Volume No.3 Issue No. 1 (2022) Archives

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Real-Time Emotion Recognition System using Facial Expressions and Soft Computing methodologies

S.Arun Inigo, V.Rajesh Kumar, P.Ashokram

Facial Expression conveys non-verbal cues, which plays an important role in interpersonal relations. The Cognitive Emotion AI system is the process of identifying the emotional state of a person. The main aim of our study is to develop a robust system which can detect as well as recognize human emotion from live feed. There are some emotions which are universal to all human beings like angry, sad, happy, surprise, fear, disgust and neutral. The methodology of this system is based on two stages- facial detection is done by extraction of Haar Cascade features of a face using Viola Jones algorithm and then the emotion is verified and recognized using Artificial Intelligence Techniques. The system will take image or frame as an input and by providing the image to the model the model will perform the preprocessing and feature selection after that it will be predict the emotional state.

Emotional Quotient, Emotional Intelligence, Ethnicity.

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A VLSI-BASED MULTI-LEVEL ECG COMPRESSION SCHEME FOR WEARABLE SENSOR NODE

P.Balasubramani, S.Swathi Krishna

Wearable sensor nodes, possessing constant monitoring features, produce a large amount of data. Besides, power consumption is a major constraint in these nodes in ensuring longer battery life as approximately 3/4th of the power of the sensor node is consumed during data transmission. During smart long-term monitoring of any biomedical signal in wireless body area networks, wearable sensor nodes generate and transmit a large amount of data, increasing transmission power consumption. In order to reduce data storage and power consumption, a lossless data compression technique for an electrocardiogram signal monitoring system is proposed. For this, a hybrid lossless multi-level compression algorithm based on Golomb–Rice coding and dictionary selection based on bitmask method is proposed to enhance the bit compressing rate. Golomb–Rice coding is one of the efficient lossless coding techniques that has been used where amplitude values of the input bit stream are significantly lower and with continuous runs of ones and zeros. An efficient bitmask with dictionary selection technique can create a large set of matching patterns that can significantly reduce the memory requirement for a set of repeated random data. The lossless encoding scheme is implemented on the MIT-BIH arrhythmia database, achieving a compression ratio better than the existing architecture.

High compression ratio, Wearable Sensor Nodes, Golomb–Rice coding, Compression Techniques

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LOW POWER AND AREA OPTIMIZED APPROXIMATE BOOTH MULTIPLIERS WITH ERROR COMPENSATION AND COMPRESSION

R.Gowri Shankar1 , D.R. Ananthi, 2

Approximate computing is a promising technique to elevate the performance of digital circuits which curtail the hardware requirements by exploiting the inherent error resilience of certain applications such as digital signal processing, multimedia and machine learning. Approximate multipliers and other approximation techniques can be integrated to increase the performance of applications such as convolutional Neural Networks. In the proposed work, a truncation based Booth multiplier is designed based on multi-level compressors such as 4:2, 5:2 and 6:3 counter. A compensation circuit is generated by selective modifications in k-map to circumvent the carry appearing from the truncated part. By efficient mapping, hardware pruning and output error reduction is achieved simultaneously. In the quest of power and accuracy tradeoff, Truncated and Approximate Booth Multipliers using compressors and counters (TABM-CC) are proposed with a range of designs based on truncation factor w. When compared with the state-of-the-art multipliers, TACBM outperforms in terms of accuracy and Area-Power savings.

Truncated and Approximate Booth Multipliers using compressors and counters (TABM-CC), Compressors, Counters, Digital Signal Processing

Reference:

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LOW POWER AND TIMING ERROR MITIGATION CIRCUIT DESIGN BASED ON CLOCK GATING

Parimala Gandhi Ayyavu, Jemima S

Timing error is now getting increased attention due to the high rate of error-occurrence on semiconductors. Even slight external disturbance can threaten the timing margin between successive clocks since the latest semiconductor operates with high frequency and small supply voltage. To deal with a timing error, many techniques have been introduced. Nevertheless, existing methods that mitigate a timing error mostly have time-delaying mechanisms and too complex operation, resulting in a timing problem on clock-based systems and hardware overhead. In the proposed work a novel timing-error-tolerant method that can correct a timing error instantly through a simple mechanism is demonstrated. By modifying a clock in a flip-flop, the proposed system can recover a timing error without the loss of time in the clock-based system. Furthermore, in order to reduce power consumption in the stages were operation is not performed clock gating mechanism is used to reduce the unwanted transition. Look-Ahead Clock Gating (LACG) computes the clock enabling signals of each FF one cycle ahead of time, based on the present cycle data of those FFs on which it depends. It has however a big advantage of avoiding the tight timing constraints of earlier methods, by allotting a full clock cycle for the enabling signals to be computed and propagate to their gaters.

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Volume No.3 Issue No. 1 (2022)

Real-Time Emotion Recognition System using Facial Expressions and Soft Computing methodologies

A VLSI-BASED MULTI-LEVEL ECG COMPRESSION SCHEME FOR WEARABLE SENSOR NODE

LOW POWER AND AREA OPTIMIZED APPROXIMATE BOOTH MULTIPLIERS WITH ERROR COMPENSATION AND COMPRESSION

LOW POWER AND TIMING ERROR MITIGATION CIRCUIT DESIGN BASED ON CLOCK GATING

ARTIFICIAL INTELLIGENT BASED COMPUTATIONAL MODEL FOR DETECTING CHRONIC-KIDNEY DISEASE

A DEEP LEARNING APPROACH FOR LSTM BASED COVID-19 FORECASTING SYSTEM

COLLABORATE FRAMEWORK BASED ON SOFTWARE DEFINED NETWORK IN MANET

THE IMPROVED MATHEMATICAL MODEL FOR CONTINUOUS FORECASTING OF THE EPIDEMIC

Recurrent Neural Network Based Speech emotion detection using Deep Learning

Disease Identification using Machine Learning and NLP

PREDICTION OF EDUCATIONAL DATA USING DEEP CONVOLUTIONAL NEURAL NETWORK

MACHINE LEARNING IMPROVED ADVANCED DIAGNOSIS OF SOFT TISSUES TUMORS

NEUTROSOPHIC THORY AND SENTIMENT ANALYSIS TECHNIQUE FOR MINING AND RANKING BIG DATA FROM ONLINE EVALUATION

Herbal treatment of Dengue Fever