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Gas leakage in industrial area causes many health issues. Thus, to prevent such disasters happen, the atmosphere of a workplace should be regularly monitored and controlled, in order to maintain the clean air environment. However, efforts in industrial air quality control have been impeded by the lack of science-based approaches to identify and assess atmosphere air quality and level of dangerous gas. Therefore, a monitoring system for gas leakage detection needs to be developed. For the development of this system, the combustible gas sensor (MQ9) was used in order to detect the present of methane (CH4) and carbon monoxide gas (CO). This sensor will detect the concentration of the gas according to the voltage output of sensor and operated in the alarm system, autonomous control system and monitoring system by using Arduino uno as the microcontroller for the whole system. Whereas the Zigbee will send the data reading from the gas sensor to monitoring system that display on LabVIEW Graphical User Interface (GUI). Besides, user can take immediate action upon the leakage occurs, else the gas supply and the system will shut down automatically within 10 minutes to prevent the condition becoming worst.

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An advance approach of distributed power amplifier (DPA) design has been introduced in this study, based on broadband impedance transformer integration. By identifying the DPA's optimum impedance over the frequency range with load pull measurement technique, the impedance transformer with mixed‐lumped elements using real‐frequency technique is designed. The prototype of the transformer network is integrated with the three‐stage DPA with pseudomorphic High‐Electron‐Mobility Transistor (HEMT) and gallium nitride HEMT technologies. Experimental output power performance achieves 40 dBm over bandwidth operation from 0.1 to 2.4 GHz, and the power added efficiency reaches 30% in average with gain of 30 dB. This proposed technique is essential to maximise DC–radio frequency conversion to the load termination and the transformer is having advantages over size area, implementation in small form factor and low‐cost approach particularly compatible for radio communications applications.

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Gestures constitute an important form of nonverbal communication where bodily actions are used for delivering messages alone or in parallel with spoken words. Recently, there exists an emerging trend of WiFi sensing enabled gesture recognition due to its inherent merits like device-free, non-line-of-sight covering, and privacy-friendly. However, current WiFi-based approaches mainly reply on domain-specific training since they don't know "where to look" and "when to look ". To this end, we propose WiGRUNT, a WiFi-enabled gesture recognition system using dual-attention network, to mimic how a keen human being intercepting a gesture regardless of the environment variations. The key insight is to train the network to dynamically focus on the domain-independent features of a gesture on the WiFi Channel State Information (CSI) via a spatial-temporal dual-attention mechanism. WiGRUNT roots in a Deep Residual Network (ResNet) backbone to evaluate the importance of spatial-temporal clues and exploit their inbuilt sequential correlations for fine-grained gesture recognition. We evaluate WiGRUNT on the open Widar3 dataset and show that it significantly outperforms its state-of-the-art rivals by achieving the best-ever performance in-domain or cross-domain.