Shahpour Alirezaee

Optimized implementation of memristor-based full adder by material implication logic

Recently memristor-based applications and circuits are receiving an increased attention. Furthermore, memristors are also applied in logic circuit design. Material implication logic is one of the main areas with memristors. In this paper an optimized memristor-based full adder design by material implication logic is presented. This design needs 27 memristors and less area in comparison with typical CMOS-based 8-bit full adders. Also the presented full adder needs only 184 computational steps which enhance former full adder design speed by 20 percent.

Memristor-based linear feedback shift register based on material implication logic

Memristor as an emerging history dependent nanometer scaled element will play an important role in future nanoelectronic computing technologies. Some pure and hybrid memristor-based implementation techniques have been proposed in recent years. Material implication logic is one of the significant areas for memristor-based logic implementation. In this paper a memristor-based linear feedback shift register is implemented based on material implication logic. It is implemented by 8 memristors which is considerably used less area in comparison with conventional CMOS-based peers. Also the proposed memristor-based LFSR circuit needs 55 computational steps for generating a 4-bits number.

A Hopf Resonator for 2-D Artificial Cochlea: Piecewise Linear Model and Digital Implementation

The mammalian auditory system is able to process sounds over an extraordinarily large dynamic range, which makes it possible to extract information from very small changes both in sound amplitude and frequency. Evidently, response of the cochlea is essentially nonlinear, where it operates within Hopf bifurcation boundaries to maximize tuning and amplification. This paper presents a set of piecewise linear (PWL) and multiplierless piecewise linear (MLPWL1 and MLPWL2) active cochlear models, which mimic a range of behaviors, similar to the biological cochlea. These proposed models show similar dynamical characteristics of the Hopf equation for the active nonlinear artificial cochlea. Accordingly, a compact model structure is proposed upon which a 2-D cochlea is developed. The proposed models are investigated, in terms of their digital realization and hardware cost, targeting large scale implementation. Hardware synthesis and physical implementation on a FPGA show that the proposed models can reproduce precise active cochlea behaviors with higher performance and considerably lower computational costs in comparison with the original model. Results indicate that the MLPWL1 model has a lower computational overhead, precision, and hardware cost, while the PWL model has a higher precision and dynamically tracks the original model. On the other hand, the MLPWL2 model outperforms the others in terms of accuracy, dynamical tracking of the original model and implementation cost. The gain variations of the original, PWL, MLPWL1, and MLPWL2 models are 230, 100, 105, and 230 dB, respectively. The mean normalized root mean square errors (NRMSEs) of the PWL, MLPWL1, and MLPWL2 models are 0.11%, 11.97%, and 0.34%, respectively, as compared to the original cochlear model.

Comparison of the Legendre, Zernike and Pseudo-Zernike Moments for Feature Extraction in Iris Recognition

In this paper we compare the performance of Legendre moments, Zernike moments and Pseudo-Zernike moments in feature extraction for iris recognition. We have increased the moment orders until the best recognition rate is achieved. Robustness of these moments in various orders has been evaluated in presence of White Gaussian Noise. Numerical results indicate that recognition rate by the Legendre, Zernike and Pseudo-Zernike moments in higher orders are approximately identical. However, average computation time for feature extraction is 4.5, 18 and. 75 seconds respectively for the Legendre, Zernike and Pseudo-Zernike moments of order 14. On the other hand, the result indicates the Legendre moment is more robust than the others against the white Gaussian noise.

A multiplierless implementation of cascade integrator comb filter

This paper presents a cascaded integrator comb compensator filter with a multiplierless implementable structure. A second order linear phase filter is realized with minimum error approximation of the coefficients. The compensator filter coefficients are presented in a canonical signed digits form. Presented design can be implemented using low cost hardware units such as add and shift operations. The results show that the compensator filter not only reduces the power consumption but also improves the pass band drop of the CIC filter.

A novel memristor based integrate-and-fire neuron implementation using material implication logic

Neural network computing philosophy is proposed to model the major features of human brain and to apply neurons functionality to build Computers capable of simulating features of the brain. Memristor is a new device that stores data as memory element and perform logic operations as a computational element with low surface area and power consumption features. These characteristics of memristors have introduced them as a brilliant candidate for neural networks realization. In this paper, a threshold Integrate-and-Fire memristor based neuron is presented and is implemented by IMPLY logic with two 4-bits inputs, which is easily extendable to higher dimensions in terms of network scale and/or precision. Corresponding calculations are performed using adders and comparators, which requires 30 memristors in 131 computational steps.

Dual-Band Bandstop Filter Using Modified Stepped-Impedance Hairpin Resonators

In this paper, a dual-band band stop filter is designed based on a modified stepped impedance hairpin resonator in which, two open stubs are embedded in the structure. There are two transmission zeros in each one of the stop bands. The center frequencies of the stop bands are placed at 2 and 5.92 GHz and their widths are 0.94 and 0.55 GHz, respectively. These stop bands can be adjusted by changing the dimensions of the embedded open stubs. This filter is then transferred to another substrate, fabricated and measured and the results for the two substrates are compared.

Performance Improvement of Microstrip LPF Based on Transfer Function Analysis

A high performance microstrip LPF with adjustable cut-off frequency and outstanding specifications is designed, analyzed, and implemented. The structure comprises a meandered transmission line and modified and improved T-shaped resonators. In order to describe the design theory, the transfer function based on an LC equivalent circuit is calculated to adjust the cut-off frequency and the location of the transmission zeros (TZs). The proposed LPF has a -3-dB cut-off frequency equal to 1.208 GHz and a narrow transition band of 0.188 GHz. The proposed LPF achieves a wide stopband with 14th harmonic suppression, insertion loss less than 0.4 dB, and low VSWR. Finally, the fabricated LPF reaches an extremely high figure-of-merit of 102182, which is considered as a significant achievement.

True RMS-DC converter based on Differential Difference Current Conveyor

This paper presents a true RMS-DC converter based on DDCC building block. Working over a wide range of input current (10μA-100μA), utilizing a general purpose building block and using a small value capacitor; makes this circuit a good candidate for biomedical and analog signal processing applications. Circuit is implemented using standard current-mode building block (DDCC). Compared with previous works, this circuit calculates True RMS of the arbitrary shaped input current over a wider range of amplitude with a higher accuracy where the output RMS value deviate the real value no more than 3% in selected input shapes.

Primary user activity prediction using the hidden Markov model in cognitive radio networks

Cognitive radio (CR) is a system for sense and access of spectrum opportunistically. It is designed on spectrum holes in primary users (PU) over licensed frequency bands. Determining access time for the secondary user (SU) is one of the most important issues in cognitive radio systems. This spectrum availability can be optimized by applying learning methods. In this paper, the hidden Markov model (HMM) is applied to determine and predict channel activity patterns. Specifically, a sensing frame structure is proposed to learn the channel activity pattern and apply the patterns as training vectors; afterward, the HMM model is modified for predicting the channel usage activity by PU. Three traffic patterns are considered as Heavy Traffic, Balanced Traffic and Slow Traffic. The results indicate 72% validity in Balanced Traffic while unbalanced traffic decreases prediction validity to 56%.