With the growing need for adoption of smarter resource control system in existing infrastructure, the proliferation of occupancy sensing is slowly increasing its pace. After reviewing an existing system, we find that utilization of Doppler radar is less progressive in enhancing the accuracy of occupancy sensing operation. Therefore, we introduce a novel analytical model that is meant for incorporating granularity in tracing the psychological periodic characteristic of an object by emphasizing on the mobility and uncertainty movement of an object in the monitoring area. Hence, the model is more emphasized on identifying the rate of change in any periodic physiological characteristic of an object with the aid of mathematical modelling. At the same time, the model extracts certain traits of frequency shift and directionality for better tracking of the unidentified object behavior where its applicabilibility can be generalized in majority of the fields related to object detection.

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The human brain is one of the most complex machines on the planet. Being the only method to get real-time data with high temporal resolution from the brain makes EEG a highly sought upon signal in the neurological and psychiatric domain. However, recent developments in this field have made EEG more than just a tool for medical professionals. The decreasing size and increasing complexity of EEG acquisition systems have brought it out of the lab and into the field where it is used for varied applications like neurofeedback, person recognition and other recreational activities. Amalgamation of the EEG signal with new developing standards of Industry 4.0 to control basic IOT devices using edge computing techniques marks the next step in the design and development our low-cost yet robust Brain Computer Interface (BCI); which is just one of the many applications that a versatile and well-built EEG acquisition system can be used for.