Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust Voltage Regulation if Unmanned Electric Vehicles

Stable and reliable voltage supply is crucial for unmanned vehicle to operate safely and autonomously. The electrification of unmanned vehicle involves the widespread use of power electronics and the integration of large amounts of power electronics loads in DC distribution systems. These electronic loads typically exhibit non-linear load characteristics, and the circuit resistance exhibits nonlinear changes may cause system instability. Using traditional methods to control DC distribution systems with these non-linear loads will be a challenge. In this paper, a nonlinear controller-stabilized DC/DC buck converters integrated Robust control method is used to eliminate the DC bus voltage fluctuation caused by These nonlinear resistances. Firstly, the model takes the error between the state quantity and the reference quantity as the control object. Then, a Robust control algorithm is used to ensure the large signal stability of the system. Finally, a nonlinear controller was established. The proposed controller not only ensures the global stability of the circuit system under the operating conditions of these non-linear loads, but also has a fast dynamic response and precise tracking over a wide operating range. Simulation was performed to verify the proposed strategy.
Comparative Analysis of Full Adder Custom Design Circuit using Two Regular Structures in Quantum-Dot Cellular Automata (QCA)

Regular structures in Quantum-dot Cellular Automata (QCA) allow for ease of design and uniform clocking structure. The paper presents a comparative analysis of QCA realization using regular structures: Shannon-Lattices and Programmable Logic Arrays (PLAs). A software tool was developed that generates complete QCA-Shannon-Lattice and QCA-PLA Layouts for Boolean functions based on an input characterized macro-cell library. The correctness of equations were verified by performing simulations of the generated layouts. It is shown that the new regular lattices outperform PLAs and are closer to full custom design on Full Adders.
Small Bowel Mucosa Segmentation for Frame Characterization in Videos of Endoscopic Capsules

Endoscopic capsules are vitamin-sized devices that leverage from a small wireless camera to create 8 to 10 hour videos of the patients’ entire digestive tract, still being the leading tool to diagnose small bowel diseases. The revision of the produced videos is a very time-consuming task, currently conducted manually and frame-by-frame by an expert. Since endoscopic videos usually contain a considerable amount of frames where the mucosa is not clearly visible, the segmentation of the informative regions is a vital component to reduce the necessary time to review each exam. In this work, a CNN encoder-decoder architecture is applied to segment informative regions in small bowel frames of videos of endoscopic capsules. The network was trained and tested with a dataset of 2,929 manually annotated images, achieving a 91.2% Dice coefficient and 83.9% IoU. Furthermore, a video-wise analysis based on the amount of informative pixels in each frame is done.
A novel nonsingular adaptive terminal sliding mode controller for MIMO linear systems with uncertainty

A novel nonsingular adaptive terminal sliding mode control (NATSMC) for MIMO linear systems is proposed in this paper. It is shown that the new method can force the states of system to converge to a region near the equilibrium point in finite time. Meanwhile, the singularity problem is avoided. The combination of barrier-function adaptive law can compensate the matched uncertainty without overestimation, and the size of the sliding mode (SM) convergence region can be further reduced. Finally, a simulation example is given to validate the effectiveness and robustness of the proposed method.
Effective Contexts for UAV Vehicle Detection

Vehicle detection based on unmanned aerial vehicle (UAV) images is a challenging task for the small size of objects, complex background, and the imbalance of various vehicle samples. This paper proposes a high-performance UAV vehicle detector. We use the single-shot refinement neural network (RefineDet) as a base network, which employs the top-down architecture to offer contextual information, achieving accurate detection. However, for the small size of vehicles, the top–down architecture introduces too much context, which brings surrounding interference. We present a multi-scale adjacent connection module (ACM) to provide effective contextual information and reduce interference for vehicle detection. In addition, we adopt an alternate double loss training strategy (ADT) to solve the problem of imbalance between hard and easy examples during training, and we design suitable default boxes according to the distribution of the UAV dataset to improve the recall rate. Our method achieves 92.0% and 90.4% accuracy on the collected UAV dataset and the publicly available Stanford drone dataset, respectively. And, the proposed detector can run at 58 FPS on a single GPU.
UAV Beamwidth Design for Ultra-Reliable and Low-Latency Communications with NOMA

In this paper, we investigate the non-orthogonal multiple access (NOMA) based unmanned aerial vehicle (UAV) assisted ultra-reliable and low-latency communications (URLLC) without the presence of base station. To achieve URLLC, we minimize the block error probability by appropriately reducing the beamwidth. Since the user grouping approach can reduce the coverage of user-free area to achieve the beamwidth reduction by only covering the users in each group, we propose two user grouping algorithms for UAV-assisted NOMA transmission. The first one is the improved K-means algorithm. It needs to set the number of groups in advance and has good performance in solving the problem considered in this paper. The second one is an extended affinity propagation (AP) clustering algorithm. This algorithm is easier to implement than the first one because it does not need to pre-define the number of groups, but the performance is inferior to the first algorithm. After user grouping, we introduce a location-based beamwidth design for each group to further minimize the beamwidth and solve the optimization problem. Numerical results show that the performance of the proposed user grouping algorithms with the location-based beamwidth design schemes are superior to the benchmark schemes under the URLLC constraints.
Time-varying, frequency-domain modeling and analysis of phase-locked loops with sampling phase-frequency detectors

This paper presents a new, frequency-domain based method for modeling and analysis of phase-locked loop (PLL) small-signal behavior, including time-varying aspects. Focus is given to PLLs with sampling phase-frequency detectors (PFDs) which compute the phase error only once per period of the reference signal. Using the harmonic transfer matrix (HTM) formalism, the well known continuous-time, linear time-invariant (LTI) approximations are extended to take the impact of time-varying behavior arising from the sampling nature of the PFD, into account. Especially for PLLs with a fast feedback loop, this time-varying behavior has severe impact on, for example, loop stability and cannot be neglected. Contrary to LTI analysis, our method is able to predict and quantify these difficulties. The method is verified for a typical loop design.
Students’ Viewpoint about Using MIT App Inventor in Education

The paper presents Ruse University students’ point of view about using Android application development environment MIT App Inventor in different levels of education. The research was done via the survey method. A questionnaire was made for the purpose. It consisted of 21 questions, merged in several parts: personal information about the respondent (age, gender, speciality, year of education, mobile devices possessed, programming skills and experience), students’ opinion about the qualities of the environment, its user-friendliness and which level of education it is most suitable for. The final part of the questionnaire included 2 open questions and the answers had to contain the advantages and disadvantages of MIT App Inventor compared to other programming environments. Within three consecutive years – 2016, 2017 and 2018 – a total number of 73 students (11 women and 62 men) from Computer Systems and Technologies master program were asked to answer the questions. The analysis of the results from all the three years shows that, according to Ruse University students, MIT App Inventor is most appropriate for use in a bachelor degree of education, followed by a master degree and a secondary school.
Collaborative virtual environments, real-time video and networking

Two real-life workplaces, a studio and an office, are described. Both have virtual reality (VR) and mixed reality counterparts. Issues of work processes and social interaction taken from CSCW are utilised to understand the functionalities that virtual studios and offices need to provide. It is broadly concluded that different media (documents, audio, video, VR) all have different strengths and weaknesses, and each may be appropriate for different purposes in different contexts. Offices and studios are best extended into virtuality by a mix of media (mixed realities) with a VR interface. The integration of video into VR environments presents the greatest technical difficulties, and some of these are considered from the viewpoints of computational load and networking. It is concluded that an optimal solution would be to provide separate network architectures for real-time interactive VR and video.
Cuda Parallelization of Commit Framework for Efficient Microstructure-Informed Tractography

Brain tractography allows estimating in vivo long-range connections between groups of neurons. However, it is well known there is a huge amount of false-positive connections in the estimators that are represented as 3D streamlines. The COMMIT framework allows reducing those false positives, however the required computational time may become very long due to the, usually, huge number of streamlines per brain-volume, and the need to process thousands of brain images to increase the statistical power of current medical studies. In this work, we provide a programming model to parallelize the COMMIT framework on the CUDA language framework. Our results demonstrate that it is possible to reduce the computational burden by one order of magnitude by using this proposal.
Calculating average shortest path length using Compute Unified Design Architecture (CUDA)

Large graphs with millions and even billions of vertices are found in many real-life network analysis, the processing of which is challenging. One of the toughest tasks is computing the average shortest path length in a large network, which requires a lot of memory and processing time while calculating many independent paths. Hence, this task becomes a good candidate for parallelizing. The idea of using graphics processing units (GPUs) for general purpose computing is not new, and with recent increases in performances and memory capacity, they make a perfect candidate for working with graphs. We will explore how the Dijkstra’s algorithm can be used to calculate the average shortest-path length, and how it can be used in a massively parallelized system and compare the performance gains and drawbacks.
Fault management API for SNMP agents

In previous works we have concluded that fault management SNMP integration state of the art is not idyllic from the integrator’s point of view. Every umbrella fault management system has to implement number of different SNMP manager modules capable to communicate with different SNMP agents, irrespective of the fact that management information structure is almost the same among those agents (e.g., alarm message always consists of start time, perceived severity, probable cause, additional information, affected network element info and other attributes). In this paper, we are going to present Fault management API (Application Programming Interface) for SNMP agents (FM SNMP API). This C+ + API is aimed to be used during SNMP agent software development process. Using this API, software developers will focus on fault management data handling while SNMP support for fault management will be covered by FM SNMP API. That approach should decrease SNMP agent software development time, but also it should simplify SNMP management process and new equipment integration into umbrella fault management systems. This paper is structured as follows: first, API architecture will be described. After that, functions to be used by developers are described briefly. Finally, FM API pilot use case will be presented.
A Novel Hybrid Method of Spatially Filtered FDTD and Subgridding Technique

A novel hybrid method of spatially filtered finite-difference time-domain (SF-FDTD) scheme and subgridding technique is presented to analyze the multiscale objects. Because of the controllable stability of the SF-FDTD scheme, which is applied in the subgrid region, the uniform temporal increment can be employed in the whole simulation region. Therefore, the temporal interpolation of the field components can be avoided at the coarse-dense grids connecting boundary. In addition, by selecting the appropriate spatial interpolation field points in the Yee cells and only eliminating the unstable frequency domain magnetic fields, the influence of the filtering operation at the connecting boundary is removed. The simulation results are presented to validate the accuracy and efficiency of the hybrid method.
Simultaneous Safe Feature and Sample Elimination for Sparse Support Vector Regression

Sparse support vector regression (SSVR) is an effective regression technique. It has been successfully applied to many practical problems. However, it remains challenging to handle the large-scale problems. A nice property of SSVR is double sparsity in the sense that most irrelevant features and samples have no effect on the regressor. Inspired by it, we propose a simultaneous safe feature and sample screening rule based on the strong convexity of objective function (FSSR1) to accelerate SSVR, including the linear and nonlinear cases. Most inactive features and samples can be simultaneously discarded before training SSVR. Only one reduced SSVR (RSSVR) needs to be solved. Moreover, to further speed up RSSVR, the screening rule based on duality gap (FSSR2) is applied to continuously discard the remaining inactive variables during the reduced model training process. Combining the rule with the grid-search method, the framework of our final method (FSSR–SSVR) is to alternatively conduct FSSR1 and FSSR2, which leads to substantial savings in both the memory usage and the computational cost. There are two appealing advantages of our FSSR-SSVR: first, it is safe, i.e., the features and samples discarded by FSSR are guaranteed to be inactive; second, it has synergy effect (in other words, the results of the previous feature screening can improve the performance of the next sample screening, and vice versa). Furthermore, the stochastic dual coordinate ascent (SDCA) method is employed as an efficient solver. Experiments on three synthetic datasets and 11 real-world datasets demonstrate the efficiency of our method.
Secrecy Performance of AF Assisted Cooperative Spatial Modulation System with Destination-Based-Jamming

As a low-complexity and energy efficient MIMO technique, spatial modulation (SM) can avoid inter-channel interference and inter-antenna synchronization. Combining SM with cooperative relaying, the performance of SM can be effectively improved. Since wireless communication is vulnerable to be eavesdropped, the destination-based-jamming (DBJ) technique is applied to the cooperative SM system for security enhancement. In the DBJ scheme, the jamming signal is sent from the legitimate receiver (Bob) to interfere the eavesdropper (Eve). Based on the amplify-and-forward (AF) relaying, we investigate the secrecy performance of the AF relay assisted cooperative SM system with DBJ over Rayleigh fading channels. The achievable rates by Bob and Eve and the corresponding lower bounds are respectively derived. With these low bounds, the approximated closed-form ergodic rates by Bob and Eve can be obtained, respectively. Using these results, the theoretical closed-form ergodic secrecy rate (ESR) of the system can be further derived. The simulation results are provided to confirm the validity of our theoretical analysis. The derived theoretical expressions are consistent with the corresponding simulations and the DBJ scheme is well capable of enhancing the secrecy performance of the cooperative SM system over Rayleigh fading channels.
The impact of English on language education policy in Iceland

Iceland, a small Nordic country, is confronting choices about the languages to be used in education, especially in the light of the growing importance of English as an international lingua franca in higher education. This article examines the growth of teaching through English, mainly as a means of attracting international students who lack the ability to study through a Nordic language. It investigates the policies adopted by the European Union to promote multilingualism and suggests that their intentions are not consistently carried out in pragmatic decision-making. The experience of the Nordic countries has been that English is becoming more widely used in higher education, but in Iceland the enthusiasm for English is accompanied by an interest in other foreign languages, as well as a commitment to maintain the presence of Icelandic in all academic domains.
An Uncertainty Propagation Mechanism Analysis Method for Three-Dimensional Quadrilateral Localization

Three-dimensional localization methods could provide important position information for various application systems, including PHM (Prognostics and Health Management), aerospace, emergency scheduling, rescue and relief, and many other related location-based services systems. Due to the negative influence of various uncertain factors on the localization accuracy, the localization result may lead to wrong navigation result, or even could not be utilized. So except for the localization result, the uncertainty of localization result should be evaluated, which could provide important priori information for later processing procedure or decision-making. For this problem, this paper analyzes the uncertainty propagation mechanism during a three-dimensional least square localization method, which is referred to as a guide to represent uncertainty in measurement (GUM). In this method, we make a comprehensive uncertainty propagation analysis during three-dimensional least square localization, and provide important uncertain information of localization result. We first analyze the uncertain sources of three-dimensional least square localization. Then we analyze the sensitivity and propagation mechanism of uncertainty during least square localization computation. Finally, we output the localization result and its uncertainty through synthesizing all the input uncertainties. The simulation and experimental results illustrate that the proposed method could provide localization result with accurate uncertain information.
Quantitative Photoacoustic Blood Oxygenation Imaging Using Deep Residual And Recurrent Neural Network

Diffusive light scattering in biological tissue causes a heterogeneous and variable spectrum of light fluence, which is a significant challenge to predict due to unknown optical properties of tissues. Then it is difficult to accurately and quantitatively image blood oxygen saturation (sO2) in photoacoustic imaging because of unknown fluence distribution. To tackle this problem, we develop a deep residual, and recurrent neural network, i.e., DR2U-net for the quantitative estimation of blood oxygenation of photoacoustic imaging. The fine-tuned DR2U-net we developed can extract fluence distribution information from optical absorption images only using two wavelengths of light in Monte Carlo simulation, and afterward generate a quantitative image of sO2. The measurement results of sO2 show a very high accuracy by testing in simulated biological tissue, and its error is as low as 1.27% compared with conventional linear mixing method (48.76%). Besides, our model is high-speed, about 18. 4ms is achieved for every quantitative sO2 image.
Bintaint: A Static Taint Analysis Method for Binary Vulnerability Mining

Vulnerabilities in the current network space has been increasingly concerned by all parties. More and more software is currently in the form of binary code in practical applications. Therefore, the research on vulnerability mining technology for binary code attracts more attention from the researchers. In order to address the problem of binary vulnerability mining, this paper focuses on the taint analysis, and proposes a method called Bintaint, which can perform static taint analysis and generate the taint control flow graph (TCFG). In addition, we implement a system based on Bintaint to reduce the path explosion in the traditional vulnerability analysis process. Combined with our bidirectional search technology and path guidance algorithm to assist the symbol execution technology to generate test cases, the high computational overhead caused by complex constraints can be reduced. Finally, we evaluate our method on X86 programs and six core programs in different architecture embedded devices, and the result shows that our method detect all known vulnerability without false negative and mitigates the problem of path explosion and computational overhead effectively.
Research of D2D Communications Mode for 5G Vehicular Networks

Aiming at the poor operational reliability and high latency in the communication links of vehicular networks, this paper proposes a resource allocation algorithm, which gives priority to quality of service and is suitable for wireless transmission of short distance traffic information. By the proposed algorithm, 5G device-to-device (D2D) communications adopt a small antenna mounted in the car for sending the base station signal to the place that needs signal coverage, to effectively reduce and eliminate signal blind spots, after that suppress the interference of the same frequency signal to guarantee the network quality. The simulation results show that the proposed algorithm can effectively improve the throughput of the system, outperform the stochastic resource allocation algorithm. It implements the mode switch and gets a better experience for D2D users.
Effect of Use Parameters on Fatigue-Life of Flexible Substrates under Bending Loads

Flexible electronics have been emerging into the wearable application and their reliability in terms of failure mechanisms and lifetime plays a vital role in its survival in the application industry. Reliability of Flex-PCB under different human body working parameters are not been studied. Proper test standards and reliability study are not been done until now to provide confidence that flexible electronics will perform as required in the wearable application as compared to the Rigid-PCB. In this paper, a reliability study has been done based on the parameters - bend angle, the geometry of trace, trace width, pad size, aging, stress state(tension/compression) and sweat. The amount of strain on the traces is measured experimentally for quantification. The reliability study is done in order to compare the fatigue life with the human body data measurement at different joint angles. Many of the joints in a human body are V -shaped like elbow bend, knee bend etc. due to which this paper addresses the v-bend motion. The multi-layer connectivity reliability is also studied in this paper by considering different pad-to-trace ratio. The damage location of the trace is determined with the help of an optical microscope. The effect of cyclic bending under the exposure of human body conditions is been studied and analyzed. The failure of the traces is determined as per the IPC standards. The Flex-PCB are fabricated with subtractive itching method. The flexible substrate used is polyimide and traces are of copper (Cu).