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International Conference on Emerging Technologies and Factory Automation (ETFA)

作者： Fabian Kummer Frank Golatowski Willi Brekenfelder Helge Parzyjegla Peter Danielis Gero Mühl Institute of Applied Microelectronics and Computer Engineering University of Rostock Rostock Germany Institute of Computer Science University of Rostock Rostock Germany

ISBN: (数字)9798350361230

ISBN: (纸本)9798350361247

Time-Sensitive Networking (TSN) facilitates the implementation of realtime data traffic with deterministic delay and jitter in Ethernet networks. In its time-triggered communication variant, network paths as well as transmission time slots along the paths need to be planned and reserved for all data streams, respectively. Conventionally, such a feasible (i. e., realtime compliant) network schedule is computed and optimized offline, installed upon deployment, and cannot be changed anymore at runtime without stopping the communication. The latter becomes unsuitable for future industrial use cases with a growing demand for flexibility including schedule adjustments. In this paper, we present an online scheduler capable to incrementally integrate new streams at runtime and to adapt the existing schedule, if necessary, while retaining the realtime guarantees given to active streams. For this purpose, the scheduling heuristic identifies streams whose adjustment requires minimal reconfiguration efforts. In a thorough evaluation, we analyze the scheduler's trade-off between resource usage and schedule quality showing its suitability for diverse application scenarios.

关键词： Schedules Runtime Job shop scheduling Conferences Transforms Jitter Servers Resource management Streams Smart manufacturing

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Texture-based Feature Extraction and Machine Learning Model for the Detection of Acute Lymphoblastic Leukemia 5

Texture-based Feature Extraction and Machine Learning Model ...

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5th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2024

作者： Rangini, M. Pundir, Sumit Soudagar, Manzoore Elahi M. Vekariya, Daxa Patil, Harshal Rajkumar, S. R.M.D. Engineering College Department of Computer Science and Engineering Tamil Nadu Kavarapettai601 206 India Graphic Era Deemed to be University Department of Computer Science & Engineering Uttarakhand Dehradun248002 India Saveetha Institute of Medical and Technical Sciences Department of VLSI Microelectronics Saveetha School of Engineering Tamil Nadu Chennai602105 India Parul Institute of Engineering and Technology Parul University Department of Computer Science & Engineering Post Limda Gujarat 391760 India Symbiosis Institute of Technology Maharashtra Pune412 115 India Hawassa University Faculty of Manufacturing Institute of Technology Department of Mechanical Engineering Ethiopia

ISBN: (纸本)9798350395235

Acute Lymphocytic Leukaemia (ALL) is a dangerous malignancy that not only attacks adults but also contributes to roughly 25% of children. A precise and on-time diagnosis of cancer is a critical prerequisite for effective therapy to enhance survival rates. It is challenging to identify cancer cells from normal cells because leukemic cells seem extremely identical to normal cells beneath the microscope. Normally, morphological analysis for the detection of ALL is conducted physically on blood cells by qualified medical individuals, which has various drawbacks, like a lack of medical experts, slow analysis, and prognosis which depends on the knowledge of medical professionals. For this reason, we provide a robust method for improving the reliability of ALL identification using feature extraction and a Machine Learning (ML) model to discriminate between cancerous and healthy cells in images. To extract texture features for analysis, we employ the First Order Histogram (FOH) and the Gray-Level Co-occurrence Matrix (GLCM) in our technique. Logistic Regression (LR), Naive Bayes (NB), and Artificial Neural Network (ANN) are used in the categorizing method. The simulation results demonstrate that combining GLCM feature extraction with the ANN model yields the highest accuracy, correctly predicting 595 out of 600 data samples. The accuracy was 99.17%, which is quite remarkable. © 2024 IEEE.

关键词： Feature extraction

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Expanding chiral metamaterials for retrieving fingerprints via vibrational circular dichroism

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Light(Science & Applications) 2023年第7期12卷 1401-1414页

作者： Cheng Xu Zhihao Ren Hong Zhou Jingkai Zhou Chong Pei Ho Nan Wang Chengkuo Lee Department of Electrical and Computer Engineering National University of SingaporeSingapore 117583Singapore Center for Inteligent Sensors and MEMS(CISM) National University of SingaporeSingapore 117608Singapore institute of Microelectronics(IME) Agency for ScienceTechnology and Research(A*STAR)Singapore 138634Singapore NUS Graduate School for Integrative Science and Engineering Program(ISEP) National University of SingaporeSingapore 117456Singapore

Circular dichroism(CD)spectroscopy has been widely demonstrated for detecting chiral ***,the determination of chiral mixtures with various concentrations and enantiomeric ratios can be a challenging *** solve this problem,we report an enhanced vibrational circular dichroism(VCD)sensing platform based on plasmonic chiral metamaterials,which presents a 6-magnitude signal enhancement with a selectivity of chiral *** by coupled-mode theory,we leverage both in-plane and out-of-plane symmetry-breaking structures for chiral metamaterial design enabled by a two-step lithography process,which increases the near-field coupling strengths and varies the ratio between absorption and radiation loss,resulting in improved chiral light-matter interaction and enhanced molecular VCD ***,we demonstrate the thin-film sensing process of BSA andβ-lactoglobulin proteins,which contain secondary structures a-helix andβ-sheet and achieve a limit of detection down to zeptomole ***,we also,for the first time,explore the potential of enhanced VCD spectroscopy by demonstrating a selective sensing process of chiral mixtures,where the mixing ratio can be successfully differentiated with our proposed chiral *** findings improve the sensing signal of molecules and expand the extractable information,paving the way toward label-free,compact,small-volume chiral molecule detection for stereochemical and clinical diagnosisapplications.

关键词： chiral vibrational circular

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Van der Waals integration inch-scale 2D MoSe_(2) layers on Si for highly-sensitive broadband photodetection and imaging

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Nano Research 2023年第8期16卷 11422-11429页

作者： Yupiao Wu Shuo-En Wu Jinjin Hei Longhui Zeng Pei Lin Zhifeng Shi Qingming Chen Xinjian Li Xuechao Yu Di Wu School of Physics and Microelectronics and Key Laboratory of Material PhysicsZhengzhou UniversityZhengzhou 450052China Department of Electrical and Computer Engineering University of California San DiegoLa JollaCA 92093USA School of Microelectronics Science and Technology Sun Yat-Sen UniversityZhuhai 519082China Key Laboratory of Multifunctional Nanomaterials and Smart Systems Suzhou Institute of Nano-Tech and Nano-BionicsChinese Academy of SciencesSuzhou 215123China

As one of the most promising materials for two-dimensional transition metal chalcogenides(2D TMDs),molybdenum diselenide(MoSe_(2))has great potential in photodetectors due to its excellent properties like tunable bandgap,high carrier mobility,and excellent air *** 2D MoSe_(2)-based photodetectors have been reported to exhibit admired performance,the large-area 2D MoSe_(2)layers are difficult to be achieved via conventional synthesis methods,which severely impedes its future ***,we present the controllable growth of large-area 2D MoSe_(2)layers over 3.5-inch with excellent homogeneity by a simple post-selenization ***,a high-quality n-MoSe_(2)/p-Si van der Waals(vdW)heterojunction device is in-situ fabricated by directly growing 2D n-MoSe_(2)layers on the patterned p-Si substrate,which shows a self-driven broadband photoresponse ranging from ultraviolet to mid-wave infrared with an impressive responsivity of 720.5 mA·W^(−1),a high specific detectivity of 10^(13) Jones,and a fast response time to follow nanosecond pulsed optical *** addition,thanks to the inch-level 2D MoSe_(2)layers,a 4×4 integrated heterojunction device array is achieved,which has demonstrated good uniformity and satisfying imaging *** large-area 2D MoSe_(2)layer and its heterojunction device array have great promise for high-performance photodetection and imaging applications in integrated optoelectronic systems.

关键词： molybdenum diselenide large-area synthesis broadband photodetector integrated device array van der Waals(vdW)heterojunction

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A Novel Strategy for Flexible Placement and Routing of AVS Sensors on FPGAs

A Novel Strategy for Flexible Placement and Routing of AVS S...

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International Conference on Field Programmable Logic and Applications

作者： Christoph Niemann Michael Rethfeldt Dirk Timmermann Institute of Applied Microelectronics and Computer Engineering University of Rostock Germany

Adaptive Voltage Scaling (AVS) is a well-known technique in ASICs. However, the requirements for its application in FPGAs are quite different. AVS uses run-time knowledge to determine a favorable voltage/frequency operating point. There are many suggestions in the literature for the sensors to be used to gain this run-time knowledge. However, the specific placement and routing of each sensor instance is either ignored or manually defined by hard macros or directed routing. In this paper, we propose a new approach to sensor placement. We are able to omit strict constraints like directed routing and give more freedom to the EDA tool without compromising sensor accuracy by using a proper sensor calibration. As a result, we are able to avoid placement and routing conflicts between sensors and application design. This allows the use of larger sensors with significant routing delay. An extensive experimental evaluation proves the validity of our approach.

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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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LogCAE: An Approach for Log-based Anomaly Detection with Active Learning and Contrastive Learning 35

LogCAE: An Approach for Log-based Anomaly Detection with Act...

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35th IEEE International Symposium on Software Reliability engineering, ISSRE 2024

作者： Xiao, Pei Jia, Tong Duan, Chiming Cai, Huaqian Li, Ying Huang, Gang Peking University School of Software and Microelectronics Beijing China Peking University Institute for Artificial Intelligence Beijing China Peking University School of Computer Science Beijing China Peking University National Engineering Research Center for Software Engineering Beijing China

ISBN: (纸本)9798350353884

Log-based anomaly detection plays a crucial role in maintaining the reliability of software systems. Unsupervised models are more suitable for real-world usage because they do not rely on huge data labeling efforts. However, their effectiveness is limited because of the lack of supervision of data labels. To balance model effectiveness and labeling efforts, existing approaches enhance model capabilities by incorporating relatively few but key human labels as a golden signal, thereby improving the model ability with acceptable labeling efforts. However, these methods still face limitations of complex human labels and insufficient utilization of human knowledge. In this paper, we introduce LogCAE, a two-stage log anomaly detection approach based on active learning and contrastive learning. It utilizes an unsupervised model to learn from unlabeled log data without human labels and incorporates human knowledge through active learning during online optimization. We employ contrastive learning to optimize the representation of log samples in feature space for more efficient usage of human labels. We conducted experiments on three distinct public log datasets (Thunderbird, BGL, and Zookeeper). The results show that our method improves 12.93% F1-score on average with 6.06% labeled data samples. Besides, our approach is more effective in utilizing human labels than state-of-The-Art approaches. © 2024 IEEE.

关键词： Contrastive Learning

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CIMulator: Evaluating Neuromorphic Accelerators with HZO FefinFET-Based Synaptic Device 7

CIMulator: Evaluating Neuromorphic Accelerators with HZO Fef...

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7th International Conference on Green Technology and Sustainable Development, GTSD 2024

作者： Le, Hoang-Hiep Lu, Hao-Yu Baig, Md. Aftab De, Sourav Lin, Ing-Chao Lu, Darsen D. Minghsin University of Science and Technology Department of Computer Science and Information Engineering Hsinchu Taiwan National ChengKung University Intelligent Computing Industrial Doctorate Program Tainan Taiwan Institute of Microelectronics National ChengKung University Tainan Taiwan National ChengKung University Department of Computer Science and Information Engineering Tainan Taiwan

ISBN: (纸本)9798350386073

In this study, we use the CIMulator platform to evaluate the performance of neuromorphic accelerators with novel Hf0.5Zr0.5O2 (HZO) ferroelectric fin field-effect transistor (FefinFET) as synaptic device. The MNIST handwritten digit dataset is used for training and inference processes in multilayer perceptron (MLP) neural networks. With the highly optimized synaptic device, the results demonstrate high training and inference accuracy, achieving up to 97% for the MLP, approaching the accuracy of software implementations. © 2024 IEEE.

关键词： Multilayer neural networks

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LLMeLog: An Approach for Anomaly Detection based on LLM-enriched Log Events 35

LLMeLog: An Approach for Anomaly Detection based on LLM-enri...

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35th IEEE International Symposium on Software Reliability engineering, ISSRE 2024

作者： He, Minghua Jia, Tong Duan, Chiming Cai, Huaqian Li, Ying Huang, Gang Peking University School of Software and Microelectronics Beijing China Peking University Institute for Artificial Intelligence Beijing China Peking University School of Computer Science Beijing China Peking University National Engineering Research Center for Software Engineering Beijing China

ISBN: (纸本)9798350353884

Log-based anomaly detection is an essential task in maintaining software reliability. Existing log-based anomaly detection approaches often consist of three key phases: log parsing, event embedding, and model construction. Event embedding efficiently extracts semantic information from log events and produces vector representations of log events. However, existing event embedding methods suffer from two key problems. First, semantic noises are buried in log events leading to inevitable gaps between the obtained semantics from log events and their essential meanings. Second, there exists a gap between general semantic embedding and the specific embedding requirement of anomaly detection tasks. To mitigate these problems and improve the quality of representations of log events, we propose a novel anomaly detection approach named LLMeLog. It leverages the capabilities of large language models (LLMs) to enrich the contents of log events with in-context learning techniques. Then it utilizes the enriched log events to fine-Tune a pre-Trained BERT model. At last, it trains a transformer-based anomaly detection model with the event representations produced by the pre-Trained BERT model. Evaluation results on three public log datasets show that LLMeLog achieves the best performance across all datasets, boasting F1-scores exceeding 99%. Besides, when using only 10% of labeled data as training data, our approach can still achieve over 90% F1-scores. © 2024 IEEE.

关键词： Semantics

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Leveraging Model Distillation as a Defense Against Adversarial Attacks Based on Deep Learning 2

Leveraging Model Distillation as a Defense Against Adversari...

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

作者： Suresh, K. Radha, J. Thilagaraj, T. Subramani, R. Lakineni, Prasanna Kumar Taqui, Syed Noeman Department of Computer Science Bangalore India Nandha Engineering College Department of Computer Science and Engineering Tamilnadu Perundurai India Moodlakatte Institute of Technology Department of Computer Science and Engineering Moodalakatte Karnataka Kundapura India Department of Mathematics Bangalore 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

Adversarial attacks on deep learning models threaten machine learning system security and reliability. The above attacks use modest data alterations to produce erroneous model results while being undetected by humans. This work suggests model distillation to prevent adversarial perturbations. The student model is taught to emulate the teacher model in model distillation. This is done using teacher model soft outputs. Our idea is that this strategy organically strengthens the student model against adversarial assaults by keeping the teacher model's essential knowledge and generalization capabilities while reducing weaknesses. Distilled models are more resilient to adversarial assaults than non-distilled models, according to experiments. These models also perform similarly on undamaged, uncorrupted data. The results show that model distillation may be a powerful defense against machine learning adversaries. This method protects model resilience and performance. © 2023 IEEE.

关键词： Adversarial Attacks Deep Learning Defensive Approach Machine Learning Model Distillation

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