A part-level learning strategy for JPEG image recompression detection

Multimedia Tools and Applications - Recompression is a prevalent form of multimedia content manipulation. Different approaches have been developed to detect this kind of alteration for digital...     

A Survey of PSO-Based Scheduling Algorithms in Cloud Computing

Cloud computing provides effective mechanisms for distributing the computing tasks to the virtual resources. To provide cost-effective executions and achieve objectives such as load balancing, availability and reliability in the cloud environment, appropriate task and workflow scheduling solutions are needed. Various metaheuristic algorithms are applied to deal with the problem of scheduling, which is an NP-hard problem. This paper presents an in-depth analysis of the Particle Swarm Optimization (PSO)-based task and workflow scheduling schemes proposed for the cloud environment in the literature. Moreover, it provides a classification of the proposed scheduling schemes based on the type of the PSO algorithms which have been applied in these schemes and illuminates their objectives, properties and limitations. Finally, the critical future research directions are outlined.     

Functional Nanomaterials for the Diagnosis of Alzheimer's Disease: Recent Progress and Future Perspectives

Alzheimer's disease (AD) is one of the main causes of dementia worldwide, whereby neuronal death or malfunction leads to cognitive impairment in the elderly population. AD is highly prevalent, with increased projections over the next few decades. Yet current diagnostic methods for AD occur only after the presentation of clinical symptoms. Evidence in the literature points to potential mechanisms of AD induction beginning before clinical symptoms start to present, such as the formation of amyloid beta (Aβ) extracellular plaques and neurofibrillary tangles (NFTs). Biomarkers of AD, including Aβ₄₀, Aβ₄₂, and tau protein, amongst others, show promise for early AD diagnosis. Additional progress is made in the application of biosensing modalities to measure and detect significant changes in these AD biomarkers within patient samples, such as cerebral spinal fluid (CSF) and blood, serum, or plasma. Herein, a comprehensive review of the emerging nano-biomaterial approaches to develop biosensors for AD biomarkers’ detection is provided. Advances, challenges, and potential of electrochemical, optical, and colorimetric biosensors, focusing on nanoparticle-based (metallic, magnetic, quantum dots) and nanostructure-based biomaterials are discussed. Finally, the criteria for incorporating these emerging nano-biomaterials in clinical settings are presented and assessed, as they hold great potential for enhancing early-onset AD diagnostics.     

Preparation of water-repellent cellulose fibers using a polycarboxylic acid/hydrophobic silica nanocomposite coating

This research was carried out to embed hydrophobic silica nano-particles on the cotton surface using 1,2,3,4-butanetetracarboxylic acid (BTCA) as a crosslinking agent and sodium hypophosphite as a catalyst. The influence of the amount of silica nano-particles on the performance of the cotton fibers was investigated employing Attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR), Raman spectroscopy (RS), Thermogravimetric analysis (TGA), limited oxygen index (LOI), scanning electron microscopy (SEM), water contact angle (WCA), reflectance spectroscopy (RS) and abrasion resistance (AR). The possible interactions between silica nano-particles, the crosslinking agent and the cotton functional groups at the surface were elucidated by ATR-FTIR and Raman spectroscopy. The results indicated that the embedded silica nano-particles modify the surface of fibers increasing the hydrophobicity and thermal stability of substrate. The microscopic analysis showed a uniform coating of silica nano-particles on the surface of fibers.     

Efficient Structured Policies for Admission Control in Heterogeneous Wireless Networks

In the near future, demand for heterogeneous wireless networking (HWN) is expected to increase. QoS provisioning in these networks is a challenging issue considering the diversity in wireless networking technologies and the existence of mobile users with different communication requirements. In HWNs with their increased complexity, “the curse of dimensionality” problem makes it impractical to directly apply the decision theoretic optimal control methods that are previously used in homogeneous wireless networks to achieve desired QoS levels. In this paper, optimal call admission control policies for HWNs are considered. A decision theoretic framework for the problem is derived by a dynamic programming formulation. We prove that for a two-tier wireless network architecture, the optimal policy has a two-dimensional threshold structure. Further, this structural result is used to design two computationally efficient algorithms, Structured Value Iteration and Structured Update Value Iteration. These algorithms can be used to determine the optimal policy in terms of thresholds. Although the first one is closer in its operation to the conventional Value Iteration algorithm, the second one has a significantly lower complexity. Extensive numerical observations suggest that, for all practical parameter sets, the algorithms always converge to the overall optimal policy. Further, the numerical results show that the proposed algorithms are efficient in terms of time-complexity and in achieving the optimal performance.     

Code design for adaptive coding and modulation indicator in broadcast systems

In this paper, we address the coding problem for adaptive coding and modulator indicators in communication systems where users are divided into several classes according to their channel quality. Two novel methods are described to construct codebooks with variable length codewords forsuch an application. The proposed constructions satisfy all constraints of the system model, showing considerable gain in both the maximal and average length of codebook with respect to the current state of the art. The methodology includes a systematic way for constructing variable length codebooks where codewords are not uniformly distributed in the space, and thus, some codewords are more protected than others. The proposed construction can be easily adapted, by zero padding, to obtain a fixed block‐length code, with length equal to the maximal length of the designed variable‐length code but still smaller than that of the best state‐of‐the‐art code. Copyright © 2015 John Wiley & Sons, Ltd.     

Phone-based filter parameter optimization of filter and sum robust speech recognition using likelihood maximization

Because of noise and reverberation, accuracy of speech recognition systems decreases when the distance between talker and microphone increases. By the using of microphone arrays and appropriate filtering of received signals, the accuracy of recognizer can be increased. Many different methods for using microphone arrays have been proposed that can be classified into two main approaches: systems that perform in two independent stages of array processing and then recognition and systems that use array processing to generate a sequence of features which maximize the likelihood of generating the correct hypothesis in recognition phase. Following second approach, in this paper a new method for microphone array processing is proposed in which the parameters of array processing are adjusted in calibration phase based on phones used in language and maximum likelihood method. Optimized filter parameters are stored and used during recognition phase. A new modified Viterbi algorithm using optimal phone-based filter parameters is used for recognition phase. The proposed algorithm is analytically formulated and Persian language is used to find any improvement in speech recognition accuracy compared with results of delay and sum and utterance-based filter and sum algorithms. The results show 12.2% improvement in accuracy compared to utterance-based algorithm.     

Controlled AFM manipulation of small nanoparticles and assembly of hybrid nanostructures

We demonstrate controlled manipulation of semiconductor and metallic nanoparticles (NPs) with 5-15 nm diameters and assemble these NPs into hybrid structures. The manipulation is accomplished under ambient environment using a commercial atomic force microscope (AFM). There are particular difficulties associated with manipulating NPs this small. In addition to spatial drift, the shape of an asymmetric AFM tip has to be taken into account in order to understand the intended and actual manipulation results. Furthermore, small NPs often attach to the tip via electrostatic interaction and modify the effective tip shape. We suggest a method for detaching the NPs by performing a pseudo-manipulation step. Finally, we show by example the ability to assemble these small NPs into prototypical hybrid nanostructures with well-defined composition and geometry.