Multiple UAV Formations for Cooperative Source Seeking and Contour Mapping of a Radiative Signal Field

In this paper, four scenarios are presented for cooperative source seeking and contour mapping of a radiative signal field by multiple UAV formations. A source seeking strategy is adopted with saturation, and then it is modified to achieve contour mapping of the signal field with the moving source situation considered. A formation controller used for consensus problem is simplified and applied in the scenarios to stabilize the multiple UAV formation flight during source detection. The contour mapping strategy and the formation control algorithm are combined to guarantee stable source seeking and contour mapping in both circular flight path and square flight path via multiple UAV formations.     

Low-power DSP system for real-time correction of fish-eye cameras in automotive driver assistance applications

The development of an embedded system for real-time correction of fish-eye effect is presented. The fish-eye lens is applied to driver assistance video systems because of its wide-angled view. A large field of view can reduce the number of cameras needed for video system and their cost, installation, maintenance and wiring issues. On the other hand, this lens causes inherent radial distortion to image that has to be corrected in real-time with a low-cost and low-power processing platform. This paper proposes a solution that can be easily adapted to different types of lens and camera, and meets real-time constraints with a power budget within 100 mW and a board size of few cm². Starting from mathematical equations, given by the geometrical optics, a state-of-the-art correction method is presented, then optimizations are introduced at different levels: algorithmic level, where a real-time correction parameter calculation avoids extra non-volatile off-chip memory cards; data transfer level, where a new pixel pair management reduces memory access and storage burden; HW-SW implementation level, where a low-power board has been developed and tested in real automotive scenarios. Other applications of the developed system, such as multi-camera and multi-dimensional video systems, are finally presented.     

全源矿井定位:一种智能煤矿位置服务新范式

矿山动目标定位系统为煤矿智能化建设提供基于位置的服务。而传统的矿井定位系统中定位信标与定位标签之间大多采用单一或组合通信技术,结构不灵活,覆盖范围受限,鲁棒性不高,定位精度有限;使用单一或组合测距技术,无法根据使用场景的不同自适应选择最优测距技术;位置求解算法固定,无法根据使用场景的不同和信号变化自适应选择最优求解算法。提出了全源矿井定位新范式,可根据定位环境和目标承载平台的变化,从所有可用定位信号测量设备中动态选择满足当前需求的最优设备组合,自适应地从可用的测距算法、位置解算算法、结果优化算法中选择最优的算法组合,计算出目标节点的最优位置。给出了全源矿井定位系统架构,探讨了矿井中可用的定位信号测量设备和可用于定位的测量属性值类型,对比了单一矿井定位与全源矿井定位在定位服务器方面的功能差异。研究了全源矿井定位中的3项关键技术:统一定位框架技术,旨在解决全源定位的模块化、组合式、可扩充需求;全源定位信息融合技术,对测量属性值进行有机组织,达到提高定位精度的目的;通信定位一体化技术,通过无线通信网络本身所具有的定位能力,简化全源矿井定位系统的设计并提高定位精度。提出了基于“端-边-云”的全源矿井定位系统实施架构,以便与煤矿智能化的主流建设模式相适应,大幅降低实施难度和系统成本。     

Real-time Soundprism

This paper presents a parallel real-time sound source separation system for decomposing an audio signal captured with a single microphone in so many audio signals as the number of instruments that are really playing. This approach is usually known as Soundprism. The application scenario of the system is for a concert hall in which users, instead of listening to the mixed audio, want to receive the audio of just an instrument, focusing on a particular performance. The challenge is even greater since we are interested in a real-time system on handheld devices, i.e., devices characterized by both low power consumption and mobility. The results presented show that it is possible to obtain real-time results in the tested scenarios using an ARM processor aided by a GPU, when this one is present.

     

Robust fault detection of non-linear systems using set-membership state estimation based on constraint satisfaction

In this paper, the robust fault detection problem for non-linear systems considering both bounded parametric modelling errors and measurement noises is addressed. The non-linear system is monitored by using a state estimator with bounded modelling uncertainty and bounded process and measurement noises. Additionally, time-variant and time-invariant system models are taken into account. Fault detection is formulated as a set-membership state estimation problem, which is implemented by means of constraint satisfaction techniques. Two solutions are presented: the first one solves the general case while the second solves the time-variant case, being this latter a relaxed solution of the first one. The performance of the time-variant approach is tested in two applications: the well-known quadruple-tank benchmark and the dynamic model of a representative portion of the Barcelona's sewer network. In both applications, different scenarios are presented: a faultless situation and some faulty situations. All considered scenarios are intended to show the effectiveness of the presented approach.     

Normal-boundary intersection based parametric multi-objective optimization of green sand mould system

In manufacturing engineering optimization, it is often that one encounters scenarios that are multi-objective (where each of the objectives portray different aspects of the problem). Thus, it is crucial for the engineer to have access to multiple solution choices before selecting of the best solution. In this work, a novel approach that merges meta-heuristic algorithms with the Normal Boundary Intersection (NBI) method is introduced. This method then is used generate optimal solution options to the green sand mould system problem. This NBI based method provides a near-uniform spread of the Pareto frontier in which multiple solutions with gradual trade-offs in the objectives are obtained. Some comparative studies were then carried out with the algorithms developed and used in this work and that from some previous work. Analysis on the performance as well as the quality of the solutions produced by the algorithms is presented here.     

A Bayesian solution and its approximations to out-of-sequence measurement problems

Target tracking using delayed, out-of-sequence measurements is a problem of growing importance due to an increased reliance on networked sensors interconnected via complex communication network architectures. In such systems, it is often the case that measurements are received out-of-time-order at the fusion center. This paper presents a Bayesian solution to this problem and provides approximate, implementable algorithms for both cluttered and non-cluttered scenarios involving single and multiple time-delayed measurements. Such an approach leads to a solution involving the joint probability density of current and past target states.In contrast, existing solutions in the literature modify the sensor measurement equation to account for the time delay and explicitly deal with the resulting correlations that arise in the process noise and current target state. In the Bayesian solution proposed in this paper, such cross correlations are treated implicitly. Under linear Gaussian assumptions, the Bayesian solution reduces to an augmented state Kalman filter (AS-KF) for scenarios devoid of clutter and an augmented state probabilistic data association filter (AS-PDA) for scenarios involving clutter. Computationally efficient versions of AS-KF and AS-PDA are considered in this paper. Simulations are presented to evaluate the performance of these solutions.     

On the Design of Safety Communication Systems for Vehicles

Statistics show that the number of casualties due to traffic accidents exceeds one million each year. For the development of systems that prevent vehicle collisions, vehicular communication is considered a promising technology. This paper focuses on design aspects of communication systems that support the development of collaborative active safety systems such as collision warning and collision avoidance. We introduce a design method for safety communication systems that includes a set of analyses and a reasoning system for modeling and analyzing traffic scenarios. An overview of a specific solution for communication is presented in this paper. This solution proposes techniques for network organization and data dissemination that make use of contextual information. This allows the development of a communication system that is adaptable to the specifics of the traffic situation.     

Statistical Cosmological Fermion Systems with Phantom Scalar Interaction of Particles

The article represents a research of the cosmological evolution of fermion statistical systems with phantom scalar interaction where the “kinetic” term’s contribution to the total energy of a scalar field is negative. As a result of analytical and numerical simulation of such systems, we have revealed the existence of four possible scenarios depending on the system’s parameters and the initial conditions. Among them are scenarios with an early, intermediate and late nonrelativistic stages of the cosmological evolution, all of which also have a necessary inflationary stage. This paper contains only a small part of the research results. In a more complete form, they are presented in arXiv: 1608.05020 [1].     

Distributed and adaptive traffic signal control within a realistic traffic simulation

As traffic congestion rises within urban centers around the world, the intelligent control of traffic signals within cities is becoming increasingly important. Previous research within the area of intelligent traffic signal control has several shortcomings, including a reliance on historical data, the use of centralized systems which cannot handle city-sized problem instances and solutions which are not capable of addressing real-world traffic scenarios (e.g., constantly varying volumes and complex network structures). The research reported here proposes algorithms capable of controlling traffic signals that rely on traffic observations made by available sensor devices and local communication between traffic lights. This solution allows signals to be updated frequently to match current traffic demand, while also allowing for significantly large problem sizes to be addressed. To evaluate the developed system, a realistic traffic model was developed using information supplied by the City of Ottawa, Canada. It was found, through simulation within the SUMO traffic simulation environment, that the proposed adaptive system resulted in higher overall network performance when compared to the current fixed signal plan controllers, which were recreated using information from the City of Ottawa. This work also includes examples of why fixed signal controllers are inferior to an adaptive control system.     

基于CPLD实现的数字信号通道滤波方法

在数字系统设计时难免要遇到干扰问题,而可编程器件在电子设计中的广泛应用给我们提供了一个很好的平台。本文介绍了一种基于可编程器件实现的数字信号通道滤波方法,希望能给大家提供借鉴。     

Control and Virtual Reality Simulation of Tendon Driven Mechanisms

In this paper the authors present a control strategy for tendon driven mechanisms. The aim of the control system is to find the correct torques which the motors have to exert to make the end effector describe a specific trajectory. In robotic assemblies this problem is often solved with closed loop algorithm, but here a simpler method, based on a open loop strategy, is developed. The difficulties in the actuation are in keeping the belt tight during all working conditions. So an innovative solution of this problem is presented here. This methodology can be easily applied in real time monitoring or very fast operations. For this reason several virtual reality simulations, developed using codes written in Virtual Reality Markup Language, are also presented. This approach is very efficient because it requires a very low cpu computation time, small size files, and the manipulator can be easily put into different simulated scenarios.     

一种基于阵列信号处理的短波侦察系统的设计及实现

本文研究并实现了一个集宽带搜索、宽带测向、跳频搜索、窄带测向、窄带分析等功能于一体的短波阵列信号侦察系统.该系统采用阵列天线和短波射频信号低通采样的软件无线电结构,结合先进的空间谱估计、波束形成、零点对准等阵列信号处理技术,使新的短波侦察接收机与传统接收机相比,大大改善了整体性能.现场试验表明,本文设计的阵列信号处理系统性能优良、信号处理算法有效.     

Convolutional Neural Network Auto Encoder Channel Estimation Algorithm in MIMO-OFDM System

Higher transmission rate is one of the technological features of prominently used wireless communication namely Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing (MIMO–OFDM). One among an effective solution for channel estimation in wireless communication system, specifically in different environments is Deep Learning (DL) method. This research greatly utilizes channel estimator on the basis of Convolutional Neural Network Auto Encoder (CNNAE) classifier for MIMO-OFDM systems. A CNNAE classifier is one among Deep Learning (DL) algorithm, in which video signal is fed as input by allotting significant learnable weights and biases in various aspects/objects for video signal and capable of differentiating from one another. Improved performances are achieved by using CNNAE based channel estimation, in which extension is done for channel selection as well as achieve enhanced performances numerically, when compared with conventional estimators in quite a lot of scenarios. Considering reduction in number of parameters involved and re-usability of weights, CNNAE based channel estimation is quite suitable and properly fits to the video signal. CNNAE classifier weights updation are done with minimized Signal to Noise Ratio (SNR), Bit Error Rate (BER) and Mean Square Error (MSE).     

A Decision Support System for a small scale industry in a developing country

This paper describes the design and development of a Decision Support System (DSS) for a small scale manufacturing company in a developing country. The DSS provides a solution for an integrated problem of planning, shopfloor scheduling, and control. The DSS system accepts the demand data with priorities, then uses the information from the imbedded data base system regarding product structure, production process, available resources with the status, and inventory. This in turn is used by the imbedded models to solve planning and scheduling problems, and provide solutions containing job order release, shopfloor scheduling and material requirement report. What if analysis feature provided by DSS allows the managers to incorporate their knowledge and experience to improve the quality of the solution by evaluating different scenarios presented by the manager. The laboratory equipment manufactured by the company is made to order. Product information regarding twenty-two high demand items is incorporated into the data base system, and the scheduling problem is solved for these items. The jobshop is a mixed shop problem containing a flow shop, an open shop, and a flow shop. A heuristic method used for solving this problem is presented.     

Numerical integration of partial differential equations using cubic splines

This paper presents techniques for the numerical solution of partial differential equations using cubic spline collocation.

The main spline relations are presented and incorporated into solution procedures for partial differential equations. The computational algorithm in every case is a tridiagonal matrix system amenable to efficient inversion methods. Truncation errors and stability are briefly discussed. Finally, some examples of their application to parabolic and hyperbolic systems with mixed boundary conditions are presented.

The results obtained are encouraging and justify further research in this field.     

Design and validation of knowledge acquisition tools in a business domain

This paper presents an approach for the design and the validation of a prototype knowledge acquisition tool in the domain of business planning. Results from previous work in the area of problem-solving in business domain indicate that there are wide differences in both the ways problems are represented, and solution strategies are selected. These differences can have a significant effect on the suitability of knowledge acquisition techniques. The knowledge acquisition tool has been designed to accommodate these differences. Problem decomposition and simplification techniques are employed by the tool in order to elicit the appropriate information for managerial decision making. The prototype tool has been validated in the field with 35 managers using ten test scenarios. The results of the validation process are presented, and implications for the design of such tools in business domain are discussed.     

Hybrid heuristic-waterfilling game theory approach in MC-CDMA resource allocation

This paper discusses the power allocation with fixed rate constraint problem in multi-carrier code division multiple access (MC-CDMA) networks, that has been solved through game theoretic perspective by the use of an iterative water-filling algorithm (IWFA). The problem is analyzed under various interference density configurations, and its reliability is studied in terms of solution existence and uniqueness. Moreover, numerical results reveal the approach shortcoming, thus a new method combining swarm intelligence and IWFA is proposed to make practicable the use of game theoretic approaches in realistic MC-CDMA systems scenarios. The contribution of this paper is twofold: (i) provide a complete analysis for the existence and uniqueness of the game solution, from simple to more realist and complex interference scenarios; (ii) propose a hybrid power allocation optimization method combining swarm intelligence, game theory and IWFA. To corroborate the effectiveness of the proposed method, an outage probability analysis in realistic interference scenarios, and a complexity comparison with the classical IWFA are presented.     

A generic stochastic model for supply-and-return network design

This paper presents a generic stochastic model for the design of networks comprising both supply and return channels, organized in a closed loop system. Such situations are typical for manufacturing/re-manufacturing type of systems in reverse logistics. The model accounts for a number of alternative scenarios, which may be constructed based on critical levels of design parameters such as demand or returns. We describe a decomposition approach to this model, based on the branch-and-cut procedure known as the integer L-shaped method. Computational results in an illustrative numerical setting show a consistent performance efficiency of the method. Moreover, the stochastic solution features a significant improvement in terms of average performance over the individual scenario solutions. A modeling and solution methodology as presented here can contribute to the efficient solution of network design models under uncertainty for reverse logistics.