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

Effects of fluoxetine on functional outcomes after acute stroke (FOCUS): a pragmatic, double-blind, randomised, controlled trial

Background Results of small trials indicate that fluoxetine might improve functional outcomes after stroke. The FOCUS trial aimed to provide a precise estimate of these effects. Methods FOCUS was a pragmatic, multicentre, parallel group, double-blind, randomised, placebo-controlled trial done at 103 hospitals in the UK. Patients were eligible if they were aged 18 years or older, had a clinical stroke diagnosis, were enrolled and randomly assigned between 2 days and 15 days after onset, and had focal neurological deficits. Patients were randomly allocated fluoxetine 20 mg or matching placebo orally once daily for 6 months via a web-based system by use of a minimisation algorithm. The primary outcome was functional status, measured with the modified Rankin Scale (mRS), at 6 months. Patients, carers, health-care staff, and the trial team were masked to treatment allocation. Functional status was assessed at 6 months and 12 months after randomisation. Patients were analysed according to their treatment allocation. This trial is registered with the ISRCTN registry, number ISRCTN83290762. Findings Between Sept 10, 2012, and March 31, 2017, 3127 patients were recruited. 1564 patients were allocated fluoxetine and 1563 allocated placebo. mRS data at 6 months were available for 1553 (99·3%) patients in each treatment group. The distribution across mRS categories at 6 months was similar in the fluoxetine and placebo groups (common odds ratio adjusted for minimisation variables 0·951 [95% CI 0·839–1·079]; p=0·439). Patients allocated fluoxetine were less likely than those allocated placebo to develop new depression by 6 months (210 [13·43%] patients vs 269 [17·21%]; difference 3·78% [95% CI 1·26–6·30]; p=0·0033), but they had more bone fractures (45 [2·88%] vs 23 [1·47%]; difference 1·41% [95% CI 0·38–2·43]; p=0·0070). There were no significant differences in any other event at 6 or 12 months. Interpretation Fluoxetine 20 mg given daily for 6 months after acute stroke does not seem to improve functional outcomes. Although the treatment reduced the occurrence of depression, it increased the frequency of bone fractures. These results do not support the routine use of fluoxetine either for the prevention of post-stroke depression or to promote recovery of function. Funding UK Stroke Association and NIHR Health Technology Assessment Programme

Application of Soft Computing Techniques to a Linear Quadratic Gaussian Controller Design

The Linear Quadratic Gaussian (LQG) controller is a very popular control methodology and is being used for wide range of commercial application from domestic appliances to autopilot designs especially autonomous vehicles. LQG is the combination of a Linear Quadratic Regulator (LQR) and a Kalman filter. As an LQR assumes fiall state feedback which is not always possible, a Kalman filter is used to estimate all the unavailable states and remove noise from the output of the system. LQG has four design parameters, namely, system state weighting matrix, control weighting matrix, process noise covariance matrix and measurement noise covariance matrix. Owing to the use of trial and error procedure, it takes a significant amount of time and effort to properly tune these four matrices for optimal performance of LQG controller. Here in this thesis, fiizzy logic is applied for online tuning of the measurement noise covariance matrix of the Kalman filter and it is combined with LQR to form a novel LQG controller. The controller is tested on a twin rotor MIMO system (TRMS) which resembles a helicopter. To start with the design of LQG controller, system identification techniques have been used to find the mathematical model of the TRMS. The design of LQR and fuzzy Kalman filter is then carried out separately and then combined to form the fuzzy LQG control strategy. After obtaining satisfactory simulation results, the controller is tested online. The introduction of this technique improves the performance of the controller. In a similar manner the other three matrices can be tuned online to completely automate the LQG design processSchool of Engineerin

Adaptive and reinforcement learning control methods for active compliance control of a humanoid robot arm

Safety is an important requirement for human-robot interaction. Compliance control can often help to ensure safety in human-robot interaction (HRI). The aim of this work is to develop a compliance control strategy for safe HRI. Compliance can be achieved through passive means (mechanical structure or passive actuation) or through active compliance methods, employing force/torque feedback. This thesis deals with the compliance control of Bristol-Elumotion Robot Torso (BERT) II robot arm which is inherently rigid and heavy. As the dynamic model of the arm is difficult to obtain and prone to inaccuracies, parametric uncertainties and un-modelled nonlinearities, a model-free adaptive compliance controller is employed. The control scheme is using a mass-spring-damper system as reference model to produce compliant behaviour. The adaptive control scheme may cause actuator saturations, which could lead to instabilities and eventually windup. Hence, an anti-windup compensator is employed to address actuator saturation issues. The control scheme is a Cartesian one (tracking x, y and z coordinates) and employing four joints (namely, shoulder flexion, shoulder abduction, humeral rotation and elbow flexion joints) of the BERT II arm. Although, this needs three degrees of freedom (DOF), the fourth redundant DOF is employed to generate human-like motion, minimising a gravitational function. The adaptive compliance control scheme works efficiently for the application and produces good tracking and compliance results. It is often the case that adaptive control schemes are not necessarily (control) optimal, which may create difficulties in the controller design. Furthermore, it is difficult to incorporate constraints or any other desired behaviour. Therefore, bio-inspired reinforcement learning (RL) schemes are explored. A recently formulated RL based optimal adaptive controller scheme is employed and modified for real time testing on our robot arm. The RL based scheme is implemented for non-constrained and constrained cases in the joint space. Particularly, the results produced with the constrained case are encouraging, where the controller learns to deal with the constraints in the form of joint limits. An RL based Cartesian model reference compliance controller is also tested for two links of the BERT II arm. Generally, the results with this scheme are very good. However, there are limitations on the representation of the RL cost functions and the control scheme using neural networks (NNs). To a large extent these limitations have been overcome through a novel practical approach of representing the cost function and the control via a simple neural network. Nevertheless, available computational power permitted only two link experimental implementation. Integration of these new control approaches into practical HRI system is important. A final achievement is an initial HRI experiment for passing of objects between human and robot employing the model reference adaptive compliance control scheme mentioned in the beginning. This experimental scenario is implemented using also separate hand controller and speech interface.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Adaptive Neural Network Q-Learning-Based Full Recurrent Adaptive NeuroFuzzy Nonlinear Control Paradigms for Bidirectional-Interlinking Converter in a Grid-Connected Hybrid AC-DC Microgrid

The stability of a hybrid AC-DC microgrid depends mainly upon the bidirectional interlinking converter (BIC), which is responsible for power transfer, power balance, voltage solidity, frequency and transients sanity. The varying generation from renewable resources, fluctuating loads, and bidirectional power flow from the utility grid, charging station, super-capacitor, and batteries produce various stability issues on hybrid microgrids, like net active-reactive power flow on the AC-bus, frequency oscillations, total harmonic distortion (THD), and voltage variations. Therefore, the control of BIC between AC and DC buses in grid-connected hybrid microgrid power systems is of great importance for the quality/smooth operation of power flow, power sharing and stability of the whole power system. In literature, various control schemes are suggested, like conventional droop control, communication-based control, model predictive control, etc., each addressing different stability issues of hybrid AC-DC microgrids. However, model dependence, single-point-failure (SPF), communication vulnerability, complex computations, and complicated multilayer structures motivated the authors to develop online adaptive neural network (NN) Q-learning-based full recurrent adaptive neurofuzzy nonlinear control paradigms for BIC in a grid-connected hybrid AC-DC microgrid. The proposed strategies successfully ensure the following: (i) frequency stabilization, (ii) THD reduction, (iii) voltage normalization and (iv) negligible net active-reactive power flow on the AC-bus. Three novel adaptive NN Q-learning-based full recurrent adaptive neurofuzzy nonlinear control paradigms are proposed for PQ-control of BIC in a grid-connected hybrid AC-DC microgrid. The control schemes are based on NN Q-learning and full recurrent adaptive neurofuzzy identifiers. Hybrid adaptive full recurrent Legendre wavelet-based Neural Network Q-learning-based full recurrent adaptive NeuroFuzzy control, Hybrid adaptive full recurrent Mexican hat wavelet-based Neural Network Q-learning-based full recurrent adaptive NeuroFuzzy control, and Hybrid adaptive full recurrent Morlet wavelet-based Neural Network Q-learning-based full recurrent adaptive NeuroFuzzy control are modeled and tested for the control of BIC. The controllers differ from each other, based on variants used in the antecedent part (Gaussian membership function and B-Spline membership function), and consequent part (Legendre wavelet, Mexican hat wavelet, and Morlet wavelet) of the full recurrent adaptive neurofuzzy identifiers. The performance of the proposed control schemes was validated for various quality and stability parameters, using a simulation testbench in MATLAB/Simulink. The simulation results were bench-marked against an aPID controller, and each proposed control scheme, for a simulation time of a complete solar day

Experimental validation of an integral sliding mode-based LQG for the pitch control of a UAV-mimicking platform

In this paper, an enhanced Integral Sliding Mode-based Linear Quadratic Gaussian (ISM-LQG) controller has been proposed and verified in real-time on a Twin Rotor multi-input-multi-output MIMO System (TRMS). A TRMS serves as a suitable laboratorybased platform to evaluate the performance of control algorithms for complex Unmanned Aerial Vehicle (UAV) systems such as rotocraft. In the proposed scheme, an ISM enhancement to an LQG has been introduced, which attempts to overcome modelling inaccuracies and uncertainties. The novelty of the proposed control law lies in hybridizing a robust control approach with an optimal control law to achieve improved performance. Experimental results on the TRMS demonstrate that the ISM-LQG strategy significantly improves the tracking performance of the TRMS pitch and hence confirm the applicability and efficiency of the proposed scheme

Academic advising models and practices of two Asian universities

Academic advising is a valuable academic service and an essential component of higher education. This service is very important for students' satisfaction, retention, recruitment and success. This paper compares a successful example of decentralized academic advising from Malaysia and a successful example of centralized advising from Japan. Using a qualitative approach, the study focused on students' perspectives on academic advising practice at their universities. Ten undergraduate students volunteered for semi-structured interviews focusing on their academic advising experience. The interviews were recorded and transcribed verbatim, and the data were analysed using thematic analysis. The themes emerged from the analysis were roles of academic advising, delivery of academic advising, and outcome of academic advising. Each of these themes produced three more sub-themes. It can be concluded that the respondents need some elements organizational practices of academic advising. This result has broad implications for administrators in establishing, maintaining and improving their institutions' academic advising system

Nonlinear control systems - A brief overview of historical and recent advances

Last five decades witnessed remarkable developments in linear control systems and thus problems in this subject has been largely resolved. The scope of the present paper is beyond linear solutions. Modern technology demands sophisticated control laws to meet stringent design specifications thus highlighting the increasingly conspicuous position of nonlinear control systems, which is the topic of this paper. Historical role of analytical concepts in analysis and design of nonlinear control systems is briefly outlined. Recent advancements in these systems from applications perspective are examined with critical comments on associated challenges. It is anticipated that wider dissemination of this comprehensive review will stimulate more collaborations among the research community and contribute to further developments