The electrification of the transportation system is double-edged for the smart grid. Although it is green and eco-friendly, uncontrolled charging of electric vehicles (EVs) could cause not only distribution network congestion but also long queues at the charging stations. Hence, it is necessary to ensure existing charging resources are efficiently utilized. The main objective of this article is to develop an algorithm that assigns EVs to charge stations such that distribution network congestion and time spent by the user from requesting a charging service to accessing it is minimized. The Lyapunov function is utilized for developing an EV assignment algorithm to manage a dynamic population of EVs ensuring queuing stability. Moreover, as the EV assignment algorithm relies on the communication network, an intrusion cyber-attack can occur resulting in an unstable queuing system. An intrusion detection technique is, hence, proposed which utilizes existing transportation network sensor data with the EV charging stations operator information to detect such attacks. A case study using the IEEE 69 bus system is then developed to test the proposed framework.

Line current differential relays (LCDRs) detect faults accurately and promptly, by comparing all currents flowing into the line. This type of relay has been identified in the literature as a reliable protection for lines in DC microgrids (MGs). LCDRs, however, lack the required resiliency against cyber intrusions, such as false data injection attacks (FDIAs) and time synchronization attacks (TSAs), due to their high dependence on communication infrastructure and/or the Global Positioning System (GPS). This paper first introduces coordinated attacks-i.e., several almost-simultaneous FDIAs or TSAs that are carried out independently to achieve a specific objective-as potential threats for MGs. Then, through a case study, it shows how coordinated FDIAs and TSAs can initiate a sequence of events that result in instability of an entire MG. Afterwards, an approach is presented to detect FDIAs and TSAs, and to distinguish them from real faults. The proposed method is comprised of passive oscillator circuits (POCs) installed in series with each converter. During faults, the resultant RLC circuit causes the POCs to resonate and generate a damped sinusoidal component with a specific frequency, i.e., fd. However, fdis not generated during FDIAs and TSAs, since unlike faults, which are physical events that trigger the natural frequencies of a system, cyber-attacks happen in the cyber layer without provoking natural frequencies of the physical layer. Thus, an LCDR pickup without detecting fddenotes an FDIA or a TSA. Since fdis locally measured and analyzed by each LCDR, the proposed detection approach cannot be targeted by cyber-attacks. The proposed method is evaluated on a simulated ±2.5 kV DC MG. Numerical analysis confirms that the proposed method (i) is system-independent; (ii) detects FDIAs and TSAs in less than 1 ms; (iii) is sensitive to high-resistance faults; (iv) can determine fault types, and (v) reduces faults' peak currents.

There has been a growing interest in image fusion technologies, but how to objectively evaluate the quality of fused images has not been fully understood. Here, we propose a method for objective quality assessment of multiexposure multifocus image fusion based on the evaluation of three key factors of fused image quality: 1) contrast preservation; 2) sharpness; and 3) structure preservation. Subjective experiments are conducted to create an image fusion database, based on which, performance evaluation shows that the proposed fusion quality index correlates well with subjective scores, and gives a significant improvement over the existing fusion quality measures.

Sharpness is an important determinant in visual assessment of image quality. The human visual system is able to effortlessly detect blur and evaluate sharpness of visual images, but the underlying mechanism is not fully understood. Existing blur/sharpness evaluation algorithms are mostly based on edge width, local gradient, or energy reduction of global/local high frequency content. Here we understand the subject from a different perspective, where sharpness is identified as strong local phase coherence (LPC) near distinctive image features evaluated in the complex wavelet transform domain. Previous LPC computation is restricted to be applied to complex coefficients spread in three consecutive dyadic scales in the scale-space. Here we propose a flexible framework that allows for LPC computation in arbitrary fractional scales. We then develop a new sharpness assessment algorithm without referencing the original image. We use four subject-rated publicly available image databases to test the proposed algorithm, which demonstrates competitive performance when compared with state-of-the-art algorithms.

Sharpness is one of the most determining factors in the perceptual assessment of image quality. Objective image sharpness measures may play important roles in the design and optimization of visual perception-based auto-focus systems and image enhancement, restoration and compression algorithms. Here we propose a new sharpness measure where sharpness is identified as strong local phase coherence evaluated in the complex wavelet transform domain. Our test using the LIVE blur database shows that the proposed algorithm correlates well with subjective quality evaluations. An additional advantage of our approach is that other image distortions such as compression, median filtering and noise contamination that may affect perceptual sharpness can also be detected.

The major challenges in automatic multi-sensor image registration are the inconsistency in intensity or contrast patterns, and the existence of partial or missing information between images. Here we propose a novel image registration method based on local phase coherence features, which are insensitive to changes in intensity or contrast. Furthermore, a new objective function based on weighted mutual information is proposed, where less weight is given to the objects that have no correspondence between images. The proposed method has been tested on both synthetic and medical images and evaluated based on registration accuracy. Our experiments demonstrate good performance of the proposed approach with missing or partial data, with significant changes in contrast, and with the presence of noise.

Evolutionary algorithms (EAs) are well-known optimization approaches to deal with nonlinear and complex problems. However, these population-based algorithms are computationally expensive due to the slow nature of the evolutionary process. This paper presents a novel algorithm to accelerate the differential evolution (DE). The proposed opposition-based DE (ODE) employs opposition-based learning (OBL) for population initialization and also for generation jumping. In this work, opposite numbers have been utilized to improve the convergence rate of DE. A comprehensive set of 58 complex benchmark functions including a wide range of dimensions is employed for experimental verification. The influence of dimensionality, population size, jumping rate, and various mutation strategies are also investigated. Additionally, the contribution of opposite numbers is empirically verified. We also provide a comparison of ODE to fuzzy adaptive DE (FADE). Experimental results confirm that the ODE outperforms the original DE and FADE in terms of convergence speed and solution accuracy.

This paper focuses on extracting and analyzing different spectral features from transrectal ultrasound (TRUS) images for prostate cancer recognition. First, the information about the images' frequency domain features and spatial domain features are combined using a Gabor filter and then integrated with the expert radiologist's information to identify the highly suspicious regions of interest (ROIs). The next stage of the proposed algorithm is to scan each identified region in order to generate the corresponding 1-D signal that represents each region. For each ROI, possible spectral feature sets are constructed using different new geometrical features extracted from the power spectrum density (PSD) of each region's signal. Next, a classifier-based algorithm for feature selection using particle swarm optimization (PSO) is adopted and used to select the optimal feature subset from the constructed feature sets. A new spectral feature set for the TRUS images using estimation of signal parameters via rotational invariance technique (ESPRIT) is also constructed, and its ability to represent tissue texture is compared to the PSD-based spectral feature sets using the support vector machines (SVMs) classifier. The accuracy obtained ranges from 72.2% to 94.4%, with the best accuracy achieved by the ESPRIT feature set.

In this paper, an enhanced version of the opposition-based differential evolution (ODE) is proposed. ODE utilizes opposite numbers in the population initialization and generation jumping to accelerate differential evolution (DE). Instead of opposite numbers, in this work, quasi opposite points are used. So, we call the new extension quasi- oppositional DE (QODE). The proposed mathematical proof shows that in a black-box optimization problem quasi- opposite points have a higher chance to be closer to the solution than opposite points. A test suite with 15 benchmark functions has been employed to compare performance of DE, ODE, and QODE experimentally. Results confirm that QODE performs better than ODE and DE in overall. Details for the proposed approach and the conducted experiments are provided.

Differential evolution (DE) is a simple, reliable, and efficient optimization algorithm. However, it suffers from a weakness, losing the efficiency over optimization of noisy problems. In many real-world optimization problems we are faced with noisy environments. This paper presents a new algorithm to improve the efficiency of DE to cope with noisy optimization problems. It employs opposition-based learning for population initialization, generation jumping, and also improving population's best member. A set of commonly used benchmark functions is employed for experimental verification. The details of proposed algorithm and also conducted experiments are given. The new algorithm outperforms DE in terms of convergence speed.

Evolutionary Algorithms (EAs) are well-known optimization approaches to cope with non-linear, complex problems. These population-based algorithms, however, suffer from a general weakness; they are computationally expensive due to slow nature of the evolutionary process. This paper presents some novel schemes to accelerate convergence of evolutionary algorithms. The proposed schemes employ opposition-based learning for population initialization and also for generation jumping. In order to investigate the performance of the proposed schemes, Differential Evolution (DE), an efficient and robust optimization method, has been used. The main idea is general and applicable to other population-based algorithms such as Genetic algorithms, Swarm Intelligence, and Ant Colonies. A set of test functions including unimodal and multimodal benchmark functions is employed for experimental verification. The details of proposed schemes and also conducted experiments are given. The results are highly promising.

A new robust image thresholding technique is introduced in this paper. Comprehensive experiments show that a single thresholding method can not be successful for all kind of images. The proposed approach uses fusion of some well-known thresholding methods by applying weighted voting at the decision level. The main objective is improving robustness of thresholding approach by participating several methods. Although, the proposed approach can not guaranty the best result for all kind of images but it shows higher performance and consistent/smoother behavior in overall. The performance of the new approach and nine well-established thresholding methods are compared by applying to an image set with high image diversity. The comparison results show that the proposed approach outperforms other nine well-established thresholding approaches. The proposed approach has been explained in details and experimental results are provided.

In this paper a new approach to object extraction and recognition based on reinforcement learning is presented. We use this novel idea as a method to optimally segment the image and increase the recognition rate. The success rate is compared with a classical approach. Preliminary results demonstrate increase in recognition rate.

This paper introduces a new method to medical image segmentation using a reinforcement learning scheme. We use this novel idea as an effective way to optimally find the appropriate local thresholding and structuring element values and segment the prostate in ultrasound images. Reinforcement learning agent uses an ultrasound image and its manually segmented version and takes some actions (i.e., different thresholding and structuring element values) to change the environment (the quality of segmented image). The agent is provided with a scalar reinforcement signal determined objectively. The agent uses these objective reward/punishment to explore/exploit the solution space. The values obtained using this way can be used as valuable knowledge to fill a Q-matrix. The reinforcement learning agent can use this knowledge for similar ultrasound images as well. The results demonstrate high potential for applying reinforcement learning in the field of medical image segmentation.

In this paper a novel method for automatic prostate segmentation in transrectal ultrasound images is presented. Morphological grey level transformations are first used to generate an image with enough bright intensity around the prostate. This image is then thresholded to produce a binary image. Then by finding and using a point as the inside point for the prostate, a Kalman estimator is used to isolate the prostate boundary from any irrelevant parts and produce a roughly segmented version (as coarse estimation). Consequently, a fuzzy inference system describing regional and gray level information is employed to enhance the contrast of the prostate with respect to the background. Using strong edges obtained from this enhanced image and information from pixels gradients and also the characteristics in the vicinity of the coarse estimation, the final boundary is extracted. A number of experiments are conducted to validate this method.

A time-series approach has been employed to devise neural-network topologies for time dependent partial discharge pulse pattern recognition applications. The cascaded output neural-network structure was found to provide the highest success rate in differentiating between two different partial discharge patterns. This was accomplished by utilizing the indexed feature of the first stage output as one of the inputs into the second stage of the cascaded neural network.