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IEEE International Conference on Electro-Information Technology

作者： Hans Johnson Silvia Zorzetti Jafar Saniie Department of Electrical and Computer Engineering Embedded Computing and Signal Processing (ECASP) Research Laboratory Illinois Institute of Technology Chicago IL U.S.A. Superconducting Quantum Materials and Systems Center (SQMS) Fermi National Accelerator Laboratory Batavia IL U.S.A.

This work explores avenues and target areas for optimizing FPGA-based control hardware for experiments conducted on superconducting quantum computing systems and serves as an introduction to some of the current research at the intersection of classical and quantum computing hardware. With the promise of building larger-scale error-corrected quantum computers based on superconducting qubit architecture, innovations to room-temperature control electronics are needed to bring these quantum realizations to fruition. The QICK (Quantum Instrumentation Control Kit) is one leading experimental FPGA-based implementations. However, its integration into other experimental quantum computing architectures, especially those using superconducting radiofrequency (SRF) cavities, is largely unexplored. We identify some key target areas for optimizing control electronics for superconducting qubit architectures and provide some preliminary results to the resolution of a control pulse waveform. With optimizations targeted at 3D superconducting qubit setups, we hope to bring to light some of the requirements in classical computational methodologies to bring out the full potential of this quantum computing architecture, and to convey the excitement of progress in this research.

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KANsformer for Scalable Beamforming

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IEEE Transactions on Vehicular Technology 2025年

作者： Xie, Xinke Lu, Yang Chi, Chong-Yung Chen, Wei Ai, Bo Niyato, Dusit Beijing Jiaotong University State Key Laboratory of Advanced Rail Autonomous Operation Beijing100044 China Beijing Jiaotong University School of Computer Science and Technology Beijing100044 China National Tsing Hua University Institute of Communications Engineering Department of Electrical Engineering Hsinchu30013 Taiwan Beijing Jiaotong University School of Electronics and Information Engineering Beijing100044 China Nanyang Technological University College of Computing and Data Science 639798 Singapore

This paper proposes an unsupervised deep-learning (DL) approach by integrating Transformer and Kolmogorov-Arnold networks (KAN) termed KANsformer to realize scalable beamforming for mobile communication systems. Specifically, we consider a classic multi-input single-output energy efficiency maximization problem subject to the total power budget. The proposed KANsformer first extracts hidden features via a multi-head self-attention mechanism and then reads out the desired beamforming design via KAN. Numerical results are provided to evaluate the KANsformer in terms of generalization performance, transfer learning and ablation experiment. Overall, the KANsformer outperforms the existing benchmark DL approaches, and is adaptable to the variation in the number of mobile users with real-time and near-optimal inference. © 1967-2012 IEEE.

关键词： Budget control

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cVTS: A Constrained Voronoi Tree Search Method for High Dimensional Analog Circuit Synthesis

cVTS: A Constrained Voronoi Tree Search Method for High Dime...

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Design Automation Conference

作者： Aidong Zhao Xianan Wang Zixiao Lin Zhaori Bi Xudong Li Changhao Yan Fan Yang Li Shang Dian Zhou Xuan Zeng Microelectronics Department State Key Laboratory of Integrated Chips and Systems Fudan University Shanghai China Department of Electrical Engineering University of Texas Dallas USA Department of Electrical and Computer Engineering University of California San Diego USA School of Data Science Fudan University Shanghai China School of Computer Science Fudan University Shanghai China

A constrained Voronoi tree-based domain decomposition method for high-dimensional Bayesian optimization is proposed to solve large scale analog circuit synthesis problems, which can be formulated as high-dimensional heterogeneous black-box optimization. Hierarchical Voronoi tree progressively breaks down the design space into partitions with implicit performance boundaries such that promising regions are efficiently explored. Fast exploitation is ensured in Voronoi nest via local Bayesian optimization with a few observations. A slice-enhanced Gibbs sampling method is proposed to sample acquisition function cMES in irregular polyhedrons with design constraints. Compared with state-of-the-art methods, cVTS achieves significant speed up without loss of accuracy.

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Low-Power Envelope Detector for WSN Wake-up Receiver Applications

Low-Power Envelope Detector for WSN Wake-up Receiver Applica...

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IEEE International Conference on Design & Test of Integrated Micro & Nano-systems (DTS)

作者： Mariem Bouraoui Amel Neifar Imen Barraj Mohamed Masmoudi Electrical Engineering Department Systems Integration and Emerging Energies Laboratory National Engineers School of Sfax University of Sfax Sfax Tunisia National Engineers School of Gabes University of Gabes Tunisia Department of Computer Engineering College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Al-Kharj Saudi Arabia

This paper highlights the importance of the wake-up receiver in applications using WSN while offering a brief description of this element. In this work, the design of a low-power envelope detector for wake-up receiver applications is presented. By using RF-TSMC CMOS 0.18μm, the proposed envelope circuit is designed for 52 GHz frequency. The forward body biasing method is employed to decrease the circuit energy consumption. The proposed circuit can achieve 152MHz cut-off frequency over the operating frequency range and has power dissipation of 87.5μW under 1V.

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computer vision for road imaging and pothole detection:a state-of-the-art review of systems and algorithms

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Transportation Safety and Environment 2022年第4期4卷 3-18页

作者： Nachuan Ma Jiahe Fan Wenshuo Wang JinWu Yu Jiang Lihua Xie Rui Fan Department of Control Science and Engineering Frontiers Science Center for Intelligent Autonomous Systemsand State Key Laboratory of Intelligent Autonomous SystemsTongji UniversityShanghai 201804P.R.China Department of Civil Engineering McGill UniversityMontréalQC H3A 0C3Canada Department of Electronics and Computer Engineering the Hong Kong University of Science and TechnologyHong Kong SAR 999077P.R.China CTO Office ClearMotion Inc.BillericaMA 01821USA School of Electrical and Electronic Engineering Nanyang Technological University50 Nanyang Avenue639798Singapore

computer vision algorithms have been utilized for 3-D road imaging and pothole detection for over two ***,there is a lack of systematic survey articles on state-of-the-art(SoTA)computer vision techniques,especially deep learningmodels,developed to tackle these *** article first introduces the sensing systems employed for 2-D and 3-D road data acquisition,including camera(s),laser scanners and Microsoft *** then comprehensively reviews the SoTA computer vision algorithms,including(1)classical 2-D image processing,(2)3-D point cloud modelling and segmentation and(3)machine/deep learning,developed for road pothole *** article also discusses the existing challenges and future development trends of computer vision-based road pothole detection approaches:classical 2-D image processing-based and 3-D point cloud modelling and segmentation-based approaches have already become history;and convolutional neural networks(CNNs)have demonstrated compelling road pothole detection results and are promising to break the bottleneck with future advances in self/un-supervised learning for multi-modal semantic *** believe that this survey can serve as practical guidance for developing the next-generation road condition assessment systems.

关键词： computer vision road imaging pothole detection deep learning image processing point cloud modelling convolutional neural networks

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Towards Depth-continuous Scene Representation with Displacement Field for Robust Light Field Depth Estimation

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IEEE Transactions on Multimedia 2025年 27卷 3637-3649页

作者： Chen, Rongshan Sheng, Hao Yang, Da Cong, Ruixuan Cui, Zhenglong Wang, Sizhe Wang, Tun Zhao, Mingyuan Beihang University State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beijing100191 China Beihang University Key Laboratory of Data Science and Intelligent Computing International Innovation Institute 166 Shuanghongqiao Street Pingyao Town Yuhang District Hangzhou31 1115 China Macao Polytechnic University Faculty of Applied Sciences Macao999078 China

The light field (LF) captures both the spatial and angular information of scenes, enabling accurate depth estimation. However, previous deep learning methods typically model surface depth only while ignoring the continuous nature of depth in 3D scenes. In this paper, we use the displacement field (DF) to describe this continuous property, and propose a novel depth-continuous scene representation for robust LF depth estimation. Experiments demonstrate that our representation enables the network to generate highly detailed depth maps with fewer parameters and faster speeds. Specifically, inspired by the signed distance field in 3D object descriptions, we aim to exploit the intrinsic depth-continuous property of 3D scenes via DF and define a novel depth-continuous scene representation. Then, we introduce a simple, yet general, learning framework for depth-continuous scene embedding, and the proposed network, DepthDF, achieves state-of-the-art performance on both synthetic and real-world LF datasets, ranking 1st on the HCI 4D light field benchmark. Furthermore, previous LF depth estimation methods can also be seamlessly integrated into this framework. Finally, we extend this framework beyond LF depth estimation to various tasks, including multiview stereo depth inference, LF superresolution, and LF salient object detection. Our experiments demonstrate an improved performance when continuous scene representation is applied, suggesting that our framework can potentially provide insights for more fields. © 1999-2012 IEEE.

关键词： Stereo image processing

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Improving Signal-to-Noise Ratio (SNR) for Readout Signals Using Adaptive Filters on Reconfigurable Controls Hardware

Improving Signal-to-Noise Ratio (SNR) for Readout Signals Us...

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Quantum computing and engineering (QCE), IEEE International Conference on

作者： Hans Johnson Silvia Zorzetti Jafar Saniie Department of Electrical and Computer Engineering Embedded Computing and Signal Processing (ECASP) Research Laboratory Illinois Institute of Technology Chicago IL U.S.A. Fermi National Accelerator Laboratory Superconducting Quantum Materials and Systems Center (SQMS) Batavia IL U.S.A.

This study investigates the optimization of Signal-to-Noise Ratio (SNR) in superconducting quantum computing readout signals through adaptive filtering. Quantum computing technology has the potential to revolutionize various fields by delivering exponential speedup in solving certain computational problems. However, the technology's practical implementation is hindered by the difficulty of extracting clean, reliable signals during the readout phase, with various sources of noise presenting a significant barrier to clean signals. This noise, often present in readout profiles due to imperfect isolation, degrades the system's overall SNR, thus impeding the ability to extract the quantum state accurately. The research leverages the power of adaptive filtering to improve the SNR of quantum computing readout signals. Specifically, an adaptive filter is implemented in a PYNQ overlay on an FPGA, and eventually will be connected to a quantum computing system. The system models the noise with a Least Mean Squares (LMS) adaptive filter, and then subtracts the estimated noise from the received signal to improve the SNR. A Direct Memory Access (DMA) channel is used to handle the signal processing, delivering efficient, high-speed data transfer between the PYNQ system and the hardware. The study explores the benefits of this adaptive filtering technique, potentially providing a significant contribution to practical and fast quantum computing.

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A Classification and Pathology Study on Schizophrenia Based on Self-Attention Model

A Classification and Pathology Study on Schizophrenia Based ...

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computer Technology and Information Science (ISCTIS), International Symposium on

作者： Guibing Li Weidong Jin Rude Lin Ying Tan Jiaqing Miao School of Electrical Engineering Southwest Jiaotong University Chengdu China The Key Laboratory for Computer Systems of State Ethnic Affairs Commission Southwest Minzu University Chengdu China School of Mathematical Southwest Minzu University Chengdu China

Schizophrenia is a severe mental disorder characterized by symptoms such as hallucinations and delusions that significantly impair patients' daily life. Magnetic resonance imaging has provided high-quality data support for research on psychiatric disorders due to its high imaging resolution and low side effects. However, conventional medical analysis methods based on statistics require a considerable amount of manual feature engineering. Moreover, machine learning and traditional deep learning methods struggle to perform well on high-dimensional MRI data, resulting in a bottleneck in further schizophrenia research. In this study, the self-attention mechanisms are applied to design a more efficient neural network for disease classification and pathological analysis, which does not depend on conventional convolutional approach. One significant advantage of self-attention mechanisms is that they can fully extract global representations, enabling the model to effectively leverage the complex MRI data. Additionally, by extracting the self-attention maps of the model, the proposed approach can be applied to detect critical pathological brain regions related to schizophrenia. It achieved end-to-end, purely data-driven pathological analysis and significantly reduced the cost of feature engineering. Experimental results show that the average classification accuracy of the self-attention-based neural network model is 76.76%, superior to traditional classification methods. It is also revealed that that structural damage to the thalamus, cerebellum, and surrounding regions is the main cause of schizophrenia. This model provides new insights for schizophrenia research using MRI data and for understanding the disease mechanism.

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An AoI-Guaranteed Sensor Data Collection Strategy for RIS-assisted UAV Communication System

An AoI-Guaranteed Sensor Data Collection Strategy for RIS-as...

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IEEE International Conference on Communications in China (ICCC)

作者： Bowen Yang Yao Yu Jianqi Li Tongze Xu Der-Jiunn Deng Hui-Hsin Chin Yonghui Li School of Computer Science and Engineering Northeastern University Shenyang China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang China Department of Computer Science and Information Engineering National Changhua University of Education Department of Information Technology Overseas Chinese University School of Electrical and Information Engineering University of Sydney Sydney Australia

The recently proposed concept of Age of Information (AoI) measures the freshness of the sensor data sampled by remote Internet of Things (IoT) devices, which is an important indicator for the smart city. Unmanned Aerial Vehicles (UAV) communication can significantly reduce the sensor data’s AoI due to its high maneuverability. However, the UAV’s high energy consumption remains a challenging issue. In this paper, we propose a Reconfigurable Intelligent Surface (RIS) assisted UAV data collection framework to minimize UAV energy consumption. By adjusting the phase of the RIS, the UAV can collect more sensor data simultaneously. A RIS phase alignment method is introduced to reduce the complexity of RIS phase solving. The IoT devices are clustered to discretize UAV trajectory. The optimal clustering scheme to maximize the AoI suppression efficiency is found by adjusting the RIS phase alignment points continuously. Therefore, the AoI requirement can be achieved with a shorter UAV flight distance. Finally, the UAV trajectory is solved by the Deep Q network (DQN) algorithm to guarantee the long-term AoI requirement. Simulation results validate the effectiveness of the proposed method.

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Adrias: Interference-Aware Memory Orchestration for Disaggregated Cloud Infrastructures

Adrias: Interference-Aware Memory Orchestration for Disaggre...

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IEEE Symposium on High-Performance computer Architecture

作者： Dimosthenis Masouros Christian Pinto Michele Gazzetti Sotirios Xydis Dimitrios Soudris Microprocessors and Digital Systems Laboratory School of Electrical and Computer Engineering National Technical University of Athens Greece IBM Research Europe Dublin Ireland Department of Informatics and Telematics Harokopio University of Athens Greece

Workload co-location has become the de-facto approach for hosting applications in Cloud environments, leading, however, to interference and fragmentation in shared resources of the system. To this end, hardware disaggregation is introduced as a novel paradigm, that allows fine-grained tailoring of cloud resources to the characteristics of the deployed applications. Towards the realization of hardware disaggregated clouds, novel orchestration frameworks must provide additional knobs to manage the increased scheduling *** present Adrias, a memory orchestration framework for disaggregated cloud systems. Adrias exploits information from low-level performance events and applies deep learning techniques to effectively predict the system state and performance of arriving workloads on memory disaggregated systems, thus, driving cognitive scheduling between local and remote memory allocation modes. We evaluate Adrias on a state-of-art disaggregated testbed and show that it achieves 0.99 and 0.942 R 2 score for system state and application’s performance prediction on average respectively. Moreover, Adrias manages to effectively utilize disaggregated memory, by offloading almost 1/3 of deployed applications with less than 15% performance overhead compared to a conventional local memory scheduling, while clearly outperforms naive scheduling approaches (random and round-robin), by providing up to ×2 better performance.

关键词： Deep learning Memory management Interference Hardware Resource management

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