Quantum‐dot cellular automata (QCA) is an emerging field‐coupled nanotechnology that promises to outperform existing CMOS technology. Single‐layer QCA designs of full adder, full subtractor, ripple carry adder, and ripple borrow subtractor is proposed. The proposed designs are improved over both existing single‐layer designs and multilayer designs.

Stress may be defined as the reaction of the body to regulate itself to changes within the environment through mental, physical, or emotional responses. Recurrent episodes of acute stress can disturb the physical and mental stability of a person. This subsequently can have a negative effect on work performance and in the long term can increase the risk of physiological disorders like hypertension and psychological illness such as anxiety disorder. Psychological stress is a growing concern for the worldwide population across all age groups. A reliable, costefficient, acute stress detection system could enable its users to better monitor and manage their stress to mitigate its long-term negative effects. In this article, we will review and discuss the literature that has used machine learning based approaches for stress detection. We will also review the existing solutions in the literature that have leveraged the concept of edge computing in providing a potential solution in real-time monitoring of stress.

Quantum circuits for mathematical functions such as division are necessary to use quantum computers for scientific computing. In this work, we propose two quantum integer division circuits. The first proposed quantum integer division circuit is based on the restoring division algorithm and the second proposed design implements the non-restoring division algorithm. Both proposed designs are optimized in terms of T-count, T-depth and qubits. Both proposed quantum circuit designs are based on (i) a quantum subtractor, (ii) a quantum adder-subtractor circuit, and (iii) a novel quantum conditional addition circuit. Our proposed restoring division circuit achieves average T-count savings from 79.03% to 91.69% compared to the existing works. Our proposed non-restoring division circuit achieves average Tcount savings from 49.75% to 90.37% compared to the existing works. Further, both our proposed designs have linear T-depth. We also illustrated the application of the proposed quantum division circuits in quantum image processing with a case study of quantum bilinear interpolation.

In this work, our objective is to design, develop, and evaluate the effectiveness of a stress detection model for older adults using a system of wrist-worn sensors. Our system uses four signals, EDA, BVP, IBI, and ST from EDA, PPG, and ST sensors, embedded in a smart wristband, to classify between stressed and not-stressed state. The stress reference is obtained from salivary cortisol measurement, which is a well established clinical biomarker for measuring physiological stress. This work is the result of year-long data collection and analysis of 40 older adults (28 females and 12 males) and age 73.625 ± 5.39. EDA, BVP, IBI, and ST signals were collected during TSST (Trier Social Stress Test), which is a well known experimental protocol to reliably induce stress in humans in a social setting. 47 features were extracted from EDA, BVP, IBI, and ST signals, out of which 27 features were selected using a supervised feature selection method. Results and analysis show that combining the features from all the four signal streams increases the model's ability to accurately distinguish between the stressed and not-stressed states. The proposed model achieved a macro-average F1-score of 0.92 and an accuracy of 94% in distinguishing between the two states when features from all the four signals were used. Further, we prototype the proposed stress detection model in a consumer end device with voice capabilities, so that users can receive feedback on their vitals and stress levels by querying on voice-enabled consumer devices such as smartphones and smart speakers.