Showing 6 results for Razavi
F. Askarian, Dr. S.m. Razavizadeh, Dr. F. Haddadi,
Volume 11, Issue 4 (December 2015)
Abstract
In this channel,we study rate region of a Gaussian two-way diamond channel which operates in half-duplex mode. In this channel, two transceiver (TR) nodes exchange their messages with the help of two relay nodes. We consider a special case of the Gaussian two-way diamond channels which is called Compute-and-Forward Multiple Access Channel (CF-MAC). In the CF-MAC, the TR nodes transmit their messages to the relay nodes which are followed by a simultaneous communication from the relay nodes to the TRs. Adopting rate splitting method in the terminal encoders and then using Compute-and-Forward (CF) relaying and decoding the sum of messages at the relay nodes, an achievable rate region for this channel is obtained. To this end, we use a superposition coding based on lattice codes. Using numerical results, we show that our proposed scheme has better performance than other similar methods and achieves a tighter gap to the outer bound.

S. M. Razavi, S. M. Razavi,
Volume 15, Issue 3 (September 2019)
Abstract
Probabilistic-based methods have been used for designing noise tolerant circuits recently. In these methods, however, there is not any reliability mechanism that is essential for nanometer digital VLSI circuits. In this paper, we propose a novel method for designing reliable probabilistic-based logic gates. The advantage of the proposed method in comparison with previous probabilistic-based methods is its ultra-high reliability. The proposed method benefits from Markov random field (MRF) as a probabilistic framework and triple modular redundancy (TMR) as a reliability mechanism. A NAND gate is used to show the design methodology. The simulation results verify the noise immunity of the proposed MRF-based gate in the presence of noise. In addition, the values from reliability estimation program show the reliability of 0.99999999 and 0.99941316 for transistor failure rates of 0.0001 and 0.001, respectively, which are much better as compared with previous reported MRF-based designs.
S. M. Razavi, S. M. Razavi,
Volume 16, Issue 4 (December 2020)
Abstract
The Markov random field (MRF) theory has been accepted as a highly effective framework for designing noise-tolerant nanometer digital VLSI circuits. In MRF-based design, proper feedback lines are used to control noise and keep the circuits in their valid states. However, this methodology has encountered two major problems that have limited the application of highly noise immune MRF-based circuits. First, excessive hardware overhead that imposes a great cost, power consumption and propagation delay on the circuits and second, separate implementation of feedback lines that adds further delay to the circuits. In this paper, we propose a novel approach for minimal-cost inherent-feedback implementation of low-power MRF-based logic gates. The simulation results, which are based on 32nm BSIM4 models, demonstrate that besides excellent noise immunity of the proposed method, it has the least propagation delay in comparison with all of the previously reported MRF-based gates due to its inherent feedbacks. In addition, the proposed method outperforms competing ones, which have comparable noise immunity, in other circuit metrics like cost and power consumption. Specifically, the proposed method achieves at least 18%, 29%, and 39% reductions in cost, delay and power consumption with considerable noise immunity improvement compared with competing methods.
A. Karizi, S. M. Razavi, M. Taghipour-Gorjikolaie,
Volume 18, Issue 1 (March 2022)
Abstract
There are two serious issues regarding gait recognition. The first issue presents when the walking direction is unknown and the other one presents when the appearance of the user changes due to various reasons including carrying a bag or changing clothes. In this paper, a two-step view-invariant robust system is proposed to address these. In the first step, the walking direction is determined using five features of pixels of the leg region from gait energy image (GEI). In the second step, the GEI is decomposed into rectangular sections and the influence of changes in the appearance is confined to a small number of sections that could be eliminated by masking these sections. The system performs very well because the first step is computationally inexpensive and the second step preserves more useful information compared to other methods. In comparison with other methods, the proposed method shows better results.
M. Soruri, S. M. Razavi, M. Forouzanfar,
Volume 18, Issue 3 (September 2022)
Abstract
Power amplifier is one of the main components in the RF transmitters. It must provide various stringent features that can lead to complicating the design. In this paper, a new optimizing method based on the inclined planes system optimization algorithm is presented for the design of a discrete power amplifier. It is evaluated in a 2.4-3 GHz power amplifier, which is designed based on “Cree’s CGH40010F GaN HEMT”. The optimization goals are input and output return losses, Power Added Efficiency, and Gain. Large signal simulation of the optimized power amplifier shows a good performance across the bandwidth. In this frequency range, the input and output return losses are about lower than -10 dB, the Power Added Efficiency is greater than 51%, while the Gain is higher than 13.5 dB. A two-tone test with a frequency space of 1 MHz is applied for the linearity evaluation of the designed power amplifier. The obtained result shows that the power amplifier has good linearity with a low memory effect.
Ehsan Ghasemi, Seyyed Mohammad Razavi, Sajad Mohamadzadeh,
Volume 20, Issue 4 (December (Special Issue on ADLEEE) 2024)
Abstract
This study proposes a descriptor-based approach combined with deep learning, which recognizes facial emotions for safe driving. Paying attention to the driver's facial expressions is crucial to address the increasing road accidents. This project aims to develop a Facial Emotion Recognition (FER) system that monitors the driver's facial expressions to identify emotions and provide instant assistance for safety control. In the initial stage, Viola-Jones face detection was employed to detect the facial region, followed by Butterworth high-pass filtering to enhance the identified region for locating the eye, nose, and mouth regions, using Viola-Jones face detection. Secondly, the Local Binary Patterns (LBP) feature descriptor is utilized to extract features from the identified eye, nose, and mouth regions. Using 3 RGB channels, the extracted features from these three regions are fed into RessNet-50 and EfficientNet deep networks. The outputs of the two deep learning models' classifiers are combined and integrated using two ensemble methods: ensemble maximum voting and ensemble mean. Based on these combining classifier rules, the performance was evaluated on the JAFFE and KMU-FED databases. The experimental results demonstrate that the proposed method can effectively and with higher accuracy than other competitors recognize emotions in the JAFFE and KMU-FED datasets. The novelty and originality of this paper lie in its significant application in the automotive industry. Implementing our proposed method in a system capable of high accuracy and precision can help mitigate numerous driving hazards. Our approach has achieved 99% and 98% accuracy on the JAFFE and KMU-FED databases, respectively. This high level of accuracy, coupled with its practical relevance, underscores the innovative nature of our work.