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Delhi Section Conference (DELCON), IEEE

作者： Kiran Sharma Keshav Kumar Arnika Patel Rocky Kumar Arpita Meet Vaidya Suraj Singh Department of Computer Science Engineering Parul University Vadodara Gujrat India Department of Electronics and Communication Engineering Pranveer Singh Institute of Technology Kanpur Uttar Pradesh India Department of Computer Scince Engineering Poornima University Jaipur Rajasthan India

ISBN: (数字)9798331518592

ISBN: (纸本)9798331518608

In the age of Internet of Things (IoT), IoT gadgets are getting popularity. The IoT devices better work when they have better lifetime and this better lifetime of such devices are achieved when they consume low amount of power. The low power consumption (PC) can be achieved when the processor of such IoT devices also consumes low power. This work highlights all such points which enhances the life of IoT devices. In this work we are designing and implementing a Reduced Instruction Set Computing (RISC) processor on Kintex Ultrascale+ FPGA devices. To optimize the power of RISC processor for better IoT services we have used Pseudo Open Drain (POD) IO standard at different range of frequency between 100 MHz to 5 GHz. After the power calculation, it is detected that the RISC processor delivers low power when it is operated at low frequency values and consumes more power at high frequency values.

关键词： Reduced instruction set computing Power demand Internet of Things High frequency Standards Optimization Field programmable gate arrays

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AI-based Learning Techniques for Bladder Cancer Detection

AI-based Learning Techniques for Bladder Cancer Detection

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Pervasive Computational Technologies (ICPCT), International Conference on

作者： Sandeep Kumar Saurabh Ankita Aggarwal Aryan Institute of Technology Ghaziabad India Department of Computer Science & Engineering Chandigarh College of Engineering Chandigarh Group of Colleges Mohali India Department of Electronics & Communication Engineering Chandigarh Engineering College Chandigarh Group of Colleges Mohali India

ISBN: (数字)9798331508685

ISBN: (纸本)9798331519476

There is no doubt that the main issue to address in these patients is the early diagnosis of Bladder carcinoma. This work aims at initiating a new path in this research and undertakes advanced artificial intelligence (AI) and machine learning to enhance bladder cancer diagnosis. Bladder cancer is one of the more widespread malignant neoplasms, it is more prevalent in the male population and particularly in male gerontocracy. Conventional diagnostic approaches are hypertrophied and crippled by constraints that lead to diagnostic imperfections and unkind lateness. There stands a great opportunity to deal with these challenges through the implementation of Artificial Intelligence in healthcare. By incorporating the modern artificial intelligence models such as CNN and RNN along with other modern techniques, this research intends to improve the diagnostic outcomes and provide strong prognosis. It is through a comprehensive method of data preprocessing and feature engineering, an incorporation of ethical factors, and the use of complex algorithms that this research hopes to advance the future of precision medicine. In this context, the customized solutions based on the most effective data also expand the scope of the diagnosis of bladder cancer and bring positive changes to the treatment of patients.

关键词： Recurrent neural networks Precision medicine Medical services Learning (artificial intelligence) Bladder Convolutional neural networks Artificial intelligence Prognostics and health management Neoplasms Cancer

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An Active Reconfigurable Mixer in GaN Technology for 5G Power Amplifier Wi-Fi communications

An Active Reconfigurable Mixer in GaN Technology for 5G Powe...

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engineering and Technology (PICET), Parul International Conference on

作者： Rajesh Kumar Tosif Abdul Gaffar Babu Binod Kumar Kanaujia Devesh Tiwari Department of Computer Science & Engineering Galgotias College of Engineering &Technology Greater Noida India Department of Electronics & Communication of Engineering PIET Vadodara Vadodara Gujarat India Department of Electronics & Communication of Engineering National Institute of Technology Jalandhar Jawaharlal Nehru University New Delhi India Department of Computer Science & Engineering Graphic Science and Engineering Graphic Era Era Hill University Dehradun Uttarakhand India

ISBN: (数字)9798350369748

ISBN: (纸本)9798350369755

The mixer's key attributes in this article are its high power output, high conversion gain, high efficiency, and good isolation between board positions. We have designed and implemented a mixer that can be reconfigured dynamically. Using a voltage gain of 12.3 dB, the local oscillator (LO) signal is amplified. The purpose of the active reconfigurable mixer is to enhance the cross-driver platform's performance. Alongside, with the occurrence of a mixer a high-power level is attained. It is noteworthy to highlight that the third-order intermodulation (IIP3) of the new configuration is −9 dBm, which is regarded as negligible. The mixer runs at 6 GHz and is powered by $\mathbf{0.15}-\boldsymbol{\mu} \mathbf{m}$ GaN technology.

关键词： Local oscillators Power supplies Noise Power amplifiers Linearity Voltage Mixers Gain Wireless fidelity Power generation

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Dynamic range enhancement for the sensing signals of peak-saturated fiber Bragg grating spectra

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Optics Letters 2023年第21期48卷 5555-5558页

作者： Sung, Jiun-Yu Liaw, Shien-Kuei Kishikawa, Hiroki Department of Electronic and Computer Engineering National Taiwan University of Science and Technology Taipei10607 Taiwan Graduate Institute of Electro-Optical Engineering National Taiwan University of Science and Technology Taipei10607 Taiwan Heterogeneously-Integrated Silicon Photonic Integration Center National Taiwan University of Science and Technology Taipei10607 Taiwan Department of Optical Science Tokushima University Tokushima770-8506 Japan

Fiber Bragg grating (FBG) sensors applying time-delay interrogators with wavelength swept lasers (WSLs) are popular for their great potentials in high sensing resolution and power budget. In these systems, well-calibrated WSLs with reduced wavelength nonlinearity and jitter are critical for the sensing performance. However, high-performance WSLs are expensive and could significantly increase the cost of the systems. The overall cost may be reduced by maximally sharing each WSL with multiple sensing FBGs through mechanisms like power splitting, which distribute the WSL signal into multiple independently operated serial FBG chains. Under such scenarios, the sensing processing unit (SPU) of each serial FBG chain must be synchronized with the WSL for correctly estimating the FBGs' respective spectra from the signal return time delays. We previously propose a self-synchronized scheme relying on the dual-polarity spectrum signal, which reduces the synchronization labor. The dual-polarity signal has a wider dynamic range, which may limit the system response speed or accuracy, considering the amplifiers' responses or the analog-to-digital converters' (ADCs') quantization noise. In this Letter, we apply peak-saturated FBG spectra for the sensors to increase the receivers' equivalent dynamic range. The flattopwaveforms of the saturated peaks result in uncertainty for the peak positions. An artificial neutral network (ANN)-based method is further studied to enhance the peak detection accuracy. We show an ∼88% receiver dynamic range improvement with an inaccuracy reduction of about a half compared to the filter-and-maximum-readout (FMR) method. © 2023 Optica Publishing Group.

关键词： Fiber Bragg gratings

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Technologically Assisted Multifunctional Smart Bag Based on Solar Energy

Technologically Assisted Multifunctional Smart Bag Based on ...

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Sustainable Computing and Integrated communication in Changing Landscape of AI (ICSCAI), International Conference on

作者： Meenakshi Maindola Zaid Alsalami Arti Badhoutiya Shrikant Upadhyay Ramya R B Santhosh Kumar Ruchika Vats Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Department of computers Techniques engineering College of technical engineering The Islamic university Najaf Iraq Department of Electrical Engineering GLA University Mathura Department of Electronics and Communication Engineering MLR Institute of Technology Hyderabad India Department of Electronics and Communication Engineering Saveetha school of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Institute of Aeronautical Engineering Hyderabad Department of Management G.L. Bajaj Institute of Technology & Management Greater Noida

ISBN: (数字)9798350355338

ISBN: (纸本)9798350355345

The solar power industry's technological advances are continually evolving, and advancements will accelerate in the next years. The efficiency of solar panels and the electrical output of solar power systems could both be improved by innovations in solar energy potential uses. Solar energy is absolutely renewable, which is the most significant advantage of solar panels. Solar panels for homes and businesses are becoming increasingly widespread, although they have not yet been made portable. electronic devices like laptops, smartphones, and tablets have grown significantly in recent years. Additionally, regular charging must be done for these utilities to operate properly. The biggest problem, however, is that these gadgets need to be charged frequently. This paper suggests a smart bag model based on IoT and solar panels. The smart bag has a foldable solar panel on it and can hold weight. This baggage includes a variety of tools, including Tiffin heaters, spy cameras, GPS trackers, and connectors for charging mobile phones.

关键词： Renewable energy sources Technological innovation Tracking Solar energy Cameras Power industry Power systems Solar panels Smart phones Global Positioning System

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Random Access Preamble Detection in OFDM Systems Using Constant-Amplitude Sequences

Random Access Preamble Detection in OFDM Systems Using Const...

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IEEE Conference on Vehicular Technology (VTC)

作者： Po-Jen Chen Yen-Chia Tseng Shih-Hao Lu Wei-Chang Chen Char-Dir Chung Graduate Institute of Communication Engineering National Taiwan University Taipei Bomdic Inc. Taipei Department of Electronic Engineering National Taipei University of Technology Taipei

ISBN: (数字)9798331517786

ISBN: (纸本)9798331517793

In uplink cellular environments, random access preamble detection (RAPD) is achieved by identifying possibly multiple received requesting orthogonal frequency-division multiplexing (OFDM) signals carrying arbitrarily-chosen constant-amplitude (CA) preamble sequences through threshold comparison. Such threshold-based RAPD is analytically studied in terms of false alarm, outage, and false identification probabilities for the receiver operating under independent Rician/Rayleigh multipath channels and in the presence of time offsets among multiple received requesting OFDM signals. The RAPD system is shown to operate under less influence of inter-sequence interference when the preamble sequence set consists of mutually orthogonal CA sequences and contains as many ϖ min -permissible cyclically-shiftable constant-amplitude (CSCA) sequences as possible, where assigning (MPMA) and Zadoff-Chu sequence families, which are min -permissible CSCA sequences are cyclically shifted from each other with a minimum cyclic-shift-distance ϖ min in time domain. The performance characteristics are thus demonstrated for the RAPD systems adopting the modified phase-model-assigning (MPMA) and Zadoff-Chu sequence families, which are comprised of orthogonal and nonorthogonal, respectively, ϖ min -permissible CSCA sequence subfamilies. While constraining upper false-alarm and outage probability bounds, the system adopting the MPMA sequence family is shown to provide much smaller false-identification probability than the system adopting the Zadoff-Chu sequence family.

关键词： Vehicular and wireless technologies OFDM Receivers Interference Probability Power system reliability Frequency division multiplexing Uplink Time-domain analysis Multipath channels

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Transmit Power Optimization of IoT Devices over Incomplete Channel Information

Transmit Power Optimization of IoT Devices over Incomplete C...

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IEEE Conference on Global communications (GLOBECOM)

作者： Nirmal D. Wickramasinghe Indrakshi Dey Department of Electronic Engineering Maynooth University Ireland Walton Institute of Information and Communication Science Ireland

Efficient resource allocation (RA) strategies within massive and dense Internet of Things (IoT) networks is one of the major challenges in deployment of IoT-network based smart ecosystems involving heterogeneous power-constrained IoT devices operating in varied radio and environmental conditions. In this paper, we focus on the transmit power minimization problem for IoT devices while maintaining a threshold channel throughput. The established optimization literature is not robust against the fast-fading channel and the interaction among different transmit signals in each instance. Besides, realistically, each IoT node possesses incomplete channel state information (CSI) on its neighbors, such as the channel gain being private information for the node itself. In this work, we resort to Bayesian game theoretic strategies for solving the transmit power optimization problem exploiting incomplete CSIs within massive IoT networks. We provide a steady discussion on the rationale for selecting the game theory, particularly the Bayesian scheme, with a graphical visualization of our formulated problem. We take advantage of the property of the existence and uniqueness of the Bayesian Nash equilibrium (BNE), which exhibits reduced computational complexity while optimizing transmit power and maintaining target throughput within networks comprised of heterogeneous devices.

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Improved Political Optimizer and Deep Neural Network-based Resource Management Strategy for fog Enabled Cloud Computing 2

Improved Political Optimizer and Deep Neural Network-based R...

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2nd IEEE Mysore Sub Section International Conference, MysuruCon 2022

作者： Prakash, M. Vijayaganth, V. Shadrach, Finney Daniel Menaha, R. Daniya, T. Guha, Tapas Vit University School of Computer Science and Engineering Vellore India Kpr Institute of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Coimbatore India Kpr Institute of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Coimbatore India Sri Eshwar College of Engineering Department of Information Technology Tamil Nadu Coimbatore India Gmr Institute of Technology Department of Information Technology Andhra Pradesh Rajam India Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India

ISBN: (数字)9781665497909

ISBN: (纸本)9781665497909

Cloud-based processing of Internet of Things (IoT) applications isn't always the best option, especially when it comes to time-sensitive applications. For managing the massive amounts of data bandwidth required by end devices, fog and edge computing offer a viable alternative. Large volumes of newly generated data must be processed locally, rather than in the cloud, according to these paradigms. Allocating resources, balancing workloads, providing resources, scheduling tasks, and ensuring quality of service are all aspects of resource management that play an important role in cloud-based IoT systems with the goal of improving system performance. In this paper, we discuss different approaches to resource management in the context of cloud, fog, and edge computing. We introduce a methodology Improved Political optimizer (IPO) with Deep Neural Networks (DNN) for ranking resource management algorithms in the context of cloud, fog, and edge systems. Therefore, we start with the challenges of resource management in that industry. Current research contributions can be better categorised, which will aid in the creation of an evaluation scheme. There is a substantial addition made by reviewing and analysing research papers on methods for resource management. Existing Fog resource managers only account for a subset of Fog resource management criteria, such as system network bandwidth and Response Time. The administration of computing assets in the cloud, fog, and at the network's periphery are the primary topics of our studies. The purpose of this analysis is to develop a set of criteria for judging algorithms used for managing resources in cloud, fog, and edge environments. We employed Deep Neural Networks and the Improved Political Optimizer in our search for better methods of managing available resources (DNN). © 2022 IEEE.

关键词： Deep neural networks

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Personalized Multimedia Recommendation Systems Using Higher-Order Tensor Singular-Value-Decomposition

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IEEE Transactions on Broadcasting 2024年第1期70卷 148-160页

作者： Chang, Shih Yu Wu, Hsiao-Chun Yan, Kun Chen, Xinjia Huang, Scott Chih-Hao Wu, Yiyan San Jose State University Department of Applied Data Science San JoseCA95192 United States Louisiana State University School of Electrical Engineering and Computer Science Baton RougeLA70803 United States Yuan Ze University Innovation Center for AI Applications Chungli32003 Taiwan Guilin University of Electronic Technology Guangxi Key Laboratory of Wireless Wideband Communication and Signal Processing Department of Information and Telecommunication Guilin541004 China Northwestern State University Department of Engineering Technology NatchitochesLA71497 United States National Tsing Hua University Department of Electrical Engineering Hsinchu300 Taiwan Communications Research Centre Canada OttawaK2H 8S2 Canada

Because of rapid growth of multimedia data over the Internet, the infobesity has been emerging in recent years. Many recommender systems (RSs) have been proposed using a variety of techniques, including artificial intelligence, machine learning, statistical analysis, and data mining. Nowadays, E-commerce companies such as Amazon, eBay, Netflix, etc. have applied RSs conveniently to provide better customer services than before by enabling customized multimedia-content recommendations. It is well known that classical RSs are almost all based on the user-item attribute matrices. However, this two-fold user-item relationship should be extended to the emerging many-fold relationship among an arbitrary number of attribute groups so that the corresponding new RSs may lead to better performances. Therefore, in this paper, we introduce a novel personalized multimedia recommender system (PMRS) according to our proposed new parallel higher-order tensor singular-value-decomposition (PHOTSVD) algorithm. This PHOTSVD algorithm is capable of accommodating an arbitrary number of entities such as social-media channels, users, queries, webpages, etc. in a robust RS. Furthermore, we also investigate the cold start and data bias problems critical to RSs. Finally, we compare the performances of our proposed new PMRS and three other existing tensor-based RSs over realworld data with respect to the well-adopted metric, namely normalized root-mean-square error (NRMSE). Our proposed new PMRS greatly outperforms the other three existing tensor-based RSs in terms of NRMSE. © 1963-12012 IEEE.

关键词： Singular value decomposition

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Enhancing gait parameter analysis for Cerebral Palsy using Attention modules

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Gait & Posture 2024年 113卷 225-226页

作者： John Bosco Uroko Donging Gu Haider Raza Liang Hu University of Essex School of Computer Science and Electronic Engineering Essex United Kingdom Harbin Institute of Technology Department of Automation- School of Mechanical and Electrical Engineering and Automation Shenzhen China

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