A programmable high-frequency operational transconductance amplifier (OTA) is proposed and analyzed. A general configurable analog block (CAB) is presented, which consists of the proposed programmable OTA, programmable capacitor and MOSFET switches. Using the CABs, the universal tunable and field programmable analog array (FPAA) can be constructed, which can realize many signal-processing functions, including filters. A tuning circuit is also discussed. The proposed OTA has been simulated and fabricated in CMOS technology. The results show that the OTA has the transconductance tunable/programmable in a wide range of 700 times and the -3-dB bandwidth larger than 20 MHz. A universal 5/spl times/8 CAB array has been fabricated. The chip has also been configured to realize OTA-C 60-kHz and 500-kHz bandpass filters based on ladder simulation and biquad cascade.

In this article, a stress monitoring system tailored for individuals with Autism Spectrum Disorders (ASD) and developed for the educational institution is presented. People with ASD face problems with effective stress management due to their high self-perceived levels of stress, poor ability to cope with it, and difficulties with the accurate detection of the source of stress. Consistently, being able to measure stress appears to be highly vital for this specific group. The design and construction of the measuring system are introduced. The stress monitoring system including autonomic wearable device (wristband), both the electronics and mechanical part, as well as the software application for data analysis are shown. The wearable device includes sensors for measuring heart rate, skin resistance, temperature and movement. A dedicated software application allows for generating reports to evaluate therapeutic effects. The complex stress monitoring system has been tested in an educational institution. It can provide significant support in the stress management of people with ASD and become a meaningful assistance in the therapy of neurodiverse individuals.

This paper presents an algorithm for the measurement of the human heart rate, using photoplethysmography (PPG), i.e., the detection of the light at the skin surface. The signal from the PPG sensor is processed in time-domain; the peaks in the preprocessed and conditioned PPG waveform are detected by using a peak detection algorithm to find the heart rate in real time. Apart from the PPG sensor, the accelerometer is also used to detect body movement and to indicate the moments in time, for which the PPG waveform can be unreliable. This paper describes in detail the signal conditioning path and the modified algorithm, and it also gives an example of implementation in a resource-constrained wrist-wearable device. The algorithm was evaluated by using the publicly available PPG-DaLia dataset containing samples collected during real-life activities with a PPG sensor and accelerometer and with an ECG signal as ground truth. The quality of the results is comparable to the other algorithms from the literature, while the required hardware resources are lower, which can be significant for wearable applications.

The application of a Bluetooth skin resistance sensor in assisting people with Autism Spectrum Disorders (ASD), in their day-to-day work, is presented in this paper. The design and construction of the device are discussed. The authors have considered the best placement of the sensor, on the body, to gain the most accurate readings of user stress levels, under various conditions. Trial tests were performed on a group of sixteen people to verify the correct functioning of the device. Resistance levels were compared to those from the reference system. The placement of the sensor has also been determined, based on wearer convenience. With the Bluetooth Low Energy block, users can be notified immediately about their abnormal stress levels via a smartphone application. This can help people with ASD, and those who work with them, to facilitate stress control and make necessary adjustments to their work environment.

This paper describes the FPGA-based hardware implementation of an algorithm for an automatic traffic surveillance sensor network. The aim of the algorithm is to extract moving vehicles from real-time camera images for the evaluation of traffic parameters, such as the number of vehicles, their direction of movement and their approximate speed, using low power hardware of a sensor network node. A single, stationary, monochrome camera is used, mounted at a location high above the road. Occlusions are not detected, however simple shadow and highlight elimination is performed. The algorithm is designed for frame-rate efficiency and is specially suited for pipelined hardware implementation. The authors, apart from the careful selection of particular steps of the algorithm and the modifications towards parallel implementation, also proposed novel improvements such as backgrounds’ binary mask combination or non-linear functions in highlight detection, resulting in increasing the robustness and efficiency of hardware realization. The algorithm has been implemented in FPGA and tested on real-time video streams from an outdoor camera.