Implementation of RNS-Based Distributed Arithmetic Discrete Wavelet Transform Architectures Using Field-Programmable Logic

Currently there are design barriers inhibiting the implementation of high-precision digital signal processing (DSP) objects with field programmable logic (FPL) devices. This paper explores overcoming these barriers by fusing together the popular distributed arithmetic (DA) method with the residue number system (RNS) for use in FPL-centric designs. The new design paradigm is studied in the context of a high-performance filter bank and a discrete wavelet transform (DWT). The proposed design paradigm is facilitated by a new RNS accumulator structure based on a carry save adder (CSA). The reported methodology also introduces a polyphase filter structure that results in a reduced look-up table (LUT) budget. The 2C-DA and RNS-DA are compared, in the context of a FPL implementation strategy, using a discrete wavelet transform (DWT) filter bank as a common design theme. The results show that the RNS-DA, compared to a traditional 2C-DA design, enjoys a performance advantage that increases with precision (wordlength).     

A Neural Network Approach for Signal Detection in Digital Communications

In this paper, we propose a new approach for signal detection in wireless digital communications based on the neural network with transient chaos and time-varying gain (NNTCTG), and give a concrete model of the signal detector after appropriate transformations and mappings. It is well known that the problem of the maximum likelihood signal detection can be described as a complex optimization problem that has so many local optima that conventional Hopfield-type neural networks fail to solve. By refraining from the serious local optima problem of Hopfield-type neural networks, the NNTCTG makes use of the time-varying parameters of the recurrent neural network to control the evolving behavior of the network so that the network undergoes the transition from chaotic behavior to gradient convergence. It has richer and more flexible dynamics rather than conventional neural networks only with point attractors, so that it can be expected to have much ability to search for globally optimal or near-optimal solutions. After going through a transiently inverse-bifurcation process, the NNTCTG can approach the global optimum or the neighborhood of global optimum of our problem. Simulation experiments have been performed to show the effectiveness and validation of the proposed neural network based method for the signal detection in digital communications.     

A study on flowshop scheduling problem combining Taguchi experimental design and genetic algorithm

As genetic algorithm parameters vary depending on different problem types when applying genetic algorithm to reach global optimum, appropriate design value selection has significant impact on the efficiency of genetic algorithm. However, most users adjust parameters manually based on the reference values of previous literature. Such trial-and-error method is time-consuming, ineffective, and often it could not locate the optimal combination. Therefore, in flowshop scheduling problems, this research anticipates to complete optimal parameter combination design in genetic algorithm using Taguchi experimental design. According to the research results, different ways of producing initial solution have significant influence on this research topic. Consequently, confirmation experiment is conducted using the optimal parameter combination obtained from the research results. It is found that the predicted value of signal-to-noise ratio (S/N ratio) and its actual value exists deviation of 0.238%, indicating repetitiveness and robustness of the obtained parameter combination. Hence, this research method can effectively reduce time spent on parameter design using genetic algorithm and increase efficiency of algorithm.     

Spare parts inventory management with demand lead times and rationing

We study an inventory system that consists of two demand classes. The orders in the first class need to be satisfied immediately, whereas the orders in the second class are to be filled in a given demand lead time. The two classes are also of different criticality. For this system, we propose a policy that rations the non-critical orders. Under a one-for-one replenishment policy with backordering and for Poisson demand arrivals for both classes, we first derive expressions for the service levels of both classes. The service level for the critical class is an approximation, whereas the service level for the non-critical class is exact. We then conduct a computational study to show that our approximation works reasonably, the benefits of rationing can be substantial, and the incorporation of demand lead time provides more value when the demand class with demand lead time is the critical class. The research is motivated by the spare parts service system of a major capital equipment manufacturer that faces two types of demand. For this company, the critical down orders need to be satisfied immediately, while the less critical maintenance orders can be satisfied after a fixed demand lead time. We conduct a case study with 64 representative parts and show that significant savings (as much as 14% on inventory on hand) are possible through incorporation of demand lead times and rationing.     

Silver nanocluster formation in silicate glass by single step ion-exchange

We report the formation of silver nanoclusters of average size ∼ 1.1 nm in a single step ion-exchanged silica glass. Silver clusters are formed in a glass during ion-exchange by fine-tuning the ion-exchange parameters such as salt composition, temperature and time. Such nanocluster formation during ion-exchange preserves the waveguiding properties of the ion-exchanged surface of the glass making them suitable for nonlinear waveguide device fabrication.     

The influence of Johnson–Cook material constants on finite element simulation of machining of AISI 316L steel

In literature, five different sets of work material constants used in the Johnson–Cook's (J–C) constitutive equation are implemented in a numerical model to describe the behaviour of AISI 316L steel. The aim of this research is to study the effects of five different sets of material constants of the J–C constitutive equation in finite-element modelling of orthogonal cutting of AISI 316L on the experimental and predicted cutting forces, chip morphology, temperature distributions and residual stresses. Several experimental equipments were used to estimate the experimental results, such as piezoelectric dynamometer for cutting forces measurements, thermal imaging system for temperature measurements and X-ray diffraction technique for residual stresses determination on the machined surfaces; while an elastic–viscoplastic FEM formulation was implemented to predict the local and global variables involved in this research. It has been observed that all the considered process output and, in particular the residual stresses are very sensitive to the J–C's material constants.     

Design space exploration of reliable networked embedded systems

In this paper, a new methodology is presented for topology optimization of networked embedded systems as they occur in automotive and avionic systems as well as wireless sensor networks. By introducing a model which is (1) suitable for heterogeneous networks with different communication bandwidths, (2) modeling of routing restrictions, and (3) flexible binding of tasks onto processors, current design issues of networked embedded systems can be investigated. On the basis of this model, the presented methodology firstly allocates the required resources which can be communication links as well as computational nodes and secondly binds the functionality onto the nodes and the data dependencies onto the links such that no routing restrictions will be violated or capacities on communication links will be exceeded. Due to the often error-prone communication in networks, we allow for routing each data dependency over multiple routes in the networks. With this strategy, our methodology is able to increase the reliability of the entire system. This reliability analysis is based on Binary Decision Diagrams (BDDs) and is integrated in our multi-objective design space exploration. By applying Evolutionary Algorithms, we are able to consider multiple objectives simultaneously during the optimization process and allow for a subsequent unbiased decision making. An experimental evaluation as well as a demonstration of a case study from the field of automotive electronics will show the applicability of the presented approach.     

An objective comparison of the cost effectiveness of three testing methods

Branch testing is a well established method for exercising software. JJ-path testing, whilst employed by some practitioners, is less popular, and the testing of JJ-pairs finds few adherents. In this paper an objective, practical study of the cost-effectiveness of these three testing methods is reported. The effectiveness of each method is assessed, in the presence of infeasible paths, not only on its ability to cover the specific structural element of code that it targets, but also on its ability to cover the structural elements targeted by the other two methods – the collateral coverage it achieves. The assessment is based on the results derived from experiments in which each of the three methods is applied to 35 units of program code.     

The effects of aperture jitter and clock jitter in wideband ADCs

Designing leading-edge systems (e.g., communication systems) requires knowledge about the technological limits. Jitter is the limiting effect in wideband ADCs with a digitization bandwidth between 1 MHz and 1 GHz. The effects of aperture jitter and clock jitter have been investigated previously. However, some very important aspects are still missing, in particular investigations on the spectral distribution of the jitter induced error. This gap is filled by this article.