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UVM-Based Verification of OFDM Baseband processing System

UVM-Based Verification of OFDM Baseband Processing System

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International conference on Communication Engineering and Technology (ICCET)

作者： Chengcheng Zhao Jianning Yang Jiansong Tian Xin Deng Yuanhui Cai Mei Yang School of Information and Electronics Beijing Institute of Technology Beijing China School of Arts and Sciences Beijing Institute Of Fashion Technology Beijing China

With the development of very large scale integrated circuit (VLSI), verification becomes an indispensable step to ensure the normal operation of digital circuits. In the face of increasing complex large-scale digital design, universal verification methodology (UVM) has become the mainstream verification method due to its scalability, standardization, portability and other characteristics. Orthogonal frequency division multiplexing (OFDM) is an advanced multi-carrier transmission technology, which is widely used in the field of wireless communication. This paper presents a verification framework of OFDM baseband processing system based on UVM. UVM generates random excitation and passes it to our design, and automatically collects the results and compares them with the reference model to achieve sufficient verification of the system. Simulation results show the final functional coverage reaches 100%, demonstrating the validity of our verification framework.

关键词： Wireless communication Baseband OFDM Simulation Scalability signal processing algorithms Standardization

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FineQ: Software-Hardware Co-Design for Low-Bit Fine-Grained Mixed-Precision Quantization of LLMs

FineQ: Software-Hardware Co-Design for Low-Bit Fine-Grained ...

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Design, Automation and Test in Europe conference and Exhibition

作者： Xilong Xie Liang Wang Limin Xiao Meng Han Lin Sun Shuai Zheng Xiangrong Xu School of Computer Science and Engineering Beihang University Beijing China Jiangsu Shuguang Optoelectric Co. Ltd

ISBN: (数字)9783982674100

ISBN: (纸本)9798331534646

Large language models (LLMs) have significantly advanced the natural language processing paradigm but impose substantial demands on memory and computational resources. Quantization is one of the most effective ways to reduce memory consumption of LLMs. However, advanced single-precision quantization methods experience significant accuracy degradation when quantizing to ultra-low bits. Existing mixed-precision quantization methods are quantized by groups with coarse granularity. Employing high precision for group data leads to substantial memory overhead, whereas low precision severely impacts model accuracy. To address this issue, we propose FineQ, software-hardware co-design for low-bit fine-grained mixed-precision quantization of LLMs. First, FineQ partitions the weights into finer-grained clusters and considers the distribution of outliers within these clusters, thus achieving a balance between model accuracy and memory overhead. Then, we propose an outlier protection mechanism within clusters that uses 3 bits to represent outliers and introduce an encoding scheme for index and data concatenation to enable aligned memory access. Finally, we introduce an accelerator utilizing temporal coding that effectively supports the quantization algorithm while simplifying the multipliers in the systolic array. FineQ achieves higher model accuracy compared to the SOTA mixed-precision quantization algorithm at a close average bit-width. Meanwhile, the accelerator achieves up to 1.79x energy efficiency and reduces the area of the systolic array by 61.2%.

关键词： Quantization (signal) Accuracy Large language models Memory management Clustering algorithms Systolic arrays Encoding Energy efficiency Natural language processing Protection

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Optimization of supply chain in iron and steel industry based on pre flow advancing network flow algorithm and computer simulation

Optimization of supply chain in iron and steel industry base...

引用

signal processing, Communications and Engineering Management conference (ISPCEM), International

作者： Runtao Zhang Zhichao Zhang Green and Intelligent Building Industrial Internet Joint Laboratory Institute of Advanced Information Technology Peking University Hangzhou China

Under the relatively mature background of the iron and steel industry, the focus of industrial optimization is focused on the optimization of the logistics supply chain. In this paper, a large amount of data is extracted from the preventive maintenance inspection of the iron and steel industry supply chain, and a model to improve the transportation efficiency and test the maximum production and transportation capacity is constructed. Using the pre-flow propulsion network flow algorithm to solve the problem at high speed, it aims to quickly solve the model target value under the dynamic and diverse conditions. Taking the simulated industry situation in Zhejiang as an example, the network transport flow model and method proposed in this paper are verified to solve the problem of supply chain optimization and calculation in the iron and steel industry.

关键词： Heuristic algorithms Supply chains signal processing algorithms Steel industry Transportation Propulsion Iron

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International Youth conference on Electronics, Telecommunications, and Information Technologies, YETI 2021

International Youth Conference on Electronics, Telecommunica...

引用

International Youth conference on Electronics, Telecommunications, and Information Technologies, YETI 2021

ISBN: (纸本)9783030811181

The proceedings contain 64 papers. The special focus in this conference is on Electronics, Telecommunications, and Information Technologies. The topics include: Application of Deep Learning Techniques for Detection of COVID-19 Using Lung CT Scans: Model Development and Validation;preface;synthesis and Analysis of Avionics Functions Digital Twins Using Machine Learning Classification algorithms;algorithm for the Joint Analysis of Beat-To-Beat Arterial Pressure and Stroke Volume for Studying Systemic Vasoconstrictor and Vasodilator Responses;development of Simple Model of the Arterial Baroreflex;Analysis of Classification Methods for Wi-Fi signals Based on the Use of Channel State Information Spatial Features and CNN;application of Top-Down Deconvolution Tools to Bottom-Up Mass Spectrometry Data: Advantages and Limitations;algorithm for Fetal Activity Passive Monitoring;lossless Genome Data Compression Using V-Gram;The Method of Generating Random OQPSK signal Packets Based on Optimal FTN Pulses with Reduced PAPR and Frequency Band;iterative Algorithm with Feedback for Coherent Detection of Non-orthogonal Spectrally Efficient Multi-frequency signals;development of a Universal System for Three-Dimensional Data Visualization;Extracting Human Brain signals from the EEG Records Using LabVIEW and advanced signal processing;on the Application of a Fully Connected Neural Network for Multifrequency signals Demodulation;identification of Mortality Predictors for Elderly Patients with Airway Cancer;FPGA Implementation of Outer Decoders for Sequential Decoder of Polar Codes;integrated Digital Voltage Regulator with Phase-Frequency Control for Wireless Passive Microdevices;Aerosol Transport Modeling for Evaporator of CVD Reactor;Numerical Simulation of CVD Reactor for Oxide Semiconductor Layer Deposition;design of GaAs Flip-Flop Using Depletion-Mode-Only Transistors;comparative Study of Ion-Induced Damage Formation in GaN and beta-Ga2O3.

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Machine Learning-based Spectrum Sensing Techniques for Optimized Spectrum Utilization in Cognitive Radio Networks

Machine Learning-based Spectrum Sensing Techniques for Optim...

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Advancement in Technology (ICONAT), International conference for

作者： Pavan Chaudhary Rajesh Gupta H Malathi Maharishi University of Information Technology Uttar Pradesh India Department of Management Sanskriti University Mathura Uttar Pradesh India Department of Biotechnology and Genetics School of Sciences JAIN (Deemed to be University) Bangalore Karnataka India

ISBN: (数字)9798350354171

ISBN: (纸本)9798350354188

the usage of cognitive radios has enabled dynamic spectrum to get the right of entry to spectrum-congested regions. It has revolutionized how spectrum is allotted with the aid of turning the conventional static spectrum allocation into a dynamic and opportunistic method. Gadget studying-based totally spectrum sensing techniques have been advanced to permit optimized spectrum usage with better accuracy in cognitive radio networks. Those strategies use machine learning algorithms to learn the behaviors of spectrum assets and adaptively replace sensing strategies by intelligently deciding on the maximum suitable spectrum. This manner permits faster and more correct sensing in comparison to traditional spectrum sensing strategies. Furthermore, various fashions were proposed for specific spectrum sensing functionalities together with signal category, spectrum prediction, and function extraction. Those fashions are employed that allow you to maximize the spectrum assets utilization in cognitive radio networks. Furthermore, diverse processes have been proposed with the purpose of improving the overall performance of spectrum sensing techniques. Those consist of the usage of deep mastering fashions, exploiting heterogeneous facts sources, and using ensemble getting-to-know strategies. These approaches remember the complexity of the environment and permit extra correct and reliable choices. To conclude, device learning-based spectrum sensing techniques preserve a fantastic capacity for optimized spectrum utilization in cognitive radio networks. Consequently, they constitute a vital step forward closer to the improvement of green and reliable verbal exchange structures.

关键词： Performance evaluation Accuracy Noise signal processing algorithms Turning Sensors Complexity theory Cognitive radio Reliability Resource management

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Research on imaging coordinate system of time-domain algorithm in BI-SAR system

Research on imaging coordinate system of time-domain algorit...

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IET International Radar conference (IRC 2023)

作者： Peiyun An Tianyi Cai Yachao Li Qin Zhong Xuanqi Wang Yuzhou Liu National Key Laboratory of Radar Signal Processing Xidian University Xi'an People's Republic of China Beijing Institute of Remote-Sensing Equipment Beijing People's Republic of China Academy of Advanced Interdisciplinary Research Xidian University Xi'an People's Republic of China

ISBN: (数字)9781837240982

The time-domain imaging algorithm can meet the imaging requirements of bistatic synthetic aperture radar(Bi-SAR) system with flexible and complex configurations, and establishing the imaging coordinate system is the key step especially for Fast Time-Domain algorithms. In this paper, to simplify the establishment of imaging coordinate system, the orthogonal elliptical mapping polar (OEMP) coordinate system is proposed and used in the frame of the fast factorized back projection(FFBP) algorithm. Compared to the orthogonal elliptical polar(OEP) coordinate system, the proposed coordinate system has simpler coordinate solving, and can also reduce the computational burden of the FFBP algorithms. The applicability and performance of the OEMP-based FFBP algorithm is verified through simulation experiments.

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Multiple Target Vital Sign Detection Using Ultra-Wideband Radar

Multiple Target Vital Sign Detection Using Ultra-Wideband Ra...

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International conference on signal and Information processing (IConSIP)

作者： Asmita Wankhade Abhijeet V. Nandedkar S. M. Rathod Yashodip Amritkar Suryakant Rathod Ajay Sharma Shri Guru Gobind Singhji Institute of Engineering and Technology Nanded Maharashtra India Defence R&D- KCTI Bharat Forge Limited Pune Maharashtra INDIA

ISBN: (纸本)9781728168869

Detection of living beings through wall or under the rubble/debris is difficult in Radar due to low signal to noise ratio (SNR). To improve SNR and extract vital signs from Radar echo signal advanced signal processing techniques are required. The existing methods focus on detection of vital sign for single target only. This paper proposes an improvised method to detect multiple vital signs by determining the number of targets and their breathing frequency behind the wall using fast Fourier transform (FFT). Additionally an alternative technique using deep learning has been proposed for faster pre-processing and classification of number of targets. The proposed convolutional neural network (CNN) model is compared with other machine learning classifiers. It is found that if we used CNN as a feature extractor layer and Random Forest (RF) classifier it works the best with 94.38% accuracy (for two targets) and 73.33% accuracy (for three targets) without any pre-processing on Radar data. This method reduces the detection time needed as compared to the conventional algorithms available in literature

关键词： Radar detection signal processing algorithms Radar Feature extraction Convolutional neural networks Data mining Standards

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Intelligent Traffic Management System Using Real-Time Vehicle Detection and Dynamic signal Control with Client-Server Architecture

Intelligent Traffic Management System Using Real-Time Vehicl...

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Next Generation Communication & Information processing (INCIP), International conference on

作者： S.K. Lokeshwaran N.S. Lohith K. Venkatasubramanian Department of ECE Amrita School of Engineering Amrita Vishwa Vidyapeetham Chennai India

ISBN: (数字)9798331528140

ISBN: (纸本)9798331528157

This paper addresses one of the major problems that are present in modern society these days is traffic congestion, because it destroys productivity and creates frustration in the large crowds of commuters. Considering this prevalent issue in general populations, our project aims to use the new science developments to produce an effective solution for traffic management. Thus, we designed a smart traffic light system with respect to using multi-faceted approaches toward this critical problem. We began with implementing on the Raspberry Pi with YOLOv3. This was able to give decent results, but it had to be improved for better performance and scalability. So, we upgraded the architecture of the system to Jetson Nano, which grants more processing power and efficiency. Thus, we will have Jetson Nano acting as a client device, capturing the real-time video frames of traffic conditions in real time. These frames are then sent to a much larger server (PC) where they may be analyzed. That server will take in this data, apply advanced decision-making algorithms, and then provide back to the Jetson Nano the changes it needs to make in response in real-time. Our solution holds queuing of conventional traffic timers and balances the wait time among all the road users involved. Our smart traffic light system with machine learning applied to real-time data analysis presents a scalable and efficient framework to improve traffic flow, thus by consequence the general commuting experience. This paper discusses the methodologies applied, the results obtained, and the implications of our findings for future solutions in traffic management.

关键词： YOLO Productivity Vehicle detection Scalability Roads Streaming media Real-time systems Servers Vehicle dynamics Traffic congestion

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processing of experimental signals to assess the dynamic behavior of a model rotor system with passive and active magnetic bearings 2

Processing of experimental signals to assess the dynamic beh...

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2nd IEEE KhPI Week on advanced Technology, KhPI Week 2021

作者： Martynenko, Gennadii National Technical University Kharkiv Polytechnic Institute Department of Dynamics and Strength of Machines Kharkiv Ukraine

ISBN: (纸本)9781665412056

The paper considers a complete magnetic suspension of a small-sized horizontal rotor in passive radial and active axial magnetic bearings. It describes experimental studies of the dynamics of a model rotor in a combined passive-active magnetic suspension. The studies were performed using the developed specialized software and hardware complex for measuring and analyzing the dynamic characteristics of rotors with magnetic suspension systems. Its software includes special algorithms of the mathematical data processing for measuring and evaluating the main parameters of the signals characterizing the vibration displacement of the rotor points. The results are presented in the form of vibrograms of rotor support section motion in the radial direction, spectrograms of these signals and amplitude-frequency characteristics. The work shows how the proposed experimental analysis of the vibration state of a rotor system with magnetic bearings allows modifying the rotor design to reduce the vibration amplitudes at rotation speed values close to the critical ones. © 2021 IEEE.

关键词： signal processing

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Separation of Mixed Near-field and Far-field Sources and DOA Estimation based on Deep Learning

Separation of Mixed Near-field and Far-field Sources and DOA...

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International conference on advanced Infocomm Technology (ICAIT)

作者： Meibin Qi Weijie Ren Houhong Xiang Institude of Signal and Information Processing Hefei University of Technology Hefei China Radar Technology and Innovative Application Laboratory Hefei University of Technology Hefei China

Aiming at the common mixed sources including near-field(NF) and far-field(FF), this paper proposes a mixed sources direction-of-arrival(DOA) estimation method based on Deep Learning(DL). By using the real part and imaginary part of the mixed sources as the input of the neural network, after full separation, the expected FF signals and NF signals can be obtained at the output of the network. Then, the traditional estimation methods for a single source can be used to estimate DOA. The proposed method optimizes some defects existing in the estimation of mixed sources containing near and far fields in the past, such as the inability to separate the mixed sources effectively, and a large loss of array aperture. Simulation results show that the proposed method is effective. Under different SNR or channel error conditions, the proposed method has better DOA estimation accuracy and generalization ability than most super-resolution DOA estimation algorithms.

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