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检索条件"机构=Dept. of Electrical and Computer Engineering and Computer Science"

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A Wearable RF Energy Harvester Based on Spoof Surface Plasmonic Antenna Array

A Wearable RF Energy Harvester Based on Spoof Surface Plasmo...

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2022 International Applied Computational Electromagnetics Society Symposium, ACES-China 2022

作者： Liu, Zhu Li, Jinxin Xiao, Pei Hunan Normal University Dept. Physics and Electronics Changsha China Hunan University Dept. Computer Science and Electronic Engineering Changsha China Hunan University Dept. Electrical and Information Engineering Changsha China

ISBN: (纸本)9781665452366

This paper presents a wearable RF energy harvester based on spoof surface plasmonic (SSP) antenna array. Four separate antennas are connected by the four bending SSP waveguides, and the SSP antenna array is formed. The loop is applied to excite the SSP structure to generate surface waves and converted to the radiative wave into the space. The rectify circuit is integrated with loop to output DC power and fabricated by the polyimide so that it can conform with different sensing modules. The proposed harvester is fabricated by the conductive and nylon fabrics in a compact and low-profile structure to be easily integrated with clothing. The maximum harvested RF-DC power is 126.24 μ W under a power density of 2.75 μ W/cm2 when the proposed structure is worn on phantom. The simulation and measurement results demonstrate that the wearable RF energy harvester has the potential to power body sensor network. © 2022 IEEE.

关键词： Antenna arrays

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Exploring emerging spreaders through GCN-based link prediction and a novel centrality method

Exploring emerging spreaders through GCN-based link predicti...

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International Communication Systems and Networks and Workshops, COMSNETS

作者： Suman Nandi Giridhar Maji Animesh Dutta Dept. of Computer Science and Engineering National Institute of Technology Durgapur West Bengal India Dept. of Electrical Engineering Asansol Polytechnic West Bengal India

ISBN: (数字)9798331531195

ISBN: (纸本)9798331531201

Exploring influential spreaders and predicting missing links in complex networks is essential for understanding and effectively controlling network dynamics. This paper presents a Graph Convolutional Network (GCN)-based link prediction method to estimate the probability of future link formation. We incorporate node features that capture local and global topological connectivity structures and feed these into the GCN model, where convolutional layers aggregate neighboring information and transform node features. This approach enables the model to capture structural patterns by integrating local and global information from neighboring nodes. In the final layer, the GCN model computes a prediction score representing the likelihood of an edge’s existence, using insights gained during training. Finally, considering the predicted links, we update the network structure and introduce a novel centrality method called Emerging Spreader Centrality (ESC) to identify emerging spreaders within this augmented network. We conduct two separate experiments to evaluate the performance of the GCN-based link prediction and the ESC method, comparing their effectiveness with various state-of-the-art methods. Results demonstrate that our approach not only effectively predicts future links but also identifies emerging spreaders in the augmented networks.

关键词： Training Graph convolutional networks Communication systems Computational modeling Aggregates Transforms Complex networks Predictive models Feeds

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Classification Anterior and Posterior of Knee Osteoarthritis X-Ray Images Grade KL-2 Using Deep Learning with Random Brightness Augmentation

Classification Anterior and Posterior of Knee Osteoarthritis...

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2022 International Conference on computer engineering, Network and Intelligent Multimedia, CENIM 2022

作者： Supatman Yuniarno, Eko Mulyanto Purnomo, Mauridhi Hery Institut Teknologi Sepuluh Nopember FTI UMBY Dept. of Electrical Engineering Dept. of Informatics Surabaya60111 Indonesia Institut Teknologi Sepuluh Nopember Dept. of Electrical Engineering Dept. of Computer Engineering Surabaya60111 Indonesia

ISBN: (纸本)9781665476508

Osteoarthritis of the knee (KOA) is a narrowing of the joint space area (JSA) due to lack of fluid in the knee joint, resulting in pain when moving and when it is severe, the femur and tibia meet. Medical personnel and existing computer-based methods have been able to detect patients with X-rays but have not been able to detect where visually the posterior AJ is still wide while the anterior AJ is actually narrow, so that the patient still feels joint pain. A new approach is proposed for the classification of X-Ray Grade Kellgren-Lawrence (KL)-2 Osteoarthritis Initiative image datasets using Deep Learning Neural Networks (DCNN) by setting the Random Brightness Augmentation hyperparameter. The experimental results obtained X-Ray Grade KL-2 narrowing image classification 'Anterior View KOA' and 'Posterior View KOA' with training accuracy of 83.33% and validation accuracy of 54.69% at Random Brightness with a value of 30. © 2022 IEEE.

关键词： computer vision

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Leg Movement Controlled Robotic Leg for Menace Preclusion

Leg Movement Controlled Robotic Leg for Menace Preclusion

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2024 IEEE SmartBlock4Africa, SmartBlock4Africa 2024

作者： Sitti, Mensah Antwiwaa, Anita Danquah-Amoah, Archibald Govindan, Kannan Martey, Ezekiel Mensah University of Mines and Technology Dept of Computer Science and Engineering Tarkwa Ghana All Nations University College Dept. of Electronics and Communication Engineering Koforidua Ghana Accra Technical University Dept. of Medical Laboratory Accra Ghana University of Health and Allied Sciences Dept. of Orthotics and Prosthetics Ho Ghana

ISBN: (纸本)9798350376838

Movement of autonomous humanoid is a key area, as we gradually accept robots. This research proposes an 'Artificial Human Leg Model' which can precisely follow the real movements of human leg by means of image processing thereby avoiding sensor noise and damage and providing more adjustable response. Webcams are used to capture the leg images followed by Morphological image processing to extract information from human leg movement. The four degrees of freedom implemented in the proposed method are rotation of base, leg like motions, knee like motions, and steps motions. The robot leg is controlled successfully using human leg movement, where the system can travel a distance, manoeuvre in an uneven surface and move the robot from one place to other with precision. The user can control the leg entirely based on his/her intentions to accomplish the required task. The proposed real-time method of implementing human leg movement also ensures immediate control and performance. Distant control of the robotic leg does not only ensure complete safety to human, but also allows flexibility in controlling distant objects. The overall goal of the proposed system is to guarantee safety, flexibility and accuracy in movements when performing sensitive tasks in day-to-day life activities thus offering suitability for menace preclusion. © 2024 IEEE.

关键词： Motion capture

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Accelerating supersingular isogeny Diffie-Hellman (SIDH) cryptosystem for the security of resource-constrained IoT devices with FPGA 3

Accelerating supersingular isogeny Diffie-Hellman (SIDH) cry...

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3rd IEEE International Conference on Electronic engineering, ICEEM 2023

作者： Khadra, Shaimaa Abo Ismail, Nabil A. Attiya, Gamal M. Abdulrahman, Salah Eldin S. E. High Institute of Engineering and Technology Dept. Mechatronics Engineerng Al-Mahalla-El-Kobra Egypt Menoufia University Faculty of Electronic En. Dept. Computer Science & Engineering Menouf Egypt

ISBN: (纸本)9798350323511

It is imperative to note that post-quantum cryptography, such as supersingular isogeny Diffie-Hellman (SIDH), is essential for ensuring that Internet of Things (IoT) devices have a restricted amount of resources. The primary challenges in assuring the security of IoT devices are addressed by this work's analysis of SIDH implementations designed for field programmable gate array (FPGA) architecture. Extensive efforts towards implementing the architecture for a rapid and constant-time FPGA implementation of SIDH. This quantum-resistant cryptographic primitive is a crucial component for adhering to NIST's PQC standardization. Our goal with this paper is to demonstrate how FPGA architectures can enhance the parallelism of SIDH, making it a more practical option for securing resource-limited IoT devices. Our focus is on providing a reliable speed record for SIDH, which is crucial for ensuring the security of IoT devices. The design for isogeny computation was built over p434 in Xilinx Virtex 6 and produced 45ms for public key generation in addition to 35ms for secret key generation. © 2023 IEEE.

关键词： Internet of things

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Performance Comparison of IEEE 802.11be and 802.11ax in Terms of Throughput

Performance Comparison of IEEE 802.11be and 802.11ax in Term...

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computer sciences, engineering, and Technology Innovation (ICoCSETI), International Conference on

作者： Elang Prasakti Ghani Bellia Dwi Cahya Putri Adian Fatchur Rochim Erwin Adriono Delphi Hanggoro Adnan Fauzi Dept. of Electrical Engineering Diponegoro University Semarang Indonesia Dept. of Computer Engineering Diponegoro University Semarang Indonesia

ISBN: (数字)9798331508616

ISBN: (纸本)9798331508623

IEEE 802.11be, the seventh-generation wireless protocol, will be released soon and will perform better than IEEE 802.11ax, the sixth-generation wireless technology. The performance of competing wireless protocols at the same operating frequency—5 GHz—is reviewed in this study. We use Network Simulator (NS-3) as a simulation tool that offers flexibility, shorter setup time, and makes it easy to experiment for any scenario we need to do. Furthermore, this paper focuses on analyzing and comparing the throughput of the IEEE 802.11be (Extremely High Throughput MCS) (EhtMcs) and 802.11ax (High Efficiency MCS) (HeMcs) protocols with different client counts and specific payload sizes. Spatial flow, channel width, Guard Interval (GI), Modulation and Coding Scheme (MCS), and simulation duration are among the other parameters that are set to certain values. Simulation results show that the IEEE 802.11be Modulation and Coding Scheme (MCS-13) protocol has better throughput performance than the IEEE 802.11ax Modulation and Coding Scheme (MCS-11) with many clients. In the simulation, the access point node is accessed from 2 to 128 clients. Simulation results show that in every situation, IEEE 802.11be performs better than IEEE 802.11ax. Wi-Fi 7 achieves up to 18 percent higher throughput for lower client counts and up to 35 percent higher throughput in scenarios with 64 to 128 clients, demonstrating its enhanced efficiency and reliability for high-density networks.

关键词： Wireless communication Technological innovation Protocols Simulation computer network reliability Modulation IEEE 802.11ax Standard Throughput Encoding Wireless fidelity

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Battery Degradation Heuristics for Predictive Energy Management in Shipboard Power Systems

Battery Degradation Heuristics for Predictive Energy Managem...

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Annual Conference of Industrial Electronics Society

作者： Satish Vedula Ayobami Olajube Koto Omiloli Olugbenga Moses Anubi Dept. of Electrical and Computer Engineering FAMU-FSU College of Engineering Tallahassee USA

ISBN: (数字)9781665464543

ISBN: (纸本)9781665464550

The presence of Pulse Power Loads (PPLs) in the Notional Shipboard Power System (SPS) presents a challenge in the form of meeting their high ramp rate requirements. Considering the ramp rate limitations on the generators, this might hinder the power flow in the grid. Failure to meet the ramp rate requirements might cause instability. Aggregating generators with energy storage elements usually addresses the ramp requirements while ensuring the power demand is achieved. This paper proposes an energy management strategy that adaptively splits the power demand between the generators and the batteries while simultaneously considering the battery degradation and the generator’s efficient operation. Since it is challenging to incorporate the battery degradation model directly into the optimization problem due to its complex structure and the degradation time scale which is not practical for real-time implementation, two reasonable heuristics in terms of minimizing the absolute battery power and minimizing the battery state of charge are proposed and compared to manage the battery degradation. A model predictive energy management strategy is then developed to coordinate the power split considering the generator efficiency and minimizing the battery degradation based on the two heuristic approaches. The designed strategy is tested via a simulation of a lumped notional shipboard power system. The results show the impact of the battery degradation heuristics for energy management strategy in mitigating battery degradation and its health management.

关键词： Degradation Phase change materials Power demand Power system stability Predictive models Generators Real-time systems Batteries State of charge Energy management

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Real-Time Flow Scheduling in Industrial 5G New Radio

Real-Time Flow Scheduling in Industrial 5G New Radio

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Real-Time Systems Symposium (RTSS)

作者： Tianyu Zhang Jiachen Wang Xiaobo Sharon Hu Song Han Dept. of Computer Science and Engineering University of Connecticut Dept. of Computer Science and Engineering University of Notre Dame

Among the many industrial wireless solution candidates, 5G New Radio (NR) has drawn significant attention in recent years due to its capabilities to support ultra-high-speed communication, ultra-low latency, and massive connectivity. Despite its great potential, 5G NR also brings significant complexity in scheduling industrial data flows to meet their hard real-time requirements. In this paper, we first leverage a real-world 5G RAN testbed to benchmark the downlink throughput and explore the impact of modulation and coding scheme (MCS) selection on the network performance. We then formulate a real-time flow scheduling problem in industrial 5G NR, which features per-flow real-time schedulability guarantees through time-frequency-space resource allocation. We propose a novel two-phase scheduling framework, named 5G-TPS, to construct the schedule that meets the deadlines of all the flows. To adapt to dynamic channel conditions, 5G-TPS enables online schedule adjustment for affected flows to meet their timing requirements. To evaluate the performance of 5G-TPS, we present a case study of a motion control panel use case and perform extensive experiments. The results show that 5G-TPS can achieve schedulability ratios comparable to the Satisfiability Modulo Theory (SMT)-based exact solution and outperform many other state-of-the-art scheduling approaches, including the built-in 5G NR schedulers.

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On-Demand Current Pulse Activation of RF Monopole Antenna Biosensor Arrays with Nitrocellulose Membranes

On-Demand Current Pulse Activation of RF Monopole Antenna Bi...

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2022 United States National Committee of URSI National Radio science Meeting, USNC-URSI NRSM 2022

作者： Shanmugadas, Sindu Lundsquit, Jonathan Topsakal, Erdem Ozgur, Umit Avrutin, Vitaliy Virginia Commonwealth University Dept. of Electrical and Computer Engineering RichmondVA23284 United States

ISBN: (数字)9781946815156

ISBN: (纸本)9781946815156

Continuous glucose monitors currently on the market are expensive and uncomfortable due to their short operational lifespans of 14 days or less limited by biofouling. To address this problem, we propose a biosensor array skin patch that would increase the device functional lifetime significantly. Each sensor, based on a quarter wave monopole antenna, is housed in a PDMS well. The sensors read changes in voltage reflection coefficient (dB). Combustible nitrocellulose membranes prevent early contact of sensors with the interstitial fluid. A current pulse passed through a metal filament sufficiently disintegrated the membrane on top to allow interstitial fluid to enter thereby activating a new sensor. © 2022 USNC-URSI.

关键词： Monopole antennas

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Lung Disease Classification Using Deep Learning Models from Chest X-ray Images 3

Lung Disease Classification Using Deep Learning Models from ...

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3rd International Conference on Intelligent Communication and Computational Techniques, ICCT 2023

作者： Sultana, Salma Pramanik, Anik Rahman, Md. Sadekur Jahangirnagar University Dept. of Statistics Dhaka Bangladesh Virtual Multidisciplinary Research Lab Dhaka Bangladesh Daffodil International University Dept. of Computer Science & Engineering Dhaka Bangladesh

ISBN: (纸本)9781665453578

In the very recent past, Infectious disease-related sickness has long posed a concern on a global scale. Each year, COVID-19, pneumonia, and tuberculosis cause a large number of deaths because they all affect the lungs. Early detection and diagnosis can increase the likelihood of receiving quality treatment in all circumstances. A low-cost, simple imaging approach called chest X-ray imaging enables to detection and screen lung abnormalities brought on by infectious diseases for example Covid-19, pneumonia, and tuberculosis. This paper provided a thorough analysis of current deep-learning methods for diagnosing Covid-19, pneumonia, and TB. According to the research papers reviewed, Deep Convolutional Neural Network is the most used deep learning method for identifying Covid-19, pneumonia, and TB from chest X-ray (CXR) images. We compared the proposed DNN to well-known DNNs like Efficient-NetB0, DenseNet169, and DenseNet201 in order to more accurately assess how well it performed. Our findings are equivalent to the state-of-the-art, and since the proposed CNN is lightweight, it may be employed for widespread screening in areas with limited resources. From three diverse publicly accessible datasets merged into one dataset, the suggested DNN generated the following precisions for that dataset: 99.15%, 98.89%, and 97.79% for EfficientNetB0, DenseNet169, and DenseNet201 respectively. The proposed network can help radiologists make quick and accurate diagnoses because it is effective at identifying COVID-19 and other lung contagious disorders utilizing chest X-ray images. This paper also gives young scientists a good insight into how to create CNN models that are highly efficient when used with medical images to identify diseases early. © 2023 IEEE.

关键词： COVID-19

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