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Digital filter design optimization counts as one of the most challenging problems in circuit development. Furthermore, the bit-width allocation has a crucial impact on the efficiency and accuracy of fixed-point digital filters. The conventional implementation procedure involves studying the bit-width allocation problem after determining the filter coefficients. This sequential procedure leads frequently to an excessive bit-width allocation, hence an extra implementation cost. The main contribution of this paper is to propose a new implementation procedure based on the simultaneous handling of both design issues and the bit-width optimization for the infinite impulse response filter. In this study, the problem is formalized by a multi-objective programming technique using the non-dominated sorting genetic algorithm II. The joint consideration of the two problems offers a better trade-off between design and bit-width allocation. The output of our approach is compared to the results of existing algorithms. Simulation results show that the multi-objective method significantly reduces the implementation cost while ensuring filter stability and an acceptable mean square error (MSE) compared to the existing algorithms. [ABSTRACT FROM AUTHOR]

This paper addresses the precision analysis of filters in the parallel form framework. The precision analysis consists of determining suitable fractional bit-widths to set a tradeoff between resource consumption and computational accuracy. Although the stability of the parallel form is relatively better controlled than the related direct form, its bit-width optimization did not receive much consideration in the literature, despite its significant contribution to the optimization of the filter's physical implementation. To carry out the needed bit-width optimization, we present two heuristics based on the Estimation of Distribution Algorithm, which falls within the category of probabilistic model-building genetic algorithm. The performance of the proposed approach is discussed, and compared to chosen benchmarks, the results show that our hardware implementations reduce the cost of the resulted circuits up to 37 % . [ABSTRACT FROM AUTHOR]

Word-length allocation is the most important design phase to optimize hardware resources while guaranteeing a determined accuracy for circuits with fixed-point numbers. This paper presents an enhanced precision analysis for degree-n polynomial Horner's rule. It is based on affine arithmetic and introduces an error propagating formula for a degree-n polynomial Horner's rule. It takes into account quantization error of all the circuit's connections including the inputs. Furthermore, a tighter upper bound error is defined, exploiting the dependencies between intermediate connections. Hardware implementations show that the proposed upper bound results in an area reduction that reaches 70 percent. [ABSTRACT FROM AUTHOR]

Elementary functions are widely integrated in many embedded applications such as speech recognition and signal processing. This paper deals with the issues related to the hardware implementation of those functions. Indeed, a novel efficient Error‐Margin aware Approach for non‐uniform Segmentation of Elementary Functions using degree‐N Polynomial Approximation (EMASEF‐NPA) has been proposed. The bit‐width optimization (BWO) process has been used to obtain a better representation of the approximation polynomial coefficients. The principle of content‐addressable memory (CAM) and Horner's rules have been used in the physical implementation. Functional verification, logical synthesis, and physical implementation were performed using the FPGA platform. The results obtained using EMASEF‐NPA approach show a significant reduction, up to 70%, in the number of segments compared to conventional approaches. The findings of the physical implementation show a considerable decrease in area and delay. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Abstract The IEEE 802.11p standard introduces new specifications to improve communications in vehicular ad hoc networks (VANETs). The MAC sub-layer of the IEEE 802.11p standard uses Enhanced Distributed Channel Access (EDCA) to introduce service differentiation when accessing the channel, by using four access categories (ACs). In this paper we propose an analytical model, based on two-dimensional Markov chain, for the IEEE 802.11p EDCA mechanism under saturation traffic conditions and error-prone channel. The major key aspects of the IEEE 802.11p EDCA mechanism are captured by the model as defined in the standard. These include the AIFS waiting procedure, backoff counter freezing, virtual collisions between the ACs of the same station, external collisions with other stations in the network, post-collision period, and retransmission limit. Moreover, the probability of error in the channel is explicitly considered. This model allows us to analyze the performance of VANET. Normalized throughput and average access delay are considered as quality of service (QoS) metrics. In addition, the impact of Bit Error Rate (BER) and frame size on network performance is also investigated. The obtained results confirm that the probability of error is a parameter that has a significant impact on network performance accuracy. Discarding this parameter in the IEEE 802.11p EDCA model limits the capabilities of the analytical model, because the model will not be able to correctly predict the performance of the network in an error-prone channel. They also confirm that the increase in the density of vehicles and the BER result in less efficient use of the network by increasing the collision rate; and that the use of small frames is one of the necessary aspects for applications with strict constraints of delay. [ABSTRACT FROM AUTHOR]

Magnetic resonance imaging (MRI) is widely used in the medical field, especially for detecting serious abnormalities affecting the organs of the human body, such as tumors. Automatic detection of tumors needs high‐performance recognition techniques. In this paper, we have developed a new automatic method based on the multisegmentation of brain tumor region. We used an improved region‐growing algorithm, which is based on quasi‐Monte Carlo and expectation maximization methods to define the desired classes. Several metrics were calculated to evaluate the performance of our technique. The fully automatic multisegmentation approach, developed in this study, showed good performance, and it can offer a new option to replace conventional techniques used for tumor detection in MRI images. [ABSTRACT FROM AUTHOR]