Miracle-dz/UniFAR-FTUs huggingface dataset:
Simplified and approximation autofocus back‐projection algorithm for SAR

Due to accuracy limited inertial measurement unit systems, residual synthetic aperture radar (SAR) motion errors need to be compensated by autofocus method. Back-projection (BP) algorithm is a time-domain imaging process suitable for arbitrary trajectory and imaging on digital elevation model. However, traditional SAR autofocus algorithm requires a Fourier transform relationship between image domain and phase history domain. This study introduces an autofocus algorithm based on phase-error estimation. By selecting the image sharpness as target cost function, the exact phase error is directly estimated in closed form by solving the fourth-order polynomial with simplified approach. Another straightforward approximation autofocus method is also proposed to simplify calculation with high accuracy. The vast storage requirement for filtered BP data is reduced through a trade between time and space. Computer simulation and airborne experimental results validate the efficiency and efficacy of the proposed method.
BSF-RCNN-VFR: Background Subtracted Faster RCNN for Video based Face Recognition

— Surveillance systems are widely deployed in various organization and public palaces to monitor suspicious activities and reduce the crime rate. Recently, visual surveillance systems has gained huge attraction from research community due to their significant impact on monitoring application. Several techniques have been developed which are based on the still image which do not provide efficient solution for real-time application. Hence, video based face recognition is considered as a tedious task. Recently, deep learning based schemes have been adopted widely for video face recognition but these techniques suffer from well-known challenges such as pose and illumination variation. Hence, we present a Convolutional Neural Network (CNN) based approach for video face recognition. According to the proposed approach, we employ background subtraction scheme which helps to reduce the scene complexity and improves the feature extraction process. Later CNN based face detection scheme is developed which uses region proposal generation networks. In this phase, we incorporate bounding box regression model to reduce the face detection error. Finally, RCNN based learning model is applied which uses Joint Bayesian learning to discriminate the classes of detected faces. Based on these stages, the proposed model is named as Background subtracted Faster RCNN for Video based Face Recognition (BSF-RCNN-VFR). An experimental study is carried out based on the proposed method where we use publically available datasets such as YouTube celebrity dataset, Buffy dataset and YouTube face dataset. The experimental study shows a significant improvement in the detection and recognition accuracy process.
Robust tracking control for Robotic Manipulators based on Super-twisting Algorithm

Robotic manipulators are broadly used in industries for different kinds of specialized operational responsibilities and it is very complicated to design of a robust and stable controller for these systems. The performance of industrial robotic manipulators has augmented concerning stability and safety due to growths in robust controller design. However, designing a stable and reliable control method for a robot manipulator is important for real-world applications. This paper proposed a robust composite super-twisting sliding mode controller (STSMC) for robotic manipulators with uncertainties. To improve the robustness of robotic manipulators and reduce the chattering problem of sliding mode control, the Super-twisting algorithm is employed to design a continuous controller, and the stability is proved. Simulations on the 2-DOF robotic manipulator are shown to illustrate the effectiveness of the proposed method in which demonstrated that the proposed model has smoothness at 10−5.
Beyond R2D2

Robots are expected to enter the everyday lives of people to entertain, educate, or support them. It is therefore important that people can intuitively understand the behavior of robots. Oftentimes, the behavior of people is used as a model because of its familiarity. However, it is as yet unclear what the best approach is to design interaction behaviors for non-humanoid robots. In this article, we explore two different approaches toward designing behavior for a service robot. The first concerns the commonly used approach of copying human behavior as closely as possible to the robot (human-translated). The second approach was inspired by product design methods. The design of the robot's behavior was optimized for the robot's interaction capabilities and hardware modalities (robot-optimized). To evaluate people's responses to the two behavior sets for a tour guide robot, an online video study (N = 204) and a two-day in-the-wild study (N > 600) were performed. Results showed that participants responded slightly more positive to robot-optimized behavior and paid attention to robot-optimized behavior for longer. However, participants remembered more details when the robot showed human-translated behavior. Together, the studies show that it is sometimes better for non-humanoid robots to have robot-optimized behaviors rather than human-translated behaviors.
Research on the Method of Reducing Transformer DCBC Considering Converter Transformer Grouping and Receiving End Power Grid Structure

Based on the study of the influence of converter marshalling and receiving grid structure on the DC bias current (DCBC), DCBC levels of typical connection structures between converter station and its adjacent substations is analysed in this paper. Then DCBC levels of converter stations and AC substations under different connection modes of substations in AC power network are analysed. The measures to reduce DCBC of converter transformer and AC transformer considering various factors are put forward. A method to reduce DCBC of converter transformer and transformer considering converter transformer grouping and receiving end power grid structure is proposed. The measures of restraining DCBC are analysed from the aspects of operation mode selection, grounding pole location, power grid structure planning and design. Suggestions are put forward for the treatment of DCBC in the planning and design stage.
Decentralized Secondary Frequency Restoration and Power Sharing Control in Islaned Microgrid Systems

In this paper, a totally decentralized control scheme is proposed to address the secondary frequency restoration and real power sharing problem in AC microgrid (MG) systems. Different from existing centralized or distributed control approaches, no communication among controllers is required with our method, which greatly eases the system implementation cost. It is theoretically proved that the designed decentralized leaky integral controllers can restore the frequency to its nominal value with bounded steady-state error, which can be arbitrarily small by choosing proper control parameters. In addition, the proposed controller can also adjust the real power sharing ratio according to different working conditions. An islanded AC MG test system consisting of 4 distributed generators (DGs) is built in MATLAB Simulink environment and the simulation results validate the effectiveness of proposed decentralized control approach.
Environment Capture and Simulation for UAV Cinematography Planning and Training

This paper presents a workflow for the generation of environmental models, which can be employed for training and planning of drone based shooting. This converts multiple 2D environmental and terrain images into 3D models and height maps, which are then imported into a simulation engine as environment assets. A subjective study has also been conducted to characterise the relationship between reconstruction quality and the number and location of input images for various environmental scenarios. Using this workflow, demonstration videos have been produced which combine extracted environments with an existing object model (cyclist) in simulation. These illustrate its utility in shot planning, rehearsal and training. Index Terms UAV, simulation, 3D reconstruction
Secure Strassen-Winograd Matrix Multiplication with MapReduce

Matrix multiplication is a mathematical brick for solving many real life problems. We consider the Strassen-Winograd algorithm (SW), one of the most efficient matrix multiplication algorithms. Our first contribution is to redesign SW with the MapReduce programming model that allows to process big data sets in parallel on a cluster. Moreover, our main contribution is to address the inherent security and privacy concerns that occur when outsourcing data to a public cloud. We propose a secure approach of SW with MapReduce called S2M3, for Secure Strassen-Winograd Matrix Multiplication with MapReduce. We prove the security of our protocol in a standard security model and provide a proof-of-concept empirical evaluation suggesting its efficiency.
Simulation of Land Use Evolution Based on Cellular Automata and Artificial Neural Network

In order to find out evolution rules of land use structure,data mining was applied to cellular automata(CA) theory to simulate real land use evolution.By establishing an integrated model made up of CA and learning vector quantization network(LVQ network),evolution rules of land cellular were mined and used to invert the land use pattern and predict land cellular status in future.According to integrated CA-LVQ model,evolution rules of land cellular mined out from past land use pattern were considerably coincident with simultaneous social evolution in research area in Shanghai.The result indicates that such model is able to simulate land use structure evolution and greatly shorten required time of establishing evolution rules.
Validation of a vibroacoustic finite element model using bottlenose dolphin simulations: the dolphin biosonar beam is focused in stages

Psychoacoustic laboratory studies with live dolphins require considerable resources and are essential for assessing the validity of our models. Computerized numerical modelling methods are a reasonable approach to simulate the vibroacoustic functions of the dolphin biosonar apparatus. In order to validate this approach, we chose a vibroacoustic finite element model to simulate sound production and sound beam formation in the bottlenose dolphin (Tursiops truncatus), based on computed tomography scans from live and postmortem dolphins. The right and left dorsal bursae were assumed to be potential sound sources. The simulations confirm several hypotheses: (1) the shape of the skull plays a role in the formation of the sound transmission beam; (2) the melon appears to concentrate the acoustic energy by a factor of four in the transmitted beam; (3) focusing the sound beam apparently occurs in a series of stages that include contributions from the skull, nasal diverticula, melon and connective tissue structures. An unexpected result is that adjustments to the focus and direction of the sound beam can result from small (millimetre scale) changes in the relative position of the anterior and posterior bursae within each sound generation complex. Comparing our results with those from dolphin psychoacoustic experiments establishes validation of our vibroacoustic model. The potential for varied effects from anthropogenic sound also emphasizes the importance of developing vibroacoustic modelling. These numerical modelling tools complement experimental data for determining exposure thresholds and may allow us to simulate exposure levels, from moderate to extreme, without impacting live animals.
Reasoning about Dynamic Networks of Infinite-State Processes with Global Synchronization

We propose a generic framework for reasoning about dynamic networks of infinite state processes such as counter processes, timed processes, or pushdown processes, with complex synchronization mechanisms, including global synchronization (i.e., broadcast communication). We define models for such networks, called CTN, based on Petri nets with transfer operations. Tokens (representing occurrences of processes) have attached colors over infinite domains (representing data values, clocks, stacks, etc.). We also define a (second-order) logic called CTSL allowing to express constraints on locations of tokens in the nets and on their colors. We prove that the $\exists^* \forall^*$ fragment of CTSL is decidable whenever the underlying logic for expressing constraints on colors is decidable. Moreover, we show that the same fragment is closed under post and pre image computations. These results can be used in verification such as in invariance checking. We show that our framework can be applied for reasoning about multithreaded programs with procedure calls and dynamic creation of process with global synchronization, and on dynamic programs with real-time constraints.
Performance Analysis of Dynamic Routing Protocols vs. Distance Vector Routing Protocols for Channel Error Rate on Multimedia Traffic.

The objective of the paper is to experimentally analyse the performance metric Peak Signal to noise ratio (PSNR) for varying channel error rates for multimedia (MPEG-4) traffic. The experiment is performed using two classes of routing protocols viz. dynamic routing protocol ie Ad-Hoc On-Demand Distance Vector (AODV)(3) and distance vector routing protocol ie Destination Sequenced Distance Vector (DSDV)(2). Both the classes of routing protocols perform routing differently based on their algorithms which are complicated in case of dynamic routing protocols and relatively simpler in Distance vector. The effect of these routing algorithms not only affects the efficiency but also determines the optimization required for transmission of a particular class of traffic. The paper discusses the effect of these routing protocols in intricate detailing and concludes that simpler technique of routing ie distance vector proves more efficient in case of UDP based multimedia traffic.
Composite reduced-kernel weighted extreme learning machine for imbalanced data classification

In order to solving the problem that the weighted extreme learning machine based on the ensemble learning method enhances the classification performance while increasing the running time of the algorithm, starting from the perspective of multi-core learning, a weighted extreme learning machine based on composite kernel functions and reduced-kernel technique is proposed. The composite kernel function based on Gaussian kernel and Polynomial kernel weighted combination is designed, which effectively improves the classification performance of weighted extreme learning machine. Meanwhile, based on the sample distribution characteristics of the imbalanced dataset, the balanced input sub-matrix is designed to reduce the computational cost of the composite kernel method. The eight binary classification imbalanced datasets of KEEL dataset repository were used for testing. The experimental results show that compared with the original weighted extreme learning machine algorithm, the G- mean and AUC classification performance indicators of the composite reduced-kernel weighted extreme learning machine algorithm are improved in each dataset, and the computation cost is effectively reduced.
Economic Dispatch of Energy Storage System in Micro-grid

Renewable energy and energy storage combined system cannot only realize load transfer, load shifting, energy saving and emission reduction, but also ensure the stability and safety of power grid. Economic dispatch of energy storage system under micro-grid environment is a typical multi-stage stochastic programming problem. The purpose of this paper is to propose an economic dispatch model for the energy storage system satisfying the non-anticipative constraints. The objective function is constructed based on the minimum dispatching cost of the micro-grid, and the accurate mathematical description of the feasible region is given. By solving these problems, we can obtain economic dispatch results of the energy storage system satisfying the non-anticipative constraints. The proposed methods are tested on four different cases, and the results suggest the methods are effective.
Multi-dimensional Bayesian Network Classifiers Based on ICA

Multi-dimensional Bayesian network classifiers( MBCs) are probabilistic graphical models proposed to deal with multi-dimensional classification problems,where each feature variable determines one or more than one class variable. For the problem of high dimensional of feature attributes and information redundancy,the Independent Component Analysis( ICA) is applied to decrease the dimension of feature variable which could completely describe data. Then,we construct a multi-dimensional Bayesian network classifier according to the decreased data. Finally,the performance of the MBCs is proved good by theoretical analysis. The experiment results show that for three benchmark multi-dimensional data sets,the multi-dimensional Bayesian network classifier based on ICA outperforms other algorithms in accuracy.
A constraint based interactive frequent pattern mining algorithm for large databases

Ovel the past decade, man)¡ fÌequeIìt-pattern mining algolithrns have been developed. Horvever, many of them rely on the availability of lar.ge metnory. Their per.formance deglades if the available memoly is limited because of the overhead and extra I/O costs. X4oreover', among the algorithms that mine large databases, many of them do not plovide users control ovel the mining process tht'ough the use of constraints. Constlaint based rrining is very important because it encourages users focus on only those patterns that are intelesting to the users. Further.more, among the algorithms that handle user constraints, many of them do not allot' users to interactively change the rnining paÌameters duling the mining process. As mining is usually an iteÌative process, it is important to have an algorithm that supports constrai,nt based mtnzng and allorvs useß lo ¿nteractiaely mine large d,atabases. In this thesis, rve design and implement a constrâint based interactive mining algolithm, named Inuerted, Matr¿î++, that uses a disk based data str.uctr¡re called inuerted matrix for mining frequent patterns from large databases and constructs a conditional tree called COFI*-tree fol each fi.equent item fr.om the inver.ted matrix. Our algorithm facilitates constraint based mining and interactive mining from large databases. Experimental results shorv the efficiency of our algorithm itr constrained interactive mining from lalge databases.
Needs and Potentials for Studying Local Malaysian Culture through Mobile Learning

Mobile learning is an emerging approach in Asian countries, particularly in the local context, such as the Malaysian universities. This research adds the perspectives of users' experiences to the mobile learning regarding potentials in the study of local culture. The motivation for this research is the limited users' voices -- students for mobile learning -- particularly, their own experiences in learning local cultural studies and the associated potentials. The aim of this study is to investigate if user experience has any mobile learning potentials to assist users in studying local culture. To find in-depth meaning, this research garnered the data from focus group discussions with students. The data were then analyzed using thematic analysis. This research indicates the students' interest in mobile learning demonstrated on the students' perspectives: the desired mobile applications paradigm and usability design concerns.
Motion sickness in a Virtual Reality cycling simulation

Children with DCD have trouble learning skills such as riding a bicycle. The Roessingh Center of Rehabilitation provides these children with extra help. But the step from therapy in a safe practice environment to practicing in traffic was deemed too large by the therapists. A virtual reality cycling simulation was developed to bridge this gap, providing a realistic yet safe practice environment. However, this simulation induces motion sickness. This report describes the process of reducing this effect from this simulation. This process started with consulting scientific literature. A number of potential solutions (changeable aspects of this simulation that have shown to decrease the severity of VR induced motion sickness in different simulations) were deducted from this literature and implemented in the cycling simulation. They were then tested over the course of 2 experiments. First, a small pilot test (N= 7) to collect qualitative data and then a larger experiment (N= 27) to collect both quantitative and qualitative data, which was used to determine which potential solution(s) actually prove effective in decreasing the severity of VR induced motion sickness. Over the course of this project, a total number of 13 solutions was narrowed down to 2 (second experiment). Only one proved consistently effective in reducing VR induced motion sickness, which was to point a fan at the user.
Web-based decision support toolbox for Spanish reservoirs

Abstract. Under the S-ClimWaRe (Seasonal Climate prediction in support of Water Reservoirs management) initiative, a climate service to support decision-making process by water managers in Spanish reservoirs has been developed. It consists in a web-based toolbox jointly designed with stakeholders. The website is organized in two main areas. The first one allows the user to explore, for any water reservoir or grid point over continental Spain, the existing hydrological variability and risk linked to climate variability. This is performed through a set of indicators obtained from time series of hydrological and meteorological observations and North Atlantic Oscillation (NAO) index, identified as main climate driver in this geographical region. The second main area provides seasonal forecasts of NAO and both reservoir inflow and precipitation, complemented by information on probabilistic forecasts skill. Currently the NAO index is the only driver implemented for display, and forecasts come from a statistical forecasting system developed only for the extended winter NDJFM period. Through the MEDSCOPE (MEDiterranean Services Chain based On climate PrEdictions) project new sources of predictability and relationships with different climate drivers will be explored. Forecast skill improvement is expected after the combination and weighting of ensemble members of the Copernicus seasonal forecasting systems. These forecasts will feed more sophisticated hydrological models. The toolbox has been flexible designed with respect to sources of seasonal forecasts and extension to additional drivers, variables and seasons. In this way, user requirements and scientific progress will be easily incorporated to new versions of this climate service.