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IEEE Symposium on Radio Frequency Integrated Circuits (RFIC)

作者： Jing-Ren Yan Yao-Wei Huang Wei-Jen Lai Jen-Hao Liao Ching-Chun Lin Yu-Te Liao Institute of Electrical and Computer Engineering National Yang Ming Chiao Tung University Taiwan Novatek Microelectronics Taiwan

ISBN: (数字)9798350359473

ISBN: (纸本)9798350359480

This work proposes a 2.4 GHz wireless energy harvesting chip with a meander dipole antenna. The design was fabricated in a 180-nm standard CMOS process and occupies a chip area of 2.3 × 2.5 mm 2 while consisting of a reconfigurable rectifier, a bandgap reference, a maximum power point tracking controller, a 3× switched-capacitor charge pump, two regulators, and an ultra-low power diode. The design achieves a peak power efficiency of 51.9% at -4 dBm and sensitivity of -19 dBm. The peak power efficiency of SCCP3× is 97.2%, and the reverse leakage current of ULPD is less than 2 nA. The automatic maximum power tracking scheme extends the >20% power efficiency range to 24 dB.

关键词： Radio frequency Wireless communication Wireless sensor networks Sensitivity Regulators Photonic band gap Rectifiers

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Study of Whisker Growth from SAC0307-SiC Composite Solder Alloy 46

Study of Whisker Growth from SAC0307-SiC Composite Solder Al...

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46th International Spring Seminar on Electronics Technology, ISSE 2023

作者： Choi, Halim Skwarek, Agata Hurtony, Tamás Byun, Jaeduk Illés, Balázs Budapest University of Technology and Economics Faculty of Electrical Engineering and Computer Science Department of Electronics Technology Budapest Hungary Lukasiewicz Research Network - Institute of Microelectronics and Photonics Department of Microelectronics Kraków Poland Dankook University College of Science & Technology Department of Physics Cheonan Korea Republic of

ISBN: (纸本)9798350334845

In the current study, tin (Sn) whisker growth was observed on the surface of the SAC0307-SiC composite solder joint. A commercial SAC0307 solder alloys were reinforced with SiC nanophases to produce composite solder joints. SiC nanoparticles and nanowires were mixed with SAC0307 solder paste using the ball milling method. The solder paste was deposited by stencil printing on the test PCBs, and 0805 chip resistors were soldered by IR reflow method. The solder joints facilitated the growth of the Sn whisker through accelerated lifetime tests. The samples were observed by scanning electron microscopy (SEM) every 1000 hours of the test. On the SiC composite solder joints, Sn whiskers and large corrosion spots were observed after 1000 hours of the THB test. In the case of SAC0307 reference samples, only minor corrosion signs were found after 1000 hours of the THB test. The main finding was that the addition of SiC nanophases promoted Sn whisker growth and decreased the corrosion resistance of the composite solder. © 2023 IEEE.

关键词： Silicon carbide

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Expeditious Diagnosis of Cerebral Aneurysm on MR Brain Angiogram Images Utilizing Soft Computing Techniques 7

Expeditious Diagnosis of Cerebral Aneurysm on MR Brain Angio...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Anjaneya, L.H. Maheswari, K. Revathi, N. Siva, K.P. Soudagar, Manzoore Elahi M. Dhanraj, Joshuva Arockia Bapuji Institute of Engineering And Technology Department of Electrical And Electronics Engineering Karnataka Davangere577004 India Cmr Technical Campus Department of Computer Science And Engineering Hyderabad Telangana Kandlakoya501401 India Panimalar Engineering College Department of Electronics And Communication Engineering Tamil Nadu Chennai600123 India Sri Eshwar College of Engineering Department of Computer Science And Business Systems Tamil Nadu Coimbatore641202 India Saveetha Institute of Medical And Techinical Sciences Saveetha School of Engineering Department of Vlsi Microelectronics Tamil Nadu Chennai602105 India Department of Mechatronics Engineering Chennai Tamil Nadu Padur603103 India

ISBN: (纸本)9798350359299

It is always a challenging task for radiologists to detect Cerebral Aneurysms (CA). Unenhanced magnetic resonance angiography (MRA) has become a popular screening tool because of its non-invasiveness, lack of contrast, and ionizing radiation. Because of its screening role, MRA requires a high recognition rate. This paper presents Machine Learning (ML) methods for detecting CA in MRA data. MRA scans of normal and aneurysms are collected and evaluated. This research study is divided into two sections: segmentation and classification. First, K-Means Clustering (KMC) is used for aneurysm segmentation from MR images. Second, image classification is done. For that, the appropriate features are retrieved using feature extraction and selection. Then Gray Level Run Length Matrix (GLRLM) is used for feature extraction and the Genetic Algorithm (GA) for feature selection. The GLRLM and GA features are provided independently to the ML models for training and testing. The ML models used in the research work are Artificial Neural Networks (ANN) and Modified Fuzzy C-Means (MFCM). The metrics are employed to evaluate the GLRM and GA features with ANN and MFCM. The combination of GA features with ANN produces the best results when compared to other combinations. The GA with ANN obtains an outstanding 98.33% accuracy, 100% specificity and precision, 96.67% sensitivity, and 98.31% F1 score. © 2024 IEEE.

关键词： Genetic algorithms

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Structural plasticity-based hydrogel optical Willshaw model for one-shot on-the-fly edge learning

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InfoMat 2023年第4期5卷 48-59页

作者： Dingchen Wang Dingyao Liu Yinan Lin Anran Yuan Woyu Zhang Yaping Zhao Shaocong Wang Xi Chen Hegan Chen Yi Zhang Yang Jiang Shuhui Shi Kam Chi Loong Jia Chen Songrui Wei Qing Wang Hongyu Yu Renjing Xu Dashan Shang Han Zhang Shiming Zhang Zhongrui Wang Department of Electrical and Electronic Engineering The University of Hong KongHong Kongthe People's Republic of China ACCESS-AI Chip Center for Emerging Smart Systems InnoHK CentersHong Kong Science ParkHong Kongthe People's Republic of China School of Computer Science and Engineering Faculty of Innovation EngineeringMacao University of Science and TechnologyMacaothe People's Republic of China Key Laboratory of Microelectronics Devices and Integrated Technology Institute of MicroelectronicsChinese Academy of SciencesBeijingthe People's Republic of China Collaborative Innovation Center for Optoelectronic Science Technology International Collaborative Laboratory of 2D Materials for OptoelectronicsScience and Technology of Ministry of EducationInstitute of Microscale OptoelectronicsShenzhen UniversityShenzhenthe People's Republic of China School of Microelectronics Southern University of Science and TechnologyShenzhenthe People's Republic of China Microelectronics Thrust Function Hub of the Hong Kong University of Science and Technology(Guangzhou)Guagndongthe People's Republic of China

Autonomous one-shot on-the-fly learning copes with the high privacy,small dataset,and in-stream data at the *** such learning on digital hardware suffers from the well-known von-Neumann and scaling *** optical neural networks featuring large parallelism,low latency,and high efficiency offer a promising ***,ex-situ training of conventional optical networks,where optical path configuration and deep learning model optimization are separated,incurs hardware,energy and time overheads,and defeats the advantages in edge ***,we introduced a bio-inspired material-algorithm co-design to construct a hydrogel-based optical Willshaw model(HOWM),manifesting Hebbian-rule-based structural plasticity for simultaneous optical path configuration and deep learning model optimization thanks to the underlying opto-chemical *** first employed the HOWM as an all optical in-sensor AI processor for one-shot pattern classification,association and *** then leveraged HOWM to function as a ternary content addressable memory(TCAM)of an optical memory augmented neural network(MANN)for one-shot learning the Omniglot *** HOWM empowered one-shot on-the-fly edge learning leads to 1000boost of energy efficiency and 10boost of speed,which paves the way for the next-generation autonomous,efficient,and affordable smart edge systems.

关键词： associative memory hydrogel one-shot learning optical neural network structural plasticity Willshaw model

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Leveraging Temporal Patterns with LSTMs Networks for Financial Forecasting: A New Stastical Machine Learning Approach 2

Leveraging Temporal Patterns with LSTMs Networks for Financi...

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2nd IEEE International Conference on Communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Thinakaran, K. Soman, Sandhya Anitha, L. Lakineni, Prasanna Kumar Dilli Ganesh, V. Taqui, Syed Noeman Saveetha School of Engineering Saveetha Institute of Medical and Technical Science Simats Department of Computer Science and Engineering Chennai India Gitam University Dept of Computer Science Karnataka Bengaluru India Saveetha Engineering College Department of Management Studies Chennai India Gitam School of Technology Gitam University Department of Computer Science and Engineering Visakhapatnam India Saveetha School of Engineering Saveetha Institute of Medical and Technical Science-SIMATS Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Vlsi Microelectronics Tamil Nadu Chennai India

ISBN: (纸本)9798350369960

The intricate temporal patterns observed in financial markets might have an impact on market outlook and trading decisions. The challenge of effectively predicting these markets remains difficult due to the limitations inherent in conventional linear forecasting methods. The aim of this study is to assess the efficacy of Long Short-Terms Memory (LSTMS) networks, which are advanced recurrent neural network designs, in accurately forecasting financial time-series data. This evaluation will entail a comparison of the performance between LSTMS networks and classical models. The present study employs data derived from the S&P 500 index spanning a twenty-year timeframe for the purpose of analysis. The information comprises not just the daily closing prices but also incorporates trading volume and a range of technical indicators, including moving averages, relative strength index (RSI), and momentum. The information provided is extensive and serves as the foundation for the development and evaluation of the forecasting models. A sequence-to-sequence Long Short-Terms Memory (LSTMS) model was devised with the explicit objective of capturing the inherent temporal dynamics inherent in financial data. The research employed a rolling window approach, wherein a predetermined sequence of daily data was employed to predict the closing price of the subsequent day. The LSTMS architecture is composed of input layers, multiple LSTMS units, and output layers, with the incorporation of dropout layers in between to mitigate the problem of overfitting. In order to develop a benchmark, a comparison was conducted between the LSTMS approach and traditional models such as ARIMA and a basic RNN structure. © 2023 IEEE.

关键词： Financial forecasting Forecasting models Long Short-Term Memory Neural networks Relative strength index Temporal patterns

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Comparing Collaborative Filtering Algorithms in AI-Powered Recommendation Systems for E-Commerce 4

Comparing Collaborative Filtering Algorithms in AI-Powered R...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Aamer, Najmuddin Mittal, Tanusha Muthusamy, Nantha Kumar Battula, R. S. S. Raju Ajith, P.S. Soudagar, ManzooreElahi M. Bharat Ratna Indira Gandhi College of Engineering Department of Computer Science and Engineering Maharashtra Solapur410003 India Graphic Era Deemed to Be University Department of Computer Science and Engineering Maharashtra Dehradun410003 India OntrailInc Dba Uprise San FranciscoCA94104 United States Aditya University Department of Computer Applications Andhra Pradesh Surampalem533432 India Brindavan College of Engineering Department of Mathematics Karnataka Bengaluru560063 India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Vlsi Microelectronics Tamil Nadu Chennai602105 India

ISBN: (纸本)9798331523923

The quick growth of e-commerce has brought attention to how important efficient recommendation systems are to enhancing user experience and accomplishing business objectives. This paper investigates the effectiveness of collaborative filtering algorithms in AI-powered recommendation systems, with a focus on user-based collaborative filtering (UBCF). A comprehensive method is employed to guarantee consistency across different rating scales, beginning with data normalisation to standardise user-item interaction matrices. To decrease dimensionality and maintain the most significant patterns of user-item interaction, Principal Component Analysis (PCA) is employed for feature selection. The classification system uses similarity measures and UBCF to predict user preferences based on historical data. The algorithms are evaluated on key metrics like precision, recall, and RMSE using publically available datasets. The outcomes demonstrate the impact of preprocessing and dimensionality reduction on UBCF's performance as well as its efficacy in providing customised recommendations. This research contributes to the improvement of collaborative filtering algorithms for e-commerce systems, which will raise customer satisfaction and enable scalability in dynamic online markets. © 2025 IEEE.

关键词： Collaborative filtering

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Organic-inorganic hybrid piezotronic bipolar junction transistor for pressure sensing

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Microsystems & Nanoengineering 2024年第3期10卷 229-244页

作者： Emad Iranmanesh Zihao Liang Weiwei Li Congwei Liao Shunyu Jin Chuan Liu Kai Wang Shengdong Zhang Charalampos Doumanidis Gehan A.J.Amaratunga Hang Zhou Guangdong Provincial Key Laboratory of In-Memory Computing Chips School of Electronic and Computer EngineeringPeking University Shenzhen Graduate SchoolShenzhen 518055P.R.China School of Mechanical Engineering Guangdong Technion-Israel Institute of TechnologyShantou 515063P.R.China State Key Laboratory of Microelectronics Device and Integrated Technology Institute of MicroelectronicsChinese Academy of SciencesBeijing 100029P.R.China Hefei National Research Center for Physical Sciences at the Microscale University of Science and Technology of ChinaHefei 23000PR China School of Electronics and Information Technology Sun Yat-sen UniversityNo.132 East Waihuan RoadGuangzhou 510006P.R.China Department of Mechanical Aerospaceand Biomedical EngineeringUniversity of South AlabamaShelby Hall3128MobileAL 36688USA Electrical Engineering Division Department of EngineeringUniversity of Cambridge9 JJ Thomson AvenueCambridge CB30FAUnited Kingdom Zhejiang University International CampusHainingChina

With the rapid development of the Internet of Things(IoTs),wearable sensors are playing an increasingly important role in daily monitoring of personal health and *** signal-to-noise-ratio has become the most critical performance factor to *** enhance it,on the one hand,good sensing materials/devices have been employed;on the other hand,signal amplification and noise reduction circuits have been ***,most of these devices and circuits work in an active sampling mode,requiring frequent data acquisition and hence,entailing high-power *** this scenario,a flexible and wearable event-triggered sensor with embedded signal amplification without an external power supply is of great ***,we report a flexible two-terminal piezotronic n-p-n bipolar junction transistor(PBJT)that acts as an autonomous and highly sensitive,current-and/or voltage-mediated pressure *** PBJT is formed by two back-to-back piezotronic diodes which are defined as emitter-base and collectorbase *** force exertion on the emitter side,as a result of the piezoelectric effect,the emitter-base diode is forward biased while the collector-base diode is reverse *** to the inherent BJT amplification effect,the PBJT achieves record-high sensitivities of 139.7 kPa^(-1)(current-based)and 88.66 kPa^(-1)(voltage-based)in sensing *** PBJT also has a fast response time of<110 ms under exertion of dynamic stimuli ranging from a flying butterfly to a gentle finger ***,the PBJT advances the state of the art not only in terms of sensitivity but also in regard to being self-driven and autonomous,making it promising for pressure sensing and other IoT applications.

关键词： bipolar transistor IoT

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Fetal Health Classification using AI from Cardiotocography Features 3

Fetal Health Classification using AI from Cardiotocography F...

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3rd International Conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Jyostna, G. Goyal, Himanshu Rai Soudagar, Manzoore Elahi M. Parikh, Swapnil Patil, Harshal Alaskar, Kamal Potti Sriramulu Chalavadi Mallikarjuna Rao College of Engineering & Technology Department of CSE-DS Andhra Pradesh Vijayawada520 012 India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun248002 India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of VLSI Microelectronics Tamil Nadu Chennai602105 India Parul Institute of Engineering and Technology Department of Computer Science and Engineering Parul University Post Limda Gujarat Waghodia391760 India Computer Science and Engineering Symbiosis Institute of Technology Maharashtra Pune412 115 India Department of Computer Application Institute of Management Maharashtra Kolhapur416003 India

ISBN: (纸本)9798350343632

Maternal health issues during pregnancy are a major global concern because they result in fetal death, which is more frequent in rising and poor countries. Cardiotocography (CTG) is the major non-invasive and cost-effective method for monitoring fetal health. CTG delivers critical parameters such as Fetal Heart Rate (FHR) and Uterine Contraction (UC), with a total of 21 features for accurate monitoring. These characteristics assist obstetricians in categorizing fetal health as normal, suspect, or pathological. However, differences in CTG interpretation among obstetricians frequently result in incorrect procedures. This work presents a unique way to detect deficient fetal states using a hybrid Machine Learning (ML) model that combines Support Vector Machine (SVM) and Random Forest (RF). The UCI repository dataset is pre-processed, which includes balancing, outlier reduction, and feature selection. The hybrid model's performance is then compared to that of classic ML models such as SVM, K-Nearest Neighbour (KNN), RF, and Decision Tree (DT). The hybrid model outperforms the other models in simulation, attaining the greatest accuracy rate of 98.83%. This novel ML-based technique has the potential to improve fetal health detection while reducing the influence of human CTG interpretation discrepancies across healthcare providers. © 2023 IEEE.

关键词： Cardiotocography Correlation Features Fetal Hybrid Machine Learning Outliers UCI Repository

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Evaluating the Robustness of Neural Networks Against Adversarial Perturbations 2

Evaluating the Robustness of Neural Networks Against Adversa...

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2nd IEEE International Conference on Communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Bhat, C. Rohith Nandhini, J. Narayanasamy, S. Karthik, B. Lakineni, Prasanna Kumar Taqui, Syed Noeman Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamilnadu Chennai India Jai Shriram Engineering College Department of Ece Tirupur India J.J College of Engineering and Technology Department of Computer Science and Engineering Trichy India Sona College of Technology Department of Electrical and Electronics Engineering Salem India Gitam School of Technology Gitam University Department of Computer Science and Engineering Visakhapatnam India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Vlsi Microelectronics Tamil Nadu Chennai India

ISBN: (纸本)9798350369960

N eural networks have impressive powers in the field of deep learning and have been used in many different applications. Nevertheless, the issue of robustness against deliberate harmful inputs, commonly referred to as adversarial perturbations, continues to be a prominent topic. The presence of these perturbations, which are frequently undetectable by human observers, has the potential to cause erroneous predictions in models, hence presenting potential hazards to crucial domains such as autonomous vehicles or medical diagnostics. The primary objective of this study is to assess the vulnerability of neural networks to adversarial perturbations, ascertain the underlying factors contributing to this susceptibility, and investigate viable solutions for mitigating such vulnerabilities. Various adversarial attack strategies were utilized on diverse neural network topologies, and their performances were assessed. The results of our study suggest that whereas deeper models exhibit enhanced classification abilities, they may not necessarily possess more resilience against hostile inputs. Additionally, it has been found that certain conventional regularization techniques might further amplify susceptibility. In order to address these problems, innovative training techniques and adjustments to the network structures are implemented, resulting in enhanced resilience against adversarial attacks. This study highlights the significance of developing neural networks that possess both accuracy and robustness, hence advancing the frontier of safe deep learning applications. © 2023 IEEE.

关键词： Adversarial Perturbations Deep Learning Neural Networks Robustness

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Efficient Hardware Design of a Secure Cancellable Biometric Cryptosystem

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Intelligent Automation & Soft Computing 2023年第4期36卷 929-955页

作者： Lamiaa A.Abou Elazm Walid El-Shafai Sameh Ibrahim Mohamed G.Egila H.Shawkey Mohamed K.H.Elsaid Naglaa F.Soliman Hussah Nasser AlEisa Fathi E.Abd El-Samie Department of Microelectronics Electronics Research InstituteAlnozhaEgypt Department of Electronics and Electrical Communications Engineering Ain Shams UniversityCairoEgypt Department of Electronics and Electrical Communications Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Laboratory Department of Computer SciencePrince Sultan UniversityRiyadh 11586Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh 11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadhSaudi Arabia

Biometric security is a growing trend,as it supports the authentication of persons using conﬁdential biometric *** of the transmitted data in multi-media systems are susceptible to attacks,which affect the security of these *** systems provide sufﬁcient protection and privacy for *** recently-introduced cancellable biometric recognition systems have not been investigated in the presence of different types of *** addition,they have not been studied on different and large biometric *** point that deserves consideration is the hardware implementation of cancellable biometric recognition *** paper presents a suggested hybrid cancellable biometric recognition system based on a 3D chaotic *** rationale behind the utilization of the 3D chaotic cryptosystem is to guarantee strong encryption of biometric templates,and hence enhance the security and privacy of *** suggested cryptosystem adds signiﬁcant permutation and diffusion to the encrypted biometric *** introduce some sort of attack analysis in this paper to prove the robustness of the proposed cryptosystem against *** addition,a Field Programmable Gate Array(FPGA)implementation of the pro-posed system is *** obtained results with the proposed cryptosystem are compared with those of the traditional encryption schemes,such as Double Random Phase Encoding(DRPE)to reveal superiority,and hence high recogni-tion performance of the proposed cancellable biometric recognition *** obtained results prove that the proposed cryptosystem enhances the security and leads to better efﬁciency of the cancellable biometric recognition system in the presence of different types of attacks.

关键词： Information security cancellable biometric recognition systems cryptanalysis 3D chaotic map encryption FPGA

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