Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficiency Improvement in Wireless Power Transfer System at 2.45 GHz by Using Metamaterials

This paper is proposed the efficiency improvement in wireless power transfer system is applied sensor device in IoT system. The study and design of the metamaterials structure to be applied with antennas in wireless power transfer (WPT) system at 2.45 GHz (ISM band) are performed to improved efficiency of power transfer. The two types of metamaterial are applied to the wireless power transfer. 1)The Artificial Magnetic Conductor (AMC) type, it is designed to be placed behind the antenna of transmission part. The Microstrip antenna is used in this paper. This AMC is use as the in-phase reflector to increase radiation intensity and efficiency of the transmit antenna. 2) Metamaterials structure type of Multiple Split-Ring Resonators (MSRR) that have negative refraction index is placed between the transmit antenna and the receiving antenna. This feature of the structure reduces the evanescence of the electromagnetic wave. From the results, it is found that the metamaterials can be optimized for both antennas in a wireless power transfer system at 2.45 GHz and improved the efficiency of wireless power transfer system
Correlation Analysis of Health and Traditional Chinese Medicine (TCM) Pulse Diagnosis for the Elderly Using Wearable Devices

This study used wearable devices and traditional Chinese medicine (TCM) diagnosis, which verifies the promotion of wearable devices on the health habits of the elderly, and the correlation between pulse diagnosis and health. We used wearable devices and TCM diagnosis (including TCM doctor diagnosis and Pulse diagnosis instrument) to record health information for the elderly. One-way repeated measures analysis of variance (ANOVA) was used to determine health changes. Correlation analysis was used to determine the relationship between pulse and health indicators. There was significant change in the number of steps for the elderly before and after using the wearable device. Systolic blood pressure (SBP) was negatively correlated with pulse wave time. Diastolic blood pressure (DBP) was negatively correlated with pulse duration. Pulse diagnosis can be used as an indicator to report the health of the elderly. And used wearable devices can quantify health indicators and improve the health awareness.
Dynamic Threading to Improve Embedded Software Performance in IoT Devices Using MQTT Protocol

Internet of Thing (IoT) and the 4th Industrial Revolution are one of the key development trends of today’s technology. In this trend, multi-core processors are being widely researched and manufactured to replace the single processors especially in IoT devices. However, most embedded software in multi-core devices still use the sequence programming model which does not take advantage of the multi-core processors. Therefore, this paper proposes and develops a method to improve software performance for multi-core processors in IoT devices using the MQTT protocol. This performance improvement method is built based on dynamic threading and balanced data partition in accordance with the characteristics of MQTT.
A Hybrid 2D and 3D Convolution Neural Network for Stereo Matching

Stereo matching plays an important role in computer vision and SLAM (simultaneous localization and mapping). In this paper, we propose a novel hybrid 2D and 3D convolution neural network for stereo matching. Unlike existing similarity metric based stereo matching methods which need extra postprocessing to finish the matching pipeline, the proposed approach is an end-to-end stereo matching method and it needs much less time for an image pair. Unlike a lot of cost volume and disparity based stereo matching methods which are too complicated to run on performance-constrained devices, the proposed method is much more simple and can run on the real-time sweeping robot that we build. Experimental results on two widely used stereo matching datasets verified the effectiveness of the proposed approach, meanwhile, our real-time SLAM system-the sweeping robot demonstrates that our method can apply to real-time applications.
Preliminary Evaluation of a Guided Usability Defect Report Form

Open source software development projects typically use generic defect reporting forms, such as BugZilla and Jira, even for specialized kinds of defects such as usability issues. These issues typically tend to be reported as unstructured text, mix multiple defect attributes, lack precise cause, and overlook emotional impact on users. The poor quality of these defect reports impacts on the ability of developers to address them efficiently and effectively. In this paper, we describe a novel guided defect reporting method for capturing usability defects. We evaluate our approach using an expert judgment approach where experienced developers and researchers evaluate the clarity of usability defects reported via conventional defect reporting tools and our method. Our results show this is a promising approach at capturing more clear usability defect descriptions.
Keyframe-Based RGB-D SLAM for Mobile Robots with Visual Odometry in Indoor Environments Using Graph Optimization

The SLAM problem is currently one of the most important topics in mobile robotics, due to the high number of applications that need its solution. This work proposes a methodology to perform SLAM in indoor environments with RGB-D data. The robot motion is estimated using FOVIS, a robust visual odometry system, and a graph-based probabilistic approach is used to minimize the errors caused by the drift in visual odometry. A keyframe selection approach is used to construct the graph and the g2o framework is used for the optimization. The proposed methodology is implemented as a Robot Operating System (ROS) package, and it is evaluated using benchmark datasets available in the literature. A comparison is made with state-of-the-art methods.
Design and Development of a Manipulator for Educational Robotics

This article aims to present the development and construction of two prototypes of a robotic manipulators with two degrees of freedom constituting the body and a third degree of freedom being the end-effector. The research goal is illustrate how a more practical development helps to increase the motivation and the learning process of the students. In the class the content of the discipline was transmitted by structuring the development of a project, each one of the projects phases were then applied with the students. To measure the viability of the method a survey were done with the students. The Different types of end-effector shows the use of educational Robotics platform geared towards learning and applications of robotic manipulators concepts in a educational environment.
Highly Assured Safety and Security of e-Health Applications

Modern medical devices aim at providing invasive e-health care services to patients with long-term conditions. Typically, these services are implemented as embedded software applications that remotely and automatically control the operations of the devices according to the patient’s condition as monitored by the underlying sensors. Such applications are neither safe nor secure mainly because of unreliable sensors, which may provide incorrect input data either due to its malfunctioning or due to some accidental (by privileged user) or intentional (by adversary) interference. Hence, the incorrect sensor data may lead to identification of inaccurate patient condition, which may threaten the patient’s life. To ensure safety and security of e-health applications, current approaches employ data analysis techniques to monitor sensor data and alarm when some unusual value is detected and employ access control strategies to ensure that controller decisions are consistent with sensor input data. However, such approaches fail to detect stealthy attacks, e.g. bad data (false data injection) and bad computations because they do not understand what the application or device is trying to do. To this end, we evaluate our existing approach (i.e., ARMET) to assure safety and security of an emerging and critically real-time application domain of e-health. The approach is based on the specification of the application and device, which has a design and a run-time component. Given an application specification, the design component employs logical verification methods to assure that the application design is resilient to some bad data, i.e., there are no sensor input data values with meaningful threshold which are admissible to the specification but are not true. Given the specification, the runtime component monitors application’s execution and assures that the execution is consistent with the specification and alarms whenever it detects a violation, i.e., there is a bad computation. We evaluate the methodology through its application to an example medical e-health application that controls and monitors blood glucose through an insulin pump.
Reduction of Ping-Pong Effect In Cognitive Radio Spectrum Handoffs Using Fuzzy Logic Based Inference

The evolution in wireless communication and networking has increased the demand for the availability of extra spectrum bands. The spectrum is a scarce resource and requires to be utilized in an efficient way. Research in this domain has envisaged that the problem of spectrum scarcity can be addressed by Cognitive Radio. Cognitive Radio Networks are comprised of multiple technologies and networks which permits the dynamic use of spectrum. Spectrum Handoffs, is thus a major necessity of Cognitive Radio Network and if not properly executed, might cause ping-pong effect. The Ping-pong effect is caused due to the motion of mobile users between boundaries of adjacent cells thus, initiating unnecessary Spectrum Handoffs. In this paper, a Fuzzy Logic based system is proposed that is capable of making efficient decisions about the execution of Spectrum Handoffs thus, and reduces the chances of the ping-pong effect.
Robust Mapping for the Autonomous Mobile Robot Considering Potential Occupied Spaces of Objects

Simultaneous Localization and Mapping (SLAM) is an important function for autonomous mobile robots. 2D or 3D maps under static or dynamic environments have been greatly developed and widely used for robot navigation and path planning. Most of the generated maps can accurately reflect the objects in the environment, but the properties of the objects have not been considered. The robot can avoid colliding with obstacles when using these kind of maps. However, the robot needs to move in a socially acceptable way like human beings. For example, human beings usually avoid moving under desks even if there are paths that can go through. Meanwhile, human beings has the ability to analyze the motion of the objects like a door and move in a considerate way without staying behind it and standing in the way. The spaces under a desk, behind a door, in front of a refrigerator etc. are not occupied by real objects but actually occupied by the objects because of their properties. These kinds of spaces are defined as potential occupied spaces in this paper and considered when generating the map. The objects in the environment are detected and reflected to the may in the same way of conventional methods. Besides, the objects are also recognized and their properties are analyzed to generated virtual areas in the map. In this way, human beings will naturally avoid entering these potentially occupied spaces and the robots can move considerately like human beings. The basic map is generated by immobile area grid map based SLAM. The objects are recognized by Single Shot multi-box Detector (SSD) and other methods, and their potential occupied spaces are generated and reflected to the map base on potential filed method. The effectiveness of the proposed method is proven by mapping under the indoor environment.
Remote Blind Motion Separation Using a Single-Tone SIMO Doppler Radar Sensor

It is a challenging task for a continuous-wave Doppler radar sensor (DRS) to linearly separate independent motions without any prior information. In this paper, a low-IF single-input multiple-output (SIMO) DRS system is designed and implemented for motion separation. The SIMO DRS system detects the signals scattered off multiple objects, and a two-step blind motion separation approach is proposed to separate motions from the DRS output. Experiments show that the SIMO DRS and the proposed motion separation approach can successfully separate combinations of triangular, sinusoidal, and random motions, as long as the velocities or initial phases of such motions are different. With the two-step approach sequentially tackling the signal separation and the nonlinear demodulation problems, both small- and large-scale independent motions can be linearly separated. These results imply the potential in practical applications of short-range tracking and localization of moving objects.
Unified Active Damper Enhances Stability of Multi Inverters System

There are serious problems tending to result in resonance and instability because of the mutual coupling of multi inverters in the system through the grid impedance. The traditional active damper can suppress the system resonance by impedance adaptation with virtual impedance. However, it cannot fully decouple multi inverters system and the grid impedance. What is more, voltage source harmonics such as grid background noise cannot be effectively suppressed. To address them, this paper proposes a unified active damper that uses a combination of a parallel structure and a serial structure, it can reduce the influence of the grid impedance and suppress system’s resonance and harmonic amplification at the PCC. In order to verify the effectiveness of the proposed scheme, a simulink of the multi inverters system setup are carried out, the simulation results show that the proposed unified active damper can enhance the stability of the system.
Robust Lifelong Multi-task Multi-view Representation Learning

The state-of-the-art multi-task multi-view learning (MTMV) tackles the learning scenario where multiple tasks are associated with each other via multiple shared feature views. However, in online practical scenarios where the learning tasks have heterogeneous features collected from multiple views, e.g., multiple sources, the state-of-the-arts with single view cannot work well. To tackle this issue, in this paper, we propose a Robust Lifelong Multi-task Multi-view Representation Learning (rLM2L) model to accumulate the knowledge from online multi-view tasks. More specifically, we firstly design a set of view-specific libraries to maintain the intra-view correlation information of each view, and further impose an orthogonal promoting term to enforce libraries to be as independent as possible. When online new multi-view task is coming, rLM2L model decomposes all views of the new task into a common view-invariant space by transferring the knowledge of corresponding library. In this view-invariant space, capturing underlying inter-view correlation and identifying task-specific views for the new task are jointly employed via a robust multi-task learning formulation. Then the view-specific libraries can be refined over time to keep on improving across all tasks. For the model optimization, the proximal alternating linearized minimization algorithm is adopted to optimize our nonconvex model alternatively to achieve lifelong learning. Finally, extensive experiments on benchmark datasets shows that our proposed rLM2L model outperforms existing lifelong learning models, while it can discover task-specific views from sequential multi-view task with less computational burden.
GaN Integration Technology, an Ideal Candidate for High-Temperature Applications: A Review

In many leading industrial applications such as aerospace, military, automotive, and deep-well drilling, extreme temperature environment is the fundamental hindrance to the use of microelectronic devices. Developing an advanced technology with robust electrical and material properties dedicated for high-temperature environments represents a significant progress allowing to control and monitor the harsh environment regions. It may avoid using cooling structures while improving the reliability of the whole electronic systems. As a wide bandgap semiconductor, gallium nitride is considered as an ideal candidate for such environments, as well as in high-power and high-frequency applications. We review in this paper the main reasons that offer superiority to GaN devices over better-known technologies such as silicon (Si), silicon-on-insulator, gallium arsenide (GaAs), silicon germanium (SiGe), and silicon carbide (SiC). The theory of operation and main challenges at high temperature are discussed, notably those related to materials and contacts. In addition, the main limitations of GaN, including the technological (thermal and chemical) and intrinsic (current collapse and device self-heating) features are provided. In addition, the GaN devices recently developed for high-temperature applications are examined.
Enterprise Architecture Driven and User-Friendly SaaS Service Selection

In the last decade the number of cloud services has grown significantly, and its nature challenges the traditional process of requirements elicitation for software solutions. Most of the conducted cloud research address nonfunctional requirements, such as payment strategies, performance or security. Methods are required which provide an appropriate level of abstraction for describing the functional aspects of cloud services. We suggest an approach which exploits the potential of enterprise architecture modeling for cloud service selection. This study contributes to cloud service selection in two ways. First, we suggest a concept for the description of functional requirements for vertical SaaS services. Second, we provide a method that enables various kinds of stakeholders to describe their requirements considering their individual knowledge level.
Rapid Run-Time DRAM PUF Based on Bit-Flip Position for Secure IoT Devices

IoT services are expanding beyond basic sensor networks to various services such as connected cars, smart factories, and smart cities. As a result, security issues are also growing rapidly, especially for low-cost IoT devices. One of good security solutions is to use the Physical Unclonable Function (PUF) technology to uniquely identify hardware components. In this paper, we propose a new approach to reduce the latency of the decay based DRAM PUF at run-time in commodity IoT devices. Experimental results based on Intel Galileo Gen2 Boards show that our methods can quickly generate the PUF values with good uniqueness and uniformity within 15 seconds.
Identification of Eye Blink Artifacts Using Wireless EEG Headset for Brain Computer Interface System

Research work on Brain Computer Interface is explored worldwide with the support of wireless EEG headsets for innumerable Bio-medical applications. Usually, EEG signal gets adulterated with undesirable noise and artifacts such as eye blink, jaw clench, muscular movement, power supply etc. This paper presents some experimental procedures for detecting and analyzing eye blink artifacts with eye closed and open condition by using histogram and Fast Fourier Transform.
A Proposal to Orchestrate Test Cases

This paper presents the concept of test orchestration, understood as a novel way to select, order, and execute in parallel a group of tests. Our view of test orchestration can be seen as a process in which different test cases are organized, assembled and executed following a topology that determines how their executions coordinate. We distinguish two types of orchestrations techniques: i) verdict-driven, which organizes tests using their outcome (i.e., passed or failed) to drive the workflow; and ii) data-driven, in which test data (input) and test outcomes (output) are handled within the graph. Both approaches are being implemented in the project ElasTest, an open source platform aimed to simplify the end-to-end test process of large software systems.
An Improved Method for Building A 3D Model from 2D DICOM

The development of images processing techniques in recent years brings us a lot of useful application in practice. In the field of medical image processing, several researches and their application have been widely applied in practice such as diagnostics, treatment, training and education. This paper presents an improved method for reconstructing the shape of 3D objects from 2D DICOM datasets. We first focus on DICOM data processing based on techniques of digital image processing to get a 3D point cloud. This step includes extracting features, boundary of the shape, removing noisy data and inserting new pixels to obtain a regular dataset on each 2D DICOM slice. In the next step, we reconstruct the 3D object based on triangulation of 3D point clouds. In the last step, we build an application for rendering and visualizing the 3D medical image objects. Comparing to the existing methods, the proposed method is very fast for creating a 3D object from DICOM images. The application can be used to support medical staffs and doctors in diagnostic, treatment and training.
Rate-adaptation scheme for multiband WLAN system

To improve the spectral efficiency for an unlicensed band from 2.4 to 5 GHz, a multiband wireless local area network (WLAN) system has been proposed. When a rate-adaptation scheme such as an auto rate fallback is used on the sender side to this multiband WLAN system, it cannot select the optimal combination of modulation schemes for different frequency bands since it is difficult to accurately estimate each channel condition from the number of successful and failed frame transmissions. To overcome this problem, this paper proposes a rate-adaptation scheme on the receiver side that includes a notification method for the sender side in the multiband WLAN system. The proposed scheme can select the optimal combination of modulation schemes and the coding rate that has the maximum mutual information and ensure the backward compatibility with conventional WLAN devices by informing the sender side of the selected result using IEEE802.11 frame format.
Feature-Based Dividing Symbolic Time Series Representation for Streaming Data Processing

Along with the continuous development of infor- mation technology, massive numbers of detecting instruments or systems in various fields are continuously producing plenty number of streaming time series data. In recent years, many representation approaches for time series has been proposed with the main objective of dimensionality reduction to support various data mining algorithms in the domain of time series data processing. Symbolic Aggregate approXimation(SAX) is a major symbolic representation and dimensionality reduction algorithm which has been widely used in many application scenarios of time series data mining, such as motifs discov- ery,outlier detection, etc. In this paper, we propose a symbolic representation method of streaming time series based on VTP- diving with sliding window and a similarity measurement algorithm for the proposed representation method which lower bounding the Euclidean distance on the original data. Extensive experiments on different kinds of typical time series datasets have been conducted to demonstrate the advantages of our proposed method.