Miracle-dz/UniFAR-FTUs huggingface dataset:
Performance of Smart PV Modules Under Non-Uniform Solar Irradiance

The objective of this paper is to study and help understanding the performance of smart PV modules when exposed to non-uniform illumination. A PV string composed of five smart modules connected in series are employed to conduct the study. Each smart module is composed of a single PV incorporated with its own perturb and observe MPPT and a power conditioner as distributed dc power optimizer. The performance of PV string of smart modules is compared with that of conventional single MPPT system at the same operating conditions. According to the obtained results, the string of smart PV modules has superior performance in terms of: elimination of local maximum power points that exist in conventional PV system (due to partial shading) and extracting higher power from the entire PV string with nil domino effect. Under non-uniform illumination, the power produced from the PV string of smart modules is 76 % higher than that produced from the conventional configuration, and is 59 % higher than the conventional configuration operating with Global Maximum Power Point (GMPP) tracking system. The results indicate that the performance degradation in any non-uniformly illuminated module is kept localized and does not deteriorate the performance of other modules. While in the conventional PV system, the poorest performing module drags down the performance of the entire array resulting in sever lower power capability.
Loss of Side-to-Side Connections Affects the Relative Contributions of the Sodium and Calcium Current to Transverse Propagation Between Strands of Atrial Myocytes

Background: Atrial fibrillation (AF) leads to a loss of transverse connections between myocyte strands that is associated with an increased complexity and stability of AF. We have explored the interaction between longitudinal and transverse coupling, and the relative contribution of the sodium (INa) and calcium (ICa) current to propagation, both in healthy tissue and under diseased conditions using computer simulations. Methods: Two parallel strands of atrial myocytes were modeled (Courtemanche et al. ionic model). As a control condition, every single cell was connected both transversely and longitudinally. To simulate a loss of transverse connectivity, this number was reduced to 1 in 4, 8, 12, or 16 transversely. To study the interaction with longitudinal coupling, anisotropy ratios of 3, 9, 16, and 25:1 were used. All simulations were repeated for varying degrees of INa and ICa block and the transverse activation delay (TAD) between the paced and non-paced strands was calculated for all cases. Results: The TAD was highly sensitive to the transverse connectivity, increasing from 1 ms at 1 in 1, to 25 ms at 1 in 4, and 100 ms at 1 in 12 connectivity. The TAD also increased when longitudinal coupling was increased. Both decreasing transverse connectivity and increasing longitudinal coupling enhanced the synchronicity of activation of the non-paced strand and increased the propensity for transverse conduction block. Even after long TADs, the action potential upstroke in the non-paced strand was still mainly dependent on the INa. Nevertheless, ICa in the paced strand was essential to provide depolarizing current to the non-paced strand. Loss of transverse connections increased the sensitivity to both INa and ICa block. However, when longitudinal coupling was relatively high, transverse propagation was more sensitive to ICa block than to INa block. Conclusions: Although transverse propagation depends on both INa and ICa, their relative contribution, and sensitivity to channel blockade, depends on the distribution of transverse connections and the axial conductivity. This simple two-strand model helps to explain the nature of atrial discontinuous conduction during structural remodeling and provides an opportunity for more effective drug development.
Ultrasonic Ray-Tracing Based Endocardial Surface Reconstruction

Accurate and fast reconstruction of the endocardium is a fundamental step for performing a successful ablation operation. This paper proposes an ultrasonic ray-tracing based endocardial surface reconstruction algorithm, which utilizes a new proposed non-contact ultrasonic catheter. The proposed catheter is composed of an electromagnetic position sensor and three miniature transducers, and it can sample a point cloud from the targeted endocardium in real-time. The 3D Delaunay triangulation of the sampled point cloud is first calculated, and then each tetrahedron is marked internal or external via ultrasonic ray-tracing and the boundary of all internal tetrahedra is extracted as a coarse surface mesh. Finally, HC Laplacian is applied to smooth the coarse mesh for the benefit of avoiding shrinkages. The basic idea of the proposed surface reconstruction algorithm relies on the fact that tetrahedra intersecting with the ultrasonic rays provide a volumetric estimation of the measured heart. Simulations on a heart phantom are given to support the superiority of the proposed algorithm. Compared to the prior arts, the proposed algorithm could reconstruct a realistic endocardial surface while preserving the features of vena cava and atrium appendage without shrinkages.
Evaluation of Halstead and Cyclomatic Complexity Metrics in Measuring Defect Density

Software metrics are used to get reproducible measurements that can be useful for quality assurance. A recent study analyzed the relation between Halstead Complexity (HC), Cyclomatic Complexity (CC), and the number of defects found in software. The number of software defects does not represent a uniform software metric and may not be generalized. This work examines the density of defects (DD) and its relationship with HC and CC metrics. We have used a well-coded open-source project. We have focused our analysis at the class level and examined potential patterns and correlations that may exist between these two software metrics and the density of defects. We found that HC and CC exhibit similar relationships to Defect Density metric. Furthermore, their strong positive linear correlation leads to the conclusion that HC and CC are two consistent software metrics with respect to density of defects.
Neuroimmune mechanisms of psychostimulant and opioid use disorders

Substance use disorders are global health problems with few effective treatment options. Unfortunately, most potential pharmacological treatments are hindered by abuse potential of their own, limited efficacy, or adverse side effects. As a consequence, there is a pressing need for the development of addiction treatments with limited abuse potential and fewer off target effects. Given the difficulties in developing new pharmacotherapies for substance use disorders, there has been growing interest in medications that act on non‐traditional targets. Recent evidence suggests a role for dysregulated immune signaling in the pathophysiology of multiple psychiatric diseases. While there is evidence that immune responses in the periphery and the central nervous system are altered by exposure to drugs of abuse, the contributions of neuroimmune interactions to addictive behaviors are just beginning to be appreciated. In this review, we discuss the data on immunological changes seen in clinical populations with substance use disorders, as well as in translational animal models of addiction. Importantly, we highlight those mechanistic findings showing causal roles for central or peripheral immune mediators in substance use disorder and appropriate animal models. Based on the literature reviewed here, it is clear that brain‐immune system interactions in substance use disorders are much more complex and important than previously understood. While much work remains to be done, there are tremendous potential therapeutic implications for immunomodulatory treatments in substance use disorders.
A SDN-based WiFi-VLC Coupled System for Optimised Service Provision in 5G Networks

Visible Light Communication (VLC) is a powerful supplement, which has gained tremendous attention recently and has become a favorable technology in short-range communication scenarios for the Fifth Generation (5G) networks. VLC possesses a number of prominent features to address the highly demanding 5G system requirements for high capacity, high data rate, high spectral efficiency, high energy efficiency, low battery consumption, and low latency. However, this prominent performance is limited by the imperfect reception, since line of sight channel condition may not always exist in practice. This paper presents and experimentally validates a SDN-assisted VLC system, which is coupled with WiFi access technology in order to improve the reliability of VLC system, reassuring zero packet loss reception quality due to misalignment or path obstructions or when the user is moving between two consecutive VLC transmitters and experience “dead coverage zones”.
Object Tracking based on KCF and Sparse Prototypes

Recently, many correlation filter-based tracking methods have received lots of attention and achieved great success in visual object tracking. Among correlation filter-based methods, the most influential one is kernelized correlation filter (KCF) which has excellent performance both in efficiency and accuracy. However, due to the virtual nature of cyclic shifts samples, the training and detecting of KCF are imprecise. To alleviate the influence of virtual samples, we take the following two measures. 1) We extract image patches at positions of samples which have local maximum KCF responses and treat them as candidates. We further evaluate their true responses. 2) Instead of estimate the target position directly according to the KCF results, we use the sparse prototypes (SP) as the target model to evaluate the similarities between candidates and target. The results of KCF and SP are combined by adaptive weight to estimate the target position. In addition, KCF degrades due to its unreasonable update scheme. To do reliable update, we set different update modes and generate an adaptive update rate based on two tracking confidence indices. Experiments on a commonly used tracking benchmark show that the proposed method improves KCF about 8% on the average success rate and 10% on the precision, and achieves better performance than other state-of-the-art trackers.
TCP Phases Classification Using Long Short-Term Memory Recurrent Neural Networks

TCP counts as the protocol being most frequently adopted for communications in terms of reliable data via Internet, and can fall into four phases in line with different adjustments of the congestion window and the slow start threshold. In this work, we build an open dataset for classifying TCP phases and develop a tandem architecture for TCP phases classification where LSTM is used to learn the feature space and to attain temporal dependencies, while the Decision-Tree Classifier is employed for classification. The comparison is drawn between the proposed method and a raw Decision-Tree Classifier through adopting the extracted expert features. And as indicated, the proposed one outperforms the latter one. Furthermore, a comparative study is presented to evaluate the availability of varying number of cells and of varying depths. And a more efficient structure is recommended based on two-layer LSTM network. Additional as the numerical results demonstrate, the proposed structure takes on an optimized performance of nearly 7%-10% in terms of slow start phase accuracy.
Selecting Individual and Population Models for Predicting Human Mobility

A large plethora of models to predict human mobility exists in the literature. The problem of how to select the most appropriate model to solve a specific mobility prediction task has however received only little attention. Yet, a wrong model choice may lead to severe performance losses. In this paper, we address the model selection problem in human mobility prediction and make the following contributions. We present SELECTOR, a generic framework to explore human mobility data and compute both population models and individual models to predict human mobility. The former are models that are adapted to the characteristics of an entire population of users and can be used to overcome the cold-start problem. The latter are prediction models optimized for individual users. We present and analyze the results obtainable using SELECTOR on the Nokia data set, which is one of the largest and richest, publicly available data sets of human mobility data. We show that for many users, generic population models can be used in place of individual models with negligible performance losses. Yet for about 25 percent of the users, individual models perform at least three percentage points better than population models. Thereby, we show that the use of phone context data does not lead to significantly better performance of human mobility predictors with respect to the case in which only temporal and spatial features are used. We further observe that the population models we derive are robust against the demographics of the users and that building different population models for different periods of the day leads to performance improvements. We make SELECTOR publicly available to allow other researchers and practitioners to explore further mobility data sets and to embed the code base of SELECTOR in their applications.
Query Forwarding with Multi-Mobile Agents for Content Sharing in Peer-to-Peer Network

Various researches on query forwarding for content sharing over unstructured Peer-to-Peer network have been carried out. With the methods proposed in the past researches, there exist problems such as mis-recognition of information required for contents' searching due to dynamic change of network such as joining and leaving of peers, and increase of cost for keeping the information up to date, then the methods are not necessarily effective. In this research, we propose a method in which multiple dynamic network agents circulate in the network, always grasp the latest topology of contents sharing network and contents' deployment, propagate the grasped information to each peer, and efficiently transfer queries for contents to be searched. Furthermore, the proposed method is evaluated by using computer simulations, and its effectiveness is shown by the result.
Body Area Networks: Path Loss Modeling and Antenna Design

In this paper, a new approach of channel modeling and antenna design for a wireless Body area network (WBAN) is presented. To achieve this work, first a detailed analysis and modeling of the channel is developed including all physical parameters of the propagation medium and the intrinsic human body. The proposed model is based on the evaluation of the link budget and the path attenuation. Second, to complete the design, compact size rectangular patch antennas (RPA) with inset feed is presented for on-body communications. The antennas are printed on FR4 substrate at 2.4 GHz. The electromagnetic properties of antennas are fully characterized. Simulated radiation pattern, gain, and associated loss budget are also presented and discussed. The results of RPA simulations are in good agreement with the results of theoretical proposed model. Furthermore, since the antenna is implemented with low-cost and microstrip technology, it is a good candidate for WBAN and wireless mobile communication systems.
A Low-Cost Test Bench for the Characterization of Microwave Devices Using Modulated Envelope Signal

Thirst for efficient design and modeling of nonlinear microwave devices require accurate characterization of devices in the presence of modulated signals. Vector network analyzer, which is used for such characterization is a costly tool. This paper presents a low-cost test bed, which can characterize the microwave devices in the presence of wideband 4G/5G signals for the measurement of harmonics as well as in-band characteristics. Example case for the second harmonic along with the fundamental signal characterization has been reported.
Single-Phase Charging Operation of a Three-Phase Integrated Onboard Charger for Electric Vehicles

Although three-phase integrated onboard chargers for electric vehicles (EVs) are important for high-power fast battery charging, the single-phase battery charging is still highly demanded in the consumer markets. The single-phase charging provides EV owners the ability to conveniently charge their vehicles through standard single-phase power outlets. In this paper, the single-phase charging operation of a three-phase integrated onboard charger is investigated. The single-phase charging operation can be achieved without any hardware modification to a three-phase integrated onboard charger. The circuit topology, operation modes, control strategy, and controller design for the single-phase operation are presented in detail. A three-phase integrated onboard charger designed by the authors is operated and tested as a single-phase charger with up to 1.6kW output power. Experimental results show that unity power factor (PF) can be achieved with a peak efficiency of 93.7%.
Task Proactive Caching Based Computation Offloading and Resource Allocation in Mobile-Edge Computing Systems

For the recent emerging applications such as augmented reality (AR), delay is a key performance evaluating the quality of user experience (QoE). Caching the execution results of the popular AR applications’ computational tasks can significantly reduce the execution delay. In this paper, we consider the mobile edge computing (MEC) server and the cloud can proactively cache the execution results of computational tasks. Then, in our proposed scenario, there are four optional ways to process a task. They are, respectively, computing tasks locally, offloading tasks to the MEC server to computing, returning the task’s computation results directly from the MEC server’s cache, and returning the task’s computation results from the cloud’s cache. The computation offloading, resource allocation and task proactive caching are jointly optimized to minimize the execution latency subject to the constraints of the radio, computation and storage resources. To solve this complex mixed-integer nonlinear programming (MINLP) problem, we first propose a proactive caching algorithm for collaboration between the cloud and the MEC server to determine the task’s caching status. Then, we propose a heuristic algorithm based on greedy strategy to solve the remaining problem, which includes resource allocation and the selections of task’s execution mode. By analyzing the simulation results and comparing with an exhaustive algorithm, effectiveness and optimality of our proposed schemes are verified.
Interest point detection using the superpixel segmentation and a binary descriptor

Extracting interest points is one of the most important issues in techniques such as an object recognition and classification or a place recognition. Correct feature extraction can efficiently find out which parts of the image are likely to be unique, and can also match similar or identical parts in two different images. However, previous feature extraction algorithms typically extract features based on corner points with a large gradient, which results in degraded performance in less textured images, such as flat walls. In this paper, we propose a novel feature extraction method that can extract points in the texture-less images. Our method extracts interest points from the contour region of neighboring superpixels, which has a little or more changes of color values, then use a BRIEF descriptor to extract low-dimensional descriptors. Experimental results show that our method can extract features from texture-less regions and perform the matching between two different images with a high accuracy.
A neural-level model of spatial memory and imagery

We present a model of how neural representations of egocentric spatial experiences in parietal cortex interface with viewpoint-independent representations in medial temporal areas, via retrosplenial cortex, to enable many key aspects of spatial cognition. This account shows how previously reported neural responses (place, head-direction and grid cells, allocentric boundary- and object-vector cells, gain-field neurons) can map onto higher cognitive function in a modular way, and predicts new cell types (egocentric and head-direction-modulated boundary- and object-vector cells). The model predicts how these neural populations should interact across multiple brain regions to support spatial memory, scene construction, novelty-detection, ‘trace cells’, and mental navigation. Simulated behavior and firing rate maps are compared to experimental data, for example showing how object-vector cells allow items to be remembered within a contextual representation based on environmental boundaries, and how grid cells could update the viewpoint in imagery during planning and short-cutting by driving sequential place cell activity.
A recurrent neural network model for generation of humanlike reaching movements

ABSTRACT We constructed a recurrent neural network model that can generate human reaching motion. Given a target position, the neural network model is capable of producing muscular activation signals that move the hand to the target endpoint, while solving the problem of musculoskeletal redundancy by dynamic relaxation of the energy functions embedded in the network. The proposed neural network model was integrated with a two-dimensional three-link eight muscle musculoskeletal model of the human arm to simulate arm reaching movements in the horizontal and sagittal planes. Our results demonstrate that the model is capable of generating natural arm movements that have spatiotemporal features, such as a slightly curved hand path and the characteristic bell-shaped velocity profile, that are similar to those of actual human movements. Some aspects of the proposed computational framework might be utilized in the central nervous system for generation of reaching movements. GRAPHICAL ABSTRACT
Learning with Detail and Morphological Refinement for Satellite Image Analysis Based on Convolutional Neural Network

We present a multiple stages way to analyze the content of satellite images. Our methodology is divided into three major steps. First, Convolutional Neural Networks (CNNs) for semantic segmentation between buildings and nature scene. Then, the output semantic image would be refined in morphology way. In the last stage, Depth-First Search (DFS) algorithm is used for buildings counting. The experimental results show that refined images have smother boundaries. Base on the output images, we can count buildings using DFS algorithm accurately by refined image.
HangerOVER: Development of HMO-Embedded Haptic Display Using the Hanger Reflex and VR Application

The Hanger Reflex is a phenomenon in which the head rotates unintentionally when it is sandwiched by a wire hanger. The reflex is effectively generated by pressing on specific points, and can be reproduced by pressing with an actuator. We propose the HangerOVER, an HMD-embedded haptic display that can provide both force and motion senses using the Hanger Reflex. In this paper, we designed HangerOVER that HMD-embedded force and motion display using the Hanger Reflex, and developed four VR applications for demonstration.