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Ubiquitous Computing and Intelligent Information Systems (ICUIS), International Conference on

作者： Sefemo Molefinyane Abid Yahya V. Hamsadhwani R. Rohini Ravi Samikannu K.Maruliya Begam Department of Electrical and Communications Systems Engineering Botswana International University of Science and Technology Palapye Botswana Department of Electrical and Electronics Engineering Periyar Maniammai Institute of Science and Technology Thanjavur India Department of Computer Science and Engineering Vivekanandha College of Engineering for Women Tiruchengode India Department of Computer Science and Communications Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technology Science Tiruchengode India

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529642

Intravenous (IV) therapy is a critical component of modern healthcare, yet traditional IV systems are prone to human errors, manual adjustments, and limited monitoring capabilities, which can jeopardize patient safety and hinder healthcare efficiency. This paper presents the development of the Smartdrip IV Infusion System, a sophisticated and automated solution that integrates advanced sensors, control algorithms, and intuitive user interfaces to address these limitations. The Smartdrip system minimizes human intervention, provides real-time monitoring, and automates fluid administration with enhanced precision and safety features. The system demonstrates high reliability through simulation and prototype testing in detecting anomalies, issuing alerts, and ensuring accurate infusion rates, thereby improving patient outcomes and reducing healthcare provider workload. The results validate the Smartdrip system as a transformative approach to optimizing IV therapy in diverse clinical settings.

关键词： Fluids Medical treatment Prototypes User interfaces Sensor phenomena and characterization Ubiquitous computing Sensor systems Real-time systems Safety Testing

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Security Improvement for Deep Learning-Based Semantic Communication Systems

Security Improvement for Deep Learning-Based Semantic Commun...

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International Conference on Advanced Technologies for communications, ATC

作者： Hung Ngo Luu Tan Van-Dinh Nguyen Thien Huynh-The Toan-Van Nguyen Phuong Luu Vo School of Computer Science and Engineering International University-VNU HCMC Vietnam Vietnam National University Ho Chi Minh City Vietnam College of Engineering and Computer Science & Center for Environmental Intelligence VinUniversity Hanoi Vietnam Department of Computer and Communications Engineering HCMC University of Technology and Education

ISBN: (数字)9798350353983

ISBN: (纸本)9798350353990

Semantic communication is becoming increasingly popular for wireless image transmission due to its superior communication efficiency. However, current deep learning-based semantic systems designed for semantic communication, though efficient, remain vulnerable to eavesdropping and often overlook security measures at the physical layer. To address this issue, this paper presents a deep learning-based system with joint source-channel coding (JSCC) and cyclical consistent generative adversarial network that enhances the security of semantic communication systems. We also design a convolutional neural network for the encoder and decoder which is trained to extract and transmit semantic features while minimizing the risk of privacy leakage. The artificial noise at the physical layer is employed at the source to degrade the eavesdropping ability of the eavesdropper. We show through experiments that under the artificial noise strategy, the legitimate user achieves a higher structural similarity index measure (SSIM) and peak signal-to-noise ratio (PSNR) than the eavesdropper. Moreover, the semantic system with JSCC offers better SSIM and PSNR than the separated source and channel coding models while preserving the confidentiality of semantic information during wireless transmission. This enhanced security framework opens new opportunities for secure and reliable communication of semantic information in diverse applications.

关键词： Wireless communication PSNR Noise Semantic communication Feature extraction Physical layer Decoding Security Communication system security Eavesdropping

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Performance Analysis of CPU Utilization in Cloud Environments using Machine Learning Models

Performance Analysis of CPU Utilization in Cloud Environment...

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Distributed Systems, computer Networks and Cybersecurity (ICDSCNC), International Conference on

作者： Archana Naik Kavitha Sooda Karunakara Rai B Department of Computer Science and Engineering Nitte Meenakshi Institute of Technology Bangalore India Department of Computer Science and Engineering B. M. S. College of Engineering Bangalore India Department of Electronics and Communications Nitte Meenakshi Institute of Technology Bangalore India

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

Cloud environments offer extensive services, making them a preferred choice for businesses and startups to host and maintain databases. Efficient resource management, including automatic scaling, is crucial for sustaining virtual machine environments. This paper addresses the challenge of forecasting CPU workloads to optimize resource allocation. Terraform was utilized to create and launch instances in the cloud environment, specifically using Amazon Web Services (AWS). A CPU-intensive application was executed on the server, and CPU utilization data was collected at regular intervals to serve as the dataset for the experiment. Three models—Gated Recurrent Unit, Long Short-Term Memory Autoencoder, and Seasonal Auto-Regressive Integrated Moving Average—were compared for short-term workload prediction. The results demonstrate that SARIMA outperforms the LSTM autoencoder by 2.91%, GRU by 1.78%, and LSTM autoencoder by 1.13% based on MAPE, MSE, MAE, and RMSE metrics.

关键词： Measurement Cloud computing Web services Autoencoders Predictive models Virtual machines Performance analysis Resource management Servers Long short term memory

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A Generative Denoising Approach for Near-Field XL-MIMO Channel Estimation

A Generative Denoising Approach for Near-Field XL-MIMO Chann...

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2024 IEEE Global communications Conference, GLOBECOM 2024

作者： Jin, Zhenzhou You, Li Kwan Ng, Derrick Wing Xia, Xiang-Gen Gao, Xiqi Southeast University National Mobile Communications Research Laboratory Nanjing210096 China Purple Mountain Laboratories Nanjing211111 China University of New South Wales School of Electrical Engineering and Telecommunications Sydney2052 Australia University of Delaware Department of Electrical and Computer Engineering NewarkDE19716 United States

ISBN: (纸本)9798350351255

In this paper, we investigate the near-field (NF) channel estimation (CE) for extremely large-scale multiple-input multiple-output (XL-MIMO) systems. Considering the pronounced NF effects in XL-MIMO communications, we first establish a joint angle-distance (AD) domain-based spherical-wavefront physical channel model that captures the inherent sparsity of XL-MIMO channels in the NF region. Leveraging the sparsity of the channel, the CE is approached as a task of reconstructing sparse signals. Anchored in this framework, we first propose a compressed sensing algorithm to acquire a preliminary channel estimation. Harnessing the powerful latent representation capability of generative artificial intelligence (GenAI), we further propose a GenAI-based approach to refine the estimated channel by employing advanced image denoising techniques. Specifically, we perceive the estimated channel as a noisy color image. Then, we derive the evidence lower bound (ELBO) of the design objective utilizing variational inference and reparameterization techniques, and propose a generative diffusion probabilistic model (GDM) dedicated to denoising. Experimental results indicate that the proposed GDM is capable of offering substantial performance gain in CE compared to existing benchmark approaches in NF XL-MIMO systems. © 2024 IEEE.

关键词： Compressed sensing

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Dual-Level Data Compression and Reduction in IoT Networks: Enhancing Efficiency through Sensor Node and Gateway Optimization

Dual-Level Data Compression and Reduction in IoT Networks: E...

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Artificial Intelligence and Emerging Technology (Global AI Summit), International Conference on

作者： Pushan Kumar Dutta Pronaya Bhattacharya Sushil Kumar Singh El-Sayed M. El-kenawy Richa Pandey School of Engineering and Technology Amity University Kolkata Computer Engineering Department Marwadi University Rajkot Gujarat India Department of Communications and Electronics Delta Higher Institute of Engineering and Technology Mansoura Egypt Computer Science and Engineering Graphic Era Hill University Haldwani Uttarakhand

ISBN: (数字)9798350379716

ISBN: (纸本)9798350379723

The proliferation of Internet of Things (IoT) devices has led to unprecedented challenges in data management and transmission efficiency. This paper introduces a novel dual-level approach to data compression and reduction in IoT networks, addressing these challenges by optimizing data handling at both sensor node and gateway levels. Unlike conventional single-level techniques, our method provides a more comprehensive solution to data optimization in IoT ecosystems. At the sensor node level, we implement Run Length Encoding (RLE) compression, while at the gateway level, we integrate progressive data clustering and reduction strategies. This dual-level approach demonstrates significant improvements in data reduction compared to single-point optimization methods. Our comparative analysis reveals the synergistic effects of combining node-level compression with gateway-level data reduction, resulting in enhanced overall system performance. We evaluate the scalability and adaptability of our approach across diverse IoT applications, considering energy efficiency implications for resource-constrained devices. Additionally, we discuss alternative data reduction techniques such as attribute reduction, canonical polyadic decomposition, and edge computing-based filtering, contextualizing our method within the broader landscape of IoT data optimization. The paper also examines the impact of our approach on network bandwidth utilization and explores future directions for multi-level data optimization in complex IoT architectures. Our findings suggest that the proposed dual-level method offers a promising solution for managing the growing data challenges in IoT networks, paving the way for more efficient and scalable IoT systems.

关键词： Temperature measurement Wireless communication Wireless sensor networks Data compression Bandwidth Logic gates Encoding Energy efficiency Internet of Things Artificial intelligence

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Scene Plane Recognition via Depth-Infrared Intensity Fusion Using ToF Camera

Scene Plane Recognition via Depth-Infrared Intensity Fusion ...

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2024 Conference on Lasers and Electro-Optics, CLEO 2024

作者： Yang, Dongzhao An, Dong Wang, Qiang Pan, Zhongqi Yue, Yang School of Information and Communications Engineering Xi'an Jiaotong University Xi'an710049 China Institute of Modern Optics Nankai University Tianjin300350 China Technology co. LTD. Tianjin300450 China Department of Electrical & Computer Engineering University of Louisiana at Lafayette LafayetteLA70504 United States

A scene plane information recognition method is demonstrated based on data fusion using a single ToF camera. This approach effectively tackles general LiDAR's deficiencies in identifying planar content, achieving ... 详细信息

ISBN: (纸本)9781957171395

关键词： Cameras

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Direct sum and partitionability testing over general groups 48

Direct sum and partitionability testing over general groups

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48th International Colloquium on Automata, Languages, and Programming, ICALP 2021

作者： Bogdanov, Andrej Prakriya, Gautam Department of Computer Science and Engineering Institute of Theoretical Computer Science and Communications Chinese University of Hong Kong Hong Kong Institute of Theoretical Computer Science and Communications Chinese University of Hong Kong Hong Kong

A function f(x1,⋯, xn) from a product domain D1 ×· · ·×Dn to an abelian group G is a direct sum if it is of the form f1(x1) + · · · + fn(xn). We present a new 4-query direct sum... 详细信息

ISBN: (纸本)9783959771955

A function f(x1,⋯, xn) from a product domain D1 ×· · ·×Dn to an abelian group G is a direct sum if it is of the form f1(x1) + · · · + fn(xn). We present a new 4-query direct sum test with optimal (up to constant factors) soundness error. This generalizes a result of Dinur and Golubev (RANDOM 2019) which is tailored to the target group G = 2. As a special case, we obtain an optimal affinity test for G-valued functions on domain {0, 1}n under product measure. Our analysis relies on the hypercontractivity of the binary erasure channel. We also study the testability of function partitionability over product domains into disjoint components. A G-valued f(x1,⋯, xn) is k-direct sum partitionable if it can be written as a sum of functions over k nonempty disjoint sets of inputs. A function f(x1,⋯, xn) with unstructured product range Rk is direct product partitionable if its outputs depend on disjoint sets of inputs. We show that direct sum partitionability and direct product partitionability are one-sided error testable with O((n - k)(log n + 1/ϵ) + 1/ϵ) adaptive queries and O((n/ϵ) log2(n/ϵ)) nonadaptive queries, respectively. Both bounds are tight up to the logarithmic factors for constant ϵ even with respect to adaptive, two-sided error testers. We also give a non-adaptive one-sided error tester for direct sum partitionability with query complexity O(kn2(log n)2/ϵ). © 2021 Andrej Bogdanov and Gautam Prakriya.

关键词： Errors

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Green Machine Learning Approach for QoS Improvement in Cellular communications 2

Green Machine Learning Approach for QoS Improvement in Cellu...

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2nd IEEE International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and computer engineering, MI-STA 2022

作者： Saeed, Mamoon M. Saeed, Rashid A. Azim, Mohammad Abdul Ali, Elmustafa Sayed Mokhtar, Rania A. Khalifa, Othman Faculty of Engineering Communications and Electronics Engineering Department Yemen Taif University College of Computers and Information Technology Department of Computer Engineering Taif21944 Saudi Arabia Asian University for Women Department of Computer Science Bangladesh Department of Electrical Electronic Engineering Port Sudan Sudan College of Electronics Engineering Khartoum Sudan International Islamic University Malaysia Faculty of Engineering Dept of Electrical and Electronics Engineering Malaysia

ISBN: (数字)9781665479189

ISBN: (纸本)9781665479189

Green cellular communications are becoming an important approach due to large-scale and complex radio networks. Due to the dynamic cellular network behaviors related to interference distribution, traffic bottlenecks, congestion points, and hotspots, there is a need to evaluate the dynamic processes in cellular systems in addition to ensuring spectrum availability. The delay, loss rate, and SNR are the most issues that may affect cellular communication performance. Artificial intelligent algorithms such as machine learning (ML) enable to detection of the dynamics in cellular networks by analyzing the complex cellular network processes and evaluating the spectrum and links qualities. It enables the extraction of spectrum knowledge from the network autonomously. The extracted information helps to know about every dynamic change in wireless parameters, related to frequency, modulation, route selection, etc. This paper provides details about the use of ML in green cellular networks to efficiently upgrade the communications and enhances different related approaches including quality of services (QoS), signal traffic load, and energy efficiency, which are critical issues of green cellular communication paradigms. The paper also presents the technical concept of green ML approaches to solve significant problems in cellular communications, in addition to future aspects and considerations for energy consumption minimization using the green ML approach in cellular radio communications. © 2022 IEEE.

关键词： Energy efficiency

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Supply and Threshold Voltage Scaling for Minimum Energy Operation over a Wide Operating Performance Region

Supply and Threshold Voltage Scaling for Minimum Energy Oper...

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作者： Sonoda, Shoya Shiomi, Jun Onodera, Hidetoshi The Department of Communications and Computer Engineering Kyoto University Kyoto-shi606-8501 Japan

A method for runtime energy optimization based on the supply voltage (Vdd) and the threshold voltage (Vth) scaling is proposed. This paper refers to the optimal voltage pair, which minimizes the energy consumption of LSI circuits under a target delay constraint, as a Minimum Energy Point (MEP). The MEP dynamically fluctuates depending on the operating conditions determined by a target delay constraint, an activity factor and a chip temperature. In order to track the MEP, this paper proposes a closed-form continuous function that determines the MEP over a wide operating performance region ranging from the above-threshold region down to the sub-threshold region. Based on the MEP determination formula, an MEP tracking algorithm is also proposed. The MEP tracking algorithm estimates the MEP even though the operating conditions widely change. Measurement results based on a 32-bit RISC processor fabricated in a 65-nm Silicon On Thin Buried oxide (SOTB) process technology show that the proposed method estimates the MEP within a 5% energy loss in comparison with the actual MEP operation. Copyright © 2021 The Institute of Electronics, Information and Communication Engineers.

关键词： Voltage scaling

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24.9-GHz-bandwidth VCSEL enables 170-Gbit/s OFDM, GFDM, and DMT transmissions

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Photonics Research 2024年第2期12卷 377-390页

作者： Wu, Wei-Li Cheng, Chih-Hsien Matsumoto, Atsushi Akahane, Kouichi Yang, Yun-Cheng Su, Borching Wu, Chao-Hsin Feng, Milton Lin, Gong-Ru Graduate Institute of Photonics and Optoelectronics Department of Electrical Engineering National Taiwan University Taipei10617 Taiwan Optical Access Technology Laboratory Photonic ICT Research Center Network Research Institute National Institute of Information and Communications Technology Koganei184-8795 Japan Graduate Institute of Electrical Engineering Department of Electrical Engineering National Taiwan University Taipei10617 Taiwan Graduate Institute of Communication Engineering Department of Electrical Engineering National Taiwan University Taipei10617 Taiwan Department of Electrical and Computer Engineering Microelectronics and Nanotechnology Laboratory University of Illinois at Urbana–Champaign ChampaignIL61801 United States NTU-Tektronix Joint Research Center National Taiwan University Tektronix Inc. Taipei10617 Taiwan

The direct modulation and fiber transmission performances of a bi-layer-oxide-confined 850-nm vertical cavity surface emitting laser (VCSEL) containing an emission aperture size of 3.5 μm with versatile novel complex data formats are compared. With delivering quadrature amplitude modulation-orthogonal frequency-division multiplexing (QAM-OFDM), generalized frequency-division multiplexing (GFDM), and discrete multi-tone (DMT) data streams, the highest data rate allowable for encoding the 3.5-μm-aperture VCSEL is demonstrated under back-to-back (BtB) and 100-m OM5-multi-mode fiber (OM5-MMF) transmissions. The newly designed 3.5-μm-aperture VCSEL contains InGaAs/AlGaAs multiple quantum wells and bi-layer-oxide-confined aperture to enhance its differential gain, reduce its thermal resistance, and improve its parasitic resistance for providing broader encoding bandwidth. Optimizing the VCSEL bias at 9 mA (11Ith) makes the lasing with three to four transverse modes suffer from a low modal dispersion effect, which also provides its 3-dB bandwidth as wide as 24.9 GHz and suppresses its RIN level to −138.0 dBc∕Hz for broadband OFDM/GFDM/DMT data encoding. By using the pre-distortion technique on the OFDM to compensate the SNR degradation, the OFDM modified with a pre-leveling slope of 0.5 dB/GHz can provide 160 Gbit/s for BtB and 140 Gbit/s for 100-m OM5-MMF transmissions. By dividing the N OFDM subcarriers into K equally-sized GFDM subcarriers and M equally-sized GFDM subsymbols, the QAM-GFDM with K = 2 can effectively enhance the out-of-band suppression to 35.9 dB and reduce the peak-to-average-power ratio to 9.7 dB, which can support 168 Gbit/s under BtB and 144 Gbit/s in 100-m OM5-MMF transmissions. To achieve the maximal spectral-usage efficiency, the DMT with the adaptive bit-loading algorithm is employed to improve the data rate up to 172 Gbit/s for BtB and 146 Gbit/s for 100-m OM5-MMF conditions. These advanced pre-leveled 16-QAM OFDM, GFDM, and DMT formats carried b

关键词： Surface emitting lasers

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