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On the Feasibility of a Direct Injection Probe with a Capacitively Coupled Return and Integrated Voltage Monitor

On the Feasibility of a Direct Injection Probe with a Capaci...

引用

IEEE International Symposium on Electromagnetic Compatibility (EMC)

作者： Aaron Harmon Daniel Szanto Victor Khilkevich Daryl Beetner Dept. of Electrical and Computer Engineering Electromagnetic Compatibility Laboratory Missouri University of Science and Technology Rolla MO USA

ISBN: (数字)9798350360394

ISBN: (纸本)9798350360400

Characterizing the susceptibility of an IC while it is integrated within a system can be challenging. Characterization is even harder if one wants to know the waveform at the target IC pin when injecting a signal on the pin. In this work, the feasibility of a direct injection probe with a capacitively coupled return and integrated voltage monitor is proposed. This probe is advantageous because it does not need to be soldered to the test device and its ability to provide a measurement of the waveform on the target IC pin during the injection. Methods for reconstructing the pin waveform based on probe measurements are discussed. Initial results indicate that the presented probe is generally insensitive to landing position variations and can accurately provide the waveform at the target IC during an injection. Future work is focused on further validation of the presented probe.

关键词： Pins Probes Monitoring

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Benchmarking Network Functionality: Performance Evaluation of SDN Controllers on Different Network Functions

Benchmarking Network Functionality: Performance Evaluation o...

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IEEE Conference on Vehicular Technology (VTC)

作者： Md Fahad Monir Azwad Fawad Hasan Md Mozammal Hoque Tarem Ahmed Fabrizio Granelli Dept. of Computer Science and Engineering Independent University Bangladesh Cullen College of Engineering University of Houston Texas USA Dept. of Information Engineering and Computer Science University of Trento (UniTrento) Trento Italy

ISBN: (数字)9798331517786

ISBN: (纸本)9798331517793

Network Softwarization revolutionizes infrastructure by introducing programmable network functions, leading to scalable, agile, and efficient networks. This innovation facilitates adaptable, secure Next Generation Networks, transforming connectivity. This study conducts a comparative analysis of SDN-based network functions utilizing three different controllers: Open vSwitch (OVS), OpenFlow Reference controller, and RYU, to understand their impact on packet loss and jitter. The OpenFlow Reference controller, primarily used for educational and basic experimental purposes, serves as a baseline. The OVS controller, operating within the Open vSwitch framework in Mininet, offers a practical level of network management capability, more advanced than the OpenFlow Reference controller yet not as comprehensive as the RYU Controller, a production-grade SDN controller. Our research aims to fill the gap in comparative performance analysis of these controllers, particularly in hybrid round-robin-based load balancing and firewall implementations. Recognizing the operational reality of SDN deployments, we explore both reactive and proactive modes, acknowledging that real-world applications like firewalls and load balancers often operate proactively with pre-populated rules, affecting data plane performance. The findings provide insights into each controller’s effectiveness, with OVS and RYU showing enhanced capability in reducing packet loss and jitter compared to the OpenFlow Reference controller. This research not only sheds light on the functional behaviors and performance trade-offs of these controllers but also contributes to the broader SDN and NFV discourse, offering a foundation for future academic and practical advancements in the field.

关键词： Vehicular and wireless technologies Technological innovation Firewalls (computing) Packet loss Bandwidth Switches Jitter Topology Software defined networking Next generation networking

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Design Techniques for Sample-and-Hold with Bootstrapping in Low-Power SAR ADC

Design Techniques for Sample-and-Hold with Bootstrapping in ...

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Annual IEEE Northeast Workshop on Circuits and Systems (NEWCAS)

作者： Fei Yuan Dept. of Electrical Computer and Biomedical Engineering Toronto Metropolitan University Toronto ON Canada

ISBN: (数字)9798350361759

ISBN: (纸本)9798350361766

This paper investigates the errors of sample-and-hold (S/H) with bootstrapping in low-power SAR ADC. We show charge injection errors are of common-mode characteristics mainly and their impact is largely suppressed via differential signaling. We further show errors caused by bootstrapping voltage feed-through have both common-mode and differential-mode components. The impact of the former is suppressed by differential signaling while the latter doubles. Moreover, the loss of bootstrapping voltage due to the use of nMOS bootstrapping capacitors and the impact of the size of bootstrapping nMOS capacitors on bootstrapping voltage are investigated. Furthermore, we show errors caused by the voltage drop of sampling switches are of differential characteristics and double with differential signaling. The impact of supply voltage reduction on the voltage drop of sampling switches is also studied. We show the lower the supply voltage, the worse the voltage drop of sampling switches. Finally, we show errors caused by the voltage drop of sampling switches are most critical and can be reduced by increasing the size of sampling switches without sacrificing charge injection errors. These findings are verified using the simulation results of a differential S/H designed in TSMC 130 nm with a reduced supply voltage of 0.8 V.

关键词： Simulation Capacitors Voltage MOS devices

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Deep Learning for Drug Response Prediction with Gene Expression Data

Deep Learning for Drug Response Prediction with Gene Express...

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International Conference on Information Networking

作者： Sardar Jaffar Ali Muhammad Omer Duc Tai Le Syed M. Raza Hyunseung Choo Dept. of AI Systems Engineering Sungkyunkwan University Suwon South Korea Dept. of Computer Science and Engineering Sungkyunkwan University Suwon South Korea Dept. of Electrical and Computer Engineering Sungkyunkwan University Suwon South Korea James Watt School of Engineering University of Glasgow Glasgow United Kingdom

ISBN: (数字)9798331506940

ISBN: (纸本)9798331506957

Accurately predicting drug responses based on individual patient profiles is a critical challenge in personalized medicine, primarily due to the complex biological variability involved. This paper presents a deep learning framework for predicting changes in gene expression, providing insights into how drugs impact cells at the molecular level. Using data from the Kaggle competition, several models have been evaluated, including LSTM, GRU, Transformer, and Autoencoder architectures. Among these, the 3 -stacked GRU with Attention demonstrated superior performance, achieving the highest sign accuracy of 79% and the lowest mean absolute error across diverse biological conditions. The robust performance of the model highlights the effectiveness of attention mechanisms in capturing critical patterns in gene expression data.

关键词： Drugs Deep learning Attention mechanisms Precision medicine Biological system modeling Autoencoders Transformers Data models Gene expression Long short term memory

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Gene Essentiality Mediated Base Substitution in Escherichia coli genome: Machine Learning Analysis

Gene Essentiality Mediated Base Substitution in Escherichia ...

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2022 International Interdisciplinary Conference on Mathematics, engineering and science, MESIICON 2022

作者： Kurmi, Annushree Das, Debashis Sen, Piyali Ray, Suvendra Kumar Satapathy, Siddhartha Sankar Tezpur University Dept. of Computer Science and Engineering Tezpur India Tezpur University Dept. of Molecular Biology and Biotechnology Tezpur India

ISBN: (纸本)9781665470728

Several selection and mutation factors are known to influence evolution in the nucleotide sequence in the genome of organisms. Using the machine learning approach, we considered gene essentiality as a selection factor, and we investigated base substitution among essential and remaining gene sets in the Escherichia coli genome. RF and SVM classifiers achieved an AUC score of 75% each. The importance of the base substitution-based features differs in predicting essentiality. Features such as G to A substitutions, mutations within the amino group, and transversion mutations were found to be the most important features. In contrast, G to C, G to T, and T to C substitutions were among the least ones. This result indicates that the base substitution patterns in essential genes might differ from that in non-essential genes. The result of this manuscript will be helpful for understanding evolution at the molecular level. © 2022 IEEE.

关键词： Genes

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Unified mechanism-specific amplification by subsampling and group privacy amplification 24

Unified mechanism-specific amplification by subsampling and ...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Jan Schuchardt Mihail Stoian Arthur Kosmala Stephan Günnemann Dept. of Computer Science & Munich Data Science Institute Technical University of Munich Dept. of Engineering University of Technology Nuremberg

ISBN: (纸本)9798331314385

Amplification by subsampling is one of the main primitives in machine learning with differential privacy (DP): Training a model on random batches instead of complete datasets results in stronger privacy. This is traditionally formalized via mechanism-agnostic subsampling guarantees that express the privacy parameters of a subsampled mechanism as a function of the original mechanism's privacy parameters. We propose the first general framework for deriving mechanism-specific guarantees, which leverage additional information beyond these parameters to more tightly characterize the subsampled mechanism's privacy. Such guarantees are of particular importance for privacy accounting, i.e., tracking privacy over multiple iterations. Overall, our framework based on conditional optimal transport lets us derive existing and novel guarantees for approximate DP, accounting with Rényi DP, and accounting with dominating pairs in a unified, principled manner. As an application, we analyze how subsampling affects the privacy of groups of multiple users. Our tight mechanism-specific bounds outperform tight mechanism-agnostic bounds and classic group privacy results.

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Hyperparameter Optimized DNN for OFDM Signal Detection in the Presence of CFO and PO

Hyperparameter Optimized DNN for OFDM Signal Detection in th...

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International Conference on Microwave, Optical and Communication engineering (ICMOCE)

作者： Shubham Anand Pushp Paritosh Preetam Kumar Dept. of Electrical Engineering IIT Patna Dept. of Computer Science SRMIST Chennai

In any multi-carrier modulation (MCM) system, it is crucial to have accurate channel estimation. Typically, the choice of channel state information (CSI) based on the least-square (LS) and minimum-mean square error (MMSE) estimators has been the traditional approach. With the advancement of deep learning algorithms, it has become a state-of-the-art algorithm for signal detection. However, deep learning comes with various hyperparameters that should be tuned for maximum performance. In this paper, an optimized Deep Neural Network (DNN) based signal detection approach has been proposed for the orthogonal frequency division-multiplexing (OFDM) system in the carrier frequency offset (CFO) and phase offset (PO)impaired environment at the receiver end. Thorough experiments were carried out to determine the optimal architecture for accurate signal detection. The optimum architecture is of shape 64-12-78-22-64, where 12,78,22 are the number of neurons in the hidden layer.

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Software Code De-Compilation Techniques and Approaches: A Comparative Study

Software Code De-Compilation Techniques and Approaches: A Co...

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Information Technology (ACIT)

作者： Hamed Fawareh Yazan Al-Smadi Mohammad Hassan Faid AlNoor Fawareh Ali Elrashidi Dept. of Software Engineering Zarqa University Jordan Dept. of Computer Science Zarqa University Jordan Dept. Of Mathmitical Science Zarqa University Jordan Dept. of Electrical Engineering University of Business and Technology Jeddah Saudi Arabia

ISBN: (数字)9798331540012

ISBN: (纸本)9798331540029

De-compilation is the process of executable files as input where the de-compilation process tries to get away the source code, or the majority of it, if the file's source code is lost or damaged for whatever cause. De-compilation has many uses such as code recovery, security testing, and errors corrections, however, de-compilation is cautious since it depends on certain approaches and techniques. Nowadays de-compilation done by many automated and semi-automated tools, but the accuracy and the efficiency of it depend on the proposed approaches and techniques to stand in front of reengineering challenges. This paper aims to highlight up-to-date prior contributions in the field where 8 published research papers from May 2020 until May 2023 were collected to investigate the state-of-art. As a result, some of the researchers tend to develop their own tools and others tend to use popular automated with different approaches and techniques where the majority of the researchers focused on C, C++, and JAVA programming languages where 50% were de-compilation on C language.

关键词： Hands Java Source coding Programming Software Libraries Generators Security Information technology Testing

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Breast Cancer Prediction Using Machine Learning 5

Breast Cancer Prediction Using Machine Learning

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5th International Conference on Emerging Systems and Intelligent Computing, ESIC 2025

作者： Dhondiyal, Shiv Ashish Luthra, Pooja Sharma, Bhupender Rajni, Rajni Uniyal, Ishita Graphic Era Deemed to be University Dept. of Computer Science and Engineering Dehradun India Chandigarh University Department of Computer Science & Engineering Punjab Mohali India Coer University Department of Computer Science and Application Uttarakhand Roorkee India Guru Jambheshwar University of Science and Technology Department Of Computer Science & Engineering Haryana Hisar India Graphic Era Hill University Department of Electrical Engineering India

ISBN: (纸本)9798331522100

The number of newly diagnosed cases of breast cancer exceeds 2.3 million annually worldwide. There is a severe lack of valid prognostic and predictive indicators for the clinical treatment of breast cancer patients at the present time. In order to decrease the mortality rate and increase the survival time of breast cancer patients, early detection of this deadly disease is crucial. Improving breast cancer prognosis relies on the analysis and processing of mammography data, the gold standard for screening and early detection. In order to detect breast cancer in mammograms, the Fuzzy C-means algorithm is employed for image segmentation. Next, we train it to identify the characteristics of the segmented regions, and then we use an effective classifier to categorise the trained pictures into their respective mammography classes. This research used the Breast Cancer Wisconsin Diagnostic dataset and two ML algorithms: K-Nearest Neighbours (KNN) and Random Forest. After getting the results, we compared and evaluated the performance of these two classifiers. Potentially useful tools for both early diagnosis and treatment planning include breast cancer prediction models that account for a wide range of risk factors. Effective disease management involves the collection, storage, and administration of many data sets, as well as intelligent systems based on several aspects for the prediction of breast cancer. © 2025 IEEE.

关键词： Image segmentation

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Phase-Locked Loop vs Synchronous Machine Emulation: A Comparative Study of Phase-Angle Determination Dynamics

Phase-Locked Loop vs Synchronous Machine Emulation: A Compar...

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IEEE Energy Conversion Congress and Exposition, ECCE

作者： Prithwiraj Roy Chowdhury Ehab Shoubaki Madhav Manjrekar Dept. Electrical and Computer Engineering University of North Carolina at Charlotte Charlotte USA

ISBN: (数字)9798350376067

ISBN: (纸本)9798350376074

This paper presents a comparative analysis of a Phase-Locked Loop (PLL) and a Synchronous Machine Emulation (SME) in terms of phase-angle determination, dynamic performance, and control characteristics. Internally, SME does not have a phase angle controlling loop like in PLL. Instead, it controls active power and determines phase angle in consequence. Additionally, unlike the PLL, which employs an ABC to DQ transformation, the SME operates in the time domain with instantaneous values, resulting in a fast response of phase synchronization. This control architecture has the potential to overcome the challenges in the future power grid consisting of large numbers of small-scale Inverter-Based Resources. Moreover, the SME can offer additional control features, such as voltage and frequency control in black-start, and power control by enabling appropriate droop characteristics. This paper compares the dynamic performance of the SME-based controllers with the PLL-based controllers, establishing their capability to accurately determine phase angles for grid synchronization without PLL. The performance of this controller is validated in MATLAB/Simulink and Hardware-in-Loop experiment.

关键词： Torque Power system dynamics Emulation Power grids Synchronization Phase locked loops Synchronous machines Voltage control Tuning Grid forming

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