Muneeb Ahmed

Muneeb Ahmed, Mohd Majid Akhtar

This study discloses the development of a solution for realizing a smart home in the post COVID-19 era using the Internet of Things domain knowledge. COVID-19 outbreak has been catastrophic and impacted everyone's lives due to its rapid transmission from one body to another. This study aims to reduce virus transmission by eliminating the need to touch any common-point surface in a home, such as switches, doorknobs, and remotes. We provide a generic solution by coupling things with the internet to control them remotely. The project aims to showcase a working solution to controlling devices like smart bulbs, smart fans, smart ACs, and smart door locks in our self-developed emulator over WWW securely using two different protocols, viz., HTTP and MQTT-over-WSS. Additionally, intent authentication over HTTP is based on digital signature that is demonstrated using RSA (encryption) and MD5 (hashing) when the system is deployed in insecure environment(s). RESTful API deployed on AWS EC2 is used to realize HTTP communication protocol, and MQTT is realized using AWS IoT service. The developed project can be applied to any smart home setting like a hotel or public place using AWS IoT, Lambda, or similar infrastructure as a broker.

Muneeb Ahmed, Rajesh Kumar, Qaim Abbas et al.

The letter focuses on Haptic Glove (HG) based control of a Robotic Hand (RH) executing in-hand manipulation of certain objects of interest. The high dimensional motion signals in HG and RH possess intrinsic variability of kinematics resulting in difficulty to establish a direct mapping of the motion signals from HG onto the RH. An estimation mechanism is proposed to quantify the motion signal acquired from the human controller in relation to the intended goal pose of the object being held by the robotic hand. A control algorithm is presented to transform the synthesized intent at the RH and allow relocation of the object to the expected goal pose. The lag in synthesis of the intent in the presence of communication delay leads to a requirement of predicting the estimated intent. We leverage an attention-based convolutional neural network encoder to predict the trajectory of intent for a certain lookahead to compensate for the delays. The proposed methodology is evaluated across objects of different shapes, mass, and materials. We present a comparative performance of the estimation and prediction mechanisms on 5G-driven real-world robotic setup against benchmark methodologies. The test-MSE in prediction of human intent is reported to yield ~ 97.3 -98.7% improvement of accuracy in comparison to LSTM-based benchmark