A unified framework for describing and verifying hardware synchronous sequential systems

This paper presents a unified framework for expressing and solving the different functional verification problems of the circuit designers. This approach is based on the synchronous data flow language Lustre that was originally designed for programming real-time systems. Lustre can be used to describe digital circuits at different abstraction levels and their environments, as well as to express the properties about the behavior of these circuits. Then, the verification tool Lesar associated with the language Lustre automatically handles the different verifications.     

Illumination d’un réseau de lignes ou de câbles par une onde de foudre Modélisation par des générateurs de courant

In this paper we propose a new formalism for the analysis of electromagnetic coupling between the lightning wave and a network of lines or cables. This approach allows obtaining currents and induced voltages directly on the set of lines or cables extremities. For this objective we use the current generators representation of the lightning wave. Then by a mathematical formulation based on the topological analysis of the network, we deduce a linear equation system which resolution leads directly to the currents and induced voltages. In order to confirm our theoretical work, we present a set of applications that allows validating this formalism.     

Metaheuristic Optimization Algorithm for Signals Classification of Electroencephalography Channels

Digital signal processing of electroencephalography (EEG) data is now widely utilized in various applications, including motor imagery classification, seizure detection and prediction, emotion classification, mental task classification, drug impact identification and sleep state classification. With the increasing number of recorded EEG channels, it has become clear that effective channel selection algorithms are required for various applications. Guided Whale Optimization Method (Guided WOA), a suggested feature selection algorithm based on Stochastic Fractal Search (SFS) technique, evaluates the chosen subset of channels. This may be used to select the optimum EEG channels for use in Brain-Computer Interfaces (BCIs), the method for identifying essential and irrelevant characteristics in a dataset, and the complexity to be eliminated. This enables (SFS-Guided WOA) algorithm to choose the most appropriate EEG channels while assisting machine learning classification in its tasks and training the classifier with the dataset. The (SFS-Guided WOA) algorithm is superior in performance metrics, and statistical tests such as ANOVA and Wilcoxon rank-sum are used to demonstrate this.     

SAFT-VNDN: A Socially-Aware Forwarding Technique in Vehicular Named Data Networking

Vehicular Social Networks (VSNs) is the bridge of social networks and Vehicular Ad-Hoc Networks (VANETs). VSNs are promising as they allow the exchange of various types of contents in large-scale through Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication protocols. Vehicular Named Data Networking (VNDN) is an auspicious communication paradigm for the challenging VSN environment since it can optimize content dissemination by decoupling contents from their physical locations. However, content dissemination and caching represent crucial challenges in VSNs due to short link lifetime and intermittent connectivity caused by vehicles’ high mobility. Our aim with this paper is to improve content delivery and cache hit ratio, as well as decrease the transmission delay between end-users. In this regard, we propose a novel hybrid VNDN-VSN forwarding technique based on social communities, which allows requester vehicles to easily find the most suitable forwarder or producer among the community members in their neighborhood area. Furthermore, we introduce an effective caching mechanism by dividing the content store into two parts, one for community private contents and the second one for public contents. Simulation results show that our proposed forwarding technique can achieve a favorable performance compared with traditional VNDN, in terms of data delivery ratio, average data delivery delay, and cache hit ratio.     

A Novel Hybrid Machine Learning Approach for Classification of Brain Tumor Images

Abnormal growth of brain tissues is the real cause of brain tumor. Strategy for the diagnosis of brain tumor at initial stages is one of the key step for saving the life of a patient. The manual segmentation of brain tumor magnetic resonance images (MRIs) takes time and results vary significantly in low-level features. To address this issue, we have proposed a ResNet-50 feature extractor depended on multilevel deep convolutional neural network (CNN) for reliable images segmentation by considering the low-level features of MRI. In this model, we have extracted features through ResNet-50 architecture and fed these feature maps to multi-level CNN model. To handle the classification process, we have collected a total number of 2043 MRI patients of normal, benign, and malignant tumor. Three model CNN, multi-level CNN, and ResNet-50 based multi-level CNN have been used for detection and classification of brain tumors. All the model results are calculated in terms of various numerical values identified as precision (P), recall (R), accuracy (Acc) and f1-score (F1-S). The obtained average results are much better as compared to already existing methods. This modified transfer learning architecture might help the radiologists and doctors as a better significant system for tumor diagnosis.     

Reduced short-channel effects in submicron N-HIGFET technologyusing sidewalls

In this letter, 0.35 μm gate length pseudomorphic AlGaAs/InGaAs/GaAs heterostructure insulated-gate field-effect-transistors (HIGFETs) have been fabricated on GaAs. The short-channel effects have been reduced by using a sidewall technology. A high current density and a high transconductance were obtained, reflectively, 510 mA/mm and 550 mS/mm, in addition to a high value of extrinsic current gain cutoff frequency F_T=44 GHz. The dependencies of subthreshold current, threshold voltage, and output conductance on gate length have been emphasised     

An artificial immune system heuristic for two-stage multi-machine assembly scheduling problem to minimize total completion time

We address the two-stage multi-machine assembly scheduling problem. The first stage consists of m independently working machines where each machine produces its own component. The second stage consists of two independent and identical assembly machines. The objective is to come up with a schedule that minimizes total or mean completion time for all jobs. The problem has been addressed in the scheduling literature and several heuristics have been proposed. In this paper, we propose a new heuristic called artificial immune system (AIS). We conduct experimental analysis for comparing the newly proposed heuristic AIS with the best known heuristic in the literature. Experimental results show that our proposed heuristic AIS performs better than the best known existing heuristic. More specifically, our new heuristic AIS reduces the error of the best known heuristic by 60% while the computational times of both AIS and the best known heuristic are almost the same.     

Construction of flexible blending parametric surfaces via curves

The main purpose of this paper is to provide a method that allows to solve the blending problem of two parametric surfaces. The blending surface is constructed with a collection of space curves defined by point pairs on the blending boundaries of given primary surfaces. Bézier and C-cubic curves are used to interpolate the blending boundaries. The blending surface is Gⁿ continuously connected to the primary surfaces.     

Deadline-based connection setup in wavelength-routed WDM networks

This article addresses the ubiquitous topic of quality of service (QoS) aware connection provisioning in wavelength-routed WDM optical networks. The impact of the connection setup time of an optical connection has not been adequately addressed in the open literature. As such, this paper presents a novel approach that uses the optical connection setup time as a service differentiator during connection provisioning. The proposed approach utilizes the Earliest Deadline First (EDF) queueing algorithm to achieve deadline-based connection setup management with the deadline being the setup time requirement of an optical connection. The proposed EDF-based approach would allow the network operator to improve the QoS perceived by the end clients. Performance of this novel scheme is analyzed by accurately calculating various parameters, such as the fraction of connections provisioned on-time (i.e. prior to deadline expiration) and the average time it takes to successfully setup a connection. In addition, the presented approach is validated by a simulation that analyzes the performance of the proposed connection setup scheme in the specific context of the National Science Foundation Network (NSFNET). The obtained results show that a deadline-based setup strategy can minimize blocking probability while achieving QoS differentiation.     

Adjustable audio watermarking algorithm based on DWPT and psychoacoustic modeling

This paper presents a novel adjustable audio watermarking method with high auditory quality by exploiting the discrete wavelet packet transform (DWPT), psychoacoustic modeling and distortion compensated-dither modulation (DC-DM) quantization. While the DWPT is used to divide the audio frames into several frequency sub-bands, the psychoacoustic model is intergraded to determine the appropriate sub-bands for watermarking and to control the number of embedded bits in each one. Then, the DC-DM technique is used to embed the watermark bits into the appropriate DWPT coefficients. The synchronization code technique is adopted in the proposed method to withstand desynchronization attacks. In order to achieve an adjustable watermarking scheme, two regulator parameters are provided to manage the capacity-robustness trade-off. The performance of the watermarking scheme is evaluated by examining different host audio signals under various watermarking attacks. The results show excellent imperceptibility of watermarked signals with an average ODG of ? 0.3. In addition, the proposed scheme provides strong robustness against the attacks with low capacity. However, high capacity (about 2500 bps) can be achieved while maintaining a reasonable robustness. A comparison with some state-of-the-art audio watermarking schemes reveals that the proposed method provides competitive results.