Computational Study of a Motion Sensor to Simultaneously Measure Two Physical Quantities in All Three Directions for a UAV.

Cross-axis sensitivity is generally undesirable, and lower values are required for the accurate performance of a thermal accelerometer. In this study, errors in devices are utilized to simultaneously measure two physical quantities of an unmanned aerial vehicle (UAV) in the X-, Y-, and Z-directions, i.e., where three accelerations and three rotations can also be simultaneously measured using a single motion sensor. The 3D structures of thermal accelerometers were designed and simulated in a FEM simulator using commercially available FLUENT 18.2 software Obtained temperature responses were correlated with input physical quantities, and a graphical relationship was created between peak temperature values and input accelerations and rotations. Using this graphical representation, any values of acceleration from 1g to 4g and rotational speed from 200 to 1000°/s can be simultaneously measured in all three directions.

An AI-empowered affect recognition model for healthcare and emotional well-being using physiological signals.

Affective Computing is one of the central studies for achieving advanced human-computer interaction and is a popular research direction in the field of artificial intelligence for smart healthcare frameworks. In recent years, the use of electroencephalograms (EEGs) to analyze human emotional states has become a hot spot in the field of emotion recognition. However, the EEG is a non-stationary, non-linear signal that is sensitive to interference from other physiological signals and external factors. Traditional emotion recognition methods have limitations in complex algorithm structures and low recognition precision. In this article, based on an in-depth analysis of EEG signals, we have studied emotion recognition methods in the following respects. First, in this study, the DEAP dataset and the excitement model were used, and the original signal was filtered with others. The frequency band was selected using a butter filter and then the data was processed in the same range using min-max normalization. Besides, in this study, we performed hybrid experiments on sash windows and overlays to obtain an optimal combination for the calculation of features. We also apply the Discrete Wave Transform (DWT) to extract those functions from the preprocessed EEG data. Finally, a pre-trained k-Nearest Neighbor (kNN) machine learning model was used in the recognition and classification process and different combinations of DWT and kNN parameters were tested and fitted. After 10-fold cross-validation, the precision reached 86.4%. Compared to state-of-the-art research, this method has higher recognition accuracy than conventional recognition methods, while maintaining a simple structure and high speed of operation.

Computational Study on Thermal Motion Sensors That Can Measure Acceleration and Rotation Simultaneously.

In this study, a new technique has been proposed by numerical simulations by which multiple physical quantities can be simultaneously measured. The sensor is a modification of existing physical sensors such as a thermal motion sensor. Simultaneous measurement of acceleration and rotation is presented herein. Cross-axis sensitivity is employed such that output sensitivities observed at two perpendicular axes, X and Y sensor data, are related to the input physical quantities. The physics involved in measurement is similar to that of a conventional thermal accelerometer, hence the governing equations predicting the sensor response are based on the conservation of mass, momentum, and energy, and are discretized by using a commercially available software FLUENT. A series of computational studies are conducted and using these studies a novel idea is proposed in which the maximum temperature values are obtained at various positions around a heating source and are correlated with the applied acceleration and rotational speed. A parametric study is also presented to find the optimum distance between the heater and sensors. The influence of changing gas medium on the temperature curves has also been examined and it has been concluded that CO2 generates the maximum performance due to its higher density and lower viscosity.

Designing Deep Learning Hardware Accelerator and Efficiency Evaluation.

With the swift development of deep learning applications, the convolutional neural network (CNN) has brought a tremendous challenge to traditional processors to fulfil computing requirements. It is urgent to embrace new strategies to improve efficiency and diminish energy consumption. Currently, diverse accelerator strategies for CNN computation based on the field-programmable gate array (FPGA) platform have been gradually explored because they have edges of high parallelism, low power consumption, and better programmability. This paper first illustrates state-of-the-art FPGA-based accelerator design by emphasizing the contributions and limitations of existing research works. Subsequently, we demonstrated significant concepts of parallel computing (PC) in the convolution algorithm and discussed how to accomplish parallelism based on the FPGA hardware structure. Eventually, with the proposed CPU+ FPGA framework, we performed experiments and compared the performance against traditional computation strategies in terms of the operation efficiency and energy consumption ratio. The results revealed that the efficiency of the FPGA platform is much higher than that of the central processing unit and graphics processing unit.

A Simple and Effective Approach Based on a Multi-Level Feature Selection for Automated Parkinson's Disease Detection.

Parkinson's disease (PD), which is a slowly progressing neurodegenerative disorder, negatively affects people's daily lives. Early diagnosis is of great importance to minimize the effects of PD. One of the most important symptoms in the early diagnosis of PD disease is the monotony and distortion of speech. Artificial intelligence-based approaches can help specialists and physicians to automatically detect these disorders. In this study, a new and powerful approach based on multi-level feature selection was proposed to detect PD from features containing voice recordings of already-diagnosed cases. At the first level, feature selection was performed with the Chi-square and L1-Norm SVM algorithms (CLS). Then, the features that were extracted from these algorithms were combined to increase the representation power of the samples. At the last level, those samples that were highly distinctive from the combined feature set were selected with feature importance weights using the ReliefF algorithm. In the classification stage, popular classifiers such as KNN, SVM, and DT were used for machine learning, and the best performance was achieved with the KNN classifier. Moreover, the hyperparameters of the KNN classifier were selected with the Bayesian optimization algorithm, and the performance of the proposed approach was further improved. The proposed approach was evaluated using a 10-fold cross-validation technique on a dataset containing PD and normal classes, and a classification accuracy of 95.4% was achieved.

Phragmites australis in combination with hydrocarbons degrading bacteria is a suitable option for remediation of diesel-contaminated water in floating wetlands.

The presence of diesel in the water could reduce the growth of plant and thus phytoremediation efficacy. The toxicity of diesel to plant is commonly explained; because of hydrocarbons in diesel accumulate in various parts of plants, where they disrupt the plant cell especially, the epidemis, leaves, stem and roots of the plant. This study investigated the effect of bacterial augmentation in floating treatment wetlands (FTWs) on remediation of diesel oil contaminated water. A helophytic plant, Phragmites australis (P. australis), was vegetated on a floating mat to establish FTWs for the remediation of diesel (1%, w/v) contaminated water. The FTWs was inoculated with three bacterial strains (Acinetobacter sp. BRRH61, Bacillus megaterium RGR14 and Acinetobacter iwoffii AKR1), possessing hydrocarbon degradation and plant growth-enhancing capabilities. It was observed that the FTWs efficiently removed hydrocarbons from water, and bacterial inoculation further enhanced its hydrocarbons degradation efficacy. Diesel contaminated water samples collected after fifteen days of time interval for three months and were analyzed for pollution parameters. The maximum reduction in hydrocarbons (95.8%), chemical oxygen demand (98.6%), biochemical oxygen demand (97.7%), total organic carbon (95.2%), phenol (98.9%) and toxicity was examined when both plant and bacteria were employed in combination. Likewise, an increase in plant growth was seen in the presence of bacteria. The inoculated bacteria showed persistence in the water, root and shoot of P. australis. The study concluded that the augmentation of hydrocarbons degrading bacteria in FTWs is a better option for treatment of diesel polluted water.