Photoplethysmogram (PPG) signal reacts according to the volumetric change of blood, that is related to the total cardiovascular system. Hence PPG signals have a very close relation with cardiovascular diseases. As the number of cardiovascular disease (CVD) patients is increasing day by day, it is necessary to develop digital systems for early detection and treatment. In this study, we have aimed to design a field programmable gate array (FPGA) based hardware system that can detect various CVDs. We have chosen six different combinations of diseases from the PPG BP dataset. The selected signals are denoised using filters, and then features are extracted. Then SVM classifier is used to determine the class of the preprocessed signals. For FPGA implementation, we have targeted Xilinx zynq ultrascale+. The system utilizes different logic and signal processing blocks to detect the result. The resource utilization analysis proves the efficacy of the selected FPGA board. The power utilization analysis depicts that the designed prototype requires 1.403 W of power, which is sensible due to the requirement of large number of logic blocks. The accuracy of the system of detecting multiclass diseases is 79.83%. In the future, the system can be further extended to detect other types of CVDs.

The electrooculogram (EOG) is one of the most significant signals carrying eye movement information, such as blinks and saccades. There are many human–computer interface (HCI) applications based on eye blinks. For example, the detection of eye blinks can be useful for paralyzed people in controlling wheelchairs. Eye blink features from EOG signals can be useful in drowsiness detection. In some applications of electroencephalograms (EEGs), eye blinks are considered noise. The accurate detection of eye blinks can help achieve denoised EEG signals. In this paper, we aimed to design an application-specific reconfigurable binary EOG signal processor to classify blinks and saccades. This work used dual-channel EOG signals containing horizontal and vertical EOG signals. At first, the EOG signals were preprocessed, and then, by extracting only two features, the root mean square (RMS) and standard deviation (STD), blink and saccades were classified. In the classification stage, 97.5% accuracy was obtained using a support vector machine (SVM) at the simulation level. Further, we implemented the system on Xilinx Zynq-7000 FPGAs by hardware/software co-design. The processing was entirely carried out using a hybrid serial–parallel technique for low-power hardware optimization. The overall hardware accuracy for detecting blinks was 95%. The on-chip power consumption for this design was 0.8 watts, whereas the dynamic power was 0.684 watts (86%), and the static power was 0.116 watts (14%).

This work explores the effect of Si doped HfO₂ (Si : HfO₂) ferroelectric layer (FEL) under the gate electrode on the characteristics of the Negative Capacitance Field Effect Transistor (NCFET). Due to the ferroelectric polarization of Si doped HfO₂ a negative capacitance is produced in the ferroelectric region, which essentially reduces the total gate capacitance. Here we showed that the inclusion of 5 nm ferroelectric layer decreases the subthreshold swing (SS) to around 52 mV/decade, which is almost 30% lower, and decreases the threshold voltage to 0.71 V which is more than 40% lower compared to the conventional FET. Also, the On-Off current ratio increase to around \(1.4 \times {{10}^{{10}}}\) with 5 nm ferroelectric layer. Moreover, the increase in transconductance, output characteristics, and the effect of ferroelectric layer on saturation currents as well as off-state currents are explained in detail.

With technological advancement, automated hand gesture recognition has become an essential tool for a group of people who are deaf or mute, or both. In addition, the use of hand gestures provides an effective way of communication between humans and computers. In literature, most automatic hand gesture classification techniques are devised on neural network architecture. Although these techniques provide good classification accuracy, they often require the intensive use of additional GPU and larger training data sets. In this paper, we have proposed a heuristic approach to recognizing static hand gestures based on the Radon Cumulative Distribution Transform (RCDT) with a mean accuracy of 99.485%. This performance evaluation is conducted employing only 32 training samples with 10-fold cross-validation. The results can be reproduced by the MATLAB code available at https://github.com/KamrulHasan1743/rcdt_Classifier.