This work is motivated by the recent advances in Deep Neural Networks (DNNs) and their widespread applications in human-machine interfaces. DNNs have been recently used for detecting the intended hand gesture through the processing of surface electromyogram (sEMG) signals. Objective: Although DNNs have shown superior accuracy compared to conventional methods when large amounts of data are available for training, their performance substantially decreases when data are limited. Collecting large datasets for training may be feasible in research laboratories, but it is not a practical approach for real-life applications. The main objective of this work is to design a modern DNN-based gesture detection model that relies on minimal training data while providing high accuracy. Methods: We propose the novel Few-Shot learning- Hand Gesture Recognition (FS-HGR) architecture. Few-shot learning is a variant of domain adaptation with the goal of inferring the required output based on just one or a few training observations. The proposed FS-HGR generalizes after seeing very few observations from each class by combining temporal convolutions with attention mechanisms. This allows the meta-learner to aggregate contextual information from experience and to pinpoint specific pieces of information within its available set of inputs. Data Source & Summary of Results: The performance of FS-HGR was tested on the second and fifth Ninapro databases, referred to as the DB2 and DB5, respectively. The DB2 consists of 50 gestures (rest included) from 40 healthy subjects. The Ninapro DB5 contains data from 10 healthy participants performing a total of 53 different gestures (rest included). The proposed approach for the Ninapro DB2 led to 85.94% classification accuracy on new repetitions with few-shot observation (5-way 5-shot), 81.29% accuracy on new subjects with few-shot observation (5-way 5-shot), and 73.36% accuracy on new gestures with few-shot observation (5-way 5-shot). Moreover, the proposed approach for the Ninapro DB5 led to 64.65% classification accuracy on new subjects with few-shot observation (5-way 5-shot).

Deep learning-based Hand Gesture Recognition (HGR) via surface Electromyogram (sEMG) signals have recently shown considerable potential for development of advanced myoelectric-controlled prosthesis. Although deep learning techniques can improve HGR accuracy compared to their classical counterparts, classifying hand movements based on sparse multichannel sEMG signals is still a challenging task. Furthermore, existing deep learning approaches, typically, include only one model as such can hardly extract representative features. In this paper, we aim to address this challenge by capitalizing on the recent advances in hybrid models and transformers. In other words, we propose a hybrid framework based on the transformer architecture, which is a relatively new and revolutionizing deep learning model. The proposed hybrid architecture, referred to as the Transformer for Hand Gesture Recognition (TraHGR), consists of two parallel paths followed by a linear layer that acts as a fusion center to integrate the advantage of each module. We evaluated the proposed architecture TraHGR based on the commonly used second Ninapro dataset, referred to as the DB2. The sEMG signals in the DB2 dataset are measured in real-life conditions from 40 healthy users, each performing 49 gestures. We have conducted an extensive set of experiments to test and validate the proposed TraHGR architecture, and compare its achievable accuracy with several recently proposed HGR classification algorithms over the same dataset. We have also compared the results of the proposed TraHGR architecture with each individual path and demonstrated the distinguishing power of the proposed hybrid architecture. The recognition accuracies of the proposed TraHGR architecture for the window of size 200ms and step size of 100ms are 86.00%, 88.72%, 81.27%, and 93.74%, which are 2.30%, 4.93%, 8.65%, and 4.20% higher than the state-of-the-art performance for DB2 (49 gestures), DB2-B (17 gestures), DB2-C (23 gestures), and DB2-D (9 gestures), respectively.

The paper proposes a co-design framework for event-triggered stabilization control of a class of networked control systems (NCS) under unknown DoS attacks. To reduce the number of control inputs, a sampled-data dynamic event-triggering (S-DET) scheme is developed. Both the state measurements and monitoring of the S-DET are conducted periodically. The parameter design is based on the solution of linear matrix inequalities (LMI) obtained from a delay-dependent Lyapunov-Krasovskii functional (LKF) using the improved free weighting matrix (IFWM) technique. In the presence of DoS with unknown patterns, the proposed co-design framework is beneficial in two ways. (i): The desired level of resilience to DoS is included as a design input. (ii): The upper-bound for guaranteed resilience associated with the proposed co-design approach is less conservative (larger) as compared to those obtained from other analytical solutions. The proposed co-design approach demonstrates the trade-off between the DoS resilience level and system performance indices. Numerical experiments quantify the effectiveness of the proposed approach both in terms of reducing the number of control updates and providing higher resilience to unknown DoS attacks.

The volume, variety, and velocity of medical imaging data are exploding, making it impractical for clinicians to properly utilize such available information resources in an efficient fashion. At the same time, the interpretation of such a large amount of medical imaging data by humans is significantly error prone, reducing the possibility of extracting informative data. The ability to process such large amounts of data promises to decipher encrypted information within medical images, develop predictive and prognosis models to design personalized diagnosis, allow comprehensive study of tumor phenotype, and allow the assessment of tissue heterogeneity for diagnosis of different types of cancers.

A major challenge of free-hand palpation ultrasound elastography (USE) is estimating the displacement of RF samples between pre- and post-compressed RF data. The problem of displacement estimation is ill-posed since the displacement of one sample by itself cannot be uniquely calculated. To resolve this problem, two categories of methods have emerged. The first category assumes that the displacement of samples within a small window surrounding the reference sample is constant. The second class imposes smoothness regularization and optimizes an energy function. Herein, we propose a novel method that combines both approaches, and as such, is more robust to noise. The second contribution of this work is the introduction of the L1 norm as the regularization term in our cost function, which is often referred to as the total variation (TV) regularization. Compared to previous work that used the L2 norm regularization, optimization of the new cost function is more challenging. However, the advantages of using the L1 norm are twofold. First, it leads to substantial improvement in the sharpness of displacement estimates. Second, to optimize the cost function with the L1 norm regularization, we use an iterative method that further increases the robustness. We name our proposed method tOtal Variation Regularization and WINDow-based time delay estimation (OVERWIND) and show that it is robust to signal decorrelation and generates sharp displacement and strain maps for simulated, experimental phantom and in-vivo data. In particular, OVERWIND improves strain contrast-to-noise ratio (CNR) by 27.26%, 144.05%, and 49.90% on average in simulation, phantom, and in-vivo data, respectively, compared to our recent Global Ultrasound Elastography (GLUE) method.

Inspired by the recent surge of interest in security analysis of renewable energies, this article proposes a cyber resilient control framework for large-scale wind parks (WPs) against denial of service (DoS) and deception attacks. A detailed cyber-physical model for a doubly-fed induction generator (DFIG)-based WP is first developed. It is shown that the WP is subject to a stability issue known as the sub-synchronous interaction (SSI). Additionally, two cyber threats in the forms of DoS and deception attack attempt to destabilize the WP from the subsynchronous perspective. To stabilize the WP under the SSI phenomenon and these cyber attacks, an observer-based fuzzy control scheme is proposed which requires only periodic samples of the output. Using a looped-Lyapunov functional (LLF) method for stability analysis, the control and observer gains are computed from the solution of some linear matrix inequality (LMI) conditions. The proposed design framework allows including the desired level of cyber resilience to DoS and deception attacks for computing proper control and observer gains. The effectiveness of the proposed controller against the considered threats is demonstrated via electromagnetic transient (EMT) simulations performed using the EMTP-RV software and a co-simulation platform.