Miracle-dz/UniFAR-FTUs huggingface dataset:
Remarkable Current Collapse Suppression in GaN HEMTs on Free-standing GaN Substrates

The current collapse phenomena, particularly the trap-induced threshold voltage shift, was drastically reduced in GaN high-electron-mobility transistors (GaN-HEMTs) fabricated on GaN free-standing substrates. High breakdown voltage of 376 V was achieved and off-state leakage current was also suppressed until a hard breakdown condition. These could be attributed to the improvement of crystal quality in the homo-epitaxial GaN layer, that is, reduction in the dislocation density and absence of nucleation layer. GaN-HEMTs on GaN substrates with a gate length of 0.5 µm exhibited good RF performance with a maximum power added efficiency of 71% with an associated output power of 6.6 W/mm at a 2.45 GHz load-pull measurement. It is evident that GaN-HEMTs on the GaN substrates are promising for driving up a performance of an RF amplifier.
HOG feature descriptor based PCA with SVM for efficient & accurate classification of objects in image

In Computer vision, object recognition is a very important component and also very challenging. Intention of This paper is to exploit a high confidence object detection framework that boosts up the classification performance with less computational burden and cost efficient. Features are extracted from images by using Histograms of Oriented Gradients (HOG) technique and then for generating principle components as well as reducing dimensions Principal Component Analysis (PCA) has been applied on the extracted features. For classification of objects Support Vector Machine (SVM), Random Forest, Input mapped classifier, M5P classifier and Gaussian process classifier have been employed. A comparative study on performance of those approaches have been conducted. Moreover, for better clarification of the dataset, statistical and automated analysis have been considered. Overall findings demonstrates that, Principle Component Analysis (PCA)based Support Vector Machine (PSO) outperforms other approaches by depicting accuracy of 94.02% and highest F-Score measurement.
Kalman Filter based Path Generation and Tracking Control for Autonomous In-door Valet Parking System

This paper presents a path generation method using curve fitting based on Kalman filter of LED way-points and tracking control for autonomous in-door valet parking system. Generally, if the given way-points are either irregular or uncertain, it could generate a discontinuous path that the following vehicle cannot track. To cope with this problem, we propose methods generating virtual way-points and fitting a curve using Kalman filter. Then, we improve tracking performance by using linear-quadratic regulator (LQR) controller. The presented algorithm was evaluated via simulation depicting in-door valet parking system and experiment using a test vehicle in in-door parking space equipped with LED way-points.
A Testbed and an Experimental Public Dataset for Energy-Harvested IoT Solutions

The Internet of Things (IoT) paradigm poses a great variety of application domains where million of devices work uninterruptedly to improve some aspect of our lives. To support the continuous execution of the applications working on the devices, energy harvesting systems enable to extract the energy found naturally in the environment (for instance from the sun or from the wind) and convert it into energy able to either sustain the device’s operation and recharge its batteries which, in conjunction with an appropriate scheduling strategy, led to the device to an electrically sustainable state (i.e. an energy-neutral state). Most of the works found in literature oriented to achieve energy neutrality are however evaluated by means of simulation which means that, in spite of precisely modeling hardware features and energy productions, lack of the realism that we find in a real deployment. A minor part of the works are based on a real deployment but do not share the collected data that permit to replicate the analysis. With this purpose in mind, in this article we describe a testbed designed for outdoor monitoring purposes in the IoT context, equipped with several sensors for weather conditions monitoring and with a solar panel to provide application lifetimes potentially infinite. The testbed was deployed on the roof of a building and it executed uninterruptedly an application able to generate a dataset with the collected information over a period of more than two months. This dataset has been online published to be used for different researching purposes, as for instance, prediction models of the energy production.
Identification of Node Influence Based on Improved K-shell Algorithm

Nodes that have greater influence in complex networks play an important role in controlling rumors propagation, optimizing resource allocation, spreading information efficiently, and advertising accurately. In view of the current many methods in identifying the node’s influence had certain limitation, this paper based on the k-shell algorithm defined the concept of weighted degree, and putted forward the Modified K-shell Algorithm, shorted for MKS algorithm by measuring the potential importance of edges and considering the different contributions of neighbors. This algorithm considers the nodes’ own features, location features and local features. Through implementing this algorithm on the representative Zachary karate club network and comparing with other typical methods, it is found that this algorithm improves the coarse division of k-shell algorithm, and its result is more reasonable.
A Distributed Secondary Control Strategy for Power Sharing and Voltage Regulation by Multiple Converters in DC Micro-grids

Recent research and advancement in DC systems reveal that DC distribution may be an efficient, reliable and economical solution to avoid the inherent issues associated with AC power systems such as frequency control, harmonics and synchronization. However, some key operational issues in DC micro-grids are to ensure a stable output voltage and proper load sharing among the sources. Improper current sharing and voltage variations in the conventional droop control causes circulating currents due to the presence of droop and line resistance between converters. This paper presents a distributed secondary control strategy to smooth the output voltage and maintain power sharing based on average current/voltage control and circulating current minimization. Further, an additional current feedback loop is introduced to modify the micro-grid reference voltage during overload conditions for minimizing the line voltage drop and power losses. The conventional communication links are used for exchanging information to the relevant controllers simultaneously to implement the proposed distributed control. The performance of the proposed approach has been verified by simulating two parallel Buck converters supplying a common load. The effect of load switching on voltage regulation and power sharing has also been analyzed.
Capacity Analysis for LDM-Based Multiple-PLP Configurations in ATSC 3.0

This paper analyzes capacities for various layered division multiplexing (LDM)-based multiple-physical layer pipe (M-PLP) configurations in Advanced Television Systems Committee 3.0. Also, this paper proposes transmitter and receiver structures for multi-layer LDM. The proposed multi-layer LDM consists of one core layer and one or more enhanced layer(s) that can be combined with different power levels. The simulation results show that LDM with an appropriate injection level can improve the capacity for M-PLP compared to TDM-based PLP configurations.
A Real-Time Task Offloading Strategy Based on Double Auction for Optimal Resource Allocation in Edge Computing

Task offloading in edge computing becomes an effective method to extend the computation ability of user equipments (UEs), via migrating computation-intensive applications from UEs to edge servers. However, not only locality-aware resource allocation for UEs and various edge computing services providers (ESPs) but also network economics for profit-driven ESPs and UEs is still a big challenge in task offloading. In this paper, we propose an edge computing resource allocation model based on the continuous-cycle double auction mechanism (RABDA). Considering the emergency of task offloaded, we also propose real-time offloading strategy (RTOS) to ensure tasks are processed efficiently. We use genetic algorithm to determine the winner ESPs which are responsible for providing computational resources to UEs, and verify the performance of our algorithm by contrast experiment. The simulation results show that our algorithm can improve satisfaction between UEs and ESPs, and it has higher resource utilization than the existing algorithm.
On Spatial Diversity for LoRaWAN: Experimental Evaluation of Performance of a Dual-Gateway Network With and Without Downlink

The Low Power Wide Area Networks (LPWANs) is today seen as one of the key connectivity enablers for the massive Internet-of-Things (IoT) applications. In this paper, we experimentally study the performance of a LoRaWAN network composed of multiple end devices and two independently operating gateways (GW) for the case of uplink-only and uplink- downlink traffic. Our results demonstrate that even though the spatial diversity due to the use of multiple GWs improves the performance of the network for the packet error rate, it still does not guarantee low packet loss under heavy uplink network traffic. Nonetheless, when the downlink traffic is present, a second GW operating in the uplink-only mode can dramatically increase the uplink packets delivery rate in the network. Therefore, we consider that spatial decoupling of receiver and transmitters, or addition of uplink-only GWs may become an efficient way to improve the performance of LoRaWAN networks with downlink traffic. Even though the limited laboratory environment might have affected the results of our measurements, we expect that many of the observed trends will also stand for the real-life LoRaWAN deployments. For this reason, the presented results might be interesting both for analysts and practitioners working in the field.
Modeling of Technological Processes in Gas Production Complexes

Purpose: study of controlled technological processes of a gas production complex, the study is based on partial differential equations. Results: The analysis allows to determine the characteristics of the gas production complex as a control object. Mathematical models of technological processes of the gas production complex are presented taking into account the dependence of the velocity of the gaseous phase on flow, pressure and temperature. The results of computational experiments are given for mass transfer processes in an absorber when perturbations are applied in one, two, and three variables. They confirm the adequacy of the mathematical model to the real technological process.
FILM: Fine-Grained Book Localization with Mobile RFID Scanning

Misshelving is an age-old problem in a library. If this happens, users can hardly find their wanted books, leading to a waste of book resources. In this paper, we investigate the problem of book localization through mobile RFID scanning. According to extensive experiments, we find that the phase data (that are used to do localization) beyond the reader antenna's main lobe are noisy; taking these data as the input of book localization is likely to lower the localization accuracy. However, it is not easy to figure out which data are noisy. To address this problem, we propose a fine-grained localization method called FILM, which can achieve cm-level book localization through the following three steps. First, it collects the phase profile for each tagged book as is. Second, it sophisticatedly chooses three samples from the phase profile and locates the tagged book by modeling the three samples. Since there are a great number of 3-combinations, we can get many candidates of the book localization. Third, it uses the Gaussian kernel density model to determine whether the candidates are good or not. The basic idea is that, the noisy data give rise to uncertain estimates, whereas the clean data will always get an estimate close to the ground truth. Therefore, the position with the highest density is the most likely to be the tag's position. By this means, the noisy data do not contribute to the final estimate, which improves the localization accuracy. We setup a prototype of FILM and experiment results show that FILM is superior to the state-of-the-art, with a small localization error of 1.3cm.
A Day-ahead Adjustable Robust Dispatching Model of Integrated Energy Systems Considering Renewables Penetration

The uncertainty of renewables penetration brings challenges to the operation of the integrated energy system (IES). This paper focuses on the development of adjustable robust dispatch for this purpose. Firstly, IES is modeled based on the energy hub. Then an adjustable robust dispatching model is designed with the goal of minimum cost, where the decision variables are obtained to ensure a feasible solution for all scenarios according to the uncertainty set. On this basis, the model is further transformed into a mixed-integer linear program (MILP) so that it can be easily solved by the existing solvers. Finally, it is shown that the optimum scheme obtained by the proposed method reduces the reserve capacity and cost compared with the static robust optimization.
FIMPA: A Fixed Identity Mapping Prediction Algorithm in Edge Computing Environment

Edge computing is a research hotspot that extends cloud computing to the edge of the network. Due to the recent developments in computation, storage and network technology for end devices, edge networks have become more powerful, making it possible to integrate locator/identity separation protocol (LISP) into these networks. Accordingly, in this paper, we introduce LISP into edge routers at the edge network, focusing primarily on the delay problem of mapping resolution and cache updating in LISP with the help of edge computing. To solve this delay problem, we first analyze the communication process of the locator/identity separation network and consider using the prediction method to underpin this research. In order to achieve a good prediction result, we propose and implement a Fixed Identity Mapping Prediction Algorithm (FIMPA) based on collaborative filtering, and further verify the effectiveness of the proposed algorithm through experiments on real-world data.
A Wideband 3-dB Directional Coupler in GGW for Use in V-Band Communication Systems

In this article, a broadband 3-dB directional coupler is proposed using groove gap waveguide (GGW) structures. Gap waveguide technology has been introduced to overcome manufacturing and assembling challenges of different millimeter-wave components and devices. The presented coupler has wideband coupling flatness with low return loss in 60-GHz frequency band and can be easily adapted to other frequency ranges. Experimentally, a sample prototype of the proposed 3-dB coupler has been designed and fabricated. The measured return loss and isolation are better than 20 dB and the power-split unbalance within ±0.5 dB is obtained over the frequency range from 57 to 74 GHz (26% BW). The proposed structure has the capability of easily integrating with other millimeter-wave components.
DEKF to Estimate Hemodynamic Response and Path-length in fNIRS Data

The removal of the physiological noises and the external noises such as motion artifacts is one of the key issues in functional near-infrared spectroscopy (fNIRS) studies. In this paper, we investigate a dual extended Kalman filter (DEKF) for the data from fNIRS, which consists a state estimator for hemodynamics responses and a parameter estimator for path-lengths of individual channels simultaneously. Eight subjects participated in two experimental sessions of mental arithmetic tasks. The obtained signals are processed by the correlation based signal improvement (CBSI) method and the newly proposed method, respectively. We evaluate accuracy of the hemodynamic response estimation using the correlation between the CBSI estimation and the DEKF estimation while that of the path-lengths using a left-tail $t$-test on the relative errors between sessions. The noise-resistant performance is validated using the metrics of correlation coefficient analysis and within-subject standard deviation (SD). The results show that the DEKF-converted concentrations strongly correlate with that of CBSI and the estimated path-lengths are statistically identical in both sessions. The DEKF method is as resistant to noise as CBSI.
A Comprehensive Survey on Design Approaches for Metamaterial based Microstrip Patch Antenna

This paper offers an overview of microstrip patch antenna using metamaterial. It illustrates the comprehensive survey on antenna designing with their developing schemes. It is important to know the applications of designed antenna especially for wireless communication system. Metamaterial consists some unique features as negative permittivity and permeability and known as left-handed material antenna. These are acting as certain defect that can be developed at substrate, ground or any other regions of antennas. Generally, size of the antenna is high at low frequency. Therefore, fractal antenna can enhance the performance of antenna with minimum area at low frequency. It also provides the multiband facility through reconfiguration of antenna. This paper offers some of the trending designs of metamaterial-based microstrip patch antenna for multiband applications. Metamaterials provide the optimal bandwidth with minimum return loss including miniaturization.
Cargo-call-stack Static Call-stack Analysis for Rust

Memory safety is instrumental to the safety and security of software systems. The Rust language stands out with a type system and underlying memory model targeting memory safety without the need for dynamic garbage collection, making Rust a viable option for embedded applications. In this paper we present an integrated tool for call-stack analysis of Rust applications. We cover both theoretical and practical challenges, their solutions and open questions. The cargo-call-stack tool is useful for analyzing Rust applications in general, and embedded Rust in particular. To the latter, we show that using the call-stack analysis we can give guarantees of total memory safety, free of assumptions on operating systems and underlying memory protection mechanisms in hardware. The feasibility of the approach is demonstrated by applying the ‘call-stack’ tool on production code targeting a light-weight ARM Cortex-M platform.
MobileEdge: Enhancing On-Board Vehicle Computing Units Using Mobile Edges for CAVs

As the rapid growth of connected and autonomous vehicles (CAVs) and 5G intensifies, more third-party applications are increasingly being deployed on CAVs. They not only improve user experience but also provide more helpful services, for example, enhancing public safety by recognizing criminals in real-time videos. Current CAVs prefer to process collected data on the vehicle to avoid long transmission latency and extra network cost. However, due to the limitations of the on-board vehicle computing unit (VCU) and increasing use of computing-intensive in-vehicle applications, the burden of on-board VCU has sharply increased, which may affect driving safety. In particular, for existing vehicles on the road, adding more computing devices is a challenge if not impossible due to cost concerns. Inspired by edge computing, we propose a novel platform, MobileEdge, to enhance the computing capability of the unchangeable on-board VCU, which leverages mobile devices as edge nodes, e.g., the passengers' smartphones, by offloading computing tasks to them for collaboratively computing. Moreover, MobileEdge provides the dynamic management of mobile devices, monitoring device status and interfaces for customizable task offloading strategies and eventually achieves optimal task scheduling. We build a prototype to demonstrate the designed platform and evaluate three task offloading strategies which were implemented based on the developed interfaces. The results show that MobileEdge significantly reduces the application response latency. Compared with the baseline which does not employ task offloading, the response latency is almost near real-time when more computing resources are available. In addition, the proposed shortest response latency strategy outperforms the best overall task scheduling among the three strategies.
Performance of Relay Signal Transmission by AF Technique Influenced by k-μ Fading

This paper considers the transmission of signals under the influence of k-μ fading with powers ϒ1 and ϒ2, which model a random process of changing the amplitude of a useful signal through a single relay system using the amplify and transmit technique (Amplify-and-Forward, AF). First contribution of the paper is deriving infinite-series expressions for the probability density function (PDF) and cumulative distribution function (CDF) of co-channel interference (CCI) influenced signal. Next contribution is observing important wireless performance criteria, namely outage probability (OP). Obtained expression important wireless performance criteria, namely, outage probability, have been obtained, as functions of transmission parameters. The results have been validated through simulations which shows perfect match.
Analysis of Initial Undershoot in Step Response of Type-1 Nonminimum Phase Systems

In this paper, the analysis of initial undershoot for the step response of Type -1 nonminimum phase systems is carried out. In view of this, a theorem is proposed which enables the identification of various system parameters on the magnitude of undershoot. This involves the use of Markov parameters of the system. The theorem is accompanied by the mathematical proof and validated using two practical situations. First, a DC motor is operated in the open-loop configuration, where the input signal is delayed by a certain amount of time. The presence of delay element incites nonminimum phase behaviour, and thus can lead to the presence of initial undershoot in the system’s step response. The analysis showed how the motor’s parameters and the delay time affects the undershoot in the angular displacement curve of the motor. Following this, the concept of initial undershoot is used to analyze the effect on the system frequency deviation. The problem adopted is a single area power system consisting of a hydro turbine (a nonminimum phase plant), where system frequency is perturbed by sudden load disturbances. The effect of this system’s characteristic on the maximum initial frequency deviation of the electric power is analyzed. It aims at providing means to identify how a controller’s parameters affect the change in frequency due to sudden load disturbances.
CP Fusion based Abnormal Traffic Dedection for Process Layer Network in Digital Substation

When abormalities, including equipment failure and/or cyber attacks, occur in the substation, it is essential to specify their difference in performance, e.g., process layer of digital substation will encounter stochastic messages and burst messages in normal operation, abnormal traffic caused by equipment fault, or denial-of-service (DoS) attacks by virus intrusion. In this reason, a novel cyber physical fusion based abnormal traffic detection approach has been proposed, a difference-sequence-variance based abnormal message traffic detection method and flowchart is proposed, including a new substation traffic anomaly membership function and its parameter estimation method. The simulation verification of the T1-1 type substation network is done by OPNET, which prove that the random and burst GOOSE messages as well as DoS attacks in the layer can be identified by proposed method.