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applied Sensing Conference (APSCON), IEEE

作者： Mojtaba Jahangiri Paolo Sberna Amir Sammak Stoyan Nihtianov A Department of Microelectronics Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Mekelweg 4 Delft Netherlands B QuTech and Netherlands Organization for Applied Scientific Research (TNO) Stieltjesweg 1 Delft Netherlands

A clear understanding of the spectral components of an irradiated beam, or captured optical emission, is essential to optimize an optical system and increase its performance. Logically, for this purpose a grating-based spectrometer could be the first choice. However, in the case of a wide range spectrum, and for radiation with one dominant wavelength, this option may not work well. In this paper, we present a technique based on an array of bandpass detectors to measure accurately the power of a number of beam-specific spectral components in a wide spectrum range: from soft X-ray to infrared. The main unique features of this technique are: customization for specific wavelengths of interest; vacuum compatibility; and high sensitivity to low-energy spectral components in the presence of one or more dominant highpower spectral components.

关键词： Optical filters Sensitivity Stimulated emission Wavelength measurement Ultraviolet sources Detectors Vacuum technology

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Multilayer CVD graphene electrodes using a transfer-free process for the next generation of optically transparent and MRI-compatible neural interfaces

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Microsystems & Nanoengineering 2022年第5期8卷 183-196页

作者： Nasim Bakhshaee Babaroud Merlin Palmar Andrada Iulia Velea Chiara Coletti Sebastian Weingärtner Frans Vos Wouter A.Serdijn Sten Vollebregt Vasiliki Giagka Department of Microelectronics Faculty of Electrical EngineeringMathematics and Computer ScienceDelft University of TechnologyMekelweg 4Delft 2628 CDThe Netherlands Technologies for Bioelectronics Group Department of System Integration and Interconnection TechnologiesFraunhofer Institute for Reliability and Micro-integration IZMGustav-Meyer-Allee 25Berlin 13355Germany Department of Imaging Physics Faculty of Applied ScienceDelft University of TechnologyLorentzweg 1Delft 2628 CJThe Netherlands Erasmus University Medical Center(Erasmus MC) dr.Molewaterplein 40Rotterdam 3015 GDThe Netherlands

Multimodal platforms combining electrical neural recording and stimulation,optogenetics,optical imaging,and magnetic resonance(MRI)imaging are emerging as a promising platform to enhance the depth of characterization in neuroscientific *** conductive,optically transparent,and MRI-compatible electrodes can optimally combine all *** as a suitable electrode candidate material can be grown via chemical vapor deposition(CVD)processes and sandwiched between transparent biocompatible ***,due to the high graphene growth temperature(≥900℃)and the presence of polymers,fabrication is commonly based on a manual transfer process of pre-grown graphene sheets,which causes reliability *** this paper,we present CVD-based multilayer graphene electrodes fabricated using a wafer-scale transfer-free process for use in optically transparent and MRI-compatible neural *** fabricated electrodes feature very low impedances which are comparable to those of noble metal electrodes of the same size and *** also exhibit the highest charge storage capacity(CSC)reported to date among all previously fabricated CVD graphene *** graphene electrodes did not reveal any photo-induced artifact during 10-Hz light pulse ***,we show here,for the first time,that CVD graphene electrodes do not cause any image artifact in a 3T MRI *** results demonstrate that multilayer graphene electrodes are excellent candidates for the next generation of neural interfaces and can substitute the standard conventional metal *** fabricated graphene electrodes enable multimodal neural recording,electrical and optogenetic stimulation,while allowing for optical imaging,as well as,artifact-free MRI studies.

关键词： transparent transfer process

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Modeling Energy Consumption for Task-Offloading Decisions on Mobile and Embedded Devices 2

Modeling Energy Consumption for Task-Offloading Decisions on...

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2nd IEEE Global Conference on Life Sciences and Technologies, LifeTech 2020

作者： Niemann, Christoph Ewert, Christian Puttnies, Henning Rethfeldt, Michael Timmermann, Dirk Danielis, Peter University of Rostock Institute of Applied Microelectronics and Computer Engineering Rostock Germany University of Rostock Department of Computer Science Rostock Germany

ISBN: (纸本)9781728170633

Connected mobile and embedded devices including tablets, smartphones, and smartwatches but also IoT sensors and actuators as well as medical implants are subject to a rapid development in terms of their numbers and computing power. However, these devices have energy limitations, e.g., due to limited energy supplies. In order for networked devices to be able to manage their energy, they can offload tasks to other devices. However, they need to estimate the energy demand to execute the tasks in order to take offloading decisions. This paper proposes energy models for estimating the energy consumption for task processing derived from practical measurements for exemplary tasks. The task-processing energy consumption models are implemented in the OMNeT++ simulator to allow simulative, accurate task-offloading investigations. © 2020 IEEE.

关键词： Energy utilization

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Enhancing Four-Class Motor Imagery Detection Through Advanced Feature Extraction Techniques

Enhancing Four-Class Motor Imagery Detection Through Advance...

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ACS/IEEE International Conference on computer Systems and Applications

作者： Jihen Souissi Sourour Karmani Kais Belwafi Ridha Djemal ATMS Laboratory ENIS National Engineering School of Sfax Sfax University Tunisia Tunisia Laboratory of Electronics and Microelectronics FSM Higher Institute of Applied Sciences and Technology of Sousse University of Sousse University of Monastir Tunisia Computer Engineering Department CCI University of Sharjah Sharjah UAE Electrical Engineering Department National Engineering School of Sfax ATMS Lab. Sfax University Tunisia

ISBN: (数字)9798331518240

ISBN: (纸本)9798331518257

Brain-computer interface (BCI) technology has great potential in control, communication, and neurological diagnostics by interpreting EEG signals. This paper introduces a novel method for extracting distinguishing features from EEG signals using a Common Spatial Patterns (CSP) model, enhanced with a Phase Space Reconstruction model and followed by an autoregressive (AR) model. Initially, EEG signals are denoised using an IIR Butterworth filter before feature extraction. Two classifiers, Linear Discriminant Analysis (LDA) and Multi-Layer Perceptron (MLP), are employed to differentiate features across four motor imagery (MI) tasks. For a four-class MI task, combining spatial and temporal-frequency domain features, LDA achieves an accuracy of 67.40%, while MLP reaches 64.78%. LDA yields precision and recall scores of 67.40% and 67.20%, respectively, for the F1-score, whereas MLP records precision, recall, and F1-scores of 65.36%, 64.78%, and 64.28%, respectively. The proposed architecture is evaluated using the BCI Competition IV set 2a dataset, proving its effectiveness in EEG signal classification for BCI applications.

关键词： Accuracy Pattern classification computer architecture Signal processing Feature extraction Brain modeling Motors Electroencephalography Brain-computer interfaces Linear discriminant analysis

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BAG2 Assisted Hierarchical Analog Layout Synthesis for Planar Technologies

BAG2 Assisted Hierarchical Analog Layout Synthesis for Plana...

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Austrian Workshop on microelectronics (Austrochip)

作者： Matthew Bio Wolfgang Scherr A. S. Agbemenu Santiago Martin Sondón Johannes Sturm Vinayak Hande Josef Ressel Centre for System-on-Chip Design Automation Carinthia Institute for Microelectronics Carinthia University of Applied Sciences Department of Computer Engineering Kwame Nkrumah University of Science and Technology Kumasi Ghana Department of Integrated Systems and Circuit Design Carinthia University of Applied Sciences Villach Austria

Analog circuit design automation remains an intense area of attention and has seen both new and existing tools continued to be developed targeting different phases of the analog design flow to reduce development time and cost. One of the promising tools is the Berkeley analog generator (BAG2) framework which is an open-source analog layout generator for automating and verifying circuit layouts. It promises a process-independent flow as well as encourages design re-use due to using parameterized generators which can be scaled as required and this reduces the layout-development time compared to manual hand-made layouts. This work describes the effort and results of evaluating the BAG2 framework for the TSMC 65nm and Cadence GPDK 45nm processes. A case study is made with a number of circuits to discuss the problems in setting up and using BAG2 for the above technologies as well as the limitations and solutions required to utilize the framework effectively.

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A Novel Architecture of a fault-tolerant Memory System based on Missing Data Imputation

A Novel Architecture of a fault-tolerant Memory System based...

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2022 IEEE International Conference of Electron Devices Society Kolkata Chapter, EDKCON 2022

作者： Dey, Sowvik Chakraborty, Chandan Jana, Sushovon Mahata, Mihir Kumar Karmakar, Amiya Maulana Abul Kalam Azad University of Technology Computer Science & Engineering West Bengal Nadia Haringhata India Maulana Abul Kalam Azad University of Technology Applied Statistics West Bengal Nadia Haringhata India Maulana Abul Kalam Azad University of Technology Microelectronics & Vlsi Technology West Bengal Nadia Haringhata India

ISBN: (纸本)9781665472050

In the modern era of edge computing, each computing device is used as a resource of distributed computing elements as well as distributed storage system. Therefore, every computing device plays a crucial role in a distributed system. A huge amount of data are being collected, processed, and stored at every moment. Different kinds of AI engines are being trained by these data. The consistency of data, that is stored in distributed memories, is essential for the AI engines. The absence of data, due to memory failure, is very much effective for feature extraction. Memory failure may happen due to several causes like struck-at-fault, soft-error, etc. To get consistent data, memories should be fault-tolerant, where memory can work properly in faulty conditions. This can be done either by bypassing or by predicting the data which was stored in a faulty location. In this paper, the design of a novel architecture of memory is proposed which can predict the missing data due to memory failure. © 2022 IEEE.

关键词： Memory architecture

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Benchmarking Cryptographic Protocols for (IoT) Malware Defence

Benchmarking Cryptographic Protocols for (IoT) Malware Defen...

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International Symposium on applied Machine Intelligence and Informatics (SAMI)

作者： Khalil Ben Khaled Dönìz Borsos Jozsef Katona Kandó Kalmán Faculty of Electrical Engineering Institute of Microelectronics and Technology Obuda University Budapest Hungary Kándó Kálmán Faculty of Electrical Engineering Institute of Electronics and Communication Systems Obuda University Budapest Hungary Department of Software Development and Application Institute of Computer Engineering University of Dunaujvaros Dunaujvaros Hungary GAMF Faculty of Engineering and Computer Science John Von Neumann University Kecskemet Hungary Department of Applied Quantitative Methods Faculty of Finance and Accountancy Budapest Business University Budapest Hungary

ISBN: (数字)9798350379365

ISBN: (纸本)9798350379372

This study presents a comprehensive benchmarking analysis of cryptographic protocols for Internet of Things (IoT) malware defense. The framework was specifically tailored to evaluate cryptographic protocols such as AES-128, AES-256, ChaCha20, RSA (1024-bit, 2048-bit, and 4096-bit), SHA256, SHA512, and HMAC-SHA256 were tested in both standalone and emulated environments. The primary objective was to evaluate the performance and resource consumption of these protocols, focusing on their encryption and hashing efficiency. Key innovations include the comparative analysis of resource consumption and performance efficiency across diverse cryptographic operations, under both real-world and emulated conditions. By identifying protocols like ChaCha20 for high efficiency and minimal resource usage, and RSA 4096-bit for enhanced security at higher computational costs, this study provides actionable insights into the trade-offs between security and performance. These findings offer a foundational reference for selecting optimized cryptographic protocols, advancing IoT malware defense strategies through informed decision-making.

关键词： Performance evaluation Technological innovation Benchmark testing Malware Encryption Internet of Things Reliability Informatics Machine intelligence Cryptographic protocols

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Quantized Wavelet Dilated Convolutional Neural Network Based Intelligent Surveillance System for Public Safety in IoT Security Networks

Quantized Wavelet Dilated Convolutional Neural Network Based...

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Sustainable Expert Systems (ICSES), International Conference on

作者： R. Sowmya Ruchira Rawat Dinesh Mishra Mamtaben Kamleshbhai Megha Monikaben Maheshbhai Patel Sathish Kannan Department of CSE (AIML) S. A. Engineering College Chennai Tamil Nadu India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Computer Science and Engineering Mangalayatan University-Jabalpur Jabalpur Madhya Pradesh India Department of Computer Science Natubhai V. Patel College of Pure and Applied Sciences A Constituent College of CVM University Anand Gujarat India Department of Computer Science Natubhai V. Patel College of Pure and Applied Sciences A Constituent College of CVM University Anand Gujarat India Department of VLSI Microelectronics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

Smart cities have produced vast amounts of surveillance video data that can be processed to identify anomalies since the introduction of visual sensors. A novel Quantized Wavelet Auxiliary Encoder Self-Attention Dilated Convolutional Neural Network with Gold Rush Optimizer (QWAESDCNN-GRO) designed to enhance security and reduce crime potential. Through the integration of the Quantized Discrete Haar Wavelet Transform (QDHWT), frame-level features have been extracted. Subsequently, the recovered features are fed to the Auxiliary Guided Knowledge Encoder-Decoder (AGED), which uses them to rank the features. These features are then sent to the Self-Attention Causal Dilated Convolutional Neural Network (SACDCNN) with Gold Rush Optimization (GRO) for anomaly detection. Performance indicators including Fl-score, recall, accuracy, precision, and AUC are compared with those of earlier techniques in order to assess the effectiveness of the recommended strategy. Using the UCF-Crime dataset, the model achieved an accuracy of 99.87% and an error rate of 0.021%. On the Hockey Fights dataset, it obtained an accuracy of 99.88% and an error rate of 0.019%.

关键词： Gold Accuracy Error analysis Surveillance Feature extraction Discrete wavelet transforms Convolutional neural networks Security Optimization Sports

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A Refinement Framework of CS-MRI Using the Fast Low-Rank Approximation

A Refinement Framework of CS-MRI Using the Fast Low-Rank App...

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International Conference on computer and Communications (ICCC)

作者： Shuqi Xu Youjun Xiang Xia Zeng Wei Zhang Chunsheng Zhuang Zhen Chen School of Electronic and Information Engineering Macau University of Science and Technology Macau China School of Electronic and Information Engineering South China University of Technology Guangzhou China Department of of electrical and computer engineering Guangzhou University Sontan College Guangzhou China Henan Academy of Science Applied Physics Institute Co. Ltd Henan China Institute of Microelectronics University of Macau Macau China

ISBN: (数字)9798331507077

ISBN: (纸本)9798331507084

Compressed sensing magnetic resonance imaging (CS-MRI) enables fast acquisition, which is highly desirable for numerous clinical applications. However, the quality of the MR images will depend upon the rate of undersampling. Especially, under the extremely undersampling case, if the image obtained by a conventional CS-MRI method is not satisfied, is any remedial measure could be made? In this paper, a refinement framework of CS-MRI will be proposed as the remedial measure. In the refinement framework, the MR image obtained by using the conventional CS-MRI method, will be taken as the training image. A low-rank model associated with a self-learning scheme will be established for the refinement framework. To preserve more detail features and to reduce the complexity, a partial singular value thresholding (SVT) method and an adaptive selection of the regularization parameter will be employed to establish the solver. Numerical simulations show that the proposed framework can be the remedial measure to improve the quality and robustness of MR image reconstruction in the low sampling case.

关键词： Training Thresholding (Imaging) Dictionaries Magnetic resonance imaging Image edge detection Numerical simulation Robustness Numerical models Image reconstruction Compressed sensing

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Comparative Study of the Resolution of Ge-on-Si Photodetectors for $1\ \mu \mathrm{m}$ Infrared Signals

Comparative Study of the Resolution of Ge-on-Si Photodetecto...

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On-Line Conference (ONCON), Industrial Electronics Society Annual

作者： Mojtaba Jahangiri Paolo Sberna Amir Sammak Stoyan Nihtianov Department of Microelectronics Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft The Netherlands QuTech and Netherlands Organization for Applied Scientific Research (TNO) Delft The Netherlands

Most of the studies on narrow-band near-infrared detection reported so far are related to the $\mathbf{1}.\mathbf{3}\ \boldsymbol{\mu}\mathbf{m}$ and $\mathbf{1}.\mathbf{55}\ \boldsymbol{\mu}\mathbf{m}$ spectral windows. There is insufficient research work done on radiation detection in the narrow band around $1 \mu\mathrm{m}$ wavelength, which is just outside the Si $(\mathbf{0}.\mathbf{95}\ \boldsymbol{\mu} \mathbf{m})$ and GaAs $(\mathbf{0}.\mathbf{85}\ \boldsymbol{\mu} \mathbf{m})$ effective cut-off spectral sensitivity. This paper presents a p + n Ge-on-Si detector with a customized large active window, employing the PureGaB technology, to detect radiation in a very narrow band around $\mathbf{1}\ \boldsymbol{\mu} \mathbf{m}$ . The advantages of the proposed detector are: (1) CMOS-compatibility and micro-spectroscopic capability; (2) low dark current and high photoresponsivity, compared to similar devices reported in the literature; (3) enhanced sensitivity to weak radiation by realizing an ultra-shallow and very thin depletion region. These detectors can be good candidates for measuring the YAG laser radiation and measuring stray radiation in photolithography.

关键词： Industrial electronics Sensitivity Semiconductor lasers Current measurement Lithography Gallium arsenide Detectors

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