Showing 7 results for Mirzakuchaki
Khodabandehloo, Mirzakuchaki, Karimi,
Volume 2, Issue 1 (January 2006)
Abstract
The mixed-signal circuits with both analog and digital blocks on a single chip have wide applications in communication and RF circuits. Integrating these two blocks can cause serious problems especially in applications requiring fast digital circuits and high performance analog blocks. Fast switching in digital blocks generates a noise which can be introduced to analog circuits by the common substrate. This noise can decrease the performance of mixed-signal circuits therefore, studying this noise and the way it is transmitted will lead to solutions for reducing it and improving mixed-signal circuit’s performance. In this paper, an efficient model for substrate is extracted from Green’s function in MATLAB environment, and its accuracy is demonstrated. Using a VCO and a multiplier as analog and digital blocks, respectively and simulating them along with the proposed model of the substrate, the effects of substrate noise coupled to analog blocks are shown. Finally, some methods for reducing this noise are applied to the circuit, and the results are compared to each other. The results indicate that using P+ Guard Rings is the best method for reducing substrate noise in the mixed-signal circuits.
Gh. R. Karimi, and S. Mirzakuchaki,
Volume 4, Issue 4 (October 2008)
Abstract
During the past few years, a lot of work has been done on behavioral models and
simulation tools. But a need for modeling strategy still remains. The VHDL-AMS language
supports the description of analog electronic circuits using Ordinary Differential Algebraic
Equations (ODAEs), in addition to its support for describing discrete-event systems. For
VHDL-AMS to be useful to the analog design community, efficient semiconductor device
models must be available. In this paper, potential merits of the new IEEE VHDL-AMS
standard in the field of modeling semiconductor devices are discussed. The device models
for diodes and the principles, techniques, and methodology used to achieve the design of an
analytical third generation Spice transistor MOS model named EKV are presented. This is
done by taking into account the thermoelectrical effect in VHDL-AMS, and with relevant
parameters set to match a deep submicron technology developed in VHDL-AMS. The
models were validated using System Vision from Mentor Graphics.
S. Mirzakuchaki, Z. Paydar,
Volume 14, Issue 4 (December 2018)
Abstract
In this study a method has been introduced to map the features extracted from the recorded electromyogram signals from the forearm and the force generated by the fingers. In order to simultaneously record of sEMG signals and the force produced by fingers, 9 requested movements of fingers conducted by 10 healthy people. Estimation was done for 6 degrees of freedom (DoF) and generalized regression neural network (GRNN) was selected for system training. The optimal parameters, including the length of the time windows, the parameters of the neural network, and the characteristics of the sEMG signal were calculated to improve the performance of the estimate. The performance was obtained based on R2 criterion. The Total value of R2 for 6 DoF was 92.8±5.2% that obtained by greedy looking system parameters in all the subjects. The result shows that proposed method can be significant in simultaneous myoelectric control.
S. Mirzakuchaki, A. Heidari,
Volume 15, Issue 2 (June 2019)
Abstract
With the advent and development of the Internet of Things, new needs arose and more attention was paid to these needs. These needs include: low power consumption, low area consumption, low supply voltage, higher security and so on. Many solutions have been proposed to improve each one of these needs. In this paper, we try to reduce the power consumption and enhance the security by using SPGAL, a DPA-resistant Logic, and Carbon Nanotube FETs (CNTFETs) instead of conventional CMOS and MOSFET technology, for IoT devices. All simulations are done with HSPICE.
A. Amiri, S. Mirzakuchaki,
Volume 16, Issue 3 (September 2020)
Abstract
Watermarking has increased dramatically in recent years in the Internet and digital media. Watermarking is one of the powerful tools to protect copyright. Local image features have been widely used in watermarking techniques based on feature points. In various papers, the invariance feature has been used to obtain the robustness against attacks. The purpose of this research was based on local feature-based stability as the second-generation of watermarking due to invariance feature to achieve robustness against attacks. In the proposed algorithm, initially, the points were identified by the proposed function in the extraction and Harris and Surf algorithms. Then, an optimal selection process, formulated in the form of a Knapsack problem. That the Knapsack problem algorithm selects non-overlapping areas as they are more robust to embed watermark bits. The results are compared with each of the mentioned feature extraction algorithms and finally, we use the OPAP algorithm to increase the amount of PSNR. The evaluation of the results is based on most of the StirMark criterion.
S. A. Karimi, S. Mirzakuchaki,
Volume 17, Issue 4 (December 2021)
Abstract
Various methods have been proposed to detect the attention and perception of an operator during tasks such as radar monitoring. Due to the high accuracy of electroencephalographic signals, it is utilized for systems based on brain signal. The event-related potential (ERP) technique has been widely used for testing theories of perception and attention. Brain-computer Interface (BCI) provides the communication link between the human’s brain and an external device. In this article, we propose a method to investigate the attention of operators of very sensitive monitoring devices, in particular, the operators of navy ships’ radars in detecting fighter aircrafts. Using a Visual Stimuli, which was shown to the subjects prior to the test, the protocol utilized in this paper yielded a very high accuracy (up to 87%), which makes it a robust method to use in such conditions. Linear LDA and non-linear SVM classifiers were utilized in processing the output signal. Although several P300 systems have been used to detect attention using pattern recognition techniques, the novelty of this study is that attention detection is used for the first time for a radar operator which resulted in acceptable accuracy.
Maryam Akbari, Sattar Mirzakuchaki, Mahdi Fazeli, Mohammad Reza Tarihi,
Volume 19, Issue 4 (December 2023)
Abstract
In light of the growing prevalence of Internet of Things (IoT) devices, it has become essential to incorporate cryptographic protection techniques for high-security applications. Since IoT devices are resource-constraints in terms of power and area, finding cost-effective ways to enhance their security is necessary. Physical unclonable function (PUF) is considered a trusted hardware security mechanism that generates true and intrinsic randomness by extracting the inherent process variations of circuits. In this paper, a novel pure magnetic memory-based PUF is presented. The fundamental building blocks of the proposed PUF design are magnetic devices, the so-called mCells. These magnetoresistive devices exclusively utilize Magnetic Tunnel Junction (MTJ) components. Using purely MTJ in the main memory and sense amplifier in the proposed PUF leads to high randomness, high reliability, low power, and ultra-compact occupation area. The Monte Carlo HSPICE simulation results demonstrate that the proposed PUF achieves a uniqueness of 49.89%, uniformity of 50.02 %, power consumption of 1.43 µW, and an area occupation of 0.01 µm2 per bit.
