This paper develops a simple, cost-effective, fully Inkjet-printed flexible temperature sensor with easy fabrication steps using PiXDRO LP50 inkjet printer. The thermistor sensor employed a Poly(3,4-ethylenedioxythiophene)-poly(styrene sulfonate) (PEDOT: PSS) as a temperature-sensitive layer with silver nanoparticles (AgNps) as a contact electrode. The sensor performance was evaluated on different dimensions and types of substrates. Epson Glossy paper with 255 μm thickness and Polyimide (Kapton HN) with 50 μm thickness were used over temperatures ranging from room temperature up to 50 °C and 90 °C for paper and Kapton substrates, respectively. The sensor developed over Glossy paper showed the best sensitivity of -1.67 %/°C in the chosen temperature range while the sensor showed +0.113%/°C on PI substrate.

This work introduces a wearable, highly sensitive human body temperature sensor. The proposed thermistor sensor employs a poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS) as a temperature sensing layer and interdigitated electrodes (IDEs) from Silver nanoparticles deposited on Polyimide (PI) and Epson glossy paper substrates. The IDEs were patterned using inkjet printer Drop-on-demand PiXDRO LP50. The PEDOT:PSS layer was added by drop casting technique. The sensitivity of fabricated sensors was tested for different IDE number of fingers to investigate their response to the temperature range of 28–50 °C. The sensors performed linearly in the tested temperature range. Repeatability has been verified for five temperature measurement cycles. The achieved sensitivities are −1.462% /°C and −3.202%/°C for Epson paper and PI substrates, respectively. The sensor bendability results highlight the capability of the proposed sensor to be utilized for the wearable human body temperature reading.

Cuckoo search Algorithm (CSA) has been a candidate for numerous recent applications and showed great compatibility in solving optimization problems. It is a metaheuristic algorithm which is based on the odd breeding strategy of the Cuckoo bird spices. It is used to find an optimum or near optimum solution for a certain problem. In this research, we propose an FPGA hardware implementation for the CSA based on single precision IEEE floating point (FP). The FP format provides a wider range and higher precision when compared to fixed point format. To the best of our knowledge, this is the first study to consider implementing FP format-based CSA on FPGA. The proposed design is implemented using pipelined and parallel techniques to get a high throughput and speed. The design is controlled and coordinated using finite state machines (FSMs) modules and is configured on Cyclone IV E FPGA chip from Intel. Three common benchmark functions are used to evaluate the performance of the proposed design. The design has a maximum operating frequency of 99 MHz. It was found out the maximum power consumption for the most complex function is 610.28 mW, mainly due to the use of FP format. In addition, the proposed design is implementation and evaluated for multidimensional operation. Accordingly, the proposed design is suitable for path planning for unmanned aerial vehicles (UAVs), sensor deployments for wireless sensor networks (WSNs) in addition to medical diagnostic and DSP applications.

This paper presents a novel Human Activity Recognition (HAR) framework using wearable sensors, specifically targeting applications in gait rehabilitation and assistive robots. The new methodology includes the usage of an open-source dataset. This dataset includes surface electromyography (sEMG) and inertial measurement units (IMUs) signals for the lower limb of 22 healthy subjects. Several activities of daily living (ADLs) were included, such as walking, stairs up/down and ramp walking. A new framework for signal conditioning, denoising, filtering, feature extraction and activity classification is proposed. After testing several signal conditioning approaches, such as Wavelet transform (WT), Principal Component Analysis (PCA) and Empirical Mode Decomposition (EMD), an autocepstrum analysis (ACA)-based approach is chosen. Such a complex and effective approach enables the usage of supervised classifiers like K-nearest neighbor (KNN), neural networks (NN) and random forest (RF). The random forest classifier has shown the best results with an accuracy of 97.63% for EMG signals extracted from the soleus muscle. Additionally, RF has shown the best results for IMU signals with 98.52%. These results emphasize the potential of the new framework of wearable HAR systems in gait rehabilitation, paving the way for real-time implementation in lower limb assistive devices.

Smart grid architecture is one of the difficult constructions in electrical power systems. The main feature is divided into three layers; the first layer is the power system level and operation, the second layer is the sensor and the communication devices, which collect the data, and the third layer is the microprocessor or the machine, which controls the whole operation. This hierarchy is working from the third layer towards first layer and vice versa. This paper introduces an eco unit commitment study, that scheduling both conventional power plants (three IEEE) thermal plants) as a dispatchable distributed generators, with renewable energy resources (wind, solar) as a stochastic distributed generating units; and plug-in electric vehicles (PEVs), which can be contributed either loads or generators relied on the charging timetable in a trustworthy unit commitment. The target of unit commitment study is to minimize the combined eco costs by integrating more augmented clean and renewable energy resource with the help of field programming gate array (FPGA) layer installation. A meta-heuristic algorithm, such as the Gravitational Search Algorithm (GSA), proves its accuracy and efficiency in reducing the incorporated cost function implicating costs of CO₂ emission by optimally integrating and scheduling stochastic resources and charging and discharging processes of PEVs with conventional resources power plants. The results obtained from GSA are compared with a conventional numerical technique, such as the Dynamic Programming (DP) algorithm. The feasibility to implement GSA on an appropriate hardware platform, such as FPGA, is also discussed.