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With the advancement of modern robotics, autonomous agents are now capable of hosting sophisticated algorithms, which enables them to make intelligent decisions. But developing and testing such algorithms directly in real-world systems is tedious and may result in the wastage of valuable resources. Especially for heterogeneous multi-agent systems in battlefield environments where communication is critical in determining the system’s behavior and usability. Due to the necessity of simulators of separate paradigms (co-simulation) to simulate such scenarios before deploying, synchronization between those simulators is vital. Existing works aimed at resolving this issue fall short of addressing diversity among deployed agents. In this work, we propose SynchroSim, an integrated co-simulation middleware to simulate a heterogeneous multi-robot system. Here we propose a velocity difference-driven adjustable window size approach with a view to reducing packet loss probability. It takes into account the respective velocities of deployed agents to calculate a suitable window size before transmitting data between them. We consider our algorithm specific simulator agnostic but for the sake of implementation results, we have used Gazebo as a Physics simulator and NS-3 as a network simulator. Also, we design our algorithm considering the Perception-Action loop inside a closed communication channel, which is one of the essential factors in a contested scenario with the requirement of high fidelity in terms of data transmission. We validate our approach empirically at both the simulation and system level for both line-of-sight (LOS) and non-line-of-sight (NLOS) scenarios. Our approach achieves a noticeable improvement in terms of reducing packet loss probability (≈11%), and average packet delay (≈10%) compared to the fixed window size-based synchronization approach. 

Stay at home order during the COVID-19 helps flatten the curve but ironically, instigate mental health problems among the people who have Substance Use Disorders. Measuring the electrical activity signals in brain using off-the-shelf consumer wearable devices such as smart wristwatch and mapping them in real time to underlying mood, behavioral and emotional changes play striking roles in postulating mental health anomalies. In this work, we propose to implement a wearable, On-device Mental Anomaly Detection (OMAD) system to detect anomalous behaviors and activities that render to mental health problems and help clinicians to design effective intervention strategies. We propose an intrinsic artifact removal model on Electroencephalogram (EEG) signal to better correlate the fine-grained behavioral changes. We design model compression technique on the artifact removal and activity recognition (main) modules. We implement a magnitude-based weight pruning technique both on convolutional neural network and Multilayer Perceptron to employ the inference phase on Nvidia Jetson Nano; one of the tightest resource-constrained devices for wearables. We experimented with three different combinations of feature extractions and artifact removal approaches. We evaluate the performance of OMAD in terms of accuracy, F1 score, memory usage and running time for both unpruned and compressed models using EEG data from both control and treatment (alcoholic) groups for different object recognition tasks. Our artifact removal model and main activity detection model achieved about ≈ 93% and 90% accuracy, respectively with significant reduction in model size (70%) and inference time (31%).