A wavelet-based correlation analysis framework to study cerebromuscular activity in essential tremor

Deep brain stimulation (DBS) provides dramatic tremor relief in patients with severe essential tremor (ET). Typically, the VIM nucleus is the most effective brain area to target for high-frequency electrical stimulation in these patients. Correlation analysis between electrical local field potential (LFP) recordings from the thalamic DBS leads and electrical muscle activity from the contralateral tremulous limb has become an attractive practical tool to interpret the LFPs and their association with the tremulous clinical manifestations. Although functional connectivity analysis between brain electrical recordings and electromyographic (EMG) signals from the tremor has been of interest to an increasing number of engineering researchers, there is no well-accepted tailored framework to consistently characterise the association between thalamic electrical recordings and the tremorogenic EMG activity. Methods. This paper proposes a novel framework to address this challenge, including an estimation of the interaction strength using wavelet cross-spectrum and phase lag index while demonstrating the statistical significance of the findings. Results. Consistent results were estimated for single and multiple trials of consecutive or partially overlapping epochs of data. The latter approach reveals a substantial increase on the range of statistically significant dynamic low-frequency interrelationships while decreasing the dynamic range of high-frequency interactions. Conclusion. Results from both simulation and real data demonstrate the feasibility and robustness of the proposed framework. Significance. This study offers the proof of principle required to implement this methodology to uncover VIM thalamic LFP-EMG interactions for (i) better understanding of the pathophysiology of tremor; (ii) objective selection of the DBS electrode contacts with the highest strength of association with the tremorogenic EMG, a particularly useful feature for the implementation of novel multicontact directional leads in clinical practice; and (iii) future research on DBS closed-loop devices

Model-based optimization strategy for a liquid desiccant cooling and dehumidification system

In this paper, a model-based optimization strategy for a liquid desiccant cooling and dehumidification (LDCD) system is proposed to improve system energy efficiency. The energy models of the LDCD system are established to predict system energy consumption under different operating conditions. To minimize the system energy consumption while maintaining the system thermal performance, the system energy consumption and thermal performance indicators are normalized by introducing a weight factor in cost function, then an optimization problem considering the interactions between components and system constraints is formulated. An improved self-adaptive firefly algorithm with fast convergence rate is proposed to solve the optimization problem and obtain the optimal set-points for control settings. Tests on an experimental apparatus are carried out to verify the energy saving potential of optimal control strategy under different weight factors and operating conditions. The results indicate that the energy consumption of LDCD system in the proposed optimization strategy is reduced by 12.49% over the conventional strategy. Meanwhile, the energy saving potential of the optimal control strategy is more remarkable for high cooling and dehumidification load. The proposed optimal control strategy can work well for applications in control and energy efficiency improvement of the existing dehumidification systems.This work was supported by the National Natural Science Foundation of China (NSFC) (No. 61873239, 61803339, 61803135)

Design of Robust Sensing Matrix for UAV Images Encryption and Compression

The sparse representation error (SRE) exists when the images are represented sparsely. The SRE is particularly large in unmanned aerial vehicles (UAV) images due to the disturbance of the harsh environment or the instability of its flight, which will bring more noise. In the compressed sensing (CS) system, the projected SRE in the compressed measurement will bring a significant challenge to the recovery accuracy of the images. In this work, a new SRE structure is proposed. Following the new structure, a lower sparse representation error (LSRE) is achieved by eliminating groups of sparse representation. With the proposed LSRE modeling, a robust sensing matrix is designed to compress and encrypt the UAV images. Experiments for UAV images are carried out to compare the recovery performance of the proposed algorithm with the existing related algorithms. The results of the proposed algorithm reveal superior recovery accuracy. The new CS framework with the proposed sensing matrix to address the scenario of UAV images with large SRE is dominant

Consensus of discrete-time multi-agent systems with adversaries and time delays

This paper studies the resilient asymptotic consensus problem for discrete-time Multi- Agent Systems in the presence of adversaries and transmission delays. The network is assumed to have p loyal agents and n-p adversarial agents, and each loyal agent in the network has no knowledge of the network topology other than an upper bound on the number of adversarial agents in its neighborhood. For the considered networked system, only locally delayed information is available for each loyal agent and also the information flow is directed, a control protocol using only local information is designed to guarantee the realization of consensus with respect to communication graph which satisfies a featured network robustness. Numerical examples are finally given to demonstrate the effectiveness of theoretical results.Accepted versio

Adaptive Finite-Time Command Filtered Fault-Tolerant Control for Uncertain Spacecraft with Prescribed Performance

In this paper, an adaptive finite-time fault-tolerant control scheme is proposed for the attitude stabilization of rigid spacecrafts. A first-order command filter is presented at the second step of the backstepping design to approximate the derivative of the virtual control, such that the singularity problem caused by the differentiation of the virtual control is avoided. Then, an adaptive fuzzy finite-time backstepping controller is developed to achieve the finite-time attitude stabilization subject to inertia uncertainty, external disturbance, actuator saturation, and faults. Through using an error transformation, the prescribed performance boundary is incorporated into the controller design to guarantee the prescribed performance of the system output. Numerical simulations demonstrate the effectiveness of the proposed scheme

Dynamic model development of heat and mass transfer for a novel desiccant regeneration system in liquid desiccant dehumidification system

In this paper, from the control point of view, a simplified dynamic model of the Desiccant Regenerations System (DRS) is proposed. The DRS is a serial system of the heat pipe heat exchanger and regenerator whose models are developed by writing the thermal and moisture balance equations, and the heat and mass transfer rates of each subsystem are derived by using the effectiveness-NTU and hybrid modeling approach. The unknown Model parameters are identified through the nonlinear least squares method and unscented Kalman filter algorithm with commissioning information. The dynamic model of the whole system is obtained by integrating the subsystem models to predicts the system performance. Compared with the existing DRS models, the presented model not requires iterative computations and also can be easily transformed into a state-space model. The proposed model accurately reflects the transient and steady state performance of the DRS over the wide operating condition in the experimental validation and is expected to work well for intelligent dynamic control and optimization applications

Experimental investigations on heat and mass transfer performances of a liquid desiccant cooling and dehumidification system

The last decades have witnessed the growth interest in Liquid Desiccant Dehumidification Systems (LDDS). In the conventional LDDS, the high dilution rate of desiccant solution in dehumidifier leads to a high desiccant regeneration frequency, which consequently results in more thermal energy consumed by desiccant regeneration system. Therefore, a more energy efficient Liquid Desiccant Cooling and Dehumidification (LDCD) system is developed in this study, which mainly composes of a cooling coil and dehumidifier. A simple static model is proposed to predict the performances of heat and mass transfer process in this system. The thermal efficiency, moisture effectiveness and desiccant dilution rate are utilized as the performance indicators. The influences of several relevant parameters on the cooling and dehumidification performances of LDCD system are investigated. The model predictions are compared with the experimental data, and the results show that the model predictions are well in line with the experimental data with the maximum errors less than 10%. Moreover, the feasibility of LDCD system in reducing the dilution rate of desiccant solution and the system energy consumption is validated. The results indicate that the dilution rate of desiccant solution and energy consumption of the LDCD system are reduced by 39.64% and 22.3% over the conventional LDDS, respectively.NRF (Natl Research Foundation, S’pore