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Optimizing Channel Availability in WLAN 6GHz networks Using Automatic Frequency Coordination(AFC) System

Optimizing Channel Availability in WLAN 6GHz Networks Using ...

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Australasian Telecommunication networks and Applications conference, ATNAC

作者： Ashish Sheikh Sr.DSP Engineer (SM-IEEE) Cisco Systems Richfield USA

ISBN: (数字)9798350379112

ISBN: (纸本)9798350379129

the Federal Communications Commission (FCC) has authorized the use of 1200 MHz in the 6 GHz (5.925 - 7.125 GHz) band by Wi-Fi devices in the US, while ensuring the protection of fixed services and microwave links through Automated Frequency Coordination (AFC). As per the FCC rules, the Wi-Fi 6E/7 Access Points (APs) in the local area networks query the AFC to obtain a list of permitted 6 GHz channels for APs to operate in standard power mode (indoor APs) and allowed power levels (for outdoor deployments). the existing system operates under the assumption that the incumbents transmit continuously, which results in reduced channel availability for WLAN networks. this paper proposes improvements to the AFC system by validating the presence of incumbents in the area using an off-channel radio to monitor the spectral characteristics of incumbents. this approach enables 6 GHz WLAN networks to access an increased number of channels without causing interference to the incumbents.

关键词： Automatic frequency control FCC Microwave devices Interference Microwave communication Communications technology Protection Wireless fidelity Standards Monitoring

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A voting mechanism-based approach for identifying key nodes in complex networks

A voting mechanism-based approach for identifying key nodes ...

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International conference on Tools for Artificial Intelligence (ICTAI)

作者： Jun Chen Xuesong Jiang Xiumei Wei Yihong Li Qilu University of Technology (Shandong Academy of Sciences) Jinan city Shandong Province China Queen's University Kingston Ontario Canada

Many mechanisms, such as epidemic spread, rumor spread, and the spread of social emergencies, are closely related to complex network dynamics, and mining their key nodes plays an important role in understanding the structure and function of the network and maintaining its stable operation. In response to the problem that the key node identification methods in complex networks cannot comprehensively consider global and local information and ignore low-degree nodes, this study proposes a new method based on the voting mechanism. Firstly, the CI value of the network nodes is calculated using the CI algorithm, and initialized the voting ability of nodes by CI values, fully considering the local information of the nodes as well as the influence of low-degree nodes. Secondly, the concept of voting probability is introduced to distinguish the votes of network nodes for their different neighboring nodes through the voting probability, to consider more local information, and to comprehensively assess the importance of the nodes, and ultimately, it is more important to get nodes with the larger voting score. Comparing several classical key node identification methods, the experimental results show that this method can effectively identify key nodes and has a high accuracy rate in different complex networks.

关键词： computer science Epidemics Complex networks Artificial intelligence

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After Diversity Maximum Demand Enabled Congestion and Constraint Analyses for Distribution Transformers

After Diversity Maximum Demand Enabled Congestion and Constr...

引用

Australasian Universities Power Engineering conference, AUPEC

作者： Abdullah Attahir Yusuf Nirmal-Kumar C Nair Department of Electrical Computer and Software Engineering University of Auckland Auckland New Zealand

ISBN: (数字)9798350377941

ISBN: (纸本)9798350377958

this paper analyses low voltage (LV) networks using After Diversity Maximum Demand (ADMD) to assess and address operational challenges such as transformer overload, network congestion, and voltage constraints. Utilizing data from Horizon networks, the paper conducts congestion and constraint analyses for high-risk transformers under various future scenarios. the analyses, which are performed using PowerFactory, identify potential bottlenecks and operational limits in LV networks to inform future grid development and investment decisions. the results demonstrate that while network congestion may not pose an immediate threat, voltage constraints, especially in longer LV feeder runs, represent a significant risk by 2050. Mitigating these risks through optimizing ADMD-based planning is crucial for ensuring a sustainable and reliable power supply in the face of growing electrification demands. Recommendations for network enhancements and further research into smart meter data integration for validating ADMD assumptions are also discussed.

关键词： Electric potential Analytical models Electricity Voltage Transformers Smart meters Reliability Faces Load modeling Investment

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Resilience Enhancement Techniques in Cyber Physical Distribution System: an Overview

Resilience Enhancement Techniques in Cyber Physical Distribu...

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Australasian Universities Power Engineering conference, AUPEC

作者： Jialun Zhang Jimmy Peng Chih-Hsien Gabriela Hug Future Resilient Systems Singapore-ETH Centre Singapore Electrical and Computer Engineering National University of Singapore Singapore Power System Laboratory ETH Zurich Switzerland

ISBN: (数字)9798350377941

ISBN: (纸本)9798350377958

the evolution of power systems into cyberphysical distribution systems (CPDSs) integrates complex communication networks with traditional power infrastructures, thereby exposing systems to both cyber and physical disruptive events. this paper investigates resilience enhancement strategies designed for CPDS, focusing on countermeasures against natural hazards, malicious attacks, technical failures within communication networks, and interdependent cyber-physical failures. First, we present the structure of CPDS and define resilience within this context and differentiate it with the definition of reliability and cybersecurity. Subsequently, we categorize failures into four types of disruptive events and evaluate the state-of-the-art resilience enhancement strategies, respectively. Finally, we highlight key research gaps and propose future directions to enhance the resilience of CPDS.

关键词： Wireless communication Power engineering Reviews Network topology Microgrids Hazards Power system reliability Communication networks Reliability Resilience

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FieldHAR: A Fully Integrated End-to-End RTL Framework for Human Activity Recognition with Neural networks from Heterogeneous Sensors

FieldHAR: A Fully Integrated End-to-End RTL Framework for Hu...

引用

International conference on Application Specific Systems (ASAP), Architectures and Processors

作者： Mengxi Liu Bo Zhou Zimin Zhao Hyeonseok Hong Hyun Kim Sungho Suh Vitor Fortes Rey Paul Lukowicz German Research Center for Artificial Intelligence (DFKI) Kaiserslautern Germany Department of Computer Science RPTU Kaiserslautern-Landau Kaiserslautern Germany Department of Electrical and Information Engineering Seoul National University of Science and Technology Korea

In this work, we propose an open-source scalable end-to-end RTL framework FieldHAR, for complex human activ-ity recognition (HAR) from heterogeneous sensors using artificial neural networks (ANN) optimized for FPGA or ASIC integration. FieldHAR aims to address the lack of apparatus to transform complex HAR methodologies often limited to offline evaluation to efficient runtime edge applications. the framework uses parallel sensor interfaces and integer-based multi-branch convolutional neural networks (CNNs) to support flexible modality extensions with synchronous sampling at the maximum rate of each sensor. To validate the framework, we used a sensor-rich kitchen scenario HAR application which was demonstrated in a previous offline study. through resource-aware optimizations, with FieldHAR the entire RTL solution was created from data acquisition to ANN inference taking as low as 25% logic elements and 2% memory bits of a low-end Cyclone IV FPGA and less than 1% accuracy loss from the original FP32 precision offline study. the RTL implementation also shows advantages over MCU-based solutions, including superior data acquisition performance and virtually eliminating ANN inference bottleneck.

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SONA: An Accelerator for Transform-Domain Neural networks with Sparse-Orthogonal Weights

SONA: An Accelerator for Transform-Domain Neural Networks wi...

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International conference on Application Specific Systems (ASAP), Architectures and Processors

作者： Pierre Abillama Zichen Fan Yu Chen Hyochan An Qirui Zhang Seungkyu Choi David Blaauw Dennis Sylvester Hun-Seok Kim Department of Electrical and Computer Engineering University of Michigan MI USA

Recent advances in model pruning have enabled sparsity-aware deep neural network accelerators that improve the energy-efficiency and performance of inference tasks. We introduce SONA, a novel transform-domain neural network accelerator in which convolution operations are replaced by element-wise multiplications with sparse-orthogonal weights. SONA employs an output stationary dataflow coupled with an energy-efficient memory organization to reduce the overhead of sparse-orthogonal transform-domain kernels that are concurrently processed without any conflicts. Weights in SONA are non-uniformly quantized with bit-sparse canonical-signed-digit representations to reduce multiplications to simple additions. Moreover, for sparse fully-connected layers (FCLs), SONA introduces column-based-block structured pruning, which is integrated into the same architecture that maintains full multiply-and-accumulate (MAC) array utilization. Compared to prior dense and sparse neural networks accelerators, SONA can reduce inference energy by $5.1\times$ and $2.4 \times$ and increase performance by $5.2\times$ and $2.1\times$ , respectively, for convolution layers. For sparse FCLs, SONA can reduce inference energy by $2.4\times$ and increase performance by $2\times$ compared to prior work.

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Performance Comparison of OFDM and DFT-s-OFDM in the thz-Band Communications Channels

Performance Comparison of OFDM and DFT-s-OFDM in the THz-Ban...

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ieee International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

作者： Erfan Khorram Xiaodai Dong Department of Electrical and Computer Engineering University of Victoria British Columbia Canada

the terahertz (thz) band is a promising frequency band that ranges from 0.3 thz to 10 thz and being considered for the next generation 6G wireless networks due to the large available bandwidth and potentially ultra fast data rates. thz channels and systems are unique in three aspects: 1) high path loss; 2) rich scattering from rough surfaces; 3) severe phase noise from local oscillators. these pose questions on what physical waveforms should be used in thz channels. Since orthogonal frequency division multiplexing (OFDM) and discrete Fourier transform spread OFDM (DFT-s-OFDM) are adopted waveforms in 5G new radio (NR) and 4G, their possible use in the future generation wireless networks is an open question. In this paper, we first review a thz channel model for accurate representation of thz channels. then uncoded and coded OFDM and DFT-s-OFDM systems are investigated for their performance in thz channels, demonstrating their unique behaviors in these channels. the impact of phase noise is also examined, and a non-iterative compensation method slightly modified from the literature is used to mitigate the phase noise effect. DFT-s-OFDM shows advantages over OFDM in the studied thz environment. the simulation results in this paper can be used as a benchmark for future studies.

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Learning Diversified Feature Representations for Facial Expression Recognition in the Wild

Learning Diversified Feature Representations for Facial Expr...

引用

ieee Workshop on Machine Learning for Signal Processing

作者： Negar Heidari Alexandros Iosifidis Department of Electrical and Computer Engineering Aarhus University Denmark

ISBN: (数字)9798350372250

ISBN: (纸本)9798350372267

Diversity of the features extracted by deep neural networks is important for enhancing the model generalization ability in different learning tasks. Facial expression recognition in the wild has attracted interest recently due to the challenges existing for extracting discriminative features from occluded images in real-world scenarios. In this paper, we propose a mechanism to diversify the features extracted by CNN layers of facial expression recognition models for enhancing the model capacity in learning discriminative features. To evaluate the effectiveness of the proposed approach, we incorporate this mechanism in two state-of-the-art models to (i) diversify local/global features in an attention-based model and (ii) diversify features extracted by different learners in an ensemble-based model. Experimental results on three well-known facial expression recognition in-the-wild datasets, AffectNet, FER+ and RAF-DB, show the effectiveness of our method, achieving state-of-the-art performance of 89.99% on RAF-DB, 89.34% on FER+ and the competitive accuracy of 60.02% on AffectNet.

关键词： Image recognition Accuracy Face recognition conferences Artificial neural networks Signal processing Feature extraction Ensemble learning

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PreSyNC: Hardware realization of the Presynaptic Region of a Biologically Extensive Neuronal Circuitry 34

PreSyNC: Hardware realization of the Presynaptic Region of a...

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34th International conference on VLSI Design / 20th International conference on Embedded Systems (VLSID)

作者： Chatterjee, Rounak Chowdhury, Souradeep Mondal, Soham Raha, Arnab Paluh, Janet Mukherjee, Amitava Jadavpur Univ Dept Elect & Telecommun Engn Kolkata India Samsung Elect Co Ltd Memory Solut Bangalore Karnataka India Intel Corp Intel Edge AI Adv Architecture Res Santa Clara CA USA SUNY Polytech Inst Coll Nanoscale Sci Nanobiosci Albany NY USA SUNY Polytech Inst Coll Engn Nanobiosci Albany NY USA Adamas Univ Dept Comp Sci & Engn Kolkata India

ISBN: (纸本)9781665440875

Spiking Neural networks (SNN) have gained relevance in recent times, due to their ability to mimic the biological nature to communicate and process sparse asynchronous binary signals in a massively parallel fashion. SNN based neuromorphic hardware exhibits highly desired favourable properties such as low power consumption, fast inference, and event-driven information processing. A recognized challenge of standard SNN neuron models is their limited capabilities in biological applications, such as applying neural networks to study network responses arising from variations pertaining to damage, external influence or disruptions in channel transfer dynamics. this paper presents Pre-Synaptic Neuronal Circuit (PreSyNC), a high performance hardware realization of an input-specific presynaptic region of a generic neuron, without abstraction of primary intra-neuronal parameters. PreSyNC is configured to operate on three precision modes: ieee 754 single precision, double precision and the recently developed universal number posit number system. the developed hardware design is compared to current standards of SNN neuron models as well as biological models in terms of flexibility, resource efficiency and damage modelling capability. Error margins as low as 0.9% were obtained and suggest the capability of our hardware in handling applications involving large scale neuron networks. these architectures are synthesized on 45 nm process technology where they all operate at a minimum frequency of approximately 1GHz. the three precision modes are compared based on power, accuracy, and sensitivity handling and are expected to benefit implantation oriented applications such as neural prosthesis and Human-computer Interaction (HCI). the posit-based implementation outperforms the rest of the operating modes in terms of RMS error, while having 26.3% less area and 25.2% less power consumption compared to double precision implementation. these new architectures can be expanded in the future

关键词： Neuromorphic computation presynaptic region neural networks posit arithmetic

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ADMM-based Coordinated Control of Electric Vehicles in an Unbalanced Distribution Grid: Assessing Tolerance to Communication Failures

ADMM-based Coordinated Control of Electric Vehicles in an Un...

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Australasian Universities Power Engineering conference, AUPEC

作者： Nanduni Nimalsiri Elizabeth Ratnam CSIRO Newcastle Australia The Australian National University Canberra Australia

ISBN: (数字)9798350377941

ISBN: (纸本)9798350377958

In this paper, we propose an Alternating Direction Method of Multipliers (ADMM) approach combined with consensus optimization to coordinate electric vehicle (EV) charging and discharging in an unbalanced distribution grid. the underlying optimization problem has an objective function to minimize the customer energy costs, subject to a globally coupled network constraint in the form of a linear inequality (representing grid voltage and transformer core temperature limits) and local polyhedron constraints (representing customer charging requirements and limitations in charging infrastructure capacity). EV coordination is achieved by means of an iterative routine, wherein EVs (agents) exchange limited information with their neighboring EVs through a connected peer-to-peer communication network. Our proposed approach is also designed to accommodate time-varying communication networks arising from communication link faults and random disconnection of agents from communication channels. Numerical simulations verify the performance of our network-aware EV coordination approach to communication-based failures.

关键词： Power engineering Distribution networks Transformer cores Numerical simulation Linear programming Electric vehicle charging Peer-to-peer computing Synchronization Iterative methods Optimization

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