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2024 IEEE International Conference on Computational Electromagnetics, ICCEM 2024

作者： Cheng, Xingkai Shao, Hanru Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo315211 China

ISBN: (纸本)9798350383317

In this work, we propose a hybrid method to fast solve the optimization of array located on the PEC carrier inside the dielectric radome with parameters variation. By constructing a reusable low-rank reduced-order model (ROM) for complex carriers, a large number of repeated calculations are reduced and the analysis cost of the antenna array is reduced when the position of array is modified. By combining of frequency-and material-independent response (FMIR), the sweep of media parameters and frequency can be accelerated. The tangential equivalence principle algorithm (T -EPA) is applied to solve the array problem, and the number of unknowns on the antenna array is transferred to the equivalent surface, which is easier for the iterative convergence of the matrix equation. Therefore, the number of unknowns and the analysis cost of the antenna array can be reducecd. © 2024 IEEE.

关键词： Antenna arrays

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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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A Hybrid Non-Conformal Volume Surface Integral Equation Method

A Hybrid Non-Conformal Volume Surface Integral Equation Meth...

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2024 International Applied Computational Electromagnetics Society Symposium, ACES-China 2024

作者： Jiang, Yifei Shao, Hanru Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo315211 China

ISBN: (纸本)9798350355581

Traditional volume surface integral equation for analysis of metal-dielectric composites requires the target mesh to be conformal, which leads to over-meshing of the multi-scale model and reduces the solution efficiency. In order to deal with this problem, a non-conformal volume surface integral equation (VSIE) method based on the discontinuous Galerkin volume integral equation (DGVIE) and the grid-robust higher order vector basis (GRHOVB) surface integral equation is proposed in this paper to enhance the computing efficiency. In this method, the DGVIE is used to deal with dielectric target, which avoids the computation of higher-order singular integrals and facilitates the use of volume non-conformal meshing and mixed basis function discretization. The GRHOVB function is used to deal with PEC target, so that the surface mesh also does not need to be conformal. Numerical results verify the good performance of this non-conformal method. © 2024 IEEE.

关键词： Mesh generation

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Area-Aware Logic Mapping for MAGIC based In-Memory Computing 20

Area-Aware Logic Mapping for MAGIC based In-Memory Computing

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20th International Conference on Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design, SMACD 2024

作者： Pan, Jiaxiang Chu, Zhufei Ningbo University Faculty of Electrical Engineering & Computer Science Ningbo315211 China

ISBN: (纸本)9798350351927

In-memory computing offers promising prospects for data-intensive applications, with memristor-aided logic (MAGIC) presenting a notable avenue. Logic synthesis for MAGIC-based systems entails mapping Boolean functions to memristor crossbars, where previous efforts often suffer from accurately evaluating multi-fanout node costs. In this paper, we propose a heuristic logic mapping method tailored for MAGIC-based in-memory computing, specifically addressing multi-fanout nodes' challenges. In addition, we also propose an automated equivalence checking methodology aimed at validating the correspondence between the design specification and the resulting micro-operations post-mapping. Our proposed method yields substantial improvements, with an average reduction of 13% and 10% in the requisite non-volatile memory when compared to state-of-the-art techniques across the ISCAS'85 and IWLS'93 benchmarks. Remarkably, these enhancements are achieved with only marginal increases of 1% and 2% in the required computation cycles. © 2024 IEEE.

关键词： Memristors

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UAV-D2D Assisted Latency Minimization and Load Balancing in Mobile Edge Computing with Deep Reinforcement Learning 18th

UAV-D2D Assisted Latency Minimization and Load Balancing in...

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

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

ISBN: (纸本)9789819998951

Now Unmanned Aerial Vehicle (UAV) with Mobile Edge Computing (MEC) severs and Device-to-Device (D2D) communications provide offload computing services for User Devices (UDs). However, the UAV has relatively high transmission latency. And D2D lacks the necessary flexibility. In this paper, we introduce a novel MEC system that utilizes the collaborative advantages of flexible movement of UAV and the low latency transmission of D2D communication to process tasks from UDs. We formulate an optimization problem focused on minimizing the tasks transmission and execution delay of UDs. The problem involves joint optimization of user scheduling, UAV trajectory, and resource allocation of Virtual Machines (VMs) on the MEC server. To tackle this non-convex problem, we propose a Deep Reinforcement Learning (DRL) algorithm with Deep Deterministic Policy Gradient (DDPG). Through simulation results, we demonstrate that DDPG reduces the latency by 41% compared to Deep Q-Network (DQN) and Actor-Critic (AC) algorithm. Our collaborative UAV-D2D model has 16% and 32% lower latency than when only the UAV or D2D works alone. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Mobile edge computing

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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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An Efficient Convolutional Neural Network Hardware IP for Epilepsy Detection 17

An Efficient Convolutional Neural Network Hardware IP for Ep...

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

作者： Gong, Yi Zhang, Yuejun Tu, Jiangtao Zou, Rongxin Wen, Liang Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo315211 China

ISBN: (纸本)9798350361834

Epilepsy is a common neurological disease which is sudden and unpredictable. In response to the current problems of low efficiency and high false detection rate in epilepsy detection, this paper proposes an efficient convolutional neural network (CNN) hardware intellectual property (IP) design scheme for epilepsy detection. According to the epilepsy pathology, the oversampling technique is used to solve the sample imbalance. Filters out the noise by using band-pass filters, and improves the model by using Z-score normalization. Then, the structure of convolutional neural network is optimized to build an end-to-end network model and train the corresponding weights as well as biases. A single convolutional kernel is used to lighten the network model and reduce the parameters. The 22-channel EEG data are processed in a parallelized manner to fully extract the correlation between the channels. The 3-layer convolutional and max pooling module is designed by combining with pipelining technique. Validated on the CHB-MIT dataset, the epilepsy detection accuracy is reached 97.45%. Under TSMC 65 nm process, the IP core area is measured as 1.625 mm 2 , power consumption is measured as 4.225 mW, single detection delay is 0.01935 ms, and single detection energy consumption is 0.237 μJ/class. © 2024 IEEE.

关键词： Convolutional neural networks

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A novel low-rank blind spot reconstruction network based on multi-scale mask convolution for hyperspectral anomaly detection

A novel low-rank blind spot reconstruction network based on ...

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2024 International Conference on Remote Sensing, Mapping, and Geographic Information Systems, RSMG 2024

作者： Yang, Zhiwei Zhao, Rui Meng, Xiangchao Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo315211 China

ISBN: (纸本)9781510685826

With the emergence of blind spot reconstruction networks in hyperspectral anomaly detection, a series of new challenges have arisen. The most significant issues include local anomaly identity mapping and inaccurate detection of large anomaly targets in deep learning networks. Therefore, we propose a busy point reconstruction network for hyperspectral anomaly detection based on multi-scale mask convolution. We creatively design a multi-scale mask convolution module that uses an inner window mask to isolate the center block and edge background for reconstruction. To further enhance space-spectral information interactivity, we introduce a joint space-spectral module. Additionally, to increase the network's focus on global low-rank backgrounds, we incorporate a block low-rank penalty to guide global low-rank attention. Finally, by considering the scale difference in detection performance between different mask convolutions, we develop a dynamic learnable fusion module that performs adaptive fusion of two mask convolution results for optimal detection performance on two real data sets. © 2024 SPIE.

关键词： Anomaly detection

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Fast Analysis of Characteristic Modes Using Hybrid Integral Equation Methods

Fast Analysis of Characteristic Modes Using Hybrid Integral ...

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2024 International Applied Computational Electromagnetics Society Symposium, ACES-China 2024

作者： He, Yusheng Shao, Hanru Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo315211 China

ISBN: (纸本)9798350355581

To expedite the characteristic mode analysis (CMA) of electromagnetic target structures, this paper combines frequency- and material-independent reactions (FMIR) with characteristic mode analysis using the volume-surface integral equation (VSIE). Although the CMA based on VSIE is not affected by spurious modes when calculating targets containing dielectric, its matrix is high dimensional, resulting in slower calculations. FMIR only needs to store the calculation once for matrices that are independent of frequency and material parameters, which greatly reduces the repetitive operations when calculating multi-frequency impedance matrices. Numerical results show that combining FMIR with the CMA using VSIE can effectively improve the computational speed. © 2024 IEEE.

关键词： Matrix algebra

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Wideband Slot Antenna Array with Stable Low Sidelobe Performance

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IEEE Antennas and Wireless Propagation Letters 2025年第5期24卷 1248-1252页

作者： Zhang, Fengyuan Zhang, Hanlin You, Qingchun Huang, Jifu Ningbo University Faculty of Electrical Engineering and Computer Science Zhejiang Ningbo315211 China University of Birmingham School of Engineering B15 2TT United Kingdom

This letter presents a dispersion compensation method that integrates a comb-shaped slow-wave structure with a defected ground structure (DGS) to achieve wideband low-sidelobe performance. This method effectively ensures phase balance within the feed network, achieving a stable low sidelobe level over a wide frequency range. A prototype planar array antenna operating in the 21.6-30.8 GHz range was designed, fabricated, and measured. The experimental results demonstrate that the antenna achieves a fractional bandwidth (FBW) of 35.1%, with the first sidelobe level maintained below -21 dB across the entire band and a fluctuation confined within ±1.3 dB. © 2002-2011 IEEE.

关键词： Defected ground structures

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