Surface electromyography are bioelectrical indicators that emerge during muscle contraction and have been widely used in a variety of clinical applications. Several prosthetic control applications can benefit from analysis based on the classification of sEMG signals. However, for the real-time application of upper-limb prosthesis, EMG-based systems need robust performance and rapid response behaviour. In this study, we propose an efficient concatenated convolutional neural network (E2CNN) for classification of sEMG extracted from upper-limb. We have tested and validated the performance of the proposed E2CNN on two datasets: a longitudinal dataset comprised of ten able-bodied (healthy) subjects as well as six transradial amputee subjects and spanned the data collected for a period of seven days; and the publicly available NinaPro DB1 dataset. The raw sEMG signals are converted to Log-Mel (LM) spectrograms. This model combines input layers with the output of each convolutional block using concatenation layers. The proposed efficient concatenated CNN (E2CNN) when applied to log-Mel spectrogram-based images, provides a good response time with high-performance accuracy of 98.31% ± 0.5% and 97.97% ± 1.41% for both able-bodied and amputee subjects. When applied to NinaPro DB1, the proposed E2CNN has attained a mean accuracy of 91.27%, an increase by 24.67% with respect to baseline CNN model. The results show that the achieved results are comparable to those obtained using SSAE and other CNN models; however, E2CNN is associated with reduced training and prediction time, making it a potential candidate for real-time classification of sEMG based on LM spectrogram images.

In this paper, spectral processing of laser Self-Mixing (SM) interferometric signal phase has been carried out allowing better measurement accuracy for harmonic and arbitrarily shaped vibrations for an optical feedback based SM interferometric Laser Diode (LD) sensor. The resulting algorithm not only improves the measurement accuracy but also reduces the processing time (by a factor 3.45) as compared with a previous time-domain based displacement retrieval technique called the Phase Unwrapping Method (PUM). Fourier series based analysis of laser feedback phase is carried out to determine processing limits. The proposed algorithm has also been found to be robust against variation in optical feedback coupling C as well as additive noise. This use of spectral analysis not only increases the measurement accuracy but also retrieves information about target movement harmonics which can also be used in modal analysis applications of remote mechanical targets. Using an SM vibration sensor based on a LD emitting at 785nm, this technique has provided an average RMS error of 12.5 nm (while that of PUM is 39 nm RMS) for harmonic target vibrations of 5 µm amplitude. For reduced range of 1<C<2, an average RMS error of 8 nm (~λ/100) is obtained.

Fibroids of the uterus are a common benign tumor affecting women of childbearing age. Uterine fibroids (UF) can be effectively treated with earlier identification and diagnosis. Its automated diagnosis from medical images is an area where deep learning (DL)-based algorithms have demonstrated promising results. In this research, we evaluated state-of-the-art DL architectures VGG16, ResNet50, InceptionV3, and our proposed innovative dual-path deep convolutional neural network (DPCNN) architecture for UF detection tasks. Using preprocessing methods including scaling, normalization, and data augmentation, an ultrasound image dataset from Kaggle is prepared for use. After the images are used to train and validate the DL models, the model performance is evaluated using different measures. When compared to existing DL models, our suggested DPCNN architecture achieved the highest accuracy of 99.8 percent. Findings show that pre-trained deep-learning model performance for UF diagnosis from medical images may significantly improve with the application of fine-tuning strategies. In particular, the InceptionV3 model achieved 90% accuracy, with the ResNet50 model achieving 89% accuracy. It should be noted that the VGG16 model was found to have a lower accuracy level of 85%. Our findings show that DL-based methods can be effectively utilized to facilitate automated UF detection from medical images. Further research in this area holds great potential and could lead to the creation of cutting-edge computer-aided diagnosis systems. To further advance the state-of-the-art in medical imaging analysis, the DL community is invited to investigate these lines of research. Although our proposed innovative DPCNN architecture performed best, fine-tuned versions of pre-trained models like InceptionV3 and ResNet50 also delivered strong results. This work lays the foundation for future studies and has the potential to enhance the precision and suitability with which UF is detected.

Physiological signals such as electromyography (EMG) have been employed in Human-Computer Interaction (HCI) for medical applications. Wearable prostheses, such as robotic limbs, have seen a surge in popularity because to technological advancements in myoelectric interfaces. In spite of encouraging achievements with pattern recognition-based control systems, user acceptability of prosthetic hands still needs improvement in control robustness. The purpose of this research is to compare multi-day surface electromyography (sEMG) recordings and measure the performance of convolutional neural network (CNN) to enhance myoelectric control. The performance metrics used in this study are accuracy, macro weighted precision, macro weighted recall, and macro F1-score for 8 able-bodied and 4 amputee subjects. Using mel-spectrogram based sEMG data from both able-bodied and amputee participants, our proposed CNN has achieved a mean classification accuracy of 99.42% ± 0.42% and 98.00% ± 0.58% for able-bodied and amputee subjects for within-day analysis, respectively. The proposed CNN outperformed other classifiers (p ≤ 0.05) in between day analysis for two-fold (65.88% ± 10.1% and 58.37% ± 9.11%) and for seven-fold validation (88.73% ± 1.43% and 77.35% ± 2.72%) utilizing sEMG recordings from able-bodied and amputee subjects, respectively. The proposed CNN is compared with pre-trained transfer learning models and has achieved higher accuracy with lower computational cost. The results demonstrate that CNN can considerably increase the effectiveness of pattern recognition myoelectric control schemes and can extract deep information from EMG data.

Breast cancer ranks among the leading causes of death for women globally, making it imperative to swiftly and precisely detect the condition to ensure timely treatment and enhanced chances of recovery. This study focuses on transfer learning with 3D U-Net models to classify ductal carcinoma, the most frequent subtype of breast cancer, in histopathology imaging. In this research work, a dataset of 162 microscopic images of breast cancer specimens is utilized for breast histopathology analysis. Preprocessing the original image data includes shrinking the images, standardizing the intensities, and extracting patches of size 50 × 50 pixels. The retrieved patches were employed to construct a basic 3D U-Net model and a refined 3D U-Net model that had been previously trained on an extensive medical image segmentation dataset. The findings revealed that the fine-tuned 3D U-Net model (97%) outperformed the simple 3D U-Net model (87%) in identifying ductal cancer in breast histopathology imaging. The fine-tuned model exhibited a smaller loss (0.003) on the testing data (0.041) in comparison to the simple model. The disparity in the training and testing accuracy reveals that the fine-tuned model may have overfitted to the training data indicating that there is room for improvement. To progress in computer-aided diagnosis, the research study also adopted various data augmentation methodologies. The experimental approach that was put forward achieved state-of-the-art performance, surpassing the benchmark techniques used in previous studies in the same field, and exhibiting greater accuracy. The presented scheme has promising potential for better cancer detection and diagnosis in practical applications of mammography.

Energy demand is increasing globally due to the growing human population and progressive lifestyle. The adequate use of available energy resources, including renewable, contributes to a country’s economic sustainability and future development. Optimization–based energy management and cost minimization plays a significant role in overcoming energy crises in less developed countries. In this paper, an optimization–based dynamic energy management technique for smart grids is developed based on the integration of available renewable resources and variable consumer demand, distinctive to underdeveloped countries. Consumer demand is classified into fixed, flexible, and highly variable based on population characteristics. In this work, we developed a Dynamic Multiple Knapsack DMKNS algorithm, which automatically schedules energy provision to various users by optimally accounting for the available resources (Grid and Renewable). The proposed method provides a low-cost solution by maintaining a constant energy supply while preserving consumer comfort and grid stability. The simulation results with various intermittent availability of resources using MKNS show a saving up to 50% for a variable energy demand user. The proposed method is general and can also be applied to various underdeveloped regions with similar consumer demand and statistics.