This study proposes a convolutional neural network model trained from scratch to classify and detect the presence of pneumonia from a collection of chest X-ray image samples. Unlike other methods that rely solely on transfer learning approaches or traditional handcrafted techniques to achieve a remarkable classification performance, we constructed a convolutional neural network model from scratch to extract features from a given chest X-ray image and classify it to determine if a person is infected with pneumonia. This model could help mitigate the reliability and interpretability challenges often faced when dealing with medical imagery. Unlike other deep learning classification tasks with sufficient image repository, it is difficult to obtain a large amount of pneumonia dataset for this classification task; therefore, we deployed several data augmentation algorithms to improve the validation and classification accuracy of the CNN model and achieved remarkable validation accuracy.

ECG is a electrical signal generated by the heart and has a regular rhythm. ECG cycle, however, change depending on the person's activities and heart-related disorders. Therefore, waveform of the ECG signal can be analyzed to assess cardiac abnormalities and lesion of the heart, infer into pathological and biological mechanisms of the heart, and diagnose for various cardiac disorders. ECG enables non-invasive measuring and contains an abundance of health information. That said, this paper discusses a study concerning development of a highly convenient ECG monitoring system. Realization of such monitoring system required development of a portable, miniature, battery-powered, low power consumption, and wireless ECG measuring system featuring belt-type ECG leads worn around the chest. A Zigbee-compatible wireless sensor node was used for wireless transmission of measured ECG data, and a ultra low power consumption wireless data communications units were also developed. And then a monitoring program was coded to enable ECG signal monitoring from a personal computer. Ambulatory ECG measuring is often associated with motion artifact that forms as the subject moves about his or her daily life. Motion artifact's frequency characteristic is that it varies depending on the type of movement. Therefore, an adaptive filter was used to cancel the changing motion artifact and thereby minimize movement-induced motion artifact. Test results confirm that using the ECG monitoring system proposed herein makes it possible to measure ECG signals in non-restrained environments.

In this work a self-tuning rate and queue based proportional and integral controller called SRQ-PI is proposed to efficiently control the queue length with small overshoot and faster settling time. SRQ-PI proposes a new control tracking function that maps level of congestion to the packet drop probability dynamically. In SRQ-PI, the incoming traffic rate is estimated and used with the proportional and integral controller. The SRQ-PI tunes itself and stabilizes the system with internal feedback without requiring any external feedback. Furthermore, the stability of the SRQ-PI is analyzed using control theory and presents systematic guidelines to select the control gain parameters. NS2 is used to carry out the simulation work. The simulation result demonstrates that SRQ-PI is stable and gets faster transient response due to lower average delay jitter and robust against dynamic network parameters. The SRQ-PI outperforms proportional integral (PI), Intelligent adaptive PI (IAPI) and Random exponential marking (REM) algorithm.

In this paper, a packet generator using a pattern matching algorithm for real-time abnormal heartbeat detection is proposed. The packet generator creates a very small data packet which conveys sufficient crucial information for health condition analysis. The data packet envelopes real time ECG signals and transmits them to a smartphone via Bluetooth. An Android application was developed specifically to decode the packet and extract ECG information for health condition analysis. Several graphical presentations are displayed and shown on the smartphone. We evaluate the performance of abnormal heartbeat detection accuracy using the MIT/BIH Arrhythmia Database and real time experiments. The experimental result confirm our finding that abnormal heart beat detection is practically possible. We also performed data compression ratio and signal restoration performance evaluations to establish the usefulness of the proposed packet generator and the results were excellent.