Miracle-dz/UniFAR-FTUs huggingface dataset:
A Polyimide Based Force Sensor Fabricated Using Additive Screen-Printing Process for Flexible Electronics

A flexible capacitive force sensor was developed for force sensing applications. The force sensor consists of two electrodes and a dielectric layer. The electrodes were fabricated by depositing conductive silver (Ag) ink on a flexible polyimide platform using screen-printing process. The dielectric layer was prepared by mixing the contents of polydimethylsiloxane (PDMS) (pre-polymer and curing agent) in a 16:1 ratio. Then the PDMS dielectric layer was sandwiched between the screen-printed Ag electrodes. The capability of the fabricated force sensor was investigated by recording its capacitive response for varying applied forces of 100 N. It was observed that the capacitance increased from 5.83 pF to 6.36 pF as the force was increased from 0 N (no load) to 100 N. A sensitivity and correlation coefficient of 0.081%N−1 and 0.998 were calculated for the force sensor. In addition, a response time and recovery time of 3.7 seconds and 5.7 seconds was measured for the fabricated force sensor. The relative humidity (% RH) tests performed from 20% RH to 80% RH, in steps of 20% RH, revealed that there was a minimal effect of RH on the base capacitance of the force sensor at room temperature.
When the Decomposition Meets the Constraint Satisfaction Problem

This paper explores the joint use of decomposition methods and parallel computing for solving constraint satisfaction problems and introduces a framework called Parallel Decomposition for Constraint Satisfaction Problems (PD-CSP). The main idea is that the set of constraints are first clustered using a decomposition algorithm in which highly correlated constraints are grouped together. Next, parallel search of variables is performed on the produced clusters in a way that is friendly for parallel computing. In particular, for the first step, we propose the adaptation of two well-known clustering algorithms ( $k$ -means and DBSCAN). For the second step, we develop a GPU-based approach to efficiently explore the clusters. The results from the extensive experimental evaluation show that the PD-CSP provides competitive results in terms of accuracy and runtime.
Authentication Techniques in the Internet of Things Environment: A Survey

The Internet of Things (IoT) enables the oﬀering of services specialized for each client in real time by processing and analyzing the sensor information concerned after storing the sensor information collected from numerous things on the Internet through wired/wireless communications technology. The IoT technology currently applies to various industrial ﬁelds including home, medical service, transportation, environmentdisaster, manufacturing, construction, and energy and has been actively researched. The IoT technology closely combined with real life can cause monetary and physical damages to clients by malicious clients through the seizure and falsiﬁcation of information. Unlike computes and mobile devices, the IoT technology uses micro devices, and therefore lightweight cryptographic techniques with limited storing space are needed. In this regard, this report aims to examine security threatening factors that may occur in the IoT environment and service and to guide authentication techniques by which safe IoT service can be realized. Through this, this research presents a reference guide that can be used by companies or security protocol developers.
Implementation of Health Insurance Query Platform for Assisting Elderly People

A health insurance query platform was built based on the goals of a concise interface, a clear description, and a smooth process to assist elderly people. To solve the problems of the lack of information, fraud, or false advertising, this platform provides the function of a health insurance query. This platform is immediately clear and allows searching for multiple insurance options at one time and providing a comparison of the results.
Data Augmentation Powered by Generative Adversarial Networks

Face identification projects must be based on a highquality database of the faces to be recognized. The accuracy of the identification depends on the illumination and the diversity of the subject’s expression, among other things. Naturally, a more diverse training database and data augmentation can help in loss reduction. In this research, we attempt to increase the quality of few-shot learning face identification by using Generative Adversarial Network-based data augmentation techniques. This paper presents a novel method to embed images into GAN’s latent space, and to use the augmented versions for few-shot learning.
Air Quality and Climate Control Arduino Monitoring System using Fuzzy Logic for Indoor Environments

Maintaining good indoor air quality is critical for all people since the rate of pollution indoors is much higher than it is outdoors. Indoor ventilation to refresh indoor air is very essential, as it improves the air quality and reduces the health risks for people. The automation of the process of refreshing indoor air is very important in these kinds of situations due to the fast lifestyles of people today. The proposed method offers a fully automated system that monitors and controls the indoor air quality, temperature, and humidity using Arduino technology and a fuzzy logic controller. The system measures the air quality using the MQ-135 sensor and keeps track of the temperature and humidity using the DHT-11 sensor. The readings of these sensors are used to adjust the ventilation of the indoor area to remove unwanted gases as well as control the temperature and humidity. The results show that the proposed system achieves high performance in controlling and monitoring the air quality in a room environment. Moreover, the proposed system reduces the power consumption of the ventilation mechanics. This reduction is due to the fuzzy logic controller, which adjusts the speed and the working time interval of room ventilation. Some related works are presented to show the difference between other techniques and the proposed system.
Design of an ANFIS Automatic Voltage Regulator of a Synchronous Generator

This paper presents a procedure suitable to design an ANFIS automatic voltage regulator of a synchronous generator. Several aspects related to the design of this intelligent AVR are explored and compared, in order to determine the best procedure to perform the design. The performance of the AVR is tested and compared to the one of conventional AVRS at the 9-bus 3-machine IEEE test power system, showing an important improvement of the system dynamic response.
Active Structure Learning for Block Diagonal Subspace Clustering

The structured block diagonal representation (SBDR) subspace clustering algorithm combines the advantages of the block diagonal representation with the structured sparse subspace clustering. It considers the intrinsic relationship between affinity and segmentation, both of which are iteratively optimized to improve the clustering performance. However, getting a more accurate block diagonal structure requires additional information. In this paper, we design an active learning strategy to obtain pairwise constraints to guide SBDR clustering. The novel algorithm, named active structure learning of block diagonal representation for subspace clustering (ASBDR), is an iterative process involving two steps. First, the active learning strategy selects semi-supervised pairwise constraints. Next, the constrained block diagonal expression is obtained by ASBDR, and the reconstructed affinity matrix is input to the spectral clustering. As the amount of learning increases, better data segmentation is achieved. Experimental results on test image datasets demonstrate the effectiveness of the proposed algorithm.
Mammut pacificus sp. nov., a newly recognized species of mastodon from the Pleistocene of western North America

A new species of mastodon from the Pleistocene of western North America, Mammut pacificus sp. nov. is herein recognized, with specimens identified throughout California and from two localities in southern Idaho. This new taxon differs from the contemporaneous M. americanum in having narrower teeth, most prominently in M3/m3, as well as six sacral vertebrae, femur with a proportionally greater mid-shaft diameter, and no mandibular tusks at any growth stage. All known Pleistocene Mammut remains from California are consistent with our diagnosis of M. pacificus, which indicates that M. americanum was not present in California.
SHCFNet on Micro-expression Recognition System

Micro expression is a facial feature that can reflect the most real emotional state hidden in the human heart. This is a very short process and difficult to capture accurately. Based convolutional network, a new network architecture (SHCFNet) is proposed to extract the spatial-temporal feature of peak frames, the optical flow between onset and apex frame and its derivative (optical strain). The proposed network stacks these features from the outcomes of the previous layer. Then, the stacked feature is merged with the convolution feature of the previous layer, which enhances the learnability of neurons. The performance of the proposed SHCFNet are evaluated on four benchmark datasets: CASME I, CASME II, SAMM and SMIC, and compared with other advanced networks.
Real-time approach for gait analysis using the Kinect v2 sensor for clinical assessment purpose

Gait analysis is often performed in clinical environments to evaluate the patient’s walk capability and help the rehabilitation process. In a walking test, real-time feedback is fundamental for the healthcare professional to evaluate the gait quality, but the high cost of golden standard equipment is a limiting factor. After Microsoft Kinect v2 was released, many studies were published suggesting that this RGB-D sensor could be used for clinical purposes. This research analyzed 75 different gait samples with a real-time approach to compare the results with each parameter’s real measurements. Results showed that step length outcomes are accurate and stride length outcomes are precise. Regarding kinematic parameters, knee and hip angles indicate that these joints may be used for gait analysis, but feet angles were not reliable.
Symbolic Uniform Sampling with XOR Circuits

Uniform sampling is an important method in statistics and has various applications in model counting, system verification, algorithm design, among others. Symbolic sampling in a Boolean space is a recently proposed technique that combines sampling and symbolic representation for effective Boolean reasoning. Under the framework of symbolic sampling, we propose a method to construct compact XOR circuits achieving uniform sampling in a given Boolean space. The method is further extended to biased sampling within a focused subspace of interest. Experimental results show the effectiveness of compact sampling circuit generation and its potential to facilitate Boolean reasoning.
MeshGraphNet: An Effective 3D Polygon Mesh Recognition With Topology Reconstruction

Three-dimensional polygon mesh recognition has a significant impact on current computer graphics. However, its application to some real-life fields, such as unmanned driving and medical image processing, has been restricted due to the lack of inner-interactivity, shift-invariance, and numerical uncertainty of mesh surfaces. In this paper, an interconnected topological dual graph that extracts adjacent information from each triangular face of a polygon mesh is constructed, in order to address the above issues. On the basis of the algebraic topological graph, we propose a mesh graph neural network, called MeshGraphNet, to effectively extract features from mesh data. In this concept, the graph node-unit and correlation between every two dual graph vertexes are defined, the concept of aggregating features extracted from geodesically adjacent nodes is introduced, and a graph neural network with available and effective blocks is proposed. With these methods, MeshGraphNet performs well in 3D shape representation by avoiding the lack of inner-interactivity, shift-invariance, and the numerical uncertainty problems of mesh data. We conduct extensive 3D shape classification experiments and provide visualizations of the features extracted from the fully connected layers. The results demonstrate that our method performs better than state-of-the-art methods and improves the recognition accuracy by 4–4.5%.
Improving Resource Utilization with Virtual Media Function Decomposition

For many years, media transport and processing in professional media environments have been accomplished using specialized hardware. The flexibility of IP networking and Media Function Virtualization (MFV) can enable broadcasters to build cost-efficient architectures for the deployment of media services. These architectures can be further exploited to perform decomposition of Virtual Media Functions (VMFs) resulting in improved utilization of the MFV Infrastructure (MFVi) resources. To this end, this paper presents an algorithm aimed at optimizing the VMF Forwarding Graphs (VMF-FGs) media services. A first-fit based heuristic is also proposed for deploying media services on a given MFVi. The evaluation results indicate that VMF-FG decomposition can significantly improve the request acceptance ratio and reduce resource requirements.
Design and Implementation of eTansiyon Mobile Software Application

In this study, a mobile application called eTansyion designed and developed for global disease hypertension, which affects the whole world, can be used by both patients and doctors to follow their health status. In this study, the components of the eTansiyon mobile application are presented and the stages of the Rapid Application Development approach used in the development process are explained. eTansiyon is an application where patients can record, monitor their measured blood pressure data, create graphics of this data and share with their doctors on iOS and Android platforms. Especially in an epidemic period, it is expected that the developed application will contribute to this research field by offering a model based on the common approach of medicine and computer science.
A Pipeline for Extraction of Sharp-Wave Ripples from Multi-Channel in vivo Recording EEG

Extraction of Sharp-Wave Ripples (SWRs) from brain wave signals plays an important part in various medical studies of mammalian nervous systems. SWRs, playing a crucial role in memory consolidation, are oscillatory patterns in the mammalian brain hippocampus seen on an EEG during immobility and sleep. An SWR is composed of large-amplitude sharp waves accompanied by fast field potential oscillations known as ripple rhythms. However, most of the current commercial software for brain wave processing does not provide with an accurate SWR extraction function. Also, so far there are few literatures that fully explore the ripple detection method. Taking a fuller look at the characteristics of ripple events, an improved pipeline is presented to extract SWRs. The utility of detection based on the large-amplitude feature of SWRs will be weakened by another feature, fast oscillation. Therefore, to shield the extraction from that undesired influence, Hilbert transformation is suggested to restore the analytical signal in complex number field and then to obtain the envelope of the original EEG. Next, Gaussian window is adopted to get rid of some artifacts. Then, the central and the start and end segment of an SWR are successively determined with a sliding window. In addition, considering that the determination of the duration of a ripple also changes the frequency content of a detected, truncated ripple by the spectral leakage effect, which makes it hard to find the actual frequency of the rhythm, we add Hanning window to prevent that effect. From three sets of multi-channel in vivo recording EEG data obtained from different genotypes of mice, we detected SWR events with the proposed method, whose effectiveness and accuracy were validated.
FPGA-based Low-Batch Training Accelerator for Modern CNNs Featuring High Bandwidth Memory

Training convolutional neural networks (CNNs) requires intensive computations as well as a large amount of storage and memory access. While low bandwidth off-chip memories in prior FPGA works have hindered the system-level performance, modern FPGAs offer high bandwidth memory (HBM2) that unlocks opportunities to improve the throughput/energy of FPGA-based CNN training. This paper presents a FPGA accelerator for CNN training which (1) uses HBM2 for efficient off-chip communication, and (2) supports various training operations (e.g. residual connections, stride-2 convolutions) for modern CNNs. We analyze the impact of HBM2 on CNN training workloads, provide a comprehensive comparison with DDR3, and present the strategies to efficiently use HBM2 features for enhanced CNN training performance. For training ResNet-20/VGG-like CNNs for CIFAR-10 dataset with low batch size of 2, the proposed CNN training accelerator on Intel Stratix-10 MX FPGA demonstrates 1.4/1.7X energy-efficiency improvement compared to Stratix-10 GX FPGA with DDR3 memory, and 4.5/9.7 X energy-efficiency improvement compared to Tesla V100 GPU.
Integrating Human Hand Gestures with Vision Based Feedback Controller to Navigate a Virtual Robotic Arm

This paper reports the design and development of a real-time IMU-Vision-based hybrid control algorithm to interact with a 6-DOF Kinova virtual robotic arm. Human Robot Interaction (HRI) control scheme proposed in this paper utilizes an embedded gyroscopic sensor from a Myo Gesture Control Armband’s inertial measurement unit and an 800*600-pixel resolution from a Microsoft HD camera. The algorithm exploits the mathematical features of a numerical discrete time integrator and a mean filter to process the raw angular velocity data from the gyroscope. The processed data further provides the angular displacements to the end-effector of the robotic arm during clockwise or counterclockwise actions along x, y, and z axes from the user. This also facilitates the end effector (gripper) motion which was also controlled simultaneously by the roll action through threshold comparison in the algorithm. A vision based feedback system was designed using a computer vision toolbox and blob analysis technique in order to make the system more reliable and to control the distance of the end-effector while reaching to desired objects. The results demonstrated a significant control of the 6-DOF virtual robotic limb using the gyroscopic information and user inputs. The virtual robotic arm stopped the movement after reaching 320 mm from the desired object as expected. For three different objects, the maximum error between the real and the measured distance was calculated as 15.3 cm for cylindrical object. Due to its smooth control and arm gesture controller, this technology has the potential to assist people with either physical impairments or neurological disorders to perform activities of daily living using assistive robotic arm in the near future.
Optimal Location Management Service for RSUs Placement in VANETs

Determining optimal placement and optimal number of roadside units (RSUs) that provides location management service in terms of load distribution, accurate localization, and overhead reduction is an open issue. In this paper, Collaborative Vehicle Location Management (CVLM) is proposed to localize destination vehicles using RSUs. Optimal placement and best number of RSUs is measured. Compared to state-of-arts methods, the results show that the proposed CVLM outperforms Grid Location Service (GLS), Intersection Location Service (ILS), Time-based Vehicle-to-Roadside (V2R) in terms of both packet delivery and end-to-end delay. Additionally, it also show that, according to Packet Delivery Ratio (PDR), Packet loss Ratio (PLR), overhead, and end-to-end delay, RSUs should be placed at road intersections and the optimal number of RSUs are equal to the number of these road intersections.
A Review of Visual and Auditory Attention Computational Models

When dealing with complex scenes, human cognitive system can get useful information quickly and accurately. This is because that human visual and auditory systems have the important characteristic of selective attention. In order to simulate and realize this feature, much selective attention computational models have been proposed by scholars in the field of computer vision and auditory, which are of great value and significance to artificial intelligence, video compression, speech processing and other fields. This article provides a review of modelling studies of visual and auditory attention. The deficiency of the current research was provided, together with the future research directions.
Comparing Algorithms for Accurate Tracking in Virtual Reality with a Low-Cost MIMU

Due to recent advances in sensing technology, it has become possible to estimate the absolute orientation of a body for Virtual Reality (VR) applications using low-cost magnetic and inertial measurement units (MIMUs). A MIMU is a device that measures acceleration, rotation rate, and the Earth’s magnetic field. Using this data, the orientation can be determined with a sensor fusion algorithm (SFA). This paper compares four approaches to estimate orientation using the InvenSense MPU-9250 MIMU, namely 1- reading quaternions straight from its Digital Motion $\mathrm{P}\mathrm{r}\mathrm{o}\mathrm{c}\mathrm{e}\mathrm{s}\mathrm{s}\mathrm{o}\mathrm{r}^{\mathrm{T}\mathrm{M}}, 2$- using only accelerometer and magnetometer data, 3- using Mahony open source SFA, and 4- using Madgwick open source SFA. Basic static, dynamic and magnetic interference tests are performed. The results show that both 1 and 2 did not provide an acceptable estimate for the application, because 1 drifted 43.38° in 10 minutes and 2 presented low stability (sd $=2.46^{\circ}$) and accuracy (peak error $=59.2^{\circ}$). Therefore, either3 or 4 could be used, with 4 obtaining the best absolute orientation (sd $=0.06^{\circ}$, peak error $=4.46^{\circ})$ but presenting the slowest response and 3 having a less accurate estimation (sd $=0.41^{\circ}$, peak error $=38.72^{\circ}$) than 4, but being faster.
Moderate aortic stenosis and left ventricular dysfunction: an ominous association

Considering the high incidence of heart failure and degenerative aortic stenosis (AS) in the elderly, they often coexist. Aortic stenosis progresses gradually over time, and the aortic valvular area (AVA) decreases by 0.1 cm every year, contributing to left ventricular systolic dysfunction (LVSD) by increasing afterload. Aortic valve replacement is recommended in patients with severe AS when they experience symptoms and/or LVSD. According to this model, patients with moderate AS are a stable group, at intermediate risk, not requiring, yet, valve replacement. Nonetheless, patients affected with both conditions are subject to two different mechanisms for the increasing afterload: on one hand, the increased sympathetic activities typical of heart failure, and on the other the high transvalvular gradient of the aortic valve disease. In patients in whom AS occurs over a preexistent left ventricular dysfunction, the added afterload increases, further magnifying the LVSD. In this situation, correction of the AS, albeit moderate, could improve the long-term survival. The theoretical benefit of the afterload reduction should be confronted with the high surgical risk of these patients. The key questions in the decision-making are