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A 69MHz-Bandwidth 40V/μ s-Slew-Rate 3n V/√Hz-Noise 4.5 μ V-Offset Chopper Operational Amplifier

IEEE Transactions on Circuits and Systems I: Regular Papers 2024年第7期71卷 2977-2988页

作者： Koolivand, Yarallah Rezaeiyan, Yasser Zamani, Milad Akbari, Meysam Shoaei, Omid Tang, Kea-Tiong Moradi, Farshad Khajeh Nasir Toosi University of Technology Faculty of Electrical Engineering Tehran16317-14191 Iran Aarhus University Department of Electrical and Computer Engineering Aarhus8000 Denmark University of Kurdistan Department of Engineering Sanandaj66177-15177 Iran University of Tehran Department of Electrical and Computer Engineering Tehran*** Iran National Tsing Hua University Faculty of Electrical Engineering Hsinchu300044 Taiwan

This paper presents a chopper-stabilized three-stage operational amplifier (OpAmp) with a unity gain bandwidth of 69 MHz and an input referred noise density of 3 nV√Hz. The proposed design achieves a stable unity gain by proposing a new pole and zero scheme with very low power consumption, drawing only 3.3 mA from a 1.8 V power supply while driving a load capacitor as large as 100pF. To achieve rail-to-rail input swing, the design uses both NMOS and PMOS differential pairs at the input and biases them in the subthreshold region to provide an identical net trans-conductance over the rail-to-rail input common mode. Furthermore, an adaptive biasing is employed and the current sources are kept ON during large signal transitions at the input, thus eliminating crossover distortion and providing a high slew rate of 40 V/μs at a 100 pF load capacitor. The design employs chopping at 2.5 MHz and is enhanced with a local ripple reduction loop, making the OpAmp suitable for high gain and wide bandwidth applications with less filtering required. The design also reduces the input bias current significantly from 500 nA to 1.5 nA by buffering the input and applying it to the modified bootstrap switches. The proposed OpAmp, fabricated in a 0.18μm CMOS process, exhibits a maximum offset of 4.5μV , a flicker noise corner frequency of 246 Hz, a DC gain of 146 dB, a power supply rejection ratio of 123 dB, and a common mode rejection ratio of 116 dB. © 2004-2012 IEEE.

关键词： Operational amplifiers

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NLP Research: A Historical Survey and Current Trends in Global, Indic, and Gujarati Languages 4

NLP Research: A Historical Survey and Current Trends in Glob...

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4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Panchal, Brijeshkumar Y. Shah, Apurva The Maharaja Sayajirao University of Baroda Faculty of Technology and Engineering Computer Science and Engineering Department Gujarat Vadodara India Computer Engineering Department Gujarat Anand India

ISBN: (纸本)9798331529635

This research study presents a comprehensive survey of Natural Language Processing (NLP) research, tracing its historical evolution from its inception to the present. The survey explores the key milestones and advancements in NLP, focusing on global trends, as well as specific contributions from Indic languages, with a particular focus on Gujarati. The paper analyzes the current state of Gujarati NLP research, identifying existing gaps and challenges. By providing a comprehensive overview of NLP research, this study aims to guide future research directions and foster advancements in the field, particularly in the context of Indic languages. © 2024 IEEE.

关键词： Natural language processing systems

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Stock Market Prediction Using Genetic Algorithm Assisted LSTM-CNN Hybrid Model 14

Stock Market Prediction Using Genetic Algorithm Assisted LST...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Nikhil, Shreyas Sah, Rahul Kumar Kumar Parki, Santosh Tamang, Til Bikram Somashekhara Reddy, D. Mahesh, T.R. Faculty of Engineering and Technology Dept. of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350335095

Stock price prediction has always been a tough task for all the stakeholders involved. This paper focusses on four different models, namely LSTM, CNN, LSTM-CNN, and Genetic Algorithm-Assisted LSTM-CNN (GA-LSTM-CNN) for stock price prediction. The day-to-day stock prices of a certain company for a period of more than 10 years make up the dataset utilized in this study. Root mean square error (RMSE) has been utilized as performance metric for each model. Outcomes of our experiment show that the GA-LSTM-CNN model performed better than other models with an RMSE value of 0.0064 upon training and an RMSE value of 0.03 upon testing. The GA-LSTM-CNN model combines the strengths of LSTM as well as CNN models with the help of a genetic algorithm, which helps in optimizing the hyperparameters. According to the results, the GA-LSTM-CNN model can be very helpful for the stakeholders in predicting future stock values. © 2023 IEEE.

关键词： Genetic algorithms

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Enhancing Trust-Based Attacker Detection in 5G Social Networks Through Advanced Artificial Intelligence Control

Enhancing Trust-Based Attacker Detection in 5G Social Networ...

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2024 International Conference on Knowledge engineering and Communication Systems, ICKECS 2024

作者： Raman, Ramakrishnan Kumar, Vikram Pillai, Biju G. Rabadiya, Dhaval Patre, Smruti Meenakshi, R. India Srv Media Pvt. Ltd India Faculty of Management It & Business Analytics Pune India Parul Institute of Technology Parul University Computer Science and Engineering Faculty of Engineering and Technology India India Chennai Institute of Technology Computer Science and Engineering India

ISBN: (纸本)9798350359688

In the evolving landscape of 5G social networks, the surge in connectivity and data exchange has amplified the susceptibility to malicious attackers, undermining the trust and security integral to these networks. This paper introduces an innovative approach to bolster trust-based attacker detection mechanisms through the integration of advanced artificial intelligence (AI) control techniques. Leveraging the prowess of machine learning (ML) algorithms and AI-driven analytics, our proposed framework enhances the accuracy and efficiency of identifying potential threats within 5G social networks. By implementing a hybrid model that combines anomaly detection algorithms with trust scoring systems, we aim to establish a robust security posture that dynamically adapts to emerging threats. The core of our work lies in the development of a predictive model that utilizes deep learning techniques to analyze network behaviors and user interactions, facilitating the early detection of anomalous activities indicative of malicious intent. Through extensive simulation and real-world application, our results demonstrate a significant improvement in detection rates and a reduction in false positives, thereby fortifying the trust and reliability of 5 G social networks. This study not only contributes to the advancement of secure network architectures but also sets a new precedent for the application of AI in cybersecurity, offering a scalable and efficient solution to safeguard against sophisticated attackers. © 2024 IEEE.

关键词： 5G mobile communication systems

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Multi-Modal Facial Expression Recognition through a Hierarchical Cross-Attention Graph Convolutional Network

Multi-Modal Facial Expression Recognition through a Hierarch...

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2024 International Conference on Knowledge engineering and Communication Systems, ICKECS 2024

作者： Raman, Ramakrishnan Kumar, Vikram Pillai, Biju G. Rabadiya, Dhaval Patre, Smruti Meenakshi, R. India SRV Media Pvt. Ltd. India Faculty of Management It & Business Analytics India Parul Institute of Technology Parul University Computer Science and Engineering Faculty of Engineering and Technology India India Chennai Institute of Technology Computer Science and Engineering India

ISBN: (纸本)9798350359688

In the rapidly evolving field of computer vision and machine learning, recognizing human facial expressions with high accuracy remains a pivotal challenge. This paper introduces a novel Multi-Modal Facial Expression Recognition (MMFER) framework, leveraging a Hierarchical Cross-Attention Graph Convolutional Network (HCA-GCN) to enhance the accuracy and robustness of facial expression recognition. Our proposed HCA-GCN model systematically integrates multi-modal data sources, including visual imagery and depth information, to capture the complex and subtle nuances of human facial expressions. The hierarchical structure of our model allows for the efficient processing of multi-modal data through distinct pathways, ensuring that relevant features from each modality are effectively extracted and combined. The cross-attention mechanism within our framework meticulously aligns and emphasizes inter-modal features, fostering a synergistic integration that boosts the model's ability to discern intricate facial expressions. Through extensive experiments on benchmark datasets, our approach demonstrates significant improvements over existing methods, showcasing superior performance in recognizing a wide range of facial expressions under various conditions. The implementation details, including algorithms and mathematical expressions, are meticulously described to facilitate replication and further research. Results and discussions highlight the model's efficacy, underscored by comparative analysis and visualizations. Our findings indicate that the HCA-GCN model not only advances the state-of-the-art in facial expression recognition but also opens new avenues for research in multi-modal learning. © 2024 IEEE.

关键词： Spatio-temporal data

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Enhancing Quality of Service in Mobile Ad-Hoc Networks through Secure Routing with Multi-Constrained Network Feature Approximation

Enhancing Quality of Service in Mobile Ad-Hoc Networks throu...

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2024 International Conference on Knowledge engineering and Communication Systems, ICKECS 2024

ISBN: (纸本)9798350359688

In the realm of Mobile Ad-Hoc Networks (MANETs), achieving optimal Quality of Service (QoS) amidst the challenges of security threats and dynamic network topology remains a paramount concern. This paper introduces a groundbreaking secure routing strategy empowered by a multi-constrained network feature approximation technique aimed at bolstering QoS in MANETs. The core innovation lies in the fusion of a multi-dimensional optimization framework with an advanced feature approximation algorithm, tailored to navigate the intricacies of MANETs' decentralized architecture and fluctuating conditions. By focusing on critical QoS parameters such as throughput, delay, packet delivery ratio, and jitter, alongside ensuring robust security measures, our proposed method significantly enhances the reliability and performance of data transmission within MANETs. A comprehensive simulation environment is utilized to evaluate the effectiveness of our approach, revealing substantial improvements in QoS metrics compared to conventional secure routing protocols. Through a meticulous examination of these results, the study underscores the proposed method's efficiency in managing the dual objectives of security and QoS optimization. This research not only marks a significant advancement in secure routing protocols for MANETs but also sets the groundwork for future explorations into integrating multi-constrained optimization techniques for network feature approximation in ad-hoc networks. © 2024 IEEE.

关键词： Constrained optimization

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Dipper Throated Algorithm for Feature Selection and Classification in Electrocardiogram

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computer Systems Science & engineering 2023年第5期45卷 1469-1482页

作者： Doaa Sami Khafaga Amel Ali Alhussan Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Mohamed Saber El-Sayed M.El-kenawy Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Faculty of Computer and Information SciencesAin Shams University11566CairoEgypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Electronics and Communications Engineering Department Faculty of EngineeringDelta University for Science and TechnologyMansouraEgypt Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt

Arrhythmia has been classified using a variety of *** of the dynamic nature of electrocardiogram(ECG)data,traditional handcrafted approaches are difficult to execute,making the machine learning(ML)solutions more *** with cardiac arrhythmias can benefit from competent monitoring to save their *** arrhythmia classification and prediction have greatly improved in recent *** are a category of conditions in which the heart's electrical activity is abnormally rapid or *** year,it is one of the main reasons of mortality for both men and women,*** the classification of arrhythmias,this work proposes a novel technique based on optimized feature selection and optimized K-nearest neighbors(KNN)*** proposed method makes advantage of the UCI repository,which has a 279-attribute high-dimensional cardiac arrhythmia *** proposed approach is based on dividing cardiac arrhythmia patients into 16 groups based on the electrocardiography dataset’s *** purpose is to design an efficient intelligent system employing the dipper throated optimization method to categorize cardiac arrhythmia *** method of comprehensive arrhythmia classification outperforms earlier methods presented in the *** achieved classification accuracy using the proposed approach is 99.8%.

关键词： Feature selection electrocardiogram metaheuristics dipper throated algorithm

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Multi-hop emergency message dissemination through optimal cooperative forwarder in grid-based 5G-VANETs

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Journal of Ambient Intelligence and Humanized Computing 2023年第4期14卷 4461-4476页

作者： Ullah, Sami Abbas, Ghulam Waqas, Muhammad Abbas, Ziaul Haq Halim, Zahid Telecommunications and Networking Research Center GIK Institute of Engineering Sciences and Technology Topi23460 Pakistan Department of Computer Science Shaheed Benazir Bhutto University Sheringal Upper Dir Pakistan Faculty of Computer Science and Engineering GIK Institute of Engineering Sciences and Technology Topi23460 Pakistan School of Engineering Edith Cowan University PerthWA6027 Australia Department of Computer Engineering College of Information Technology University of Bahrain Zallaq32038 Bahrain Faculty of Electrical Engineering GIK Institute of Engineering Sciences and Technology Topi23460 Pakistan

Emergency message (EM) dissemination is a significant process in vehicular ad hoc networks (VANETs) and plays a vital role in road safety. Nonetheless, EMs’ dissemination while avoiding broadcast storms poses a considerable challenge. Moreover, selecting optimal cooperative forwarders is also challenging because of unreliability, high end-to-end delay, and packet loss caused by localization errors. Besides, the conventional broadcast-based schemes usually choose forwarder vehicles based on distance, neglecting the influence of links’ qualities. To that end, we propose multi-hop EM dissemination (MEMD) through optimal cooperative forwarder in grid-based fifth-generation (5G) VANET environment. To ensure reliable EMs’ dissemination, we incorporate vehicle-to-everything communication instead of the conventional short-range vehicle-to-vehicle communication. The proposed scheme obtains vehicle positions by integrating GPS measurements with a map-matching technique to avoid vehicle localization errors. Instead of a single parameter, the proposed scheme selects an optimal cooperative forwarder by using our proposed fitness function, which considers different mobility and social parameters. The proposed scheme categorizes EMs’ dissemination into single-hop and multi-hop broadcasting to avoid unnecessary rebroadcasting and communication congestion in the network. Simulation results demonstrate that for varying density, the proposed scheme achieves an average increase of 12%, 9%, and 8.9% in coverage, and 7.95%, 4%, and 3.4% in PDR, and an average reduction of 13.5%, 7.2%, and 12.7% in E2E delay, and 20.9%, 11.3%, and 4% in rebroadcast ratio, as compared to LCL, REMD, and EMDZoI, respectively. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Vehicular ad hoc networks

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Advanced Hydro-Informatic Modeling through Feedforward Neural Network, Federated Learning, and Explainable AI for Enhancing Flood Prediction

IEEE Open Journal of the Computer Society

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IEEE Open Journal of the computer Society 2025年第1期6卷 726-738页

作者： Mahir, Shahariar Hossain Tashrif, Md Tanjum An Karim, Md Ahsan Kundu, Dipanjali Rahman, Anichur Hamza, Md. Amir Farid, Fahim Al Miah, Abu Saleh Musa Mansor, Sarina Dhaka Bangladesh Mawlana Bhashani Science and Technology University Department of Computer Science and Engineering Tangail1902 Bangladesh Multimedia University Faculty of Engineering Cyberjaya63100 Malaysia Department of CSE Nilphamari Bangladesh

Flood prediction is one of the most critical challenges facing today's world. Predicting the probable time of a flood and the area that might get affected is the main goal of it, and more so for a region like Sylhet, Bangladesh where transboundary water flows and climate change have increased the risk of disasters. Accurate flood detection plays a vital role in mitigating these impacts by allowing timely early warnings and strategic planning. Recent advancements in flood prediction research include the development of robust, accurate, and low-cost flood models designed for urban deployment. By applying and utilizing powerful deep learning models show promise in improving the accuracy of prediction and prevention. But those models faced significant issues related to scalability, data privacy concerns and limitations of cross-border data sharing including the inaccuracies in prediction models due to changing climate patterns. To address this, our research adopts the Federated Learning (FL) framework in an effort to train state-of-the-art deep learning models like Long Short-Term Memory Recurrent Neural Network (LSTM-RNN), Feed-Forward Neural Network (FNN) and Temporal Fusion Transformer-Convolutional Neural Network (TFT -CNN) on a 78-year dataset of rainfall, river flow, and meteorological variables from Sylhet and its upstream regions in Meghalaya and Assam, India. This approach promotes data privacy and allows collaborative learning while working under cross-border data-sharing constraints, therefore improving the accuracy of prediction. The results showed that the best-performing FNN model achieved an R-squared value of 0.96, a Mean Absolute Error (MAE) value of 0.02, Percent bias (PBIAS) value of 0.4185 and lower Root Mean Square Error (RMSE) in the FL environment. Explainable AI techniques, such as SHAP, sheds light on the most significant role played by upstream rainfall and river dynamics, particularly from Cherrapunji and the Surma-Kushiyara river system, in d

关键词： Convolutional neural networks

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A Well Structured Design for Individualized Healthcare System via Fuzzy Logic System (FLS) 4

A Well Structured Design for Individualized Healthcare Syste...

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4th IEEE International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Raman, Ramakrishnan Verma, Apurv Kumar, Vikram Rastogi, Shailesh Pillai, Biju G. Meenakshi, R. India Parul Institute of Technology Parul University Computer Science and Engineering Faculty of Engineering and Technology India Srv Media Pvt. Ltd India Pune India Faculty of Management It & Business Analytics India Institute of Technology Computer Science and Engineering Chennai India

ISBN: (纸本)9798350360165

Personalized medicine is driving the need for new and customized biopharmaceuticals using proteins that have been changed. As such, it is critical to evaluate these items for allergenicity before introducing them as treatments. Even though there are a number of techniques available to predict the allergenicity of proteins, it may still be difficult to discriminate between allergens and nonallergens, particularly when the allergens have a considerable sequence resemblance to their known counterparts. To address this, we provide 'FuzzyApp,' a novel fuzzy rule-based system that evaluates a query protein's potential for allergenicity. FuzzyApp uses fuzzy IF-THEN rules that come from five different modules to determine how allergenic a protein is. FuzzyApp outperforms the state-of-the-art techniques on a variety of datasets, attaining a pleasing trade-off between specificity and sensitivity, as shown by an upbeat Mathew's correlate coefficient. FuzzyApp's capacity to distinguish allergens from allergen-like potential nonallergens (APN) is highlighted by its notable high specificity in this regard. The efficacy and uniformity of this suggested approach are further supported by error analysis and analysis of the whole proteome dataset. Furthermore, Tropomyosin from a variety of allergic and nonallergenic sources is properly predicted by FuzzyApp. The companion online service helps users comprehend the reasoning behind predictions by facilitating batch sequence filing and presenting findings in coherent words. © 2024 IEEE.

关键词： Personalized medicine

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