Wavelet based technique for multi-crack detection of a beam-like structure using the vibration data measured directly from a moving vehicle

In this paper an idea for crack detection of a multi-cracked beam-like structure by analyzing the vibration measured directly from the vehicle is presented. The crack model is adopted from fracture mechanics. The dynamic response of the bridge-vehicle system is measured directly from the moving vehicle. When the vehicle moves along the structure, the dynamic response of the vehicle is distorted by the cracks at their locations. These distortions are generally small and difficult to be detected visually. In order to detect the cracks, Wavelet Transform - an effective method of detecting such small distortions was adopted. The existence of the cracks can be revealed by large values (peaks) in the wavelet transform. Locations of the cracks can be determined by positions of the peaks and the vehicle speed. Numerical results show that the method can detect cracks as small as 10 % of the beam height with noise level up to 5%. The proposed method is applicable for low velocity-movements while high velocity-movements are not recommended

Deep Transfer Learning: A Novel Collaborative Learning Model for Cyberattack Detection Systems in IoT Networks

Federated Learning (FL) has recently become an effective approach for cyberattack detection systems, especially in Internet-of-Things (IoT) networks. By distributing the learning process across IoT gateways, FL can improve learning efficiency, reduce communication overheads and enhance privacy for cyberattack detection systems. Challenges in implementation of FL in such systems include unavailability of labeled data and dissimilarity of data features in different IoT networks. In this paper, we propose a novel collaborative learning framework that leverages Transfer Learning (TL) to overcome these challenges. Particularly, we develop a novel collaborative learning approach that enables a target network with unlabeled data to effectively and quickly learn knowledge from a source network that possesses abundant labeled data. It is important that the state-of-the-art studies require the participated datasets of networks to have the same features, thus limiting the efficiency, flexibility as well as scalability of intrusion detection systems. However, our proposed framework can address these problems by exchanging the learning knowledge among various deep learning models, even when their datasets have different features. Extensive experiments on recent real-world cybersecurity datasets show that the proposed framework can improve more than 40% as compared to the state-of-the-art deep learning based approaches.Comment: 12 page

Lecturers' adoption to use the online Learning Management System (LMS): Empirical evidence from TAM2 model for Vietnam

Online training has been a common form of training all over the world for many years ago; however, it is only a side choice alongside offline training. Not only students but also lecturers prefer offline training to online training. However, in some cases of force majeure, specifically the nCov-19 flu pandemic, online training is considered the best way to teach. This study is based on the Technology Acceptance Model 2 (TAM2) to learn about the lecturers' adoption of using the learning management system (LMS) at universities in Vietnam. Mixed research methods are used to achieve the research objectives. Online group discussions, as well as online surveys, were conducted to collect data to analyze and test the hypotheses as well as the theoretical model. The results of the study are similar to the conclusions of TAM2. Thereby, the study proposes managerial implications to improve the lecturers' adoption

Electrophysiological Excitability and Parallel Fiber Synaptic Properties of Zebrin-Positive and -Negative Purkinje Cells in Lobule VIII of the Mouse Cerebellar Slice

Heterogeneous populations of cerebellar Purkinje cells (PCs) are arranged into separate longitudinal stripes, which have different topographic afferent and efferent axonal connections presumably involved in different functions, and also show different electrophysiological properties in firing pattern and synaptic plasticity. However, whether the differences in molecular expression that define heterogeneous PC populations affect their electrophysiological properties has not been much clarified. Since the expression pattern of many of such molecules, including glutamate transporter EAAT4, replicates that of aldolase C or zebrin II, we recorded from PCs of different “zebrin types” (zebrin-positive = aldolase C-positive = Z+; and Z−) in identified neighboring stripes in vermal lobule VIII, in which Z+ and Z− stripes occupy similar widths, in the Aldoc-Venus mouse cerebellar slice preparation. Regarding basic cellular electrophysiological properties, no significant differences were observed in input resistance or in occurrence probability of types of firing patterns between Z+ and Z− PCs. However, the firing frequency of the tonic firing type was higher in Z− PCs than in Z+ PCs. In the case of parallel fiber (PF)-PC synaptic transmission, no significant differences were observed between Z+ and Z− PCs in interval dependency of paired pulse facilitation or in time course of synaptic current measured without or with the blocker of glutamate receptor desensitization. These results indicate that different expression levels of the molecules that are associated with the zebrin type may affect the intrinsic firing property of PCs but not directly affect the basic electrophysiological properties of PF-PC synaptic transmission significantly in lobule VIII. The results suggest that the zebrin types of PCs in lobule VIII is linked with some intrinsic electrophysiological neuronal characteristics which affect the firing frequency of PCs. However, the results also suggest that the molecular expression differences linked with zebrin types of PCs does not much affect basic electrophysiological properties of PF-PC synaptic transmission in a physiological condition in lobule VIII

Electrically stable carbon nanotube yarn under tensile strain

We report a highly stable electrical conductance of a compact and well-oriented carbon nanotube yarn under tensile strain. The gauge factor of the yarn was found to be extremely small of approximately 0.15 thanks to the improvements in the dry spinning process, includingmultiweb spinning and heat treatment. The threshold strain εs, below which the yarn retains its electrical conductance stability, has also been determined to be approximately 15 × 103 ppm. Owing to its highly stable resistance under mechanical strain, the yarn has a good potential as a wiring material for niche applications,where lightweight and resistance stability are required

Carbon nanotube four-terminal devices for pressure sensing applications

Carbon nanotubes (CNTs) are of high interest for sensing applications, owing to their superior mechanical strength, high Young’s modulus and low density. In this work, we report on a facile approach for the fabrication of carbon nanotube devices using a four terminal configuration. Oriented carbon nanotube films were pulled out from a CNT forest wafer and then twisted into a yarn. Both the CNT film and yarn were arranged on elastomer membranes/diaphragms which were ar-ranged on a laser cut acrylic frame to form pressure sensors. The sensors were calibrated using a precisely controlled pressure system, showing a large change of the output voltage of approximately 50 mV at a constant supply current of 100µA and under a low applied pressure of 15 mbar. The results indicate the high potential of using CNT films and yarns for pressure sensing applications

A Wearable, Bending-Insensitive Respiration Sensor Using Highly Oriented Carbon Nanotube Film

Recently, wearable electronics for health monitoring have been demonstrated with considerable benefits for early-stage disease detection. This article reports a flexible, bending-insensitive, bio-compatible and lightweight respiration sensor. The sensor consists of highly oriented carbon nanotube (HO-CNT) films embedded between electro-spun polyacrylonitrile (PAN) layers. By aligning carbon nanotubes between the PAN layers, the sensor exhibits a high sensitivity towards airflow (340 mV/(m/s)) and excellent flexibility and robustness. In addition, the HO-CNT sensor is insensitive to mechanical bending, making it suitable for wearable applications. We successfully demonstrated the attachment of the sensor to the human philtrum for real-time monitoring of the respiration quality. These results indicate the potential of HO-CNT flow sensor for ubiquitous personal health care applications

FIRST - Flexible interactive retrieval SysTem for visual lifelog exploration at LSC 2020

Lifelog can provide useful insights of our daily activities. It is essential to provide a flexible way for users to retrieve certain events or moments of interest, corresponding to a wide variation of query types. This motivates us to develop FIRST, a Flexible Interactive Retrieval SysTem, to help users to combine or integrate various query components in a flexible manner to handle different query scenarios, such as visual clustering data based on color histogram, visual similarity, GPS location, or scene attributes. We also employ personalized concept detection and image captioning to enhance image understanding from visual lifelog data, and develop an autoencoderlike approach for query text and image feature mapping. Furthermore, we refine the user interface of the retrieval system to better assist users in query expansion and verifying sequential events in a flexible temporal resolution to control the navigation speed through sequences of images

A Comprehensive Survey of Enabling and Emerging Technologies for Social Distancing—Part II: Emerging Technologies and Open Issues

This two-part paper aims to provide a comprehensive survey on how emerging technologies, e.g., wireless and networking, artificial intelligence (AI) can enable, encourage, and even enforce social distancing practice. In Part I, an extensive background of social distancing is provided, and enabling wireless technologies are thoroughly surveyed. In this Part II, emerging technologies such as machine learning, computer vision, thermal, ultrasound, etc., are introduced. These technologies open many new solutions and directions to deal with problems in social distancing, e.g., symptom prediction, detection and monitoring quarantined people, and contact tracing. Finally, we discuss open issues and challenges (e.g., privacy-preserving, scheduling, and incentive mechanisms) in implementing social distancing in practice. As an example, instead of reacting with ad-hoc responses to COVID-19-like pandemics in the future, smart infrastructures (e.g., next-generation wireless systems like 6G, smart home/building, smart city, intelligent transportation systems) should incorporate a pandemic mode in their standard architectures/designs

A Comprehensive Survey of Enabling and Emerging Technologies for Social Distancing—Part I: Fundamentals and Enabling Technologies

Social distancing plays a pivotal role in preventing the spread of viral diseases illnesses such as COVID-19. By minimizing the close physical contact among people, we can reduce the chances of catching the virus and spreading it across the community. This two-part paper aims to provide a comprehensive survey on how emerging technologies, e.g., wireless and networking, artificial intelligence (AI) can enable, encourage, and even enforce social distancing practice. In this Part I, we provide a comprehensive background of social distancing including basic concepts, measurements, models, and propose various practical social distancing scenarios. We then discuss enabling wireless technologies which are especially effect- in social distancing, e.g., symptom prediction, detection and monitoring quarantined people, and contact tracing. The companion paper Part II surveys other emerging and related technologies, such as machine learning, computer vision, thermal, ultrasound, etc., and discusses open issues and challenges (e.g., privacy-preserving, scheduling, and incentive mechanisms) in implementing social distancing in practice