Design of a Bimodal Home Automation System using ESP8266 and ATMEGA328 Microcontroller

Home automation systems are garnering increasing popularity and widespread use due to the relative ease of domestic management and comparatively high return on technology investment tied to its adoption. However, Nigeria and other emerging ICT economies are yet to fully actualize and maximize the inherent potential of these smart home technologies due to endemic challenges associated with poor infrastructure, erratic power supply and unreliable Internet connectivity. These challenges necessitate an innovative paradigmatic shift that could provide a pragmatic technological solution suitable to the context of Nigeria and other developing climes. For most smart home systems in this research context, the status quo is based on choosing whether the design would be for short- or long-range communication network. Short-range designs which are usually realized with Bluetooth technology suffer from limited range issues while poor connectivity, bandwidth and latency issues are some of the problems plaguing Wi-Fi-based long-range designs. Consequently, this research presents a hybrid adaptive architecture that combines desirable features of both short- and long-range modes. The proposed smart home system is based on using embedded systems which use mobile application to send messages to ESP8266 Wi-Fi module. Together with notifications received from the monitoring unit, these messages are parsed by Arduino's ATMEGA328 microcontroller from where instruction codes are sent for controlling the load by switching ON or OFF various relays connected to the load

Design of a Bimodal Home Automation System using ESP8266 and ATMEGA328 Microcontroller

Home automation systems are garnering increasing popularity and widespread use due to the relative ease of domestic management and comparatively high return on technology investment tied to its adoption. However, Nigeria and other emerging ICT economies are yet to fully actualize and maximize the inherent potential of these smart home technologies due to endemic challenges associated with poor infrastructure, erratic power supply and unreliable Internet connectivity. These challenges necessitate an innovative paradigmatic shift that could provide a pragmatic technological solution suitable to the context of Nigeria and other developing climes. For most smart home systems in this research context, the status quo is based on choosing whether the design would be for short- or long-range communication network. Short-range designs which are usually realized with Bluetooth technology suffer from limited range issues while poor connectivity, bandwidth and latency issues are some of the problems plaguing Wi-Fi-based long-range designs. Consequently, this research presents a hybrid adaptive architecture that combines desirable features of both short- and long-range modes. The proposed smart home system is based on using embedded systems which use mobile application to send messages to ESP8266 Wi-Fi module. Together with notifications received from the monitoring unit, these messages are parsed by Arduino's ATMEGA328 microcontroller from where instruction codes are sent for controlling the load by switching ON or OFF various relays connected to the load

Towards Enhancing Keyframe Extraction Strategy for Summarizing Surveillance Video: An Implementation Study

The large amounts of surveillance video data are recorded, containing many redundant video frames, which makes video browsing and retrieval difficult, thus increasing bandwidth utilization, storage capacity, and time consumed. To ensure the reduction in bandwidth utilization and storage capacity to the barest minimum, keyframe extraction strategies have been developed. These strategies are implemented to extract unique keyframes whilst removing redundancies. Despite the achieved improvement in keyframe extraction processes, there still exist a significant number of redundant frames in summarized videos. With a view to addressing this issue, the current paper proposes an enhanced keyframe extraction strategy using k-means clustering and a statistical approach. Surveillance footage, movie clips, advertisements, and sports videos from a benchmark database as well as Compeng IP surveillance videos were used to evaluate the performance of the proposed method. In terms of compression ratio, the results showed that the proposed scheme outperformed existing schemes by 2.82%. This implies that the proposed scheme further removed redundant frames whiles retaining video quality. In terms of video playtime, there was an average reduction of 27.32%, thus making video content retrieval less cumbersome when compared with existing schemes. Implementation was done using MATLAB R2020b

Towards Enhancing Keyframe Extraction Strategy for Summarizing Surveillance Video: An Implementation Study

The large amounts of surveillance video data are recorded, containing many redundant video frames, which makes video browsing and retrieval difficult, thus increasing bandwidth utilization, storage capacity, and time consumed. To ensure the reduction in bandwidth utilization and storage capacity to the barest minimum, keyframe extraction strategies have been developed. These strategies are implemented to extract unique keyframes whilst removing redundancies. Despite the achieved improvement in keyframe extraction processes, there still exist a significant number of redundant frames in summarized videos. With a view to addressing this issue, the current paper proposes an enhanced keyframe extraction strategy using k-means clustering and a statistical approach. Surveillance footage, movie clips, advertisements, and sports videos from a benchmark database as well as Compeng IP surveillance videos were used to evaluate the performance of the proposed method. In terms of compression ratio, the results showed that the proposed scheme outperformed existing schemes by 2.82%. This implies that the proposed scheme further removed redundant frames whiles retaining video quality. In terms of video playtime, there was an average reduction of 27.32%, thus making video content retrieval less cumbersome when compared with existing schemes. Implementation was done using MATLAB R2020b