Advanced Encryption Standard (AES) is the widely used technique in critical cyber security applications.In AES architecture S-box is the most important block.However, the power consumed by S-box is 75% of the total AES design.The S-box is also prone to Differential Power Analysis (DPA) attack which is one of the most threatening types of attacks in cryptographic systems.In this paper, a three-stage positive polarity Reed-Muller (PPRM) S-box is implemented with 45 nm FinFET using Efficient Charge Recovery Logic (ECRL) to reduce power consumption.The simulation results indicate up to 66% power savings for FinFET based S-box as compared to CMOS design.Further, the FinFET ECRL 8-bit S-box circuit is evaluated for transitional energy fluctuations and peak current traces to compare its resistance against side-channel attacks.The lower energy variations and uniform current trace exhibit the improved security performance of the circuit to withstand DPA and Differential Electromagnetic Radiation Attacks (DEMA).
A 64-bit Carry Look-ahead Adder (CLA) with radix-four structure is implemented using CMOS and FinFET-based asynchrobatic logic. The performance of designed adder circuits is assessed by comparing their power, energy, and power-delay product with those using 45 nm static CMOS technology over a range of frequencies and supply voltages. The results obtained reveal power and energy savings of up to 82 and 97% for CMOS and FinFET asynchrobatic adders, respectively, as compared to static CMOS circuits. A maximum PDP improvement of 89.47% at 0.5 V supply voltage is obtained with FinFET technology.
Technical thirst of man is in exponential rise and posing critical challenges in using this technology. This is much evident in the design of VLSI circuits. Sequential circuit designing demands lesser energy consumption, smartness and increased functional density. In this domain, every development in the recent past has energy as the focal point. MTCMOS exactly serves the purpose of reduced power consumption in digital circuits. This technique provides lower leakage current and offers enhanced speed. It uses low threshold voltage devices for low leakage and high threshold voltage components as sleep transistors. These sleep transistors are good enough to isolate the logic modules from the supply, ground in order to reduce the leakage current. Care is ensured particularly in the mode transition and also the least possible time for turn ON state in a circuit, as these are the primary concerns for power consumption and thereby for the performance degradation of integrated circuits. In this paper, a successful attempt was made in enhancing the advantage of employing MTCMOS technique towards lesser power consumption in sequential circuit designing.
This paper presents digital image processing and its representation using binary image; grayscale, color images with the help of additive color mixing, subtractive color mixing, and histogram. It is also discusses the fundamental steps involved in an image processing such as image achievement, image development, image renovation, compression, wavelets, multi-resolution processing, morphological processing, representation, description and interpretation. Finally, it presents the per-pixel and filtering operations like invert filter, grayscale, brightness and color splitting filter.
Speaker recognition approach through wavelet analysis as well as support vector machines is presented in this paper. The wavelet-based approach is used to differentiate among regular and irregular voices. The wavelet filter banks were utilized to coincide by means of support vector machine for extraction of the feature and its classification. This approach creates utilization of wavelets as well as support vector machine to separate particular speech signal through multi-dialog settings. In this approach, first we apply the wavelets to calculate audio features those have sub-band power and calculated pitch values from the given data of the speech. Multi-speaker separation of speech data is carried out by the utilization of SVM more than these audio features as well as other values of the signal. This entire database was utilized to calculate the performance of the system and it represents over 95 % accuracy.
