Robust CNN architecture for classification of reach and grasp actions from neural correlates: an edge device perspective

Brain-computer interfaces (BCIs) systems traditionally use machine learning (ML) algorithms that require extensive signal processing and feature extraction. Deep learning (DL)-based convolutional neural networks (CNNs) recently achieved state-of-the-art electroencephalogram (EEG) signal classification accuracy. CNN models are complex and computationally intensive, making them difficult to port to edge devices for mobile and efficient BCI systems. For addressing the problem, a lightweight CNN architecture for efficient EEG signal classification is proposed. In the proposed model, a combination of a convolution layer for spatial feature extraction from the signal and a separable convolution layer to extract spatial features from each channel. For evaluation, the performance of the proposed model along with the other three models from the literature referred to as EEGNet, DeepConvNet, and EffNet on two different embedded devices, the Nvidia Jetson Xavier NX and Jetson Nano. The results of the Multivariant 2-way ANOVA (MANOVA) show a significant difference between the accuracies of ML and the proposed model. In a comparison of DL models, the proposed models, EEGNet, DeepConvNet, and EffNet, achieved 92.44 ± 4.30, 90.76 ± 4.06, 92.89 ± 4.23, and 81.69 ± 4.22 average accuracy with standard deviation, respectively. In terms of inference time, the proposed model performs better as compared to other models on both the Nvidia Jetson Xavier NX and Jetson Nano, achieving 1.9 sec and 16.1 sec, respectively. In the case of power consumption, the proposed model shows significant values on MANOVA (p < 0.05) on Jetson Nano and Xavier. Results show that the proposed model provides improved classification results with less power consumption and inference time on embedded platforms

Hybrid SPF and KD Operator-Based Active Contour Model for Image Segmentation

Image segmentation is a crucial stage of image analysis systems because it detects and extracts regions of interest for further processing, such as image recognition and the image description. However, segmenting images is not always easy because segmentation accuracy depends significantly on image characteristics, such as color, texture, and intensity. Image inhomogeneity profoundly degrades the segmentation performance of segmentation models. This article contributes to image segmentation literature by presenting a hybrid Active Contour Model (ACM) based on a Signed Pressure Force (SPF) function parameterized with a Kernel Difference (KD) operator. An SPF function includes information from both the local and global regions, making the proposed model independent of the initial contour position. The proposed model uses an optimal KD operator parameterized with weight coefficients to capture weak and blurred boundaries of inhomogeneous objects in images. Combined global and local image statistics were computed and added to the proposed energy function to increase the proposed model's sensitivity. The segmentation time complexity of the proposed model was calculated and compared with previous state-of-the-art active contour methods. The results demonstrated the significant superiority of the proposed model over other methods. Furthermore, a quantitative analysis was performed using the mini-MIAS database. Despite the presence of complex inhomogeneity, the proposed model demonstrated the highest segmentation accuracy when compared to other methods

Saliency-Driven Active Contour Model for Image Segmentation

Active contour models have achieved prominent success in the area of image segmentation, allowing complex objects to be segmented from the background for further analysis. Existing models can be divided into region-based active contour models and edge-based active contour models. However, both models use direct image data to achieve segmentation and face many challenging problems in terms of the initial contour position, noise sensitivity, local minima and inefficiency owing to the in-homogeneity of image intensities. The saliency map of an image changes the image representation, making it more visual and meaningful. In this study, we propose a novel model that uses the advantages of a saliency map with local image information (LIF) and overcomes the drawbacks of previous models. The proposed model is driven by a saliency map of an image and the local image information to enhance the progress of the active contour models. In this model, the saliency map of an image is first computed to find the saliency driven local fitting energy. Then, the saliency-driven local fitting energy is combined with the LIF model, resulting in a final novel energy functional. This final energy functional is formulated through a level set formulation, and regulation terms are added to evolve the contour more precisely across the object boundaries. The quality of the proposed method was verified on different synthetic images, real images and publicly available datasets, including medical images. The image segmentation results, and quantitative comparisons confirmed the contour initialization independence, noise insensitivity, and superior segmentation accuracy of the proposed model in comparison to the other segmentation models

Self-initialized active contours for microscopic cell image segmentation

Level set models are suitable for processing topological changes in different regions of images while performing segmentation. Active contour models require an empirical setting for initial parameters, which is tedious for the end-user. This study proposes an incremental level set model with the automatic initialization of contours based on local and global fitting energies that enable it to capture image regions containing intensity corruption or other light artifacts. The region-based area and the region-based length terms use signed pressure force (SPF) to strengthen the balloon force. SPF helps to achieve a smooth version of the gradient descent flow in terms of energy minimization. The proposed model is tested on multiple synthetic and real images. Our model has four advantages: first, there is no need for the end user to initialize the parameters; instead, the model is self-initialized. Second, it is more accurate than other methods. Third, it shows lower computational complexity. Fourth, it does not depend on the starting position of the contour. Finally, we evaluated the performance of our model on microscopic cell images (Coelho et al., in: 2009 IEEE international symposium on biomedical imaging: from nano to macro, IEEE, 2009) to confirm that its performance is superior to that of other state-of-the-art models

Flood modelling and its impacts on groundwater vulnerability in sub-Himalayan region of Pakistan: integration between HEC-RAS and geophysical techniques

AbstractHydropower projects play a pivot role in the development of a country. Constructions of reservoirs create job opportunities and provide cheap energy but at the same time cause several environmental issues. The current study utilizes geoelectric and flood modelling data to develop a relationship between flood scenarios and their effect on river flows in association with the vulnerability of groundwater due to hydroelectric projects on Neelum River, Pakistan. The resistivity data delineated local aquifer systems that comprised both confined and unconfined aquifers ranging from 05 to 48 m depth, having poor to weak protective capacity with good groundwater development potential. The flood zonation models indicate a decline in the flow rate of the Neelum River from 317 to 39 m3/s with a drop in stage and flow velocity that contributes to a high risk of leachate penetration in the poorly protected shallow aquifer. The aquifer systems that mostly lie near the banks of the river face a serious threat of contamination due to low river flow. The flood modelling revealed that in case of dam burst, maximum probable flood will affect the land cover of 30,43,250 m2 and 33,64,433 m2 in Muzaffarabad and Patikka areas, respectively, affecting major population

SRIS: Saliency-Based Region Detection and Image Segmentation of COVID-19 Infected Cases

Noise or artifacts in an image, such as shadow artifacts, deteriorate the performance of state-of-the-art models for the segmentation of an image. In this study, a novel saliency-based region detection and image segmentation (SRIS) model is proposed to overcome the problem of image segmentation in the existence of noise and intensity inhomogeneity. Herein, a novel adaptive level-set evolution protocol based on the internal and external functions is designed to eliminate the initialization sensitivity, thereby making the proposed SRIS model robust to contour initialization. In the level-set energy function, an adaptive weight function is formulated to adaptively alter the intensities of the internal and external energy functions based on image information. In addition, the sign of energy function is modulated depending on the internal and external regions to eliminate the effects of noise in an image. Finally, the performance of the proposed SRIS model is illustrated on complex real and synthetic images and compared with that of the previously reported state-of-the-art models. Moreover, statistical analysis has been performed on coronavirus disease (COVID-19) computed tomography images and THUS10000 real image datasets to confirm the superior performance of the SRIS model from the viewpoint of both segmentation accuracy and time efficiency. Results suggest that SRIS is a promising approach for early screening of COVID-19

Synthesis and Activity Evaluation of Ce-Mn-Cu Mixed Oxide Catalyst for Selective Oxidation of CO in Automobile Engine Exhaust: Effect of Ce/Mn Loading Content on Catalytic Activity

A series of Mn-doped CeO2-CuO catalyst (CeO2-MnOx-CuO) (Ce/Mn molar ratio of 0.5, 1.0 2.0 and 3.0) were prepared using co-precipitation method for the selective oxidation of CO in automobile engine exhaust. The content of copper was 5.0 wt. % in each sample. Catalysts were installed on the automobile engine exhaust and CO amount was recorded with help of CO sensor, with and without the catalyst. The catalytic converter efficiency was estimated for each catalyst through efficiency formula. It was observed that Ce/Mn catalyst with a molar ratio of 2.0 shows the maximum efficiency (88.35%). Stability of conversion process was analyzed by plotting the CO amount with respect to time. The catalyst with Ce/Mn molar ratio of 2.0 performed the most streamline conversion process with least deviations

A Low-Cost Device for Measurement of Exhaled Breath for the Detection of Obstructive Lung Disease

Breath sensor technology can be used in medical diagnostics. This study aimed to build a device to measure the level of hydrogen sulfide, ammonia, acetone and alcohol in exhaled breath of patients as well as healthy individuals. The purpose was to determine the efficacy of these gases for detection of obstructive lung disease. This study was conducted on a total of 105 subjects, where 60 subjects were patients of obstructive lung disease and 45 subjects were healthy individuals. Patients were screened by means of the Pulmonary Function Test (PFT) by a pulmonologist. The gases present in the exhaled breath of all subjects were measured. The level of ammonia (32.29 &plusmn; 20.83 ppb), (68.83 &plusmn; 35.25 ppb), hydrogen sulfide (0.50 &plusmn; 0.26 ppm), (62.71 &plusmn; 22.20 ppb), and acetone (103.49 &plusmn; 35.01 ppb), (0.66 &plusmn; 0.31 ppm) in exhaled breath were significantly different (p &lt; 0.05) between obstructive lung disease patients and healthy individuals, except alcohol, with a p-value greater than 0.05. Positive correlation was found between ammonia w.r.t Forced Expiratory Volume in 1 s (FEV1) (r = 0.74), Forced Vital Capacity (FVC) (r = 0.61) and Forced Expiratory Flow (FEF) (r = 0.63) and hydrogen sulfide w.r.t FEV1 (r = 0.54), FVC (r = 0.41) and FEF (r = 0.37). Whereas, weak correlation was found for acetone and alcohol w.r.t FEV1, FVC and PEF. Therefore, the level of ammonia and hydrogen sulfide are useful breath markers for detection of obstructive lung disease

HepFREEPak: protocol for a multi-centre, prospective observational study examining efficacy and impact of current therapies for the treatment of hepatitis C in Pakistan and reporting resistance to antiviral drugs: study protocol

Background: Pakistan has one of the highest burdens of Hepatitis C virus (HCV) infection globally. To achieve the World Health Organization\u27s goals for HCV elimination, there is a need for substantial scale-up in testing, treatment, and a reduction in new infections. Data on the population impact of scaling up treatment is not available in Pakistan, nor is there reliable data on the incidence of infection/reinfection. This project will fill this gap by providing important empirical data on the incidence of infection (primary and reinfection) in Pakistan. Then, by using this data in epidemic models, the study will determine whether response rates achieved with affordable therapies (sofosbuvir plus daclatasvir) will be sufficient to eliminate HCV in Pakistan.Methods: This prospective multi-centre cohort study will screen 25,000 individuals for HCV antibody (Ab) and RNA (if Ab-positive) at various centers in Pakistan- Karachi (Sindh) and Punjab, providing estimates of the disease prevalence. HCV positive patients will be treated with sofosbuvir and daclatasvir for 12-weeks, (extended to 24-weeks in those with cirrhosis) and the proportion responding to this first-line treatment estimated. Patients who test HCV Ab negative will be recalled 12 months later to test for new HCV infections, providing estimates of the incidence rate. Patients diagnosed with HCV (~ 4,000) will be treated and tested for Sustained Virological Response (SVR). Questionnaires to assess risk factors, productivity, health care usage and quality of life will be completed at both the initial screening and at 12-month follow-up, allowing mathematical modelling and economic analysis to assess the current treatment strategies. Viral resistance will be analysed and patients who have successfully completed treatment will be retested 12 months later to estimate the rate of re-infection.Conclusion: The HepFREEPak study will provide evidence on the efficacy of available and widely used treatment options in Pakistan. It will also provide data on the incidence rate of primary infections and re-infections. Data on incidence risk factors will allow us to model and incorporate heterogeneity of risk and how that affects screening and treatment strategies. These data will identify any gaps in current test-and-treat programs to achieve HCV elimination in Pakistan