In this article, we propose a novel concept of using an existing cellular network to find the location of animals living in outdoor environments. The proposed method has simplified hardware architecture which can be implemented at a meager cost. Moreover, the sensors communicate with existing cellular networks, which will reduce the implementation cost.  The proposed system consists of a SIM 900 GSM module, a BMP280 pleasure sensor, and a battery as a wearable device that can warn the animal. The wearable device will send the real-time pressure values of the animal. In the proposed model, the pressure value, radial distance of transmitter fixed at the animal, and direction of the electromagnetic waves are used to calculate the real-time coordinates of the animals. The received pressure value and the radial distance will be used to calculate the location using this proposed model. The proposed model parameters' error was analyzed and simulated using suitable probability density distributions, and results were presented. 

An electromyography (EMG) is an analytical tool used to record muscles' electrical activity, which produces an electrical signal proportional to the level of muscle activity. EMG signal plays a vital role in bio-mechatronic engineering for designing intelligent prostheses and other rehabilitation devices. Analysis of EMG signals with powerful and advanced methodologies is an essential requirement in EMG signal processing, as the EMG signal is a complex nonlinear, non-stationary signal in nature. It is required to use advanced signal processing techniques rather than conventional methods to exact EMG signals' features. Fourier transforms (FT) are not the most appropriate tool for analyzing non-stationary signals such as EMG. In this work, we have developed a system that can be useful for disabled persons to get a regular lifestyle using a functioning part of the body. Here, we studied the electrocution gram behavior of human body parts to feature extraction and trained the neural network to simulate the movements of mechanical actuators such as robotic arms. The wavelet transformation has been used to get high-quality feature extraction from electro cardio grapy and develops proper faltering methods for cardio systems' electrical signals. Finally, an artificial neural network (ANN) is used to classify the EMG signals through exacted features. Classification results are presented in this paper.