Activity and flight trajectory monitoring of mosquito colonies for butomated behaviour analysis

Monitoring and tracking of mosquitoes using image processing is important to facilitate the mosquitos’ behaviour analysis automatically over longer period of times. In this paper, we propose a simple methodology to monitor mosquitos’ activity using multiple cameras optimally placed. In order to ensure optimal camera coverage for the area of observation and desired image quality; we propose to simulate the experimental setup in a 3D virtual environment to obtain one-off optimum camera placement parameters. Our proposed methodology is demonstrated to have improved the activity monitoring process using two cameras for accurate count of occluded mosquitoes and 3D trajectory path reconstruction. This framework will enable working out more challenging tasks of constructing 3D trajectories using information received from multiple low quality cameras, which provide inconsistent and discontinuous trajectories.

Prediction interval-based control of nonlinear systems using neural networks

Prediction interval (PI) is a promising tool for quantifying uncertainties associated with point predictions. Despite its informativeness, the design and deployment of PI-based controller for complex systems is very rare. As a pioneering work, this paper proposes a framework for design and implementation of PI-based controller (PIC) for nonlinear systems. Neural network (NN)-based inverse model within internal model control structure is used to develop the PIC. Firstly, a PI-based model is developed to construct PIs for the system output. This model is then used as an online estimator for PIs. The PIs from this model are fed to the NN inverse model along with other traditional inputs to generate the control signal. The performance of the proposed PIC is examined for two case studies. This includes a nonlinear batch polymerization reactor and a numerical nonlinear plant. Simulation results demonstrated that the proposed PIC tracking performance is better than the traditional NN-based controller.

Hybrid controller with the combination of FLC and neural network-based IMC for nonlinear processes

This work presents a hybrid controller based on the combination of fuzzy logic control (FLC) mechanism and internal model-based control (IMC). Neural network-based inverse and forward models are developed for IMC. After designing the FLC and IMC independently, they are combined in parallel to produce a single control signal. Mean averaging mechanism is used to combine the prediction of both controllers. Finally, performance of the proposed hybrid controller is studied for a nonlinear numerical plant model (NNPM). Simulation result shows the proposed hybrid controller outperforms both FLC and IMC.

Multivariate autoregressive-based neuronal network flow analysis for in-vitro recorded bursts

Neuroscientific studies of in vitro neuron cell cultures has attracted paramount attention to investigate the behaviour of neuronal networks in response to different environmental conditions and external stimuli such as drugs, optical and electrical stimulations. Microelec trodearray (MEA) technology has been widely adopted as a tool for this investigation. In this work, we present a new approach to estimate interconnectivity of neural spikes using multivariate autoregressive (MVAR) analysis and Partial Directed Coherence (PDC). The proposed approach has the potential to discover hidden intra-burst causal connectivity patterns and to help understand the spatiotemporal communication patterns within bursts, pre and post stimulations.

High thermal gradient in thermo-electrochemical cells by insertion of a poly(vinylidene fluoride) membrane

Thermo-Electrochemical cells (Thermocells/TECs) transform thermal energy into electricity by means of electrochemical potential disequilibrium between electrodes induced by a temperature gradient (ΔT). Heat conduction across the terminals of the cell is one of the primary reasons for device inefficiency. Herein, we embed Poly(Vinylidene Fluoride) (PVDF) membrane in thermocells to mitigate the heat transfer effects - we refer to these membrane-thermocells as MTECs. At a ΔT of 12 K, an improvement in the open circuit voltage (Voc) of the TEC from 1.3 mV to 2.8 mV is obtained by employment of the membrane. The PVDF membrane is employed at three different locations between the electrodes i.e. x = 2 mm, 5 mm, and 8 mm where 'x' defines the distance between the cathode and PVDF membrane. We found that the membrane position at x = 5 mm achieves the closest internal ΔT (i.e. 8.8 K) to the externally applied ΔT of 10 K and corresponding power density is 254 nWcm-2; 78% higher than the conventional TEC. Finally, a thermal resistivity model based on infrared thermography explains mass and heat transfer within the thermocells.