Object detection is an important process in surveillance system to locate objects and it is considered as major application in computer vision. The Convolution Neural Network (CNN) based models have been developed by many researchers for object detection to achieve higher performance. However, existing models have some limitations such as overfitting problem and lower efficiency in small object detection. Object detection in remote sensing hasthe limitations of low efficiency in detecting small object and the existing methods have poor localization. Cascade Object Detection methods have been applied to increase the learning process of the detection model. In this research, the Additive Activation Function (AAF) is applied in a Faster Region based CNN (RCNN) for object detection. The proposed AAF-Faster RCNN method has the advantage of better convergence and clear bounding variance. The Fourier Series and Linear Combination of activation function are used to update the loss function. The Microsoft (MS) COCO datasets and Pascal VOC 2007/2012 are used to evaluate the performance of the AAF-Faster RCNN model. The proposed AAF-Faster RCNN is also analyzed for small object detection in the benchmark dataset. The analysis shows that the proposed AAF-Faster RCNN model has higher efficiency than state-of-art Pay Attention to Them (PAT) model in object detection. To evaluate the performance of AAF-Faster RCNN method of object detection in remote sensing, the NWPU VHR-10 remote sensing data set is used to test the proposed method. The AAF-Faster RCNN model has mean Average Precision (mAP) of 83.1% and existing PAT-SSD512 method has the 81.7%mAP in Pascal VOC 2007 dataset.

The detection of ECG waves is important to cardiac disease diagnosis. A good system depends heavily upon the accurate and reliable detection of QRS complex as well as the T and P wave. In this paper first the noise is removed from the digitized ECG signal. Then Stationary Wavelet Transform (SWT) is applied to the de-noised signal. After that QRS complex, T and P waves are detected and also delineated using different amplitude threshold values. This algorithm allows delineation of wide variety of QRS complex, P and T wave. In this evolution is done for different manually annotated databases, such as MIT-BIH Arrhythmia, QT and European ST-T database. The QRS detector obtained a sensitivity Se=99.30% and positive predictivity of PP=99.39% over the well known MIT-BIH Arrhythmia Database.

Accessing the hospitals in emergency situations, from any rural area for providing appropriate medical assistance is the biggest challenge in saving the life of a patient. Also, the patients who have undergone surgery should be monitored on a 24*7 basis. The above problems can be solved with a help of Health monitoring system using IoT. The proposed system continuously monitors the diagnosis parameters such as heart beat, pressure, activeness and temperature using appropriate sensors like heartbeat sensor, pressure sensor, MEMS sensor and temperature sensor. The sensed medical information is collected and stored in the database of the server which is a Raspberry Pi device. The Raspberry Pi processes these data and displays the values on the web page. It also sends the alert messages to the Doctor, Care taker as well as the patients using the GSM module connected to the Raspberry Pi. This enables the review of reports and real-time video of the patient through patient monitoring system from a remote area thereby reducing the periodical visit.