A New PSO-based Algorithm for Two-Dimensional Non-Guillotine Non-Oriented Cutting Stock Problem

In this paper, a new algorithm is proposed for the two-dimensional non-guillotine non-oriented cutting stock problem. The considered problem consists of cutting small rectangular pieces of predetermined sizes from large but finite rectangular plates. The objective is to generate cutting patterns that minimize the unused area and fulfill customer orders. The proposed algorithm is a combination of a new particle swarm optimization approach with a heuristic criterion inspired from the literature. The algorithm is tested on twenty-two instances divided into two sets. Corresponding results show the algorithm efficiency in optimizing the trim loss that is comprised between 2.6% and 7.8% for all considered instances.     

Swarm intelligence‐based radio resource management for V2V‐based D2D communication

Internet of Things is a promising paradigm that provides the future network of interconnected devices. Device‐to‐Device (D2D) communication, which is considered as an enabler for vehicle‐to‐everything applications, has become an emerging technology to optimize network performance. In this paper, we study the Radio Resource Management (RRM) issue for D2D‐based Vehicle‐to‐Vehicle communication. The RRM key role is to assure the proficient exploitation of available resources while serving users according to their quality of service parameters. An Ant Colony Optimization (ACO)‐based Resource Allocation (ACORA) scheme is proposed in this paper. Swarm intelligence algorithm ACO is adopted to reduce the computational complexity while realizing satisfactory performance. Simulation results show promising performance of our proposed ACORA scheme.     

Experimental Investigation of Passive Fault Tolerant Control for Induction Machine Using Sliding Mode Approach

This paper presents a passive fault tolerant control approach dedicated to stator inter‐turn short‐circuit fault of an induction machine. This control is based on sliding mode strategy and is implemented for validation on real‐time data acquisition and control platform. The proposed work highlights the integral sliding mode controller benefits during healthy and faulty operations. It can make the induction machine outputs to track their desired reference signals in finite‐time and allows to obtain a better dynamic response performances even in presence of fault. Moreover to avoid the use of a flux sensor, a second order sliding mode observer is used to estimate the rotor flux. Since the used observer converges in finite time, the closed‐loop stability of the proposed system (controller+observer) is proved using the Lyapunov stability theorem. Experimental results are conducted for squirrel cage induction machine to highlight the efficiency and applicability of the proposed fault tolerant control.     

Fast mode decision algorithm for H.264/SVC enhancement layer

Scalable video coding (SVC) has been standardized to extend the capabilities of the H.264 advanced video coding (AVC). The SVC can compress several video sequences of various resolutions as a single bit-stream. In the SVC enhancement layer, for Joint Scalable Video Model (JSVM) software implementation, an exhaustive mode decision process based on the base layer mode predictions is performed to obtain the best mode for each macroblock (MB). This technique may achieve a higher coding efficiency; however, it induces a significant computational complexity in the encoding engine. In order to speedup the SVC encoder, a fast mode decision algorithm was proposed in this paper. In other words, our aim was to decrease the number of candidate modes to reduce the computational complexity and maintain the same level of coding efficiency, this approach used the spatial and temporal correlation between MB situated at the enhancement layer and its co-located MB at the base layer. Our statistical analyses were made using several HD sequences with different motion characteristics. Experimental results show a significant improvement in terms of time encoding which is a major constraint for any real-time implementation. However, this gain is accompanied with an acceptable loss in video quality and a tolerable bit rate increase for most media supports. In fact, our proposed algorithm permits a major improvement that can reach up to 64.9 % in terms of computational effort. This gain will induce an average loss yield to 10.5 or 13.87 % that is comparable to the 13.12 % of the He Li’s algorithm with an acceptable loss in terms of subjective video quality.     

An efficient microcalcifications detection based on dual spatial/spectral processing

Microcalcifications are tiny deposits of calcium located in breast tissue. They appeared as very small highlighted regions in comparison with their surrounding tissue. Spatial non linear enhancement can be applied for microcalcification detection. However, efficiency of a such approach depends on breast density: in case of extreme breast density, the contrast between microcalcification’s details and their surrounding tissue is attenuated leading to a limitation of spatially based approaches. In that case, frequency analysis such as wavelet based analysis can be more relevant for dissociating microcalcifications. The main goal of Computer Aided Detection systems (CAD) is to detect breast cancer at an early stage for all breast density classes by using entropies to enhance and then detect microcalcification details. Accordingly, we combine our approach a spatial Automatic Non Linear Stretching (ANLS) and Shannon Entropy based Wavelet Coefficient Thresholding (SE_WCT). Validation of the proposed approach is done on the Mammographic Image Analysis Society (MIAS) database. The evaluation of the contrast is based on the Second-Derivative-Like measure of enhancement(SDME). Accordingly, it yields to a mean SDME of 78.8dB on the whole database. The performance metric for evaluating our proposed CAD is the Receiver Operating Characteristic(ROC) curve and the free-response ROC (FROC). An area under the ROC curve A _z = 0.92 is obtained as well as 97.14 % of True Positives (TP) with 0,48 False positives per image (FP).     

PEAL: Power Efficient and Adaptive Latency Hierarchical Routing Protocol for Cluster-Based WSN

In wireless sensor networks, one of the most important constraints is the low power consumption requirement. For that reason, several hierarchical or cluster-based routing methods have been proposed to provide an efficient way to save energy during communication. However, their main challenge is to have efficient mechanisms to achieve the trade-off between increasing the network lifetime and accomplishing acceptable transmission latency. In this paper, we propose a novel protocol for cluster-based wireless sensor networks called PEAL (Power Efficient and Adaptive Latency). Our simulation results show that PEAL can extend the network lifetime about 47% compared to the classic protocol LEACH (Low-Energy Adaptive Clustering Hierarchy) and introduces an acceptable transmission latency compared to the energy conservation gain.     

Budget-bounded model-checking pushdown systems

We address the verification problem for concurrent programs modeled as multi-pushdown systems (MPDS). In general, MPDS are Turing powerful and hence come along with undecidability of all basic decision problems. Because of this, several subclasses of MPDS have been proposed and studied in the literature (Atig et al. in LNCS, Springer, Berlin, 2005; La Torre et al. in LICS, IEEE, 2007; Lange and Lei in Inf Didact 8, 2009; Qadeer and Rehof in TACAS, LNCS, Springer, Berlin, 2005). In this paper, we propose the class of bounded-budget MPDS, which are restricted in the sense that each stack can perform an unbounded number of context switches only if its depth is below a given bound, and a bounded number of context switches otherwise. We show that the reachability problem for this subclass is Pspace-complete and that LTL-model-checking is Exptime-complete. Furthermore, we propose a code-to-code translation that inputs a concurrent program \(P\) and produces a sequential program \(P'\) such that running \(P\) under the budget-bounded restriction yields the same set of reachable states as running \(P'\) . Moreover, detecting (fair) non-terminating executions in \(P\) can be reduced to LTL-Model-Checking of \(P'\) . By leveraging standard sequential analysis tools, we have implemented a prototype tool and applied it on a set of benchmarks, showing the feasibility of our translation.     

Advanced classification approach for neuronal phoneme recognition system based on efficient constructive training algorithm

This paper introduces a neural network optimization procedure allowing the generation of multilayer perceptron (MLP) network topologies with few connections, low complexity and high classification performance for phoneme’s recognition. An efficient constructive algorithm with incremental training using a new proposed Frame by Frame Neural Networks (FFNN) classification approach for automatic phoneme recognition is thus proposed. It is based on a novel recruiting hidden neuron’s procedure for a single hidden-layer. After an initializing phase started with initial small number of hidden neurons, this algorithm allows the Neural Networks (NNs) to adjust automatically its parameters during the training phase. The modular FFNN classification method is then constructed and tested to recognize 5 broad phonetic classes extracted from the TIMIT database. In order to take into account the speech variability related to the coarticulation effect, a Context Window of Three Successive Frame’s (CWTSF) analysis is applied. Although, an important reduction of the computational training time is observed, this technique penalized the overall Phone Recognition Rate (PRR) and increased the complexity of the recognition system. To alleviate these limitations, two feature dimensionality reduction techniques respectively based on Principal Component Analysis (PCA) and Self Organizing Maps (SOM) are investigated. It is observed an important improvement in the performance of the recognition system when the PCA technique is applied. Optimal neuronal phone recognition architecture is finally derived according to the following criteria: best PRR, minimum computational training time and complexity of the BPNN architecture.     

OS-CFAR and CMLD threshold optimization in distributed systems using evolutionary strategies

This paper proposes an improvement of the threshold optimization in distributed ordered statistics constant false alarm rate and censored mean level detector using Evolutionary Strategies (ESs). The target is assumed to be Rayleigh distributed and the observations are independent from sensor to sensor. Two fusion rules; “AND” and “OR” were considered. An ES was tested and a comparison with a genetic algorithm improved by a tournament selection was also analyzed. Among a variety of evolution strategies, the most popular proposed in the literature are the strategy (μ, λ) and the strategy (μ + λ). We proposed an (μ + λ) evolution strategy, by which a self-adaptation mutation is used. The results showed that, although the ES is more difficult to implement and is in a certain manner slower than the GA, it improves the performance of the system.