Energy modelling of fabrics wrinkle recovery behaviour

Wrinkle recovery behaviour is an important property of fabrics for apparel applications. Fifty fabrics of a variety of constructions have been objectively evaluated in order to study the wrinkle recovery after a deformation. The wrinkle recovery behaviour was studied based on energy modelling. To study their wrinkle recovery properties, fabrics are modelled with the energy method. It is shown that the more the energy dissipation is low, the more fabric is able to recover to its initial state and then the more residual deformation is low. Thus, the energy dissipation can be used to estimate the ability of the fabric to recover to its initial state. In this paper, we also studied the effect of the fatigue test after the repeated wrinkling. Further, the increase of the fatigue cycle number after repeated wrinkling resulted in an increase of the residual deformation and permanent deformation. This method allows us for better approximation of the real behaviour of multidirectional wrinkling of clothing during wearing.     

A Fittingness Factor-Based Spectrum Management Framework for Cognitive Radio Networks

In order to increase cognitive radios (CRs) operation efficiency, there has been an increasing interest in strengthening awareness level about spectrum utilisation. In this respect, this paper proposes to exploit the fittingness factor concept to capture the suitability of spectral resources exhibiting time-varying characteristics to support a set of heterogeneous CR applications. First, a new knowledge management functional architecture for optimizing spectrum management has been constructed. It integrates a set of advanced statistics capturing the influence of the dynamic radio environment on the fittingness factor. Then, a knowledge manager (KM) exploiting these statistics to monitor time-varying suitability of spectrum resources has been proposed to support the spectrum selection (SS) decision-making process. In particular, a new Fittingness Factor-based strategy combining two SS and spectrum mobility (SM) functionalities has been proposed, following either a greedy or a proactive approach. Results have shown that, with a proper fittingness factor function, the greedy approach efficiently exploits the KM support at low loads and the SM functionality at high loads to introduce significant gains in terms of the user dissatisfaction probability. The proactive approach has been shown to maintain the introduced performance gain while minimizing the signalling requirements in terms of spectrum handover rate.     

A bipolar implementation of the Beta neuron

The great flexibility of the Beta function and its universal approximation characteristics, make Beta basis function neural networks (BBFNNs) very useful. We present a hardware implementation of the Beta neuron. The proposed circuit was designed by using a standard bipolar technology. PSPICE simulations show the good concordance of the output of our circuit with the analytic Beta function. We also successfully integrated the electronic Beta neuron in the design of a BBFNN that approximates a nonlinear mapping.     

A Beta basis function neural network in CMOS subthreshold mode

Beta basis function neural networks (BBFNNs) are powerful systems for learning and universal approximation. In this paper, we present a hardware implementation of the Beta neuron using the CMOS subthreshold mode. We describe the low power–low voltage analogue Beta neuron circuit. Three main modules are used to realize the electronic Beta function: a logarithmic currentto-voltage converter, a multiplier and an exponential voltage-to-current converter. Simulation results show the validity of our neural hardware implementation. The parameters of the electronic Beta function are controlled independently by current sources. This analogue implementation could be used easily to realize analogue BBFNNs.     

New pulse mode neuro-fuzzy hardware architecture and application to image denoising

This paper focuses on a new digital architecture of pulse mode neuro-fuzzy system (PMNFS) with on-chip learning ability. The main purpose goal is to make use of the outstanding features of neuro-fuzzy in function approximation, and implement a reconfigurable architecture with on-chip learning on a field-programmable gate array (FPGA) platform. Details of the whole design with on-chip learning solutions are given. As an application illustrating the efficiency and scalability of the proposed PMNFS, we have considered the approximation of image denoising, which is a very important step in image processing. Experimental results show great efficiency of the proposed method, outperforming other denoising techniques. It was also demonstrated that such a system is strongly adaptive and gives good restored images independently of the kind of noises. Owing to learning, such feature cannot be met with conventional denoising techniques. Design synthesis results on a virtex II PRO FPGA platform are presented. Comparisons with conventional techniques as well as neural ones show higher performances of the designed PMNFS.     

Project scheduling under uncertainty using fuzzy modelling and solving techniques

In the real world, projects are subject to numerous uncertainties at different levels of planning. Fuzzy project scheduling is one of the approaches that deal with uncertainties in project scheduling problem. In this paper, we provide a new technique that keeps uncertainty at all steps of the modelling and solving procedure by considering a fuzzy modelling of the workload inspired from the fuzzy/possibilistic approach. Based on this modelling, two project scheduling techniques, Resource Constrained Scheduling and Resource Leveling, are considered and generalized to handle fuzzy parameters. We refer to these problems as the Fuzzy Resource Constrained Project Scheduling Problem (FRCPSP) and the Fuzzy Resource Leveling Problem (FRLP). A Greedy Algorithm and a Genetic Algorithm are provided to solve FRCPSP and FRLP respectively, and are applied to civil helicopter maintenance within the framework of a French industrial project called Helimaintenance.     

3–5 GHz FSK-OOK ultra wideband transmitter based on memristive ring oscillator

The compatibility of a memristor with CMOS technology has attracted the attention of many researchers to explore its application further. In this work, an ultra low-power and low-complexity ultra wideband (UWB) chirp transmitter based on memristive ring oscillator (RO) is designed in 0.18 µm TSMC CMOS technology. The Chirp waveform was chosen because of its low side-lobes and large time-bandwidth product, which allows for more spectrum use. OOK and FSK modulation are supported by the proposed UWB chirp transmitter. The chirp frequency is controlled linearly with time across the pulse duration using memristors. The binary data "1" and "0" are encoded using distinct chirp frequencies in FSK TX. The simulation results show a maximum TX output pulse of 457 mV _Vpp with a pulse width of 21 ns. The overall DC power consumption for a pulse repetition frequency (PRF) of 20 MHz is 0.328 mW, equivalent to an energy consumption of 16.4 pJ/pulse. The simulated output amplitude for OOK TX is 453 mV Vpp with a pulse width of 48 ns and a PSD of ? 10 dB over a frequency range of 3.2 to 4.8 GHz. The overall power consumption at 10 MHz PRF is 0.136 mW, which corresponds to an energy consumption of 13.6 pJ/pulse.

     

Robust controller for uncertain parameters systems

In this paper, we present the synthesis of a robust controller for Linear Time Invariant (LTI) uncertain systems. A linear parametric uncertainties model is used to describe the system dynamic behavior. The main purpose of this controller is to guarantee some step response performances such as the settling time and the overshoot. The controller synthesis is formulated as a min-max optimization problem which takes in account the desired closed-loop performances and the uncertainties on the model parameters. Then the controller parameters represent the best solution for the worst case of all possible models. In order to emphasize its performances and its efficiency, a real time implementation of the proposed controller on a laboratory pilot plant has been presented.     

Application of surface response analysis to the optimisation of nitric passivation of cp titanium and Ti6Al4V

Passivation of Ti6Al4V and cp Ti by mean of nitric acid treatment are used to reduce their surface reactivity, and consequently increasing resistance to corrosion, in physiological media.

In this work, the response surface methodology was employed to know the effects of operating parameters of the nitric passivation (HNO₃ content, temperature of solution and passivation period) on the corrosion resistance of commercially pure (cp) titanium and Ti6Al4V alloy in Ringer's solution. The experiment included two 3³ factorial designs. According to the responses from the experimental designs, the effects of each variable were calculated and the interactions between them were determined. The statistical test has revealed that the main effect of the nitric acid content is the most significant factor. The surface response methodology was applied to graphically determine the optimal working conditions.

Different electrochemical techniques (OCP, Rp and cyclic polarisation tests) were used to compare the electrochemical behaviour of cp Ti and Ti6Al4V, with and without passivation.     

VNS methods for home care routing and scheduling problem with temporal dependencies,and multiple structures and specialties

The need for optimization in the Home Care Service is becoming more and more legitimate in the face of the increase of demand and cost all over the world. Recently, many researchers in the Operation Research community have been attracted by this issue, which presents interesting aspects related to the vehicle routing problems. In this paper, we consider a new variant called the vehicle routing problem with time windows, temporal dependencies (synchronization, precedence, and disjunction), multi‐structures, and multispecialties problem (VRPTW‐TD‐2MS). This new variant is an extension of the vehicle routing problems with time windows and synchronization constraints (VRPTW‐S) that is well‐studied in literature. We present a Mixed Integer Programming method, and propose three Variable Neighborhood Search approaches. Extensive experiments show the effectiveness and efficiency of the General Variable Neighborhood Search with Ejection Chains‐based local search for solving VRPTW‐TD‐2MS and VRPTW‐S.