Search

This paper presents hardware-friendly algorithm for Gini-index (GI) cooperative-spectrum-sensing (CSS) algorithm for the data-fusion based cooperative cognitive-radio network. It simplifies the complex computations of sample-covariance-matrix (SCM) elements and test-statistics value of the conventional GI-based CSS algorithm. It delivers excellent detection performance under the realistic scenario of non-uniform dynamical noise and signal-power. Based on GI-based CSS algorithm, three different VLSI architectures are proposed for the cooperative spectrum sensor (CSR): CSR-VLAR1, CSR-VLAR2, and CSR-VLAR3. Here, CSR-VLAR1 is the first-time reported CSR-architecture for the conventional GI-based CSS algorithm. Subsequently, CSR-VLAR2 represents hardware-architecture of the proposed hardware-friendly GI-based CSS algorithm. Eventually, additional architectural optimization has been applied to CSR-VLAR2 that is transformed into the most hardware-efficient VLSI-architecture of CSR, referred as CSR-VLAR3, which is ASIC chip-fabricated in UMC 130 nm-CMOS technology node. Furthermore, both CSR-VLAR1 and CSR-VLAR2 are synthesized and post-layout simulated in the same technology node. Our ASIC-chip of CSR-VLAR3 occupies 0.35 mm2 of core-area and consumes 8.31 mW of total power at 88.8 MHz of maximum clock frequency, when the supply voltage is 1.2 V. Our CSR ASIC-chip has been functionally verified with the aid of real-world signals, using USRPs and FPGAs based test-setup of cooperative cognitive-radio network. Measured results of our design are compared with reported implementations where the proposed CSR is $4.52\times $ hardware-efficient and $2.8\times $ power-efficient than the state-of-the-art CSR-implementations. Thus, our work addresses the key challenge of designing hardware-efficient CSR that delivers excellent detection performance in the real-world scenario. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) based Speech Emotion Recognition (SER) has been widely used in the consumer field for control of smart home personal assistants, with many such devices on the market. However, with the increase in computational power, connectivity, and the need to enable people to live in the home for longer though the use of technology, then smart home assistants that could detect human emotion will improve the communication between a user and the assistant enabling the assistant of offer more productive feedback. Thus, the aim of this work is to analyze emotional states in speech and propose a suitable method considering performance verses complexity for deployment in Consumer Electronics home products, and to present a practical live demonstration of the research. In this article, a comprehensive approach has been introduced for the human speech-based emotion analysis. The 1-D convolutional neural network (CNN) has been implemented to learn and classify the emotions associated with human speech. The paper has been implemented on the standard datasets (emotion classification) Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) and Toronto Emotional Speech Set database (TESS) (Young and Old). The proposed approach gives 90.48%, 95.79% and 94.47% classification accuracies in the aforementioned datasets. We conclude that the 1-D CNN classification models used in speaker-independent experiments are highly effective in the automatic prediction of emotion and are ideal for deployment in smart home assistants to detect emotion. [ABSTRACT FROM AUTHOR]

Illumination variation generally causes performance degradation of face recognition systems under real-life environments. Therefore, we propose an illuminationrobust face recognition system using a fusion approach based on efficient facial feature called differential two-dimensional principal component analysis (D2D-PCA) for consumer applications. In the proposed method, face images are divided into two sub-images to minimize illumination effects, and D2D-PCA is separately applied to each sub-images. The individual matching scores obtained from two sub-images are then integrated using a weighted-summation operation, and the fused-score is utilized to classify the unknown user. Performance evaluation of the proposed system was performed using an extended Yale face database B which consists of 2,414 face images for 38 subjects representing 64 illumination conditions under the frontal pose. Experimental results show that the proposed fusion approach enhanced recognition accuracy by 22.02% compared to that of 2DPCA, and we confirmed the effectiveness of the proposed face recognition system under illumination-variant environments [ABSTRACT FROM PUBLISHER]