Maximizing embedding capacity for speech steganography: a segment-growing approach

It has been proven that the higher the correlation level between samples in the time-domain of a digital signal, the stronger the energy com paction property in the Discrete Cosine Transform (DCT) domain. This paper aims to investigate the limits of the DCT energy compaction property in speech signals by segmenting the cover speech signal into correlated segments and hide in each segment. The Hiding process is performed using a hiding strategy in spired by the Amplitude Modulation (AM) technique. Due to segmentation, the homogeneity is expected to increase which causes the energy of the signal to be strongly compacted in a few critical DCT coefficients, and therefore, a substantial amount of insignificant DCT coefficients can be replaced with the secret data without sacrificing the quality of the signal. Experimental results have proven the effectiveness of the proposed scheme which outperforms other speech steganography techniques recently published in the literature.

     

Robust nonblocking supervisory control of discrete-event systems under partial observation

In this paper, we examine the problem of robust nonblocking supervisory control. In the problem considered here, the exact model of the plant is not known but is assumed to be among a finite set of possible models. For each plant model a legal marked behavior is assumed given. We extend previous results for the case of control with full observation to the case of control under partial observation where only a subset of events are observable. Furthermore, we remove the limitations of previous results on ensuring the nonblocking property of the plant under supervision. We characterize the entire set of solutions of the robust control problem and obtain a set of necessary and sufficient conditions for the existence of a solution for the problem. As an illustrative example, we use our results on robust control to solve a fault recovery problem.     

Dynamic process location management in 5G

5G, as the next generation of wireless networks, promises very high throughput and low latency to mobile users that calls for a substantial innovation in computing management platforms to attend QoS metrics. Thanks to emerging technologies such as software‐defined networking (SDN)/network function virtualization (NFV), many features are available in 5G design to detect and control two types of latency caused by computation and communication. In this paper, taking features of caching mechanisms and SDN into the account, a platform is proposed to minimize latency in 5G via caching big flows intelligently and avoiding bottlenecks that may cause by virtualized computing components. First, the pioneering idea of compromising between the cloud radio access network (CRAN) and mobile edge computing (MEC)/information‐centric network (ICN) via dynamic processing location management platform is investigated. Accordingly, a mathematical optimization problem to minimize the average latency is formulated. Due to the problem complexity, a heuristic algorithm is proposed to treat the latency via dynamic orchestration of processing functionalities. Through numerical results, the performance of the proposed algorithm is analyzed, and the simulations corroborate our analytical results and illustrate the superior performance of the proposed algorithm with acceptable optimality gap.     

Identifying speakers using their emotion cues

This paper addresses the formulation of a new speaker identification approach which employs knowledge of emotional content of speaker information. Our proposed approach in this work is based on a two-stage recognizer that combines and integrates both emotion recognizer and speaker recognizer into one recognizer. The proposed approach employs both Hidden Markov Models (HMMs) and Suprasegmental Hidden Markov Models (SPHMMs) as classifiers. In the experiments, six emotions are considered including neutral, angry, sad, happy, disgust and fear. Our results show that average speaker identification performance based on the proposed two-stage recognizer is 79.92% with a significant improvement over a one-stage recognizer with an identification performance of 71.58%. The results obtained based on the proposed approach are close to those achieved in subjective evaluation by human listeners.     

Adaptive output feedback tracking controller for a class of uncertain strict feedback nonlinear systems in the absence of state measurements

In this article, design of an adaptive control scheme for a class of uncertain single-input single-output systems in strict feedback form via a backstepping technique has been proposed. It is assumed that system output and its derivatives are available. By virtue of the observability concept, it is shown that for this class of systems there exists a one-to-one map, which maps output and its derivatives to system states. By means of this mapping and using linearly parametrised approximators, such as fuzzy logic systems or neural networks, the uncertain nonlinear dynamics and unavailable states are estimated. The proposed adaptive controller guarantees that the closed-loop system is uniformly ultimately bounded and the influence of minimum approximation error on the L ₂-norm of the output tracking error is attenuated arbitrarily. The effectiveness of the proposed scheme has been demonstrated through simulation results.     

Fast voltage‐driving scheme for AMOLED displays based on internal feedback

Abstract— A new driving scheme for active‐matrix organic light‐emitting diodes (AMOLED) displays based on voltage programming is proposed. While conventional voltage drivers have a trade‐off between speed and accuracy, the new scheme is inherently fast and accurate. Based on the new driving scheme, a fast pixel circuit is designed using amorphous‐silicon (a‐Si) thin‐film transistors (TFTs). As the simulation results indicate, this pixel circuit can compensate the threshold‐voltage shift (VT shift) of the driver transistors. This pixel can be programmed in just 10 μsec, and it can compensate the threshold‐voltage shifts over 5 V with an error rate of less than 5% for a 1 ‐μA pixel current.     

On the Advice Complexity of the k-server Problem Under Sparse Metrics

We consider the k-Server problem under the advice model of computation when the underlying metric space is sparse. On one side, we introduce Θ(1)-competitive algorithms for a wide range of sparse graphs. These algorithms require advice of (almost) linear size. We show that for graphs of size N and treewidth α, there is an online algorithm that receives O (n(log α + log log N))^* bits of advice and optimally serves any sequence of length n. We also prove that if a graph admits a system of μ collective tree (q, r)-spanners, then there is a (q + r)-competitive algorithm which requires O (n(log μ + log log N)) bits of advice. Among other results, this gives a 3-competitive algorithm for planar graphs, when provided with O (n log log N) bits of advice. On the other side, we prove that advice of size Ω(n) is required to obtain a 1-competitive algorithm for sequences of length n even for the 2-server problem on a path metric of size N ≥ 3. Through another lower bound argument, we show that at least \(\frac {n}{2}(\log \alpha - 1.22)\) bits of advice is required to obtain an optimal solution for metric spaces of treewidth α, where 4 ≤ α < 2k.     

A study of the influence of REV variability in double‐scale FEM ×DEM analysis

In this work, the consequences of using several different discrete element granular assemblies for the representation of the microscale structure, in the framework of multiscale modeling, have been investigated. The adopted modeling approach couples, through computational homogenization, a macroscale continuum with microscale discrete simulations. Several granular assemblies were used depending on the location in the macroscale finite element mesh. The different assemblies were prepared independently as being representative of the same material, but their geometrical differences imply slight differences in their response to mechanical loading. The role played by the micro‐assemblies, with weaker macroscopic mechanical properties, on the initiation of strain localization in biaxial compression tests is demonstrated and illustrated by numerical modeling of different macroscale configurations. Copyright © 2016 John Wiley & Sons, Ltd.     

A biogeography-based optimization for optimum discrete design of skeletal structures

This article presents a modified biogeography-based optimization (MBBO) algorithm for optimum design of skeletal structures with discrete variables. The main idea of the biogeography-based optimization (BBO) algorithm is based on the science of biogeography, in which each habitat is a possible solution for the optimization problem in the search space. This algorithm consists of two main operators: migration and mutation. The migration operator helps the habitats to exploit the search space, while the mutation operator guides habitats to escape from the local optimum. To enhance the performance of the standard algorithm, some modifications are made and an MBBO algorithm is presented. The performance of the MBBO algorithm is evaluated by optimizing five benchmark design examples, and the obtained results are compared with other methods in the literature. The numerical results demonstrate that the MBBO algorithm is able to show very competitive results and has merits in finding optimum designs.     

对称和非对称粘合单盖板节点的变形和应力分析

采用粘合节点连接各种结构构件日益增多。只有充分了解粘合节点的性能,才能保证这种节点的功效、安全和可靠性。有些节点的构造,如单、双搭接接头受到了重视,但单盖板的构造并没有得到重视,部分原因是早期的研究显示这种节点效率低。本文的目的之一是论证只要经过合理设计,盖板节点与搭接接头同样有效。这项工作通过详细分析粘合剂中峰值应力的参数影响来完成。另一个目标是提出一个简单的方程,能够使盖板节点的设计便利及有效。为此,对解析表达式进行了简化并给出精确的结果,并对影响边缘力的参数有了进一步的理解。