Miracle-dz/UniFAR-FTUs huggingface dataset:
Blind Image Restoration Algorithm Based on l0 Total-variation Model

Given that l0-norm can enhance sparsity for restoring blind images, this study puts forward a new l0 total-variation (l0-TV) model, which replaces l1-norms of the TV model with l0-norms to boost sparsity. With a focus on handling the l0-TV model, a novel split Bregman based on blind image restoration algorithm is proposed. Via variable splitting and Bregman iteration, split Bregman algorithm first imposes equivalent transformation on the l0-TV model and then separates the model into a group of minimization problems with respect to unknown latent image and blur kernel. To acquire a stable solution of unknowns, hard thresholding and fast Fourier transforms are integrated into the solution. The continuation method is employed to iteratively update the parameter related to image regularization, which contributes to finding good solutions. In experiments, two types of degraded images are processed to verify the validity of the proposed algorithm. The restored results are also used for comparisons to evaluate the performance of the proposed algorithm.
Design, Estimation of Model Parameters, and Dynamical Study of a Hybrid Aerial-underwater Robot: Acutus

Design of multi-domain vehicles has been a focus in robotics research in the recent past. The objective behind developing such hybrid vehicle/robot is to combine the capabilities of systems operating in various domains. They can be of great use in numerous applications, as it maximizes the reach in multiple operation environments, especially in various challenging sectors to reduce risk to the human lives. This paper presents the design of multi-domain vehicle: a hybrid aerial-underwater robot, Acutus. Dynamic modelling of Acutus is one of the vital steps in the design process. The parameters involved in the model such as the hydrodynamic drag and added mass are critical in determining the accuracy of the model. Mathematical modelling and estimation of system parameters for Acutus are presented. The dynamics of the system, both in aerial and underwater domains, are initially studied individually for different possible sets of inputs. Later, simulation studies are carried out for transition between aerial and underwater domains. Preliminary mechatronic design and the experimental setup details are also presented.
Development of a Volcanic Remote Monitoring System Based on the DOAS Technique

Las condiciones extremas del ambiente volcánico y el alto riesgo asociado a las mediciones en terreno, hacen que las técnicas de sensado remoto sean una herramienta fundamental para el monitoreo geoquímico gaseoso. En este trabajo se presenta el diseño y desarrollo de un sistema optoelectrónico denominado scanning-DOAS, que permite monitorear desde una distancia segura la emisión de dióxido de azufre (SO2) de origen volcánico. El instrumento realiza un barrido o escaneo de la pluma, midiendo la radiación solar difusa UV que llega al detector. A partir de dichos espectros solares se determina la concentración de SO2 en la sección transversal de la pluma, aplicando la técnica DOAS (Differential Optical Absorption Spectroscopy). Si se cuenta con información de la altura y velocidad de desplazamiento de la pluma, es posible determinar el flujo de SO2. Existen evidencias que indican que un aumento en los flujos de SO2 podría estar asociado a un incremento de la actividad volcánica, de ahí la importancia de realizar un monitoreo sistemático. En el trabajo se destacan las características constructivas del sistema y se describe la técnica utilizada para la obtención de los datos mencionados. El diseño del sistema incorpora mejoras sustanciales basadas en los resultados obtenidos empleando un primer prototipo, durante la campaña de monitoreo realizada en el complejo volcánico Planchón-Peteroa, Malargüe, provincia de Mendoza, en enero de 2017. La variabilidad del flujo de SO2 observada durante dichas mediciones se encuentra en el orden de magnitud de lo esperado y presenta una muy buena correlación con la información brindada por el Observatorio Vulcanológico de los Andes del Sur (OVDAS), Servicio Nacional de Geología y Minería de Chile (SERNAGEOMIN), en sus Reportes de Actividad Volcánica. Los resultados obtenidos indican una tasa de emisión de SO2 muy baja, propia de la actividad de dicho complejo volcánico.
An Insight Into Test Case Optimization: Ideas and Trends With Future Perspectives

Software engineering comprises several disciplines. Testing, the subject of this paper, is an important phase which is still largely unpredictable, expensive, and ad hoc. Software testing has already been framed as an optimization problem, i.e., to solve problems in software testing various optimization techniques can be used. To adequately test the software, various rules called test adequacy criteria are used. In this paper, a review of optimization techniques used in domains, test case generation, selection, minimization, and prioritization of testing, has been presented. The review is conducted by selecting quality literature from journals, conferences, workshops, and symposium. The state-of-the-art issues in software testing have been addressed by analyzing the reviewed literature on the basis of the domain of testing, test adequacy criteria, and optimization technique used. After analysis of the considered literature, authors were able to conclude the limitations of existing work, list of traditional and recently proposed adequacy criteria as well as combinations of adequacy criteria for multi-objective optimization and optimization techniques which may be used in the optimization of software testing.
Motivating Airmen to Engage with Technical Education: Experimentation and Analysis Using Modern Gamification Techniques

The development and integration of computer systems into today’s society and the subsequent growth of cyber as a warfighting domain has led to changes in military and civilian conflict. Several traits unique to cyber, including disruption and fast pace of change, has led to issues never before seen in the military environment, especially with educating and training. A new approach that leverages crowd-sourced content has been proposed. This approach relies on motivating military members to voluntarily engage with technical (cyber) education. The application of gamification, a design practice aimed at increasing user engagement by targeting core motivators in humans, in the military context is presented in this paper. The adaptation and evaluation of unique game elements onto the platform is also discussed. A human-subject study involving a survey and engagement-tracking experiment is implemented. Results are analyzed using visualization software and a novel framework we created. We then present results explaining what core drives motivate military members on average and within subgroups. We also show that engagement data can be attributed to motivation levels. Finally, we present recommendations to military leadership and education platform designers based on our findings before discussing ideas for future work.
Distributed Product Flow Control in a Network of Inventories With Stochastic Production and Demand

This paper presents a cooperative distributed model for the control of product flow in a network of cooperative inventory systems (ISs). In each IS, a linear input/output equation describes the balance between product demand vs production, as well as the product flows among ISs. The peculiarity of the problem is that both the production and demand are stochastic and cannot be controlled. In addition, each IS has just deterministic information on its inventory level (i.e., the quantity of stored product) which is not shared in the network. Other information is available in each IS regarding the forecast of both its related production and demand. The goal of the proposed model, on the overall network, is to keep both the product stored in each IS and the product exchanged among ISs, around given values as planned a priori. The only available control is related to the product flow between neighboring ISs, which has to be locally computed. Section II describes the proposed approach, which is based on the dual decomposition of the problem, which enables reaching the optimal control. This problem can represent the abstraction of a series of problems, which are not just related to logistics. As examples, section III presents two case studies: a network of virtual power utilities; and the control of risk in the transport of dangerous goods by road.
Application of Particle Swarm Optimization to Design Control Strategy Parameters of Parallel Hybrid Electric Vehicle with Fuel Economy and Low Emission

Hybrid Electric Vehicles (HEV) provide fuel-saving and less emissions, and are one of the solutions to effectively alleviate the global energy crisis and environmental pollution problems for a large number of global vehicle demands. This study mainly uses Particle Swarm Optimization (PSO) algorithm to design appropriate control strategy parameters. To simulate a parallel hybrid electric vehicle, we used ADVISOR as a simulation tool and used the driving cycle of Federal Test Procedure (FTP) to evaluate fuel consumption (FC), exhaust emissions, and vehicle dynamics. The number of iterations is set to 50, the swarm size is set to 10, the inertia weight is set to 0.8, as well as the cognitive parameter and the social parameter are both set to 2 for the proposed PSO. Compared with ADVISOR the defined parallel HEV, the results show that PSO is a powerful tool for design of parallel HEV control strategy parameters. The proposed method can improve fuel consumption and reduce exhaust emissions without sacrificing vehicle performance. It may help to alleviate the global energy crisis and environmental pollution.
A New Image Encryption Algorithm Using Homogenized Chebyshev-Arnold Map

In this paper, a Homogenized Chebyshev-Arnold Map (HCAM) is firstly proposed by homogenizing the linear coupling of Chebyshev map and Arnold map. The proposed HCAM can avoid two problems of origin Chebyshev map which lead to potential safety hazard in image encryption algorithm: one is discontinuous choices of initial values for chaotic property, the other is nonuniform value distribution of generated sequences. Next, based on the HCAM, we propose a novel image encryption algorithm that consists of two parts: confusion and diffusion. For the confusion part, we enhance the chaotic magic transform (CMT) to adjust the shifting times of selected pixels with respect to different plaintext images for higher security level without extra time cost. For the diffusion part, to maintain the low runtime of our algorithm, a bit-operation based pixel substitution scheme is presented. Thereinto, the selection of pixels and certain values for substitution in these two parts are both decided by the HCAM. Simulation results and security analysis have been carried out and demonstrate the security and efficiency of our proposed algorithm in comparison with previous schemes.
Traffic Shaping based Queue Management for Delay Sensitive Multimedia Applications

Active queue management, as a promising approach to solve network congestion and control delay, has caught the attention of researchers in the past decades. However, most existing algorithms are sensitive to parameter settings or lack robustness in non-TCP flow scenario. This paper propose a traffic shaping method based on queue management. In order to shape the distribution of received traffic in queuing system into any desired distribution, a traffic shaping method by selectively dropping the arrival packets is then proposed. On the basis of this theoretical analysis the proposed queue management algorithm ITS (Input Traffic Shaping) turns the parameters adaptively for optimal system performance. The input traffic shaper serves as a guideline for packet dropping strategy and Poisson distribution is selected as the desired distribution. Simulation results show that compared with existing algorithms, ITS can control delay more effectively, reduce jitter and enhance system throughput in non-TCP flow scenarios.
Robust adaptive beamforming in impulsive noise environments

Adaptive beamforming is one of the most important parts of a smart antenna. The most existing adaptive algorithms are based on the second-order-moment of the error signal like least-mean-square and recursive-least-square (RLS) algorithms. Although these algorithms are popular, their performances are not robust in impulsive noise environments. A linearly constrained correntropy-based adaptive algorithm to solve the beamforming problem in impulsive noises is proposed in this study. The kernel width parameter of the proposed algorithm is considered to be time-varying to deal with non-stationary and impulsive environments. Simulation results illustrate that the proposed beamforming algorithm has a robust performance in impulsive noise. Moreover, the computational complexity of the authors’ algorithm is less than the recent algorithms presented in this literature.
Narrowband Data Transmission in TV White Space: An Experimental Performance Analysis

TV White Spaces (TVWS) are UHF or VHF TV channels that are not under use by TV broadcasters at a particular time. As a means of achieving the global objective of efficient utilization of expensive spectrum bandwidth, TVWS can be exploited by Cognitive Radio (CR) enabled Software Defined Radio (SDR) systems for alternative services like communications for disaster-time rescue teams, Internet service for hard-to-reach communities etc. In this contribution, we report the design and prototype development of a testbed for real-time testing of secondary user transmission in TVWS. Once an unused TV channel has been identified, our system uses t hat idle channel for transmitting and receiving a narrowband signal. The testbed is built on Universal Software Radio Peripheral (USRP) 2901 device powered by GNU Radio software, RTL SDR 2832U and Spectrum Analyzer Tektronix MDO4054-3. Using 25kHz narrow bands within the TV channel#23 in Windsor, ON, Canada, and considering text, voice, and image data, we show channel and transmission characteristics through measured Bit Error Rate (BER) and constellation diagrams. Based on the experimental results, we affirm that it is possible to use this approach to successfully implement narrowband dynamic spectrum access (DSA) by secondary users over an idle TV channel.
A point cloud registration algorithm based on normal vector and particle swarm optimization

Based on normal vector and particle swarm optimization (NVP), a point cloud registration algorithm is proposed by searching the corresponding points. It provides a new method for point cloud registration using feature point registration. First, in order to find the nearest eight neighbor nodes, the k-d tree is employed to build the relationship between points. Then, the normal vector and the distance between the point and the center gravity of eight neighbor points can be calculated. Second, the particle swarm optimization is used to search the corresponding points. There are two conditions to terminate the search in particle swarm optimization: one is that the normal vector of node in the original point cloud is the most similar to that in the target point cloud, and the other is that the distance between the point and the center gravity of eight neighbor points of node is the most similar to that in the target point cloud. Third, after obtaining the corresponding points, they are tested by random sample consensus in order to obtain the right corresponding points. Fourth, the right corresponding points are registered by the quaternion method. The experiments demonstrate that this algorithm is effective. Even in the case of point cloud data lost, it also has high registration accuracy.
A Deep Feature Learning Scheme for Counting the Cells in Microscopy Data

Cell counting remains a challenging task especially because of the extreme variation of the size and shape of the microscopy data. Conventional counting methods are mostly based on the utilization of the segmentation masks as the prior information for estimating the number of the cells in the image. We propose in this work a novel cell counting scheme that uses the features provided by a deep convolutional autoencoder (DCAE) as the inputs of a shallow regressor network, instead of using the segmentation masks. First, the cellular image is given to the DCAE whose task is to reconstruct the original input image with an encodingdecoding scheme. The latent representations located in the middle of the DCAE are extracted and used as the final feature representation of the images. The second step consists of using these features as the inputs of a neural network based regressor whose outputs will represent the number of the cells in the image. The results demonstrate how the proposed scheme really manages to recognize the exact number of the cells even in the case of significant overlapping situations, exactly where most of the state-of-the-art cell counting methods fail.
5G K-SimSys for System-level Evaluation of Massive MIMO

Massive MIMO is a key feature for improving spectral efficiency in 5G system. Since massive MIMO has been standardized in 3GPP Re1-13 (LTE-Advanced Pro), a next generation of massive MIMO standard is now available in Rel-15, a.k. a New Radio (NR), for 5G, which is aiming at further improving the average spectral efficiency with more antenna elements over the higher frequency band. In particular, beam-based air interface for above 6GHz band involves with various antenna configurations and feedback schemes, requiring a more complex testbed for system-level performance evaluation. In this paper, we examine the multi-antenna technologies in 3GPP NR specification, so as to develop its system-level model in 5G K-SimSys, which has been designed and implemented to provide an open platform and testbed for evaluating the system-level performance for 5G system. Its actual implementation is detailed along with the overall platform architecture for modular and flexible design. Then, its actual performance is demonstrated for one of the precoding matrices in the NR specification as an example.
Symbol Detection Using Discrete Prolate Spheroidal Sequences under the Plasma Sheath Channel

The plasma sheath has a serious impact on the propagation of electromagnetic waves, resulting in the radio blackout problem in aerospace communications. Existing studies have shown that the coherence time of the plasma sheath channel can be as low as 0. 1µs, which is smaller than the symbol period in aerospace telemetry communications. However, the traditional symbol detection method based on correlation filters only uses the orthogonality of transmission symbols, without considering the channel variation within one symbol period. In this paper, a new symbol detection scheme is proposed under the high-dynamic plasma sheath channel. Firstly, the discrete prolate spheroidal sequences are used to expand the dynamic channel. Then, based on an analysis of the log-likelihood ratio, a linear combination detection scheme is proposed, which has a simple form and is suitable for symbol detection under the plasma sheath channel. In particular, we show that the performance of communication systems under the plasma sheath channel can be improved by spreading the signal dimension. Finally, simulation results are carried out based on the shock tube experimental data, showing that the proposed detection scheme has better performance compared with the correlation-based detection method.
A Generalized Odometry for Implementation of Simultaneous Localization and Mapping for Mobile Robots

This paper proposes a novel method for calculation of generalized Odometry using velocities from Light Detection and Ranging (LiDAR) and Inertial Measurement Unit (IMU), discounting velocities from motor encoder values. Further, the estimated velocities are used for the calculation of Odometry using rigid body Newtonian equations. The generalized Odometry and laser scans are used for implementation of the particle filter Simultaneous Localization and Mapping (SLAM) algorithm. This method overcomes errors due to slippages in mobile robots. The outputs of SLAM maps are experimentally validated in both straight and curved trajectories with reference to ground truth maps. SLAM results obtained from the proposed method Odometry is better than the only LiDAR, IMU and Encoder Odometry in an indoor environment for autonomous navigation of mobile robots.
Approach for setting of pulse frequency to achieve optimal asynchronous drive performance

In this paper, an approach for asynchronous drive testing at different frequency converter parameter settings has been developed. Tests at different settings of the pulse-width modulation frequency were carried out using a specialized laboratory simulator to record the transient processes and the values of the controlled parameters in the steady-state operation. The results obtained have been analyzed to determine the impact of the examined parameter on the drive efficiency and dynamic performance. The experimental verification of the proposed approach proves its applicability in the setting of the frequency converters for drives in various fields of industry and transport.
Impact of Varying Strain Energy in Oxide on Random Telegraph Noise and Associated Time Constants in Silicon Nanowire pMOSFETs

Multilevel random telegraph noise in gate-all-around silicon nanowire pMOSFET shows gate voltage overdrive-dependent interstate transitions, statistical count of which indicates the existence of oxide traps with different time constants. Following the detail extraction of the capture and emission time with gate bias, both have been found to deviate from the usual trend, as predicted by the conventional Shockley–Read–Hall statistics. Two traps in the gate–oxide have been found, in which the near interface is found to respond through the altered RTN activation energy barrier by spatially varying oxide residual strain energy, whereas the farther responds under the weak influence of the oxide strain energy. On the other hand, the emission time of near interface trap has been found to follow similar strain influenced activation barrier, but the magnitude of the emission time of the farther trap shows the hole emission through the p+ polysilicon gate–oxide interfacial trap, while favored by the electrostatic field in oxide. From RTN distributions and time lag plots, interstate transitions and state suppression in RTN are explained under the framework of two-trap postulate, in order to elucidate the unusual gate bias dependence of the measured trap time constants in silicon nanowire pMOSFETs.
Condition Monitor System for Rotation Machine by CNN with Recurrence Plot

Induction motors face various stresses under operating conditions leading to some failure modes. Hence, health monitoring for motors becomes essential. In this paper, we introduce an effective framework for fault diagnosis of 3-phase induction motors. The proposed framework mainly consists of two parts. The first part explains the preprocessing method, in which the time-series data signals are converted into two-dimensional (2D) images. The preprocessing method generates recurrence plots (RP), which represent the transformation of time-series data such as 3-phase current signals into 2D texture images. The second part of the paper explains how the proposed convolutional neural network (CNN) extracts the robust features to diagnose the induction motor’s fault conditions by classifying the images. The generated RP images are considered as input for the proposed CNN in the texture image recognition task. The proposed framework is tested on the dataset collected from different 3-phase induction motors working with different failure modes. The experimental results of the proposed framework show its competitive performance over traditional methodologies and other machine learning methods.