Deep Scattering Transform with Attention Mechanisms Improves EMG-based Hand Gesture Recognition.

Electromyogram (EMG) signals provide valuable insights into the muscles' activities supporting the different hand movements, but their analysis can be challenging due to their stochastic nature, noise, and non-stationary variations in the signal. We are pioneering the use of a unique combination of wavelet scattering transform (WST) and attention mechanisms adopted from recent sequence modelling developments of deep neural networks for the classification of EMG patterns. Our approach utilizes WST, which decomposes the signal into different frequency components, and then applies a non-linear operation to the wavelet coefficients to create a more robust representation of the extracted features.

Cybersecurity and Cyber Forensics for Smart Cities: A Comprehensive Literature Review and Survey.

Smart technologies, such as the Internet of Things (IoT), cloud computing, and artificial intelligence (AI), are being adopted in cities and transforming them into smart cities. In smart cities, various network technologies, such as the Internet and IoT, are combined to exchange real-time information, making the everyday lives of their residents more convenient. However, there is a lack of systematic research on cybersecurity and cyber forensics in smart cities.

A Review of Functional Encryption in IoT Applications.

The Internet of Things (IoT) represents a growing aspect of how entities, including humans and organizations, are likely to connect with others in their public and private interactions. The exponential rise in the number of IoT devices, resulting from ever-growing IoT applications, also gives rise to new opportunities for exploiting potential security vulnerabilities. In contrast to conventional cryptosystems, frameworks that incorporate fine-grained access control offer better opportunities for protecting valuable assets, especially when the connectivity level is dense.

The Effectiveness of Narrowing the Window size for LD & HD EMG Channels based on Novel Deep Learning Wavelet Scattering Transform Feature Extraction Approach.

The use of the Electromyogram (EMG) signals as a source of control to command externally powered prostheses is often challenged by the signal complexity and non-stationary behavior. Mainly, two factors affect classification accuracy: selecting the optimum feature extraction methods and overlapping segmentation/window size. Nowadays, studies attempt to use deep learning (DL) methods to improve classification accuracy.

Efficient removal of selected fluoroquinolones from the aqueous environment using reduced magnetic graphene oxide/polyaniline composite.

In the present study, reduced magnetic graphene oxide/polyaniline (RmGO/PANI) composite was synthesized via in-situ oxidative polymerization method. The synthesized RmGO/PANI was characterized by fourier transform infrared, scanning electron microscope, X-ray diffraction and energy dispersive X-rays techniques. The synthesized RmGO/PANI was explored as an adsorbent for the removal of moxifloxacin (MOX) and ofloxacin (OFL) from the aqueous samples.

Cardinality and Short-Term Memory Concepts based Novel Feature Extraction for Myoelectric Pattern Recognition.

The quality of the extracted traditional hand-crafted Electromyogram (EMG) features has been recently identified in the literature as a limiting factor prohibiting the translation from laboratory to clinical settings. To address this limitation, a shift of focus from traditional feature extraction methods to deep learning models was witnessed, as the latter can learn the best feature representation for the task at hand. However, while deep learning models achieve promising results based on raw EMG data, their clinical implementation is often challenged due to their significantly high computational costs (significantly large number of generated models' parameters and a huge amount of data needed for training).

The MyHealthRecord System Impacts on Patient Workflow in General Practices.

Digital health is widely believed to have vast potential in improving patient care. MyHealthRecord (MyHR) is a digital health information system which enables Australian citizens to access their health information centrally, making it available anywhere, at anytime. The aim of this study is to explore the adoption of MyHR in general practices in Victoria and understand its impacts.

Sparse Density Estimation on the Multinomial Manifold.

A new sparse kernel density estimator is introduced based on the minimum integrated square error criterion for the finite mixture model. Since the constraint on the mixing coefficients of the finite mixture model is on the multinomial manifold, we use the well-known Riemannian trust-region (RTR) algorithm for solving this problem. The first- and second-order Riemannian geometry of the multinomial manifold are derived and utilized in the RTR algorithm.