Model-based PI–fuzzy control of four-wheeled omni-directional mobile robots

The purpose of this study is to suggest and examine a PI–fuzzy path planner and associated low-level control system for a linear discrete dynamic model of omni-directional mobile robots to obtain optimal inputs for drivers. Velocity and acceleration filtering is also implemented in the path planner to satisfy planning prerequisites and prevent slippage. Regulated drivers’ rotational velocities and torques greatly affect the ability of these robots to perform trajectory planner tasks. These regulated values are examined in this research by setting up an optimal controller. Introducing optimal controllers such as linear quadratic tracking for multi-input–multi-output control systems in acceleration and deceleration is one of the essential subjects for motion control of omni-directional mobile robots. The main topics presented and discussed in this article are improvements in the presented discrete-time linear quadratic tracking approach such as the low-level controller and combined PI–fuzzy path planner with appropriate speed monitoring algorithm such as the high-level one in conditions both with and without external disturbance. The low-level tracking controller presented in this article provides an optimal solution to minimize the differences between the reference trajectory and the system output. The efficiency of this approach is also compared with that of previous PID controllers which employ kinematic modeling. Utilizing the new approach in trajectory-planning controller design results in more precise and appropriate outputs for the motion of four-wheeled omni-directional mobile robots, and the modeling and experimental results confirm this issue.     

Trust-based minimum cost opportunistic routing for Ad hoc networks

Recently, opportunistic routing has received much attention as a new design direction. It can exploit the wireless broadcast and more highly reliable opportunistic forwarding, so as to substantially increase the throughput of network. Due to dynamic topology, distributed collaboration, limited bandwidth and computing ability, the absence of enough physical protection in Ad hoc networks, opportunistic routing is vulnerable to attacks by malicious nodes. In order to alleviate the malicious behaviors, we incorporate the concept of trust to Ad hoc networks, build a simple trust model to evaluate neighbors’ forwarding behavior and apply this model to opportunistic routing for Ad hoc networks. A new trusted opportunistic forwarding model is proposed by choosing the trusted and highest priority candidate forwarder, then a trusted minimum cost routing algorithm (MCOR) is formally formulated, the correctness and effectiveness of this algorithm from theoretical analysis are also approved. Finally, MCOR algorithm is verified by simulation using nsclick software and compared its performance with the classic protocols: ExOR, TAODV and Watchdog-DSR. The simulation results show that MCOR scheme can detect and mitigate node misbehaviors. Furthermore, MCOR scheme outperforms the other protocols in terms of: throughput, delay, Expected ETX, security-gains and cost of routing.     

An energy-efficient mobile transaction processing method using random back-off in wireless broadcast environments

Broadcast is widely accepted as an efficient technique for disseminating data to a large number of mobile clients over a single or multiple channels. Due to the limited uplink bandwidth from mobile clients to server, conventional concurrency control methods cannot be directly applied. There has been many researches on concurrency control methods for wireless broadcast environments. However, they are mostly for read-only transactions or do not consider exploiting cache. They also suffer from the repetitive aborts and restarts of mobile transactions when the access patterns of mobile transactions are skewed. In this paper, we propose a new optimistic concurrency control method suitable for mobile broadcast environments. To prevent the repetitive aborts and restarts of mobile transactions, we propose a random back-off technique. To exploit the cache on mobile clients, our method keeps the read data set of mobile transactions and prefetches those data items when the mobile transactions are restarted. As other existing optimistic concurrency control methods for mobile broadcast environments does, it works for both read-only and update transactions. Read-only transactions are validated and locally committed without using any uplink bandwidth. Update transactions are validated with forward and backward validation, and committed after final validation consuming a small amount of uplink bandwidth. Our performance analysis shows that it significantly decreases uplink and downlink bandwidth usage compared to other existing methods.     

Effective segmentation and classification for HCC biopsy images

Accurate grading for hepatocellular carcinoma (HCC) biopsy images is important to prognosis and treatment planning. In this paper, we propose an automatic system for grading HCC biopsy images. In preprocessing, we use a dual morphological grayscale reconstruction method to remove noise and accentuate nuclear shapes. A marker-controlled watershed transform is applied to obtain the initial contours of nuclei and a snake model is used to segment the shapes of nuclei smoothly and precisely. Fourteen features are then extracted based on six types of characteristics for HCC classification. Finally, we propose a SVM-based decision-graph classifier to classify HCC biopsy images. Experimental results show that 94.54% of classification accuracy can be achieved by using our SVM-based decision-graph classifier while 90.07% and 92.88% of classification accuracy can be achieved by using k-NN and SVM classifiers, respectively.     

An efficient accelerator for attribute reduction from incomplete data in rough set framework

Feature selection (attribute reduction) from large-scale incomplete data is a challenging problem in areas such as pattern recognition, machine learning and data mining. In rough set theory, feature selection from incomplete data aims to retain the discriminatory power of original features. To address this issue, many feature selection algorithms have been proposed, however, these algorithms are often computationally time-consuming. To overcome this shortcoming, we introduce in this paper a theoretic framework based on rough set theory, which is called positive approximation and can be used to accelerate a heuristic process for feature selection from incomplete data. As an application of the proposed accelerator, a general feature selection algorithm is designed. By integrating the accelerator into a heuristic algorithm, we obtain several modified representative heuristic feature selection algorithms in rough set theory. Experiments show that these modified algorithms outperform their original counterparts. It is worth noting that the performance of the modified algorithms becomes more visible when dealing with larger data sets.     

Group search optimizer based optimal location and capacity of distributed generations

This paper presents a novel efficient population-based heuristic approach for optimal location and capacity of distributed generations (DGs) in distribution networks, with the objectives of minimization of fuel cost, power loss reduction, and voltage profile improvement. The approach employs an improved group search optimizer (iGSO) proposed in this paper by incorporating particle swarm optimization (PSO) into group search optimizer (GSO) for optimal setting of DGs. The proposed approach is executed on a networked distribution system—the IEEE 14-bus test system for different objectives. The results are also compared to those that executed by basic GSO algorithm and PSO algorithm on the same test system. The results show the effectiveness and promising applications of the proposed approach in optimal location and capacity of DGs.     

A mean field annealing approach to accurate free form shape matching

The SoftAssign algorithm is an elegant free form shape matching algorithm. While its objective function can be interpreted as consisting of three desired terms: minimising a weighted sum of matching errors of combinations of all the points in the two free form shapes to be matched, equalising their weights (probabilities) of being real ones and also maximising the overlapping area between the free form shapes to be matched, the last term has no effect on the optimisation of the parameters of interest due to normalisation. In this paper, we reformulate the last two terms using the inequality about the geometric and algebraic averages and the sum of the powers of these probabilities. For the sake of computational efficiency, instead of considering combinations of all the points in the overlapping free form shapes to be matched, we employ the traditional closest point criterion to establish possible correspondences between the two overlapping free form shapes to be matched. The saddle point solution of the resulting objective function no longer yields a closed form solution to the parameters of interest. For easy computation, we then adopt a pseudo-linearisation method to linearise the first order derivative of the objective function, leading the parameters of interest to be tracked and estimated with a closed form solution. The parameters of interest are finally optimised using the efficient deterministic annealing scheme with the camera motion parameters estimated using the quaternion method in the weighted least squares sense. A comparative study based on both synthetic data and real images with partial overlap has shown that the proposed algorithm is promising for the automatic matching of overlapping 3D free form shapes subject to a large range of motions.     

Multiple costs based decision making with back-propagation neural networks

The current research investigates a single cost for cost-sensitive neural networks (CNN) for decision making. This may not be feasible for real cost-sensitive decisions which involve multiple costs. We propose to modify the existing model, the traditional back-propagation neural networks (TNN), by extending the back-propagation error equation for multiple cost decisions. In this multiple-cost extension, all costs are normalized to be in the same interval (i.e. between 0 and 1) as the error estimation generated in the TNN. A comparative analysis of accuracy dependent on three outcomes for constant costs was performed: (1) TNN and CNN with one constant cost (CNN-1C), (2) TNN and CNN with two constant costs (CNN-2C), and (3) CNN-1C and CNN-2C. A similar analysis for accuracy was also made for non-constant costs; (1) TNN and CNN with one non-constant cost (CNN-1NC), (2) TNN and CNN with two non-constant costs (CNN-2NC), and (3) CNN-1NC and CNN-2NC. Furthermore, we compared the misclassification cost for CNNs for both constant and non-constant costs (CNN-1C vs. CNN-2C and CNN-1NC vs. CNN-2NC). Our findings demonstrate that there is a competitive behavior between the accuracy and misclassification cost in the proposed CNN model. To obtain a higher accuracy and lower misclassification cost, our results suggest merging all constant cost matrices into one constant cost matrix for decision making. For multiple non-constant cost matrices, our results suggest maintaining separate matrices to enhance the accuracy and reduce the misclassification cost.     

Two-stage image denoising by principal component analysis with local pixel grouping

This paper presents an efficient image denoising scheme by using principal component analysis (PCA) with local pixel grouping (LPG). For a better preservation of image local structures, a pixel and its nearest neighbors are modeled as a vector variable, whose training samples are selected from the local window by using block matching based LPG. Such an LPG procedure guarantees that only the sample blocks with similar contents are used in the local statistics calculation for PCA transform estimation, so that the image local features can be well preserved after coefficient shrinkage in the PCA domain to remove the noise. The LPG-PCA denoising procedure is iterated one more time to further improve the denoising performance, and the noise level is adaptively adjusted in the second stage. Experimental results on benchmark test images demonstrate that the LPG-PCA method achieves very competitive denoising performance, especially in image fine structure preservation, compared with state-of-the-art denoising algorithms.     

Electrochemical performances of B doped and undoped diamond-like carbon (DLC) films deposited by femtosecond pulsed laser ablation for heavy metal detection using square wave anodic stripping voltammetric (SWASV) technique

Pure diamond-like carbon (DLC) thin films and boron-doped DLC thin films have been deposited on silicon substrates using femtosecond pulsed laser. The amorphous carbon materials (DLC), have been deposited at room temperature by ablating graphite targets with an amplified Ti:sapphire laser of 800 nm wavelength and a pulse duration of 150 fs in high vacuum conditions. Doping with boron has been performed by ablating alternatively graphite and boron targets.In this study, the DLC films were used as working electrodes for the electrochemical detection of trace heavy metals namely, Cd²⁺, Pb²⁺, Ni²⁺ and Hg²⁺, by using square wave anodic stripping voltammetry (SWASV) technique. Four metals were detected at −1.3 V deposition potential, and 90 s deposition time. The DLC films have been characterized by multiwavelength Raman spectrometry and high resolution scanning electron microscopy. The effect of the boron doping on the electrochemical behavior has been shown. The a-C:B 8%/Si₃N₄ electrode gives the more sensitive detection. The four metals are detected simultaneously with a detection limit of 1 μg/L or 2 μg/L and a dynamic range from 1 or 2 to 25 μg/L for every metal, as presented in third table of this article. The different sensitivities obtained are 6.2, 20.0, 1.2 and 6.6 μA/ppb or μA μg⁻¹ L for Cd²⁺, Pb²⁺, Ni²⁺ and Hg²⁺, respectively.