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The research and development of autonomous vehicle has entered the era of commercialization. While the vehicle self-driving technology has been growing rapidly, the validation for autonomous vehicle in terms of driving model, human factor model and traffic model is still maturing. Most of previous infrastructures are mainly focused on validation of those three models separately resorting either on real driving test at physical infrastructure or software simulation in virtualized infrastructure. However, neither the real driving test can cover all possible scenarios of autonomous driving and human factors, nor the virtualized software simulation can generate a feasible model for practical on/off-road driving. Furthermore, future autonomous transport in smart cities requires comprehensive validation. In order for autonomous vehicles to meet the autonomous transport in such complex traffic environment, an integrated testing and simulation infrastructure has been built targeting the systematic validation for autonomous vehicles: the Multi-User Environment for Autonomous Vehicle Innovation (MUEAVI). A preliminary investigation of a new autonomous vehicle validation infrastructure that can serve a multitude of research projects for smart city is presented.

Proposed is the facilitation of fault-tolerant capability in autonomous systems with particular consideration of low computational complexity and system interface devices (sensor/actuator) performance. Traditionally model-based fault-tolerant/detection units for multiple sensor faults in automation require a bank of estimators, normally Kalman-based ones. An AI-based control framework enabling low computational power fault tolerance is presented. Contrary to the bank-of-estimators approach, the proposed framework exhibits a single unit for multiple actuator/sensor fault detection. The efficacy of the proposed scheme is shown via rigorous analysis for several sensor fault scenarios for an electro-magnetic suspension testbed.

Sensor selection in control design receives substantial interest in the last few years. We disseminate work on Field Programmable Gate Array (FPGA)-based embedded software platform validating a systematic sensor selection framework and target efficient FPGA resource allocation. Sensor selection combines multi-objective optimization, Linear-Quadratic-Gaussian (LQG) control, applied to a Maglev suspension. The nonlinear Maglev model is realized on software platform forming a Hardware-in-the-loop (HIL) as an economic and reliable validation platform for the design setup. The LQG controller was modeled in fixed point, described in Verilog Hardware Description Language (HDL) and tied up with an ethernet core to form an FPGA-in-the-loop system prior to logic synthesis and FPGA place and route. The results illustrate efficient FPGA resource allocation level pertinent to extending to a core sensor fault tolerant scheme.

Virtualized servers have been the key for the efficient deployment of cloud applications. As the application demand increases, it is important to dynamically adjust the CPU allocation of each component in order to save resources for other applications and keep performance high, e.g., the client mean response time (mRT) should be kept below a Quality of Service (QoS) target. In this work, a new form of Kalman filter, called the Maximum Correntropy Criterion Kalman Filter (MCC-KF), has been used in order to predict, and hence, adjust the CPU allocations of each component while the RUBiS auction site workload changes randomly as the number of clients varies. MCC-KF has shown high performance when the noise is non-Gaussian, as it is the case in the CPU usage. Numerical evaluations compare our designed framework with other current state-of-the-art using real-data via the RUBiS benchmark website deployed on a prototype Xen-virtualized cluster.

This work presents a field programmable gate array (FPGA)-based embedded software platform coupled with a software-based plant, forming a hardware-in-the-loop (HIL) that is used to validate a systematic sensor selection framework. The systematic sensor selection framework combines multi-objective optimization, linear-quadratic-Gaussian (LQG)-type control, and the nonlinear model of a maglev suspension. A robustness analysis of the closed-loop is followed (prior to implementation) supporting the appropriateness of the solution under parametric variation. The analysis also shows that quantization is robust under different controller gains. While the LQG controller is implemented on an FPGA, the physical process is realized in a high-level system modeling environment. FPGA technology enables rapid evaluation of the algorithms and test designs under realistic scenarios avoiding heavy time penalty associated with hardware description language (HDL) simulators. The HIL technique facilitates significant speed-up in the required execution time when compared to its software-based counterpart model.

A low computational cost method is proposed for detecting actuator/sensor faults. Typical model-based fault detection (FD) units for multiple sensor faults require a bank of estimators [i.e., conventional Kalman estimators or artificial intelligence (AI)-based ones]. The proposed FD scheme uses an AI approach for developing of a low computational power FD unit abbreviated as $i$ FD. In contrast to the bank-of-estimators approach, the proposed $i$ FD unit employs a single estimator for multiple actuator/sensor FD. The efficacy of the proposed FD scheme is illustrated through a rigorous analysis of the results for a number of sensor fault scenarios on an electromagnetic suspension system.

Background Autonomous control of surgical robotic platforms may offer enhancements such as higher precision, intelligent manoeuvres, tissue-damage avoidance, etc. Autonomous robotic systems in surgery are largely at the experimental level. However, they have also reached clinical application. Methods A literature review pertaining to commercial medical systems which incorporate autonomous and semi-autonomous features, as well as experimental work involving automation of various surgical procedures, is presented. Results are drawn from major databases, excluding papers not experimentally implemented on real robots. Results Our search yielded several experimental and clinical applications, describing progress in autonomous surgical manoeuvres, ultrasound guidance, optical coherence tomography guidance, cochlear implantation, motion compensation, orthopaedic, neurological and radiosurgery robots. Conclusion Autonomous and semi-autonomous systems are beginning to emerge in various interventions, automating important steps of the operation. These systems are expected to become standard modality and revolutionize the face of surgery. Copyright © 2011 John Wiley & Sons, Ltd.

This paper presents a System on Chip (SoC) for the path following task of autonomous non-holonomic mobile robots. The SoC consists of a parameterized Digital Fuzzy Logic Controller (DFLC) core and a flow control algorithm that runs under the Xilinx Microblaze soft processor core. The fuzzy controller supports a fuzzy path tracking algorithm introduced by the authors. The FPGA board hosting the SoC was attached to an actual differential-drive Pioneer 3-DX8 robot, which was used in field experiments in order to assess the overall performance of the tracking scheme. Moreover, quantization problems and limitations imposed by the system configuration are also discussed.

In this work, a Field Programmable Gate Array (FPGA)-based embedded software platform coupled with a software-based plant, forming a Hardware-In-the-Loop (HIL), is used to validate a systematic sensor selection framework. The systematic sensor selection framework combines multi-objective optimization, Linear-Quadratic-Gaussian (LQG) control, and the nonlinear model of a maglev suspension. The physical process that represents the suspension plant is realized in a high-level system modeling environment, while the LQG controller is implemented on an FPGA. FPGAs allow to rapidly evaluate algorithms and test designs under real-world scenarios avoiding heavy time penalty associated with Hardware Description Language (HDL) simulators. Moreover, the HIL technique implemented shows a significant speed-up in the required execution time when compared to the software-based model.

In large networks due to the large number of transmissions it is unlikely that a slot will be used only by a single wireless node. In this paper, a Cutting Plane approach is proposed in order to combine the minimum latency transmission scheduling problem with power expenditure minimization. The problem is partitioned into a Master Problem and a Subproblem; the main objective of the Subproblem is to minimize the total power expenditure. The Cutting Plane algorithm used is evaluated with regard to its performance and the outcome is that the power expenditure is reduced significantly, while achieving the minimum latency transmission scheduling. One of the drawbacks of the proposed approach, however, is the fact that although it reduces the power significantly, the computational time required is quite large, pointing to further research towards that direction.

This paper presents a Hardware-in-the-loop (HIL) approach on a Field Programmable Gate Array (denoted as FPGA-in-the-loop and abbreviated as FIL) for the optimised sensor selection task of controlling a MAGnetic LEVitation (MAGLEV) system. A recently proposed systematic framework for optimised sensor selection for control and fault tolerance is developed in [1]. The framework was validated using realistic simulations on an electromagnetic suspension system. In this work, the sensor selection framework for control, and practical validation of the controller on FPGA (via HIL concept) applied on an electromagnetic suspension are considered.

A low computational power method is proposed for detecting actuators/sensors faults. Typical model-based fault detection units for multiple sensor faults, require a bank of observers (these can be either conventional observers of artificial intelligence based). The proposed control scheme uses an artificial intelligence approach for the development of the fault detection unit abbreviated as `iFD'. In contrast with the bank-of-estimators approach, the proposed iFD unit employs a single estimator for multiple sensor fault detection. The efficacy of the scheme is illustrated on an Electromagnetic Suspension system example with a number of sensor fault scenaria.

The paper describes a low computational power method for detecting sensor faults. A typical fault detection unit for multiple sensor fault detection with modelbased approaches, requires a bank of estimators. The estimators can be either observer or artificial intelligence based. The proposed control scheme uses an artificial intelligence approach for the development of the fault detection unit abbreviated as ‘i-FD’. In contrast with the bank-estimators approach the proposed i-FD unit is using only one estimator for multiple sensor fault detection. The efficacy of the scheme is tested on an Electro-Magnetic Suspension (EMS) system and compared with a bank of Kalman estimators in simulation environment.

This paper describes an improved approach to design a Takagi-Sugeno zero-order type fast parameterized digital fuzzy logic controller (DFLC) processing only the active rules (rules that give a non-null contribution for a given input data set), at high frequency of operation, without significant increase in hardware complexity. To achieve this goal, an improved method of designing the fuzzy controller model is proposed that significantly reduces the time required to process the active rules and effectively increases the input data processing rate. The DFLC discussed in this paper achieves an internal core processing speed of at least 200 MHz, featuring two 8-bit inputs and one 12-bit output, with up to seven trapezoidal shape membership functions per input and a rule base of up to 49 rules. The proposed architecture was implemented in a field programmable gate array (FPGA) chip with the use of a very high-speed integrated-circuits hardware-description-language (VHDL) and advanced synthesis and place and route tools

Fuzzy logic controllers (FLCs) are finding increasing popularity in real industrial applications, especially when the available system models are inexact or unavailable. This paper proposes a zero-order Takagi---Sugeno parameterized digital FLC, processing only the active rules (rules that give a non-null contribution for a given input data set), at high frequency of operation, without significant increase in hardware complexity. To achieve this goal, an improved method of designing the fuzzy controller model is proposed that significantly reduces the time required to process the active rules and effectively increases the input data processing rate. The digital fuzzy logic controller discussed in this paper achieves an internal core processing speed of at least 200 MHz, featuring two 8-bit inputs and one 12-bit output, with up to seven trapezoidal shape membership functions per input and a rule base of up to 49 rules. The proposed architecture was implemented in a field programmable gate array chip with the use of a very high-speed integrated-circuits hardware description language and advanced synthesis and place and route tools.

A 30-year-old woman with thyrotoxicosis sustained an acute inferolateral-wall myocardial infarction complicated by severe mitral regurgitation and left ventricular wall dysfunction. No obvious predisposing factors were present, and coronary arteriography showed normal patent vessels. Coronary embolism or primary thrombosis in situ of the right coronary artery could explain the arteriographic findings. There is an infrequent association between thyrotoxicosis and acute myocardial infarction, and excess thyroid hormone may influence the extent of the myocardial infarct.