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The Truncated Cone Effect in AFM Nanoindentation on Soft Materials

Micro and Nanosystems 2023年第2期15卷 153-158页

作者： Kontomaris, Stylianos-Vasileios Malamou, Anna Department of Research and Development BioNanoTec LTD Nicosia Cyprus Faculty of Engineering and Architecture Metropolitan College Athens Greece Radar Systems and Remote Sensing Lab of the School of Electrical & Computer Engineering of National Technical University of Athens Athens Greece

Background: Atomic Force Microscopy (AFM) nanoindentation is the principal method for the characterization of soft materials at the nanoscale. In most cases, pyramidal tips are used and approximated to perfect cones. However, the extended use of the AFM tip may alter its sharp-ness. Objective: In many cases, a truncated cone shape is appropriate for tip modeling. In this technical note, the equation that relates the force with the indentation depth when indenting an elastic half-space using a truncated cone is derived. Methods: The nanoindentation equation for a truncated cone tip is derived using the fundamental differential equation that relates the sample’s contact stiffness with Young’s modulus. Results: When fitting Sneddon’s equation (which is valid for a perfect cone) on data obtained using a truncated cone-shaped AFM tip, the results show a ‘pseudo-softening’ behavior. Conclusion: The AFM tip's sharpness in nanoindentation experiments is a crucial parameter for obtaining the correct mechanical patterns of unknown samples. © 2023 Bentham Science Publishers.

关键词： Biological materials

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Robust In-Vehicle Diagnostic-Based Intrusion Detection System

Robust In-Vehicle Diagnostic-Based Intrusion Detection Syste...

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2024 International Symposium on Networks, computers and Communications, ISNCC 2024

作者： Shafee, Ahmed Awaad, Tasneem A. Moro, Ahmed Adams State University Department of Engineering and Computer Science AlamosaCO United States Ain Shams University Faculty of Engineering Department of Computer and Systems Engineering Cairo Egypt Siemens Eda Cairo Egypt Siemens Digital Industries Software Integrated Electrical Systems Segment Cairo Egypt

ISBN: (纸本)9798350364910

Cybersecurity has become a significant concern for automotive manufacturers as modern cars increasingly incorporate electronic components. Electronic Control Units (ECUs) have evolved to become the central control units for critical car functions such as engines and brakes, experiencing rapid technological advancements. However, this swift progression in ECU technology has also made them prime targets for cyber attacks. This vulnerability has spurred researchers to focus on securing ECUs. Numerous studies have proposed intrusion detection systems (IDS) to protect against attacks on ECUs in vehicles. Yet, these IDSs are not impenetrable;attackers can exploit them by launching evasion attacks, which can trigger numerous false positive alarms. Such false alarms can be disruptive and potentially hazardous for drivers. Additionally, attackers can evade IDSs from detecting malicious data that can cause harm to the vehicle. Accordingly, in this paper, we propose a novel training framework to train a robust in-vehicle IDS that can encounter evasion attacks. Our methodology is based on implementing two rounds of mimic learning technique for training Random Forest (RF) based IDS. RF has been chosen to incorporate the randomness of the RF architecture to enhance the robustness of the model. Additionally, in each round of the two rounds of the mimic learning technique, an RF model with a different architecture is chosen to improve the resilience of the model against evasion attacks without affecting its accuracy. Our Experimental results have shown the effectiveness of our framework against evasion attacks. © 2024 IEEE.

关键词： Robustness (control systems)

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Impact of Image Sensor Input Faults on Pruned Neural Networks for Object Detection 37

Impact of Image Sensor Input Faults on Pruned Neural Network...

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37th IEEE International Symposium on Defect and Fault Tolerance in VLSI and Nanotechnology systems, DFT 2024

作者： Chen, Yizhi Zhu, Wenyao Chen, Dejiu Mohammed, Omar Khound, Parthib Lu, Zhonghai School of Electrical Engineering and Computer Science Kth Royal Institute of Technology Stockholm Sweden School of Industrial Engineering and Management Kth Royal Institute of Technology Stockholm Sweden University of Siegen Reliability of Technical Systems and Electrical Measurement Siegen Germany

ISBN: (纸本)9798350366884

Object detection is one of the most fundamental problems in computer vision, and image sensors are commonly used for this. In this paper, we present the impact of image sensor faults on pruned neural networks for object detection. We compare the error sensitivities of networks after network slimming, networks after magnitude-based pruning, and native compact models. We also explore different spatial fault types with three intensities. Furthermore, we have developed a temporal error model based on realistic aging image sensor faults. The results illuminate that the performance on clean images is important as the mean Average Precision (mAP) experiences a decrease with an increase in injected faults. Additionally, we demonstrate that the size of the model does not invariably yield a decisive impact on error tolerance when comparing small models such as pruned models and native compact models. © 2024 IEEE.

关键词： Neural networks

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Green Energy Scavenging for Powering Mobile Phones

Green Energy Scavenging for Powering Mobile Phones

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2022 International Telecommunications Conference, ITC-Egypt 2022

作者： Shafiq, Raghda R.G. Elsherbini, M.M. Omara, A.N. Elhalawany, Basem M. Benha University Shoubra Faculty of Engineering Department of Electrical Engineering Egypt Shoubra Faculty of Engineering Department of Electrical Engineering Egypt Computer and Systems Department Egypt

ISBN: (数字)9781665488082

ISBN: (纸本)9781665488082

Egypt Mobile phones have become effective devices for a variety of everyday tasks, but while running large-scale multimedia tasks and the applications such as video games require regular charging of the battery, So significant important technological advances are being made in energy harvesting technology to extend battery life. Ambient energy sources are classified as energy tanks, energy distribution methods, or energy harvesting methods, which may enable portable or wireless systems to be completely battery-independent and self-sustaining. This study provides a comprehensive survey of environmentally friendly energy harvesting methods such as radio frequency (RF), solar energy, and provides a summary of electronic circuits utilized in this field alongside various power sources which are compared concerning voltage and harvested power. © 2022 IEEE.

关键词： Energy harvesting

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Data-Driven Assessment of Operational and Harsh Environmental Stress Factors for Solar Inverters Using Field Measurements

Data-Driven Assessment of Operational and Harsh Environmenta...

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2024 IEEE Power and Energy Society General Meeting, PESGM 2024

作者： Luo, Yi Liu, Liming Yuan, Yuxuan Wang, Zhaoyu Qiu, Feng Zhao, Shijia Yildirim, Murat Iowa State University Electrical and Computer Engineering AmesIA United States Energy Systems Division Argonne National Laboratory LemontIL United States Wayne State University Industrial and Systems Engineering DetroitMI United States

The operation of inverters in distributed photovoltaic (PV) systems within distribution systems is critical for ensuring the reliability and durability of the PV system, particularly when challenged by diverse operational and environmental stress conditions. This study delves into analyzing the inverter stress factors to assess the impact of under-sizing and varying environmental stresses on the reliability and service life of PV inverters. A new operational stress metric, equivalent under-sizing rate (EUR), is proposed to quantify the actual inverter stress from the under-sizing problem. Along with the designed weather and extreme event metrics, the proposed EUR is evaluated in a real-world PV inverter dataset. To handle the censored working time variable, the Cox proportional hazards model is adopted to conduct the data-driven stress factor analysis. The regression results reveal aside from the high temperature and large temperature variance, the inverter under-sizing problem is also a significant factor that could influence the lifetime of inverters. Meanwhile, further applications and potential future directions for the proposed work are explored. © 2024 IEEE.

关键词： Photovoltaics

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Convolutional Neural Networks Based Classification of Segmented Breast Ultrasound Images - A Comparative Preliminary Study 1

Convolutional Neural Networks Based Classification of Segmen...

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1st International Conference of Intelligent Methods, systems and Applications, IMSA 2023

作者： Gadallah, Mohammed Tarek Mohamed, Abd El-Naser A. Hefnawy, Alaa A. Zidan, Hassan E. El-Banby, Ghada M. Mohamed Badawy, Samir Computers and Systems Department Cairo Egypt Menoufia University Faculty of Electronic Engineering Electronics and Electrical Communications Engineering Department Menoufia Egypt Menoufia University Faculty of Electronic Engineering Industrial Electronics and Control Engineering Department Menoufia Egypt

ISBN: (纸本)9798350335569

computer-aided diagnosis (CAD) helps physicians in tumors' identification into various biomedical tissues, breast is one. Through the last few years, image classification techniques based on deep learning (DL), have obtained noticeable success in differentiating breast ultrasound images automatically. More researches, concerned with breast cancer detection, are representing the output as a black and white segmented image (mask) or a selected region on the breast ultrasound image which will in role submitted to physicians to help them in making a better diagnosis decision. Our paper represents the second step. In this paper: ten pre-trained Convolutional neural networks (CNNs) classification models (ResNet18, ResNet50, ResNet101, InceptionV3, InceptionResNetV2, GoogleNet, MobilenetV2, SqueezeNet, DenseNet201, and Xception) have been utilized to classify segmented breast ultrasound images by transfer learning (TL). A dataset of 375 breast ultrasound images' masks (ground truths), divided as 125 normal, 125 benign, and 125 malignant, has been utilized in training and validation. A dataset of 255 breast ultrasound images' ground truths (masks), divided as 85 normal, 85 benign, and 85 malignant, has been utilized to evaluate the classification accuracy of each CNN model after TL process. Each CNN model's evaluation (over the 255 breast ultrasound images' masks) has represented different accuracy values evaluated. The best accuracy value was for ResNet50 with an accuracy of 97.25 %. The proposed classification scheme for segmented ultrasound images can be regarded as a step that may help in an automatic breast cancer diagnosis system. The produced trained ten CNNs models in this study (input image size: 128 by 128 by 3) are not in their optimum case but it can be considered as a start to any researcher interests in studying the classification of segmented binary images of breast cancers, the ten produced CNNs models are available to researchers at: https://***/

关键词： Image classification

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Learning the Eye of the Beholder: Statistical Modeling and Estimation for Personalized Color Perception 60

Learning the Eye of the Beholder: Statistical Modeling and E...

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60th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2024

作者： Chen, Xuanzhou Xu, Austin Wang, Jingyan Pananjady, Ashwin School of Electrical and Computer Engineering Georgia Institute of Technology United States Salesforce AI Research Georgia Institute of Technology United States School of Industrial and Systems Engineering Georgia Institute of Technology United States

ISBN: (纸本)9798331541033

Color perception has long remained an in-triguing topic spanning vision and cognitive science, signal processing, and computer graphics. People are often classified as either 'color-normal' or 'color-blind', and it is widely accepted there are a few types of colorblindness that are the most prevalent. At the same time, empirical evidence, such as in optometry and vision science, has repeatedly suggested that categories for colorblindness only serve as approximations to real manifestations of it. With the motivation of better understanding individual-level color perception, we propose a model for color vision that unifies existing theories for color-normal and color-blind populations. This model posits a certain type of low-dimensional structure in color space according to which any given person distinguishes colors. We design an algorithm to learn this low-dimensional structure from user queries, and prove statistical guarantees on its per-formance. To collect user data, we adapt a user interface design, termed 'perceptual adjustment queries' (PAQs), to assess color perception. This user interface efficiently infers a user's color distinguishability profile from a few cognitively lightweight responses. A user study shows that our method captures individual-level differences in both color-normal and color-blind populations. © 2024 IEEE.

关键词： Color vision

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The Group Robustness is in the Details: Revisiting Finetuning under Spurious Correlations 38

The Group Robustness is in the Details: Revisiting Finetunin...

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38th Conference on Neural Information Processing systems, NeurIPS 2024

作者： LaBonte, Tyler Hill, John C. Zhang, Xinchen Muthukumar, Vidya Kumar, Abhishek H. Milton Stewart School of Industrial and Systems Engineering Georgia Institute of Technology United States School of Electrical and Computer Engineering Georgia Institute of Technology United States Google DeepMind United Kingdom

Modern machine learning models are prone to over-reliance on spurious correlations, which can often lead to poor performance on minority groups. In this paper, we identify surprising and nuanced behavior of finetuned models on worst-group accuracy via comprehensive experiments on four well-established benchmarks across vision and language tasks. We first show that the commonly used class-balancing techniques of mini-batch upsampling and loss upweighting can induce a decrease in worst-group accuracy (WGA) with training epochs, leading to performance no better than without class-balancing. While in some scenarios, removing data to create a class-balanced subset is more effective, we show this depends on group structure and propose a mixture method which can outperform both techniques. Next, we show that scaling pretrained models is generally beneficial for worst-group accuracy, but only in conjunction with appropriate class-balancing. Finally, we identify spectral imbalance in finetuning features as a potential source of group disparities - minority group covariance matrices incur a larger spectral norm than majority groups once conditioned on the classes. Our results show more nuanced interactions of modern finetuned models with group robustness than was previously known. Our code is available at https://***/tmlabonte/revisiting-finetuning. © 2024 Neural information processing systems foundation. All rights reserved.

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SP-DSTS-MIMO Scheme-Aided H.266 for Reliable High Data Rate Mobile Video Communication

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computers, Materials & Continua 2023年第1期74卷 995-1010页

作者： Khadem Ullah Nasru Minallah Durre Nayab Ishtiaque Ahmed Jaroslav Frnda Jan Nedoma Department of Computer Systems Engineering University of Engineering and Technology PeshawarPeshawar25000Pakistan National Centre in Big Data and Cloud Computing University of Engineering and Technology Peshawar(NCBC-UETP)Peshawar25000Pakistan Department of Quantitative Methods and Economic Informatics Faculty of Operation and Economics of Transport and CommunicationsUniversity of Zilina01026ZilinaSlovakia Department of Telecommunications Faculty of Electrical Engineering and Computer ScienceVSB-Technical University of OstravaOstrava-PorubaCzech Republic

With the ever growth of Internet users,video applications,and massive data traffic across the network,there is a higher need for reliable bandwidth-efficient multimedia *** Video Coding(VVC/H.266)is finalized in September 2020 providing significantly greater compression efficiency compared to Highest Efficient Video Coding(HEVC)while providing versatile effective use for Ultra-High Definition(HD)*** article analyzes the quality performance of convolutional codes,turbo codes and self-concatenated convolutional(SCC)codes based on performance metrics for reliable future video *** advent of turbo codes was a significant achievement ever in the era of wireless communication approaching nearly the Shannon *** codes are operated by the deployment of an interleaver between two Recursive Systematic Convolutional(RSC)encoders in a parallel *** RSC encoders may be operating on the same or different architectures and code *** proposed work utilizes the latest source compression standards H.266 and H.265 encoded standards and Sphere Packing modulation aided differential Space Time Spreading(SP-DSTS)for video transmission in order to provide bandwidth-efficient wireless video ***,simulation results show that turbo codes defeat convolutional codes with an averaged E_(b)/N_(0) gain of 1.5 dB while convolutional codes outperformcompared to SCC codes with an E_(b)/N_(0) gain of 3.5 dBatBit ErrorRate(BER)of 10−*** Peak Signal to Noise Ratio(PSNR)results of convolutional codes with the latest source coding standard of H.266 is plotted against convolutional codes with H.265 and it was concluded H.266 outperform with about 6 dB PSNR gain at E_(b)/N_(0) value of 4.5 dB.

关键词： H.265 RSC turbo codes SCC SP-DSTS BP-CNN BER PSNR

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Enhancing Early Detection of Lung Cancer through Advanced Image Processing Techniques and Deep Learning Architectures for CT Scans

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computers, Materials & Continua 2024年第10期81卷 271-307页

作者： Nahed Tawfik Heba M.Emara Walid El-Shafai Naglaa F.Soliman Abeer D.Algarni Fathi EAbd El-Samie Computers and Systems Department Electronics Research InstituteEl NozhaHuckstep Cairo12622Egypt Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Lab Computer Science DepartmentPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia

Lung cancer remains a major concern in modern oncology due to its high mortality rates and multifaceted origins,including hereditary factors and various clinical *** stands as the deadliest type of cancer and a significant cause of cancer-related deaths *** diagnosis enables healthcare providers to administer appropriate treatment measures promptly and accurately,leading to improved prognosis and higher survival *** significant increase in both the incidence and mortality rates of lung cancer,particularly its ranking as the second most prevalent cancer among women worldwide,underscores the need for comprehensive research into efficient screening *** in diagnostic techniques,particularly the use of computed tomography(CT)scans,have revolutionized the identification of lung *** scans are renowned for their ability to provide high-resolution images and are particularly effective in detecting small,calcified areas,crucial for identifying earlystage lung ***,there is growing interest in enhancing computer-aided detection(CAD)*** algorithms assist radiologists by reducing false-positive interpretations and improving the accuracy of early cancer *** study aims to enhance the effectiveness of CAD systems through various ***,the Contrast Limited Adaptive Histogram Equalization(CLAHE)algorithm is employed to preprocess CT scan images,thereby improving their visual *** refinement is achieved by integrating different optimization strategies with the CLAHE *** CutMix data augmentation technique is applied to boost the performance of the proposed model.A comparative analysis is conducted using deep learning architectures such as InceptionV3,ResNet101,Xception,and *** study evaluates the performance of these architectures in image classification tasks,both with and without the implementation of the CLAHE *** empirical findings of the study demonst

关键词： Lung cancer detection CLAHE algorithm optimization deep learning classification feature extraction healthcare applications

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