Miracle-dz/UniFAR-FTUs huggingface dataset:
Generalized Content-Preserving Warp: Direct Photometric Alignment Beyond Color Consistency

Motion estimation is vital in many computer vision applications. Most existing methods require high quality and large quantity of feature correspondence and may fail for images with few textures. In this paper, a photometric alignment method is proposed to obtain better motion estimation result. Since the adopted photometric constraints are usually limited to the required illumination or color consistency assumption, a new generalized content-preserving warp (GCPW) framework, therefore, is designed to perform photometric alignment beyond color consistency. Similar to conventional content-preserving warp (CPW), GCPW is also a mesh-based framework, but it extends CPW by appending a local color transformation model for every mesh quad, which expresses the color transformation from a source image to a target image within the quad. Motion-related mesh vertexes and color-related mapping parameters are optimized jointly in GCPW to get more robust motion estimation results. Evaluation of tens of videos reveals that the proposed method achieves more accurate motion estimation results. More importantly, it is robust to significant color variation. Besides, this paper explores the performance of GCPW in two popular computer vision applications: image stitching and video stabilization. Experimental results demonstrate GCPW’s effectiveness in dealing with typical challenging scenes for these two applications.
Design and Implementation of a Highly Integrated 8-Channel Transceiver for Massive MIMO in 5G

The design and implementation of a highly integrated and reliable single board 8-channel transceiver at 3.5GHz is developed for massive MIMO system in 5G wireless communications. Based on this board, large scale 2D arrays, such as ${8\text{x}8}$ array, can be easily formed. On each board, 8 transmitting channels and 8 receiving channels in TDD mode are integrated and each Tx/Rx channel shares one antenna element. This transceiver can provide a verification platform for digital beamforming algorithm.
EmotionSense: Emotion Recognition Based on Wearable Wristband

In this paper, we develop an automatic emotion recognition system based on a sensor-enriched wearable wristband. Specifically, in order to obtain physiological data from participants, we first adopt a video induction method which can spontaneously evoke human emotions in a real-life environment. Meanwhile, a questionnaire is designed to record the emotion status of the participants, which can be used as the ground-truth for emotion recognition. Second, we collect multi-modal physiological signals by utilizing three different biosensors (including blood volume pause, electrodermal activity, and skin temperature) embedded in the Empatica E4 wristband. Furthermore, we extract time, frequency and nonlinear features from the collected physiological signals, and adopt the sequence forward floating selection (SFFS) method to search for the best emotion-related features. Finally, we classify different emotions base on SVM using the selected features in aspect of arousal, valence, and four emotions. An overall accuracy of 76% for 15 participants demonstrates that the proposed system can recognize human emotions effectively.
Ontology-Based Enhanced Word Embedding for Automated Information Extraction from Geoscience Reports

Larger amount of geoscience reports brings both challenges and opportunities for data mining and analysis. This paper proposes an ontology-based enhanced word embedding (OEWE) information extraction methodology for extracting information about geoscience topic from regional geoscience reports. We first built the geoscience ontology to obtain a controlled vocabulary, and then the Skip-Gram model of word embedding was improved by Point-wise Mutual Information (PMI). Empirical experimental results on geoscience documents and benchmark datasets showed that the method is efficient.
The Study in Edge IOT Era: A Software Framework Based on the Knowledge

In order to solve the inadaptability of traditional software and utilize the computing resources of edge nodes in the future Edge IOT Era, the paper analyzes the characteristics of IoT software and puts forward a new distributed knowledge framework. In this study, we introduce knowledge base into the normal wireless node and translate Wireless Sensor and Actor Network (WSAN) topology along with the software logic into knowledge for it. Moreover, seven heuristic rules are proposed in the process of design, based on experience, including node selection criterion and node collaboration mechanism. Finally, the proposed framework is illustrated and evaluated with a small greenhouse scenario.
GHHP: Genetic Hybrid Hierarchical Partitioning for Community Structure in Social Medias Networks

Social networks present a hidden hierarchical organization explaining some of the basic properties of the structure. In fact, the hierarchy structure of networks describes the organization of communities joined to form the entire network. Thus, hierarchical clustering for community discovering present a crucial task. In fact, most of the existing algorithms proposed for hierarchical community detection are either based on agglomerative or divisive process. For the best of our knowledge, there is no approach relying on the combination of these two processes. This paper presents a novel hybrid method based on both optimization and hybrid hierarchical clustering for community structure in social networks. Because hybrid hierarchical model usually generates the problem of convergence to a locally optimal detected community, we proposed a new modularity based opinion leader function and we introduced a metaheuristic namely genetic algorithm to optimize this introduced quality function. In fact, we introduce a genetic hybrid formulation of hierarchical clustering for community detection in social network, where hierarchical bottom-up and top-down methods are combined in order to produce the same community structure. Performances of the proposed approach are evaluated using both real and artificial networks. Experiments show the efficiency of the introduced method in improving the execution time and enhancing the quality of the clustering results.
6-DOF Inertial Sensor Development with Advanced Fabrication Platform

In this paper, we present the development of 6-Degree-Of-Freedom (6-DOF) inertial sensors based on an advanced capacitive inertial sensor fabrication platform on 8-inch wafer. The reported platform integrates 3-axis gyroscopes and 3-axis accelerometers on the same chips. Wafer-level measurement results indicate that a fabrication yield of greater than 91% and a vacuum level of around 100 mTorr have been achieved across the wafer. The fabricated 6-DOF inertial sensors are hybrid integrated with the readout circuits with the sensing electronics, respectively. The system-level characterization results indicate the reported 6-DOF inertial sensors having good scale factor nonlinearities in the full dynamic ranges.
Policy Transfer from Simulations to Real World by Transfer Component Analysis

Learning of control policies that map visual observations to actions has been drawing much attention in recent years. Since visual observations are defined as raw images or high-dimensional image features, the learning process of such policies tends to require a huge amount of training data. Therefore, their applications are often limited in simulations and difficult in the real world due to the high sampling cost of the training data. To overcome this limitation, in this paper, we propose a policy transfer learning framework with feature extraction and reinforcement learning. We utilize transfer component analysis as a feature extraction method for identifying common features across the two domains. Our framework first extracts the common features in both the simulations and the real environments. Then a control policy with the extracted features as observations is learned by reinforcement learning in simulations and subsequently transferred to the real world even with appearance errors. To validate the effectiveness of our framework, we conducted simulations and experiments with an actual robot platform.
Broadband Pattern Diversity Patch Antenna With Switchable Feeding Network

A single-feed wideband pattern diversity antenna is presented in this paper. The pattern reconfigurability is realized by a p-i-n-diodes-intergrated switchable feeding network with the ability of providing 0° in-phase or 180° out-of-phase antenna excitation. The radiating patch is excited by the feeding network through a pair of symmetric stepped probes. When the two stepped probes are with in-phase excitation, a conical mode can be generated on the patch. On the other hand, the antenna can operate at a broadside mode if the excitation is out-of-phase. A meticulous prototype is developed, manufactured, and measured. The measured impedance bandwidths of the 10-dB return loss are 60.7% from 1.95 to 3.65 GHz for the broadside mode and 72.7% from 1.65 to 3.65 GHz for the conical mode. Meanwhile, measured peak gains of up to 10.2 and 7.7 dBi and measured radiation efficiencies of higher than 80% and 76% are obtained for the broadside mode and conical mode, respectively.
A Wideband 28 GHz Fully-Integrated Power Amplifier in 65 nm CMOS Technology

A 28 GHz two-stage power amplifier is designed and measured in 65 nm CMOS technology. Using a two-stage common-source (CS) power amplifier topology with a Multi-section L-matching network, the circuit achieves 44% bandwidth while it maintains saturated output power, high gain and efficiency. Two stages are both biased in class AB with a total current of 60 mA from 1.03 V supply The power amplifier achieves a power gain of 17.5 dB and a saturated output power of 11.9 dBm with peak PAE of 22.1% at 27.5 GHz. The measured power amplifier achieves 1-dB saturated output power bandwidth of 12 GHz (44%) from 22–34 GHz.
Joint Estimation of Multiple Parameters in Bistatic MIMO Radar Based on Sparse Signal Restoration

In this paper, the direction of arrival (DOA), the direction of departure (DOD) and the reflection coefficient of multiple targets in bistatic MIMO radar are estimated based on sparse signal restoration. Firstly, a two-dimension scanning space of bistatic MIMO radar is divided into dense grids, and a sparse signal model of multiple test vectors (MMV) is constructed. Secondly, a constrained optimization model with 12 norm is transformed into an unconstrained optimization model with Lagrange multipliers and is solved iteratively. Simulation results show that the three parameters of each target are estimated simultaneously and matched automatically without additional pairing, which verifies the effectiveness of the method.
Advanced Testing Tool for .NET Applications

This article proposes a tool for testing . NET applications without the need to preconfigure the target application. By using this tool, the user can visualize and control application code in an intuitive and understandable way. The tool also has features to collect statistics from code execution to help understand how the code behaves. All this leads to significantly reduced time spent during the coding and testing phases of software application development, especially for legacy applications in which techniques like TDD (test driven development) or Unit testing are not possible.
Deep Collaborative Filtering Incorporating Auxiliary Multi-Media Information

Collaborative Filtering (CF) has been widely used in recommendation due to its brevity and effectiveness. However, rating sparsity or even cold-start is still an inevitable problem when facing CF. Although multimodal information can be easily found in today's website, most existing CF methods are not well designed to incorporate them appropriately. In this paper, we intend to explore the potential methods of fusing abundant auxiliary cross-media information of user and items to help enhance traditional CF. In general, we divide the auxiliary data into discrete partial features (tags, genres.) and continuous global features (images, videos.) for different purposes. As for discrete features, embedding is adopted to represent them. In order to merge different discrete components, we use attention mechanism to adaptively learn their respective importance in contributing to a specific rating prediction. While for the continuous features, we use feature extraction network such as CNN to exploit informative representation to help restrain item's embedding space, consequently the interpretability of factorized vectors is improved. To validate the performance of our proposed method, extensive evaluations on public MovieLens dataset are carried. Experimental results on MSE improvements demonstrate both the importance of the auxiliary information and effectiveness of the proposed approach.
Open-loop stability of overconstrained parallel robotic systems

The authors derive the object-based effective stiffness of antagonistically actuated, backdrivable, parallel linkages and show how to use this result to synthesize the generalized input loads required to create a desired system stiffness. They investigate the open-loop stability characteristics of such antagonistically actuated systems as a simple four-bar linkage, a dual-arm system, and a multifingered hand. In addition, they compare object-based and joint-based load distribution schemes in terms of their effect on system stability and input load requirements for an overconstrained planar mechanism. These investigations show an extreme interdependence between system geometry, actuation mode, and stability. <<ETX>>
Two Half-Bridge Resonant Inverters with Coupled Coils for AC/AC Induction Heating Application

In this paper a two half-bridge resonant inverter with vertical aligned coupled coils is proposed for an induction heating application. The system employs two active coupled coils which are modeled using a two-port network approach, being each coil constitutes a series resonant tank fed by a half-bridge inverter. The proposed converter guarantees unity power factor and low harmonic distortion at grid-side current whereas improves power capability in comparison with conventional ones. Power control is reached by: switching frequency, duty cycle and phase-shift displacement between inverters leg. SPICE simulations and experimental results corroborate the converter model and operation. The converter performance is evaluated adopting phase-shift and traditional (switching frequency and duty cycle) control techniques under wide load conditions. The system feasibility is validated using 2 kW prototype with 97% maximum efficiency.
Modelling and Control of a Mobile Manipulator for Trajectory Tracking

This article presents the mathematical modelling of a mobile manipulator robot to describe its kinematic and dynamic behaviour; the kinematic model is used to design a control law that allows the autonomous tracking of trajectories in structured spaces; while the dynamic model is used to compensate for speed errors in the robot. Additionally, the stability of the proposed control systems is demonstrated, and finally, simulation and experimentation tests are carried out to validate the proper functioning of the controllers.
Performance Improvement of Patch Antenna by Using Planar Metalens

We present a high-performance lens antenna, which is based on circular-patch feeding and gradient-index (GRIN) metamaterials. To demonstrate the results experimentally, we fabricated and measured the lens antenna, in which the lens is composed of closed-square-ring structures. The measurement results show that GRIN lens can effectively improve the performance of the patch antenna.
Resource Transaction Framework Based on Block Chain in Social Community

With the development of social network, network digital sources have become a main kind of information services for users. In social network, user's needs and interpersonal relationships become very rich, thus social network can quickly disseminate digital sources to users. In this paper, we propose a resource transaction framework based on block chain, which is used to disseminate digital resources through social community. Based on block chain technology, the proposed scheme changes the traditional method of charging and can provide the reliability and fairness for every transaction of digital resource.
Non-Invasive Imaging for Cardiovascular Interventions: An Evolving Paradigm

In the last decade, the medical community has seen a significant development in the treatment options for a range of cardiovascular diseases. Besides improved pharmacological options, advances in technology have allowed improved care by the use of, for example, new prosthetic cardiac valves in valvular heart disease and by further optimizing electrophysiological interventions in patients with selected rhythm disorders [1, 2]. These new procedures have increasingly relied on non-invasive imaging primarily for pre-procedural planning in order to optimize procedural success and also with increasing potential for a more effective and comprehensive follow-up after an intervention. Besides echocardiography, computed tomography (CT) and to a lesser extent magnetic resonance (MR) imaging have evolved as an essential imaging tool in the selection of patients, avoidance of potential procedural hazards and early detection of complications. In this paper, we will focus on the role of CT in the preand post-procedural evaluation of transcatheter aorta heart valves (THV).
Multilayer Artificial Neural Network Design and Architecture Optimization for the Pattern Recognition and Prediction of EEG Signals Based on Hénon Map Chaotic System

This paper investigates the training performances of multilayer artificial neural network (ANN) architectures for the implementation of chaotic systems. The designed ANN models can be employed for pattern recognition and prediction of dynamic chaotic systems, in order to simulate and analyze brain activities captured by Electroencephalogram (EEG). Previous research shows that EEG signals demonstrate chaotic features. Chaotic systems can be represented by a set of mathematical equations, which can be used to generate the target outputs for training ANN. In this research, the Henon map is selected as an example for ANN-based chaotic system design. The optimization of ANN architecture is important for improving the performance of hardware implementation. ANN architectures with up to 3 hidden layers combined with different number of hidden neurons are compared by measuring the training performance using the mean square errors (MSE). The ANN training are carried out using three training algorithms: Levenberg-Marquardt, Bayesian Regulation and Scaled Conjugated Gradient. Nonlinear autoregressive (NAR) model is used for ANN architectures design. The training results demonstrate that the training performance can not be improved simply by increasing the complexity of the ANN architecture in terms of the number of hidden layers and hidden neurons. It is therefore necessary to optimize the ANN architecture on a case-by-case basis in order to improve the efficiency of the ANN implementation for specified applications.
Role of Physical Activity in Parkinson's Disease

Parkinson's disease (PD) is common, age-dependent neurodegenerative disorder caused by a severe loss of the nigrostriatal dopaminergic neurons. Given the projected increase in the number of people with PD over the coming decades, interventions aimed at minimizing morbidity and improve quality of life are crucial. There is currently no fully proven pharmacological therapy that can modify or slow the disease progression. Physical activity (PA) can complement pharmacological therapy to manage the inherent decline associated with the disease. The evidence indicates that upregulation of neurotrophins and nerve growth factors are potentially critical mediators of the beneficial effects associated with PA. Accumulating evidence suggests that patients with PD might benefit from PA in a number of ways, from general improvements in health to disease-specific effects and potentially, disease-modifying effects. Various forms of PA that have shown beneficial effects in PD include – aerobic exercises, treadmill training, dancing, traditional Chinese exercise, yoga, and resistance training. In this review, we explored available research that addresses the impact of exercise and PA on PD. The original articles with randomized control trials, prospective cohort studies, longitudinal studies, meta-analysis, and relevant review articles from 2005 to 2017 were selected for the present review. Many gaps remain in our understanding of the most effective exercise intervention for PD symptoms, the mechanisms underlying exercise-induced changes and the best way to monitor response to therapy. However, available research suggests that exercise is a promising, cost-effective, and low-risk intervention to improve both motor and nonmotor symptoms in patients with PD. Thus, PA should be prescribed and encouraged in all PD patients.
Low-level audio descriptors-based analysis of music mixes from different Digital Audio Workstations – case study

The aim of this paper is two-fold. Firstly, we attempt to check whether objective, low-level audio descriptors may serve as a comparison tool in music mix evaluation performed using different Digital Audio Workstations (DAWs). Secondly, we seek to answer whether differences in music mixes are objectively discernible when several sound processing engines of DAWs are used. The same tracks of a song exported from different Digital Audio Workstations constitute the basis for this research study. Several song mixes are built of 24 individual tracks with no added effects, employing both commercial and non-commercial DAWs. Then, a set of time- and frequency-domain audio descriptors is calculated to find similarities and differences between the music mixes. Informal listening tests are conducted to answer to what extent experts are able to evaluate differences in these mixes. Then data are analyzed to show that in most cases similar results are obtained regardless of the DAW employed.
Semi-Supervised Classification of Wafer Map Based on Ladder Network

Wafer map analysis is a key step in semiconductor manufacturing process. The various gross failing area (GFA) patterns on wafer maps are helpful to identify the root causes of failures. In this work, a semi-supervised classification framework is proposed for wafer map analysis. We use inline defect wafer map as the example, especially for GFA pattern classification. After data preprocessing and selection, Ladder network is adopted here to classify wafer maps compared with a standard convolutional neural network (CNN) model on two real-world datasets. The results illustrate that Ladder network is consistently and substantially better than CNN model across various training data percentages by effective utilization of the unlabeled data.
Two-phase Interleaved Boost PFC Converter with Coupled Inductor under Single-phase Operation

The interleaved boost PFC converter operating in CCM is widely used in high power applications. Furthermore, the coupled inductor can be used to reduce the volume and increase the power density. At light load condition, the converter normally uses phase-shedding strategy, single-phase operation, to increase the efficiency of the converter. However, since the strong mutual effects of the coupled inductor, the converter in single-phase operation appears different operation modes in the switching cycle and half line cycle. In this paper, the interleaved boost PFC converter with coupled inductor in single-phase operation is analyzed. The operating modes of the converter in both switching cycle and a half line cycle are discussed. Finally, a 600 W prototype is built and tested in the lab to verify the analysis.