Determining speaker attributes from stress-affected speech in emergency situations with hybrid SVM-DNN architecture

Multimedia Tools and Applications - In the millions of emergency reporting calls made each year, about a quarter are non-emergencies. To avoid responding to such situations, forensic examination of...     

Integrating salient colors with rotational invariant texture features for image representation in retrieval systems

Multimedia Tools and Applications - Content based image retrieval (CBIR) systems allow searching for visually similar images in large collections based on their contents. Visual contents are...     

An efficient authentication with key agreement procedure using Mittag–Leffler–Chebyshev summation chaotic map under the multi-server architecture

The recent technological advancement and rapid development of computer networks have increased the popularity of remote password authentication protocols. Toward this end, the emphasis has shifted to protocols that apply to smart cards-empowered multi-server environments. In order to defend against the replay attack, these protocols usually depend on the nonce or timestamp. In this paper, an efficient Mittag–Leffler–Chebyshev Summation Chaotic Map (MLCSCM)-enabled multi-server authentication protocol with the key agreement is proposed and generalized to address this peculiarity in multi-server-oriented applications. The security proof and efficiency analysis of the presented MLCSCM authenticated key agreement protocol is rigorously derived and validated. Compared to the recently published literature, the proposed protocol presents high efficiency with unique features, and it is highly resistant to sophisticated attacks and achieves perfect forward secrecy.

     

Nonlinear fractional‐order power system stabilizer for multi‐machine power systems based on sliding mode technique

This paper presents two novel nonlinear fractional‐order sliding mode controllers for power angle response improvement of multi‐machine power systems. First, a nonlinear block control is used to handle nonlinearities of the interconnected power system. In the second step, a decentralized fractional‐order sliding mode controller with a nonlinear sliding manifold is designed. Practical stability is achieved under the assumption that the upper bound of the fractional derivative of perturbations and interactions are known. However, when an unknown transient perturbation occurs in the system, it makes the evaluation of perturbation and interconnection upper bound troublesome. In the next step, an adaptive‐fuzzy approximator is applied to fix the mentioned problem. The fuzzy approximator uses adjacent generators relative speed as own inputs, which is known as semi‐decentralized control strategy. For both cases, the stability of the closed‐loop system is analyzed by the fractional‐order stability theorems. Simulation results for a three‐machine power system with two types of faults are illustrated to show the performance of the proposed robust controllers versus the conventional sliding mode. Additionally, the fractional parameter effects on the system transient response and the excitation voltage amplitude and chattering are demonstrated in the absence of the fuzzy approximator. Finally, the suggested controller is combined with a simple voltage regulator in order to keep the system synchronism and restrain the terminal voltage variations at the same time. Copyright © 2014 John Wiley & Sons, Ltd.     

An enhanced distributed power control algorithm for mobile femtocells under limited dynamic range and its convergence

This paper presents a distributed power control algorithm for wireless backhaul links of mobile femtocells by using the pilot’s information. Taking into account the limited dynamic range of transmitted powers, the SINR balancing of mobile (vehicular) femto base stations in their home macro base station and the load balancing among the macrocells are achieved by the proposed approach at the cost of exchanging some limited information among both macro and vehicular femto base stations. The algorithm is very beneficial especially in a high load heterogeneous network. Monte Carlo simulation results denote that the mobile femtocells can be uniformly assigned to the macrocells and the SINR balancing is achievable via the proposed scheme.

     

Self‐Limiting Galvanic Growth of MnO2 Monolayers on a Liquid Metal—Applied to Photocatalysis

Liquid metals offer unprecedented chemistry. Here it is shown that they can facilitate self‐limiting oxidation processes on their surfaces, which enables the growth of metal oxides that are atomically thin. This claim is exemplified by creating atomically thin hydrated MnO₂ using a Galvanic replacement reaction between permanganate ions and a liquid gallium–indium alloy (EGaIn). The “liquid solution”–“liquid metal” process leads to the reduction of the permanganate ions, resulting in the formation of the oxide monolayer at the interface. It is presented that under mechanical agitation liquid metal droplets are established, and simultaneously, hydrated gallium oxides and manganese oxide sheets delaminate themselves from the interfacial boundaries. The produced nanosheets encapsulate a metallic core, which is found to consist of solid indium only, with the full migration of gallium out of the droplets. This process produces core/shell structures, where the shells are made of stacked atomically thin nanosheets. The obtained core/shell structures are found to be an efficient photocatalyst for the degradation of an organic dye under simulated solar irradiation. This study presents a new research direction toward the modification and functionalization of liquid metals through spontaneous interfacial redox reactions, which has implications for many applications beyond photocatalysis.     

Accurate Classification of EEG Signals Using Neural Networks Trained by Hybrid Population-physic-based Algorithm

Machine Intelligence Research - A brain-computer interface (BCI) system is one of the most effective ways that translates brain signals into output commands. Different imagery activities can be...     

An improved sum‐of‐squares based approach to fuzzy tracking control design of nonlinear systems

In this paper, using a more general Lyapunov function, less conservative sum‐of‐squares (SOS) stability conditions for polynomial‐fuzzy‐model‐based tracking control systems are derived. In tracking control problems the objective is to drive the system states of a nonlinear plant to follow the system states of a given reference model. A state feedback polynomial fuzzy controller is employed to achieve this goal. The tracking control design is formulated as an SOS optimization problem. Here, unlike previous SOS‐based tracking control approaches, a full‐state‐dependent Lyapunov matrix is used, which reduces the conservatism of the stability criteria. Furthermore, the SOS conditions are derived to guarantee the system stability subject to a given H_∞ performance. The proposed method is applied to the pitch‐axis autopilot design problem of a high‐agile tail‐controlled pursuit and another numerical example to demonstrate the effectiveness and benefits of the proposed method.     

Miniaturization of Monopole Patch Antenna with Extended UWB Spectrum via Novel Hybrid Heuristic Approach

Wireless Personal Communications - Many of the wireless systems employ ultra wide band (UWB) for its efficient data rate and optimal power consumption. However, all the wireless devices need to...     

An efficient computerized decision support system for the analysis and 3D visualization of brain tumor

The quality of health services provided by medical centers varies widely, and there is often a large gap between the optimal standard of services when judged based on the locality of patients (rural or urban environments). This quality gap can have serious health consequences and major implications for patient’s timely and correct treatment. These deficiencies can manifest, for example, as a lack of quality services, misdiagnosis, medication errors, and unavailability of trained professionals. In medical imaging, MRI analysis assists radiologists and surgeons in developing patient treatment plans. Accurate segmentation of anomalous tissues and its correct 3D visualization plays an important role inappropriate treatment. In this context, we aim to develop an intelligent computer-aided diagnostic system focusing on human brain MRI analysis. We present brain tumor detection, segmentation, and its 3D visualization system, providing quality clinical services, regardless of geographical location, and level of expertise of medical specialists. In this research, brain magnetic resonance (MR) images are segmented using a semi-automatic and adaptive threshold selection method. After segmentation, the tumor is classified into malignant and benign based on a bag of words (BoW) driven robust support vector machine (SVM) classification model. The BoW feature extraction method is further amplified via speeded up robust features (SURF) incorporating its procedure of interest point selection. Finally, 3D visualization of the brain and tumor is achieved using volume marching cube algorithm which is used for rendering medical data. The effectiveness of the proposed system is verified over a dataset collected from 30 patients and achieved 99% accuracy. A subjective comparative analysis is also carried out between the proposed method and two state-of-the-art tools ITK-SNAP and 3D-Doctor. Experimental results indicate that the proposed system performed better than existing systems and assists radiologist determining the size, shape, and location of the tumor in the human brain.