Miracle-dz/UniFAR-FTUs huggingface dataset:
Nonlinear Kernel Dictionary Learning Algorithm Based on Analysis Sparse Model

In the past decades, relevant sparse representation models and their corresponding dictionary learning algorithms have been explored extensively as they could be applied in various fields. However, most of them are focusing the linear model and nonlinear one is still less touched, although there are plenty of nonlinear scenarios in real applications. To further address this kind of the unmet challenge, in this work we mainly focus the following two works (i) propose a kernel transformation based method directly transforming the nonlinear analysis problem into a linear one, which is exactly the standard sparse analysis form but implies all nonlinear information of the original problem; (ii) present a nonlinear dictionary learning algorithm by leveraging the kernel trick and the KSVD-like manner, which has its root in analysis sparse model rather than synthesis model. Then, the proposed methods are employed to address the classification problem. Benchmark experimental results on three well-known datasets show that the proposed algorithm in (ii) outperforms some related linear algorithms and other existing nonlinear dictionary learning algorithms. Moreover, when the data is interfered by noise or some pixels are missing in the data, the algorithm is also effective, which proves its theoretical advantages owing to analysis sparse model’s merits of the equality of all atoms and the much smaller dimensionality for signal representation to some extent. And the classification accuracy of the proposed method in (i) is slightly lower than that of (ii), but better than that of other state of art methods.
Conceptual Design of Automotive Sensor Systems : Analyzing the impact of different sensor positions on surround-view coverage

Automotive sensor systems play an essential role for highly and fully automated driving by enabling environmental perception. Besides improving the sensors’ performance, the data quality and information processing, the arrangement of the vehicular surround sensors is crucial. The conceptual design of robust and reliable sensor systems is a complex task since they comprise a high number of different sensors, each with diverse, adjustable parameters. At this early vehicle concept phase, it is not possible to examine the performance of all system variations in field tests. Thus, we established a simulation-based evaluation methodology to investigate the interaction of multiple sensors as a system and the effects of particular sensor arrangements on object detection and vehicle’s surround-view coverage. This paper highlights the considerations while designing sensor systems and analyzes the impact of different sensor positions, especially by systematically altering the mounting height.
The WTFast’s Gamers Private Network (GPN ®) Performance Evaluation Results

The WTFast Gamers Private Network (GPN ®) is a client/server solution designed to improve network conditions with online games that utilize centralized game servers. Using a GPN ®, connections between the clients personal computer and game network are improved in terms of reduced network latency, spikes, and jitter.A common concern among players participating in online games is the game’s connection stability. A key aspect of WTFast’s GPN ® service is its greatly improved connection consistency. This is accomplished by routing data from the client to the game server in a more controlled manner.To determine what kind of conditions and improvements can be found using a GPN ®, we gathered data from thousands of GPN ® and non-GPN ® (regular internet) connections to create a statistical model of spike predictability, frequency, and severity using Markov Chains. Paired with a comparison of latency, spikes, and jitter across GPN ® / non-GPN ® lines, we provide a statistically sound report on the efficacy of WTFast’s GPN ® service.
Biodegradable, Flexible and Transparent Tactile Pressure Sensor Based on Rubber Leaf Skeletons

Capacitive sensors have many applications in tactile sensing, human-machine interfaces, on-body sensors, and patient monitoring. Particularly in biomedical applications, it would be beneficial if the sensor is disposable and readily degradable for efficient recycling. In this study, we report a biodegradable capacitive tactile pressure sensor based on sustainable and bio resourced materials. Silver-nanowire-coated rubber tree leaf skeletons are used as transparent and flexible electrodes while a biodegradable clear tape is used as the dielectric layer. The fabricated sensor is sensitive and can respond to low pressures (7.9 mN when pressed with a probe with a surface area of 79 mm2 / 0.1 kPa) ranging to relatively high pressures (37 kPa), with a sensitivity up to ≈ 4.5×10-3 kPa1. Owing to all bio resourced constituents, the sensor is biodegradable and does not create electronic waste.
A New Type of Ankle-Foot Rehabilitation Robot Based on Muscle Motor Characteristics

Ankle injury and dysfunction are always accompanied by abnormal peripheral tissues. In order to achieve a more comprehensive rehabilitation training, this article firstly studies the movement characteristics of the surrounding muscles of the ankle-foot. The human musculoskeletal model in OpenSim is used for human exercise experiment. And the relationship between different muscles around the ankle-foot and the direction of the ankle-foot movement are measured respectively, so as to formulate ankle-foot static/dynamic rehabilitation strategies related to the auxiliary training of the ankle-foot muscle. In order to implement the established rehabilitation exercise strategy and to reduce the control difficulty of rehabilitation robot, a new type of decoupling series-parallel mechanism with three rotations and one movement is proposed. After analyzing the degree of freedom and the positive kinematics solution, it is proved that it is completely decoupled in kinematics, can realize the independence of motion control, and there is no singular space. Then, under the premise of meeting the space required for the implementation of the rehabilitation strategy, the proposed new ankle-foot rehabilitation robot is optimized in terms of structure and component size, which improves its space utilization and optimizes the overall mechanical performance. Finally, the dynamic simulation experiment is carried out with the dynamic rehabilitation strategy as the goal, which proves that the robot can realize the normal gait simulation movement in sitting posture, and the motion control of the robot is independent of each other and shows good dynamic performance. Through investigation, the ankle-foot rehabilitation robot designed in this article is cost-effective.
Managing Critical Nodes in UAV assisted Disaster Networks

Device-to-device (D2D) communications serve as an alternative to cellular networks to enable communication for public safety networks (PSNs). A key requirement for PSNs is to offer alternative access to reach the responders if the communication infrastructure is partially or completely damaged due to a natural or man-made disaster. In this paper, we propose a novel unmanned aerial vehicle (UAV) assisted solution to ensure energy-efficient D2D connectivity in the disaster zone in the presence of critical nodes (CNs). The results show that the minimum average outage is achieved when the UAV is placed at the center of the region associated with the UAV, however, this scenario completely changes with the presence of CNs. Initially, with an increase in the number of CNs, the optimal UAV position shifts from the centre, however, increasing CNs causes the UAV to converge back to the center of the UAV associated region. To cater the service requirements of CNs, we analyze the impact of increasing the mobile command center (MCC) coverage range and study its impact on UAV placement by varying the ratio of CNs and non-CNs. We found that the average outage probability decreases with the increase of the MCC range.
Generative Independent Component Thermography for Improved Defect Detection of Carbon Fiber Composites

As one of the popular techniques for non-destructive evaluation, infrared thermography often requires the assistance of data analysis models to help defect detection and identification. A novel generative independent component thermography (GICT) framework for defect detection in polymer composites is proposed. It utilizes a deep convolutional generative adversarial network to generate more informative images, which enhances the diversity of thermography data. The defect detection performance of sequential ICT-based thermographic data analysis can be enhanced. The feasibility of GICT is illustrated with its application to the defect detection of a carbon fiber reinforced polymer specimen.
A Review on the Extraction of Region of Interest in Traffic Sign Recognition System

This paper analyzes the main issues of extraction of region of interest (ROI) in TSD (Traffic Sign Detection) system which include illumination inconsistency, distortion and computing efficiency. The reason why these issues occur and how they affect the performance of the system are discussed. Among all the issues, illumination inconsistency has a major influence and is often prioritized when it comes to the design of TSD system. Different methods have been brought up to deal with the issue. Some choose to improve the conventional methods to achieve better results, while others choose to utilize new tools, like CNN, to form new designs. All these methods are presented in details and compared in performance to present a overlook on the current status of TSD system.
Segment Specific Modeling of Electrocardiogram for Improved Reconstruction Error

Electrocardiogram (ECG) modeling is useful for abnormality detection and data compression. The common research problem in modeling is retaining pathological information using minimum number of model coefficients. In this paper, a new modeling technique for different wave segments of ECG signal, viz., baseline to P-onset, P wave, P-offset to Q, QRS complex, S to T-onset, T wave and T-offset to next baseline is presented. The processing steps included preprocessing, R-peak detection, beat segmentation and waveform partitioning, followed by modeling of individual partitions. For P, QRS and T wave, Gaussian model was adopted and for other segments, Fourier model was adopted to minimize reconstruction error. For testing of the proposed model, normal sinus rhythm (NSR) and myocardial infarction (MI) data records of PTB Diagnostic ECG database (ptbdb) and atrial premature (APC), premature ventricular contraction (PVC), left bundle branch block (LBBB) and right bundle branch block (RBBB) data records of MIT-BIH arrhythmia database (mitdb) under PhysioNet were used. The average SNR, and MSE using proposed method for ptbdb NSR was 86.33, and 4.41×10-6, respectively; for AMI 96.18, and 3.70×10-6 respectively; for IMI 80.86, and 1.36×10-6 respectively; for mitdb NSR 90.94 and 3.50×10-6 respectively; for APC 89.42, and 2.34×10-6 respectively; for PVC 93.28 and 3.06×10-6, respectively; for LBBB 93.77 and 2.74×10-6, respectively; for RBBB 92.83 and 3.52×10-6 respectively. Segment specific modelling approach provides better reconstruction performance in comparison with the few published works using Gaussian and Fourier model.
An Ultrafast Active Quenching Circuit for SPAD in CMOS 28nm FDSOI Technology

A very fast Active Quenching-Active Reset Circuit is proposed for the afterpulsing reduction in a Single Photon Avalanche Diode (SPAD). The proposed circuit is designed in a 28 nm CMOS FDSOI (Fully Depleted Silicon On Insulator) technology which takes benefit from the Body Biasing. By means of a very fast detection circuit, the avalanche is detected in just 27 ps. The fast detection results in a quenching time of less than 210 ps with a 46% reduction of the charge involved in the avalanche process. This reduction in the avalanche charge should strongly reduce the afterpulsing effect.
Location-Based Multichannel-Usage for ITS-G5 Car-to-Car Communication

Beside E-mobility and autonomous driving, the connectivity of vehicles is the major topics in research activities in the automotive industry, today. The ITS-G5 standards family describes a Car-to-Car communication solution for Europe, based on the IEEE 802.11p WIFI-standard using 7 communication channels at 5.9 GHz. In this paper we propose an idea of location-based channel-selection. A vehicle selects the channel depending on its position and an associated virtual radio cell. The location-based channel usage shall reduce the local channel throughput and shall allow for a content- and situation dependent choice of the dissemination area. A simulation of urban traffic was implemented to create a significant channel congestion caused by a high number of moving vehicles. For comparison to the proposed approach, two other setups were implemented, an approach without throughput control mechanism and one with a Decentralized Congestion Control (DCC) algorithm.
Machine Learning For Fast Short-Term Energy Load Forecasting

Improved energy usage data from smart meters offers a unique opportunity to apply advanced analytics that can dramatically improve load forecasting. Utility companies, policy makers, and consumers benefit with better integration of renewables and overall energy management in the IoT digital age. Accurate short-term energy forecasting is essential to improving energy efficiency, reducing blackouts, and enabling smart grid control. In this work-in-progress (WIP) paper, we use individual residential load data to perform customer segmentation based on energy profiles, introduce a unique data segmentation and feature extraction technique based on inherent load signal periodicities, and use deep learning to perform fast and accurate short-term forecasting. Partnering with Prime Solutions Group, a veteran-owned company based in Arizona, we found that we could obtain up to a 12% improvement in hourly one-day forecasting using our custom data segmentation and feature extraction techniques with neural network methods.
Implementation of a Fiware-based Integration Platform and a Web Portal as Aids to Improve the Control of Ships Navigation in a River

River Information Services (RIS) are the European standard focused on improving inland navigation by providing a series of harmonized data services available for the community. The RIS-compliant systems are expected to improve safety, efficiency and the environmental friendliness of all the accessible resources during navigation through canals or rivers. Fiware is an open source framework and one of the Connecting Europe Facility (CEF) building blocks. Its core provides tools for aggregating data from a variety of sources, generating a shared context with all the relevant information in the domain. The context may be constructed with real-time data from sensors, in addition with information provided by other systems or entities. This paper focuses on describing the details of the Fiware-based platform implemented in AIRIS project as architectural backbone for the RIS services. A web user interface adds secure access to the updated tools and configurations. New data models are also proposed to the Fiware community. It becomes an essential aid in the development of Internet of Things (IoT) platforms for inland navigation.
An Overview of Modern Thermo-Conductive Materials for Heat Extraction in Electrical Machines

This article presents a comprehensive review of the modern thermo-conductive materials that have potential to improve the heat extraction in electrical machines by conduction. Currently, there is a significant interest in thermal design and analysis of electrical machines as the demand for high power/torque density is substantially raised in applications such as marine, aerospace, e-mobility and rail. Thermal design engineering has become very important to develop smaller and more efficient electric motors, therefore electrical machine designers need to be more informed with the recent development in novel thermo-conductive (insulation) materials. Such developments can provide enhanced thermal characteristics for high power dense electrical machines. It is reported that high voltage electrical insulations can have more sophisticated thermal properties with the recent advancements in materials science. This study aims to inform electrical machine designers with and without a thermal background about recent thermo-conductive materials. Thus, a profound understanding can be gained in thermally conductive materials for the use of numerous applications in electrical machines for which thermal management is a crucial aspect of the overall multi-physics design and optimization process.
Unreliable Users Detection in Social Media: Deep Learning Techniques for Automatic Detection

Since the harmful consequences of the online publication of fake news have emerged clearly, many research groups worldwide have started to work on the design and creation of systems able to detect fake news and entities that share it consciously. Therefore, manifold automatic, manual, and hybrid solutions have been proposed by industry and academia. In this article, we describe a deep investigation of the features that both from an automatic and a human point of view, are more predictive for the identification of social network profiles accountable for spreading fake news in the online environment. To achieve this goal, the features of the monitored users were extracted from Twitter, such as social and personal information as well as interaction with content and other users. Subsequently, we performed (i) an offline analysis realized through the use of deep learning techniques and (ii) an online analysis that involved real users in the classification of reliable/unreliable user profiles. The experimental results, validated from a statistical point of view, show which information best enables machines and humans to detect malicious users. We hope that our research work will provide useful insights for realizing ever more effective tools to counter misinformation and those who spread it intentionally.
A Model of Decentralized Social Internet of Things using Blockchain Offline Channels

Social Internet of Things (SIoT) enables IoT devices to discover the services offered by each other using a social network. The social network connects two devices if they have a common owner or both manufactured by the same company or they are situated at the same location etc. Centralized SIoT platforms have security and privacy problems. We advance the state of art in SIoT as we have developed a decentralized SIoT platform. In a decentralized SIoT platform, the services provided by a device is only known to its neighbours. Due to such characteristics of decentralized SIoT platform, there are several challenges in designing it, such as verification of social neighbourhood, privacy-preserving search in SIoTs, and scalability of SIoT platform. In this paper, we propose a blockchain-based SIoT platform to mitigate these problems. Our main contributions are: We developed a decentralized SIoT platform that allows secure privacy-preserving method to verify social neighbourhood a device, we developed a protocol to find devices in the decentralized SIoT and we developed a high scale decentralized SIoT platform using blockchain offline channels. We prove the proposed decentralized SIoT platform is privacy-preserving, secure, and scalable.
A Low Noise CMOS Sensor Frontend for a TMR-based Biosensing Platform

In this paper, we propose a low noise CMOS frontend for a Point-of-Care (PoC) biosensing platform based on tunnel magnetoresistance (TMR) as sensors. The integration of a low noise and low power integrated circuit (IC) with the TMR sensors reduces power consumption compared to a realization with discrete electronics, and thereby paves the way towards a portable diagnostic system. The proposed chip uses a DC-coupled fully differential difference amplifier (FDDA) to amplify the minute signals generated by magnetic nanotags (MNTs) that will be used as biomarkers in the target biosensing application. The FDDA features a gain of around 60dB with a suitable offset calibration scheme to deal with the large DC offsets caused by TMR and/or magnetic field variations. The ability to deal with changing DC fields is crucial for a portable setup that is intended to be used in unshielded environments outside the lab. The offset cancellation is achieved by two on-chip current steering DACs that can accommodate TMR resistances between 535Ω and 4. 7kΩ. The presented chip is manufactured in a 180nm SOI CMOS technology and features a thermal noise floor of $7{\text{nV}}/\sqrt {{\text{Hz}}} $. It consumes a total of 7.7mA from a 1.8V supply.
Model of Sustainable Development Based on FinTech in Financial and Banking Industry: A Mixed-Method Research

In the past decade, it stated that the discourse on Sustainable Development and the Financial Technology(FinTech) role in the financial and banking industries, has attention on researchers, practitioners, regulators, and policymakers. The real problem arises from the discourse on whether FinTech has an effect on sustainable development in the financial and banking industry in Indonesia. This discourse needed to be understood better and more deeply. For credible research results, it is necessary to use a mixedmethod research approach. This research aims to develop a model that will help uncover the phenomenon of FinTech to sustainable development in the Financial and Banking Industry in Indonesia, using mixed methods research. In this study, Exploratory Sequential Mixed Method used as a type of mixedmethod approach. The main results of this study reveal that FinTech, which is influenced by business drivers, has a significant role in sustainable development in the Financial and Banking Industry in Indonesia. The contribution of this research can provide insight and understanding for researchers and practitioners in the financial and banking industry who want to implement a sustainable development model based on FinTech in more depth.
Predicted Safety Algorithms for Autonomous Excavators Using a 3D LiDAR Sensor

This paper proposes sensing and safety techniques for autonomous excavators working on construction sites. In autonomous construction equipment, safety is a significant concern to reduce fatal accidents and machinery damage. In taking up this point of view, this paper deals with object tracking, motion prediction, and track management for detected objects that can improve the safety function of autonomous construction equipment. By using the info of predicted motion of detected objects and actual working areas of an excavator, and calculating safety indices, the proposed safety algorithms can evaluate the degree of safety (i.e., potential collision risks).