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This paper presents an iterative, yet effective minimum mean square error (MMSE) space-time multiuser detector (MUD) with the assistance of soft interference to enhance the performance in the multipath code division multiple access (CDMA) systems. The proposed space-time MUD is of rank one and comprises of a spatial filter and a temporal filter. To offset the performance loss due to such a constraint, the new MUD alternatively updates the spatial filter or the temporal filter with the soft interference cancellation in between so as to refine the received signal. Computer simulations show that the new MUD can drastically enhance the performance compared to previous works with modest computations, especially in highly loaded scenarios.

This paper reviews the educational program development of alternative energy technology at Wayne State University and specifically describes the development and implementation of a set of 2+2+2 programs in electric drive vehicle engineering and technology, including a Master's Degree in Electric-drive Vehicle Engineering (EVE), a Bachelor's Degree in Electrical Transportation Technology (ETT), and Associate's Degrees in Automotive Technology and Electronic Engineering Technology with emphasis on electric-drive vehicles. The programs also include certificates with Electric Vehicle Technology courses, an undergraduate concentration and a graduate certificate program in EVE, and to simultaneously provide for general public and consumer education. The programs aim to prepare our current and future workforce with the education and skills necessary for the advancement and maintenance of electric vehicles (EV), plug-in hybrid electric vehicles (PHEV), and fuel cell vehicles (FCV).

This paper presents the design and the analysis of a "fish-like underwater robot". In order to develop swimming robot like a real fish, extensive hydrodynamic analysis were made followed by the study of biology of the fishes especially its maneuverability and propel styles. Swimming mode is achieved by mimicking fish-swimming of carangiform. This is the swimming mode of the fast motion using its tail and peduncle for propulsion. In order to generate configurations of vortices that gives efficient propulsion, yawing and surging with a caudal fin is applied, and in order to submerge and maintain the body balance, pitching and heaving motion with a pair of pectoral fin is used. We have derived the equation of motion of PoTuna by two methods. In first method, we use the equation of motion of underwater vehicle with the potential flow theory for the power of propulsion. In second method, we apply the method of the equation of motion of UVM (underwater vehicle-manipulator). Then, we compared these results.

The aim of this paper is to introduce a novel phase-based feature representation for robust speech recognition. This method consists of four main parts: autoregressive (AR) model extraction, group delay function (GDF) computation, compression, and scale information augmentation. Coupling GDF with an AR model results in a high-resolution estimate of the power spectrum with low frequency leakage. The compression step includes two stages similar to MFCC without taking a logarithm of the output energies. The fourth part augments the phase-based feature vector with scale information which is based on the Hilbert transform relations and complements the phase spectrum information. In the presence of additive and convolutional noises, the proposed method has led to 15% and 12% reductions in the averaged error rates, respectively (SNR ranging from 0 to 20 dB), compared to the standard MFCCs.

In this paper we propose a Sliding Mode Controller for a class of scalar bilinear systems with delay in both the input and the state. Such a class is considered since it has shown to be suitable for modelling and control of a class of turbulent flow systems. The stability and robustness analysis for the reaching phase in the controlled system are Lyapunov-based. However, since the sliding dynamics is infinite dimensional and described by an integral equation, we show that the stability and robustness analysis is simplified by using Volterra operator theory.

There is a universal problem of high professional demand in data cleaning of professional field demanding. According to this problem, this paper improves data cleaning, and it puts forward a method association mining data cleaning on this basis. Finally, this paper verifies the effectiveness of the method by an example.

The high energy consumption has become one bottleneck in the development of the data centers (DCs), where the main energy consumers are the cooling system and the servers. Therefore, the joint optimization for the energy efficiency of the cooling system and the servers is a crucial problem, while most of previous works on energy saving only studies one of these two components in an isolated manner. In this paper, we propose a real-time strategy, rTCS (real-time Task Classification and Scheduling strategy), to jointly optimize the energy efficiency of these two components in the scenario where the tasks arrive dynamically. Strategy rTCS first labels the tasks to classify them according to their run time and end time with a time complexity of O(1) and a bounded space complexity. Then, rTCS schedules the tasks in real time based on their labels and the energy consumption model of the DC. Simulation results show that rTCS can effectively improve the energy efficiency of DCs.

Reliability and fault-tolerance are essential requirements of critical, autonomous computing systems. In this paper, we propose a methodology to quantify, and maximize, the reliability of computation in the presence of transient errors when considering the mapping of real-time tasks on an homogeneous multiprocessor system with voltage and frequency scaling capabilities. As the likelihood of transient errors due to radiation is environment- and component-specific, we use machine learning to estimate the actual fault-rate of the system. Furthermore, we leverage probability theory to define a trade-off between power consumption and fault-tolerance. If a processing element fails, our methodology is able to re-map the application, establishing whether the real-time requirements will still be met, and how reliable the new, impaired system will be. Results show that the proposed methodology is able to adjust mapping and operating frequencies in order to maintain a fixed level of reliability for different fault-rates.

Due to the existing cryptographic schemes based on the satisfiability problem (SAT) lack the specific security model and the security analysis of the provable security theory, the security is questioned. For this problem, combining with the advantages of SAT and Elgamal scheme, we construct a secure, verifiable encryption scheme in this paper. Firstly, combining and improving the SRR(N, k, s) model based on the difficulty problem of the random regular formula near the phase transition point, we construct an encryption algorithm. Secondly, on the basis of the existing Damgârd Elgamal scheme, a more secure scheme based on SAT and variants of discrete logarithm problem is proposed. Lastly, the analysis results show that compared with the existing SAT-based hybrid cryptographic schemes, the private key of the hybrid encryption scheme proposed in this paper is shorter and the ciphertext satisfies the verifiability, and the scheme can secure against adaptive chosen ciphertext attacks (CCA2).

The paper studies the robust control problem of discrete-time systems with state saturation. First the paper introduces an ellipsoid and supposes that the invariant set of the system contains the ellipsoid. By using matrix norm theory we obtain one sufficient condition of the stability of the discrete-time system with state saturation. The attraction domain of the system is estimated. Then the paper researches the stability of the uncertain system with state saturation. Finally a numerical example is presented to demonstrate the result of the paper.

This paper presents a comparative assessment of Pole Placement and Linear Quadratic Regulator to control a balancing of two wheels mobile robot. Disturbance is applied to to test the balancing of the robot. The mathematical model of this robot which is highly nonlinear is derived. The final model is then represented in state space form using MATLAB/Simulink application. Simulation on MATLAB application is analyzed and the performance of position, speed, angle, and angle rate of the balancing robot are observed to find out the best controller for two wheels balancing robot model.

Electric potential difference method is widely used for diagnosing and monitoring flow conduits. We study the prospect of the method to evaluate pipe wall thinning. Based on our previous work on non-intrusive resistance thermometer for fast breeder reactor, this paper describes theoretical analysis and the 3-D exact solution of the electrical potential field in pipe wall. Complex potentials are applied for 2-D approximation of the potential on the wall surface to evaluate the pipe wall thinning.

This paper considers the characteristic mode analysis (CMA) of arbitrary electromagnetic structures using the comprehensive 3D electromagnetic field solver, FEKO [1]. The theory of characteristic modes, as presented in [2], is used to derive the real orthogonal current modes. These modes are obtained by solving a generalised symmetric eigenvalue problem defined by the real and imaginary parts of the Method-of-Moments (MoM) impedance matrix. The research presented in this article discusses the techniques used in FEKO to solve this generalised eigenproblem. Furthermore, paralleli-sation using both distributed and shared memory programming models, as well as GPU computation is considered within the FEKO framework to accelerate the CMA.

Enabling software systems to adjust their protection in continuously changing environments with imperfect context information is a grand challenging problem. The issue of uncertain reasoning based on imperfect information has been overlooked in traditional logic programming with classical negation when applied to dynamic systems. This paper sketches a non-monotonic approach based on Answer Set Programming to reason with imperfect context data in adaptive security where there is little or no knowledge about certainty of the actions and events.

This paper presents comparative study of palmprint matching algorithms for low resolution and noisy images. The principal lines and wrinkles are the only features easily extractable under low resolution. Therefore widely used Radon based approach has been studied and presented in this paper. Based on the computational complexity and fast matching, four different approaches are studied.

Malignant melanomas are the most deadly type of skin cancers however detected early enough give a high chances for successful treatment. The last years saw the dynamic growth of interest of automatic computer-aided skin cancer diagnosis. Every month brings new research results on new approaches to this problem, new methods of preprocessing, new classifiers, new ideas to follow etc. In particular, the rapid development of dermatoscopy, image processing methods, as well as the ever-increasing computing power of computers caused that researchers are able to consider significantly more features of the analyzed lesion than has been done so far using methods recognized in a medical community such as ABCD or Menzies methods. From the other hand more features not always imply an improvement in terms of efficiency of the diagnosis and its transparency. Hence, in this paper we survey the kind of features taken into account by the researchers and then, selected the most efficient set of them. Proposed method jointly selects the optimal set of features representing the analyzed lesion together with the accompanying form of the neural classifier (number of neurons, activation functions). The evolutionary algorithms are used in order to carry out the optimization. Obtained results are even better than the ones obtained by the most efficient these days deep classifiers.

In this paper we introduce the concept of Adaptive Orthogonal Frequency Division Multiplexing (AOFDM) for two-way relay systems. In order to implement AOFDM at the nodes, the Channel State Information (CSI) is required. Usually CSI is estimated and hence the effect of estimation error should be considered while implementing AOFDM. In the case of two-way relay, channel estimation at the nodes usually result in the overall channel between the source and relay. But for implementing AOFDM, we require the individual channel between node and relay. We propose a simple method to obtain the individual channel assuming that the estimate of combined channel is available. The loading algorithm used is the Levin-Campello loading algorithm. But the general frame work provided by us can be used to implement other loading algorithms for two-way relay systems.

Deep neural networks (DNNs) are successful in many computer vision tasks. However, the most accurate DNNs require millions of parameters and operations, making them energy, computation and memory intensive. This impedes the deployment of large DNNs in low-power devices with limited compute resources. Recent research improves DNN models by reducing the memory requirement, energy consumption, and number of operations without significantly decreasing the accuracy. This paper surveys the progress of low-power deep learning and computer vision, specifically in regards to inference, and discusses the methods for compacting and accelerating DNN models. The techniques can be divided into four major categories: (1) parameter quantization and pruning, (2) compressed convolutional filters and matrix factorization, (3) network architecture search, and (4) knowledge distillation. We analyze the accuracy, advantages, disadvantages, and potential solutions to the problems with the techniques in each category. We also discuss new evaluation metrics as a guideline for future research., Accepted for publication at 2020 IEEE 6th World Forum on Internet of Things (WF-IoT), New Orleans, LA, USA 2020

In recent years, chaos-based image cipher has been widely studied and a growing number of schemes based on permutation-substitution architecture have been proposed. To better meet the challenge of real-time secure image communication applications, this paper suggests a new image encryption scheme using a parallel substitution. In the permutation stage, the Arnold cat map is employed to shuffle the pixel positions so as to erase the strong relationship between adjacent pixels. In the substitution stage, the scrambled image is firstly decomposed into eight bit-planes which are then parallelly mixed with keystreams generated by the chaotic logistic map. Theoretically, the parallel substitution strategy runs eight times faster than the serial strategy on an 8-thread processor as the volume of data processed by each substitution unit is 1/8 of that of the input image. Experimental results show that the proposed parallel scheme runs more than five times faster than the serial scheme. Extensive security analysis is carried out with detailed analysis, demonstrating the satisfactory security of the proposed scheme.

The traditional teaching model of assets evaluation course generally emphasizes the theoretical knowledge impartment, lacking the cultivation of students' ability to solve practical problems. Therefore, it is difficult to satisfy the society needs of people with professional assets evaluation talent. Case teaching is a kind of heuristic teaching method, which places emphasis on the development of the students' spirit of innovation and practical ability. On the basis of characteristics of the assets evaluation course such as theoretical, technical, practical and operational, the implementation of case teaching of assets evaluation course plays a great realistic role in the development of the students' spirit of innovation and practical ability. Taking the characteristics of assets evaluation course for entry points, this paper discusses the meaning and the new teaching model of the case teaching of assets evaluation, providing a probability of wider application of case teaching of assets evaluation.

Particle swarm optimization (PSO) is a nature-inspired technique for solving continuous optimization problems. For a fixed optimization problem, the quality of the found solution depends significantly on the choice of the algorithmic PSO parameters such as the inertia weight and the acceleration coefficients. It is a challenging task to choose appropriate values for these parameters by hand or mathematically. In this paper, a novel self-optimizing particle swarm optimizer with multiple adaptation layers is introduced. In the new algorithm, adaptation takes place on both particle and subswarm level. The new idea of using virtual parameter swarms which hold modifiable parameter configurations each is introduced. The algorithmic PSO parameters can be mutated by using, for instance, well-known techniques from the field of evolutionary algorithms, in order to allow fine-granular parameter adaptation to the problem at hand. The new algorithm is experimentally evaluated, and compared to a standard PSO and the Tribes algorithm. The experimental study shows that our new algorithm is highly competitive to previously suggested approaches.

The Probabilistic Index Map (PIM) model was originally proposed for video processing to extract background of video frames. In this paper, we introduce the PIM model for texture segmentation. We first extract texture features based on Laws and Gabor filters respectively. Then we present a Fuzzy K-Means method to generate the index map and palette, and use the PIM model to improve the segmentation accuracy. Based on the comparison of experimental results produced using different features and different resolutions, we show the proposed method is effective for texture segmentation.

Cerebral aneurysm is a cerebrovascular disorder caused by a weakness in the wall of an artery or vein, which causes a localized dilation or ballooning of the blood vessel. It is life-threatening; hence, an early and accurate diagnosis would be a great aid to medical professionals in making the correct choice of treatment. HemeLB is a massively parallel Lattice-Boltzmann simulation software, which is designed to provide the radiologist with estimates of flow rates, pressures, and shear stresses throughout the relevant vascular structures, intended to eventually permit greater precision in the choice of therapeutic intervention. However, in order to allow surgeries and doctors to view and visualize the results in real-time at medical environments, a cost-efficient, practical platform is needed. In this article, we have developed and evaluated a version of HemeLB on various heterogeneous system-on-chip platforms, allowing users to run HemeLB on a low-cost embedded platform and to visualize the simulation results in real-time. A comprehensive evaluation of implementation on the Zynq SoC and Jetson TX1 embedded graphic processing unit platforms are reported. The achieved results show that the proposed Jetson TX1 implementation outperforms the Zynq implementation by a factor of 19 in terms of site updates per second. 2007-2012 IEEE. Manuscript received February 19, 2019; revised September 20, 2019; accepted November 4, 2019. Date of publication December 2, 2019; date of current version June 3, 2020. This paper was supported by the National Priorities Research Program (NPRP) under Grant 5-792-2-328 from the Qatar National Research Fund (a member of Qatar Foundation). X. Zhai is partially supported by the UK Engineering and Physical Sciences Research Council under Grants EP/R02572X/1 and EP/P017487/1. (Corresponding author: Xiaojun Zhai.) X. Zhai is with the School of Computer Science and Electronic Engineering, University of Essex, CO4 3SQ Colchester, U.K. (e-mail: xzhai@essex.ac.uk). Scopus