state, without requiring the distribution information of the wireless channel and EH process. This characteristic enhances the algorithm’s practicality and reduces complexity in real-world implementations. Through rigorous theoretical derivation and comprehensive simulation experiments, we demonstrate that our algorithm is asymptotically optimal. And compared with the benchmark algorithm LODCO, our algorithm reduces the time by 50%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Energy harvesting

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A Model-Free Approach for Load Frequency Control Using Deep Reinforcement Learning

A Model-Free Approach for Load Frequency Control Using Deep ...

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2025 IEEE Texas Power and Energy Conference, TPEC 2025

作者： Pandey, Bimal Nguyen, Nga University of Wyoming Electrical Engineering & Computer Science LaramieWY82071 United States

ISBN: (纸本)9798331541125

Frequency stability is crucial for the proper operation of the power grids. The changing landscapes in power systems, raised by continuously increasing demand and rapid decommissioning of conventional generation, have changed the frequency dynamics significantly. Load frequency control (LFC) is a mechanism for regulating frequency and ensuring stability by balancing supply and load. Conventional control strategies, which require accurate representation of the mathematical methods, can face difficulty in modeling the complexity of power dynamics. Therefore, this paper proposes a model-free strategy utilizing deep reinforcement learning (DRL) for implementing the LFC mechanism without the involvement of a central controlling. The problem is formulated as a deep deterministic policy gradient (DDPG) to find an optimal solution for regulating frequency control operations at primary and secondary stages. The proposed framework enables the generating unit, acting as an agent, to identify the optimal actions for maintaining system stability by analyzing generation and load patterns. The DRL-based method utilizes the test signal to change the dynamics of the system, and the agent modeled using the recurrent neural network (RNN) framework inside DDPG learns to balance the generation according to the load and ensures restoration of the deviated frequency. The performance of the implemented method is validated with the proportional and integral (PI) controller. © 2025 IEEE.

关键词： Deep reinforcement learning

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