Diabetes becomes a rapidly increasing global epidemic and getting serious health concern worldwide. There is no remedy except systematic management to keep blood glucose level under control. To achieve that regular glucose level monitoring is a routine task for a patient. This involves collection of blood physically from body with some discomfort and measuring using some device. To overcome this disadvantages and distress, non-invasive blood glucose measurement system is in demand. This article presents an ultra-wide band (UWB) microwave imaging and artificial intelligence based prospective solution to detect blood glucose concentration level non-invasively (without physical blood). The system consists of a pair of small UWB biomedical planar antenna, UWB transceiver as hardware and an artificial neural network with signal acquisition and processing interface as software module. The UWB signal with center frequency of 4.7 GHz was transmitted through ear lobe and forward scattering signals were received from other side. Characteristics features of received signal were extracted for pattern recognition and detection through deep artificial neural network. The system exhibits around 88% accuracy to detect glucose concentration in blood plasma. Besides, it is affordable, safe, user friendly and can be used with comfort in near future.

Breast cancer is the most common cancer among women and it is one of the main causes of death for women worldwide. To attain an optimum medical treatment for breast cancer, an early breast cancer detection is crucial. This paper proposes a multi- stage feature selection method that extracts statistically significant features for breast cancer size detection using proposed data normalization techniques. Ultra-wideband (UWB) signals, controlled using microcontroller are transmitted via an antenna from one end of the breast phantom and are received on the other end. These ultra-wideband analogue signals are represented in both time and frequency domain. The preprocessed digital data is passed to the proposed multi- stage feature selection algorithm. This algorithm has four selection stages. It comprises of data normalization methods, feature extraction, data dimensional reduction and feature fusion. The output data is fused together to form the proposed datasets, namely, 8-HybridFeature, 9-HybridFeature and 10-HybridFeature datasets. The classification performance of these datasets is tested using the Support Vector Machine, Probabilistic Neural Network and Naïve Bayes classifiers for breast cancer size classification. The research findings indicate that the 8-HybridFeature dataset performs better in comparison to the other two datasets. For the 8-HybridFeature dataset, the Naïve Bayes classifier (91.98%) outperformed the Support Vector Machine (90.44%) and Probabilistic Neural Network (80.05%) classifiers in terms of classification accuracy. The finalized method is tested and visualized in the MATLAB based 2D and 3D environment.

Bluetooth Wireless Monitoring, Managing and Control for Inter Vehicle in Vehicular Ad-Hoc Networks

Diabetes is a chronic disease and in uprising trend worldwide. There is no remedy, hence, blood glucose management is essential by screening blood glucose concentration levels (BGCL) regularly to maintain a healthy life. However, the present way of measuring BGCL is invasive by using a glucometer and drawing a blood sample directly from the human body. To overcome this discomfort-problem, a non-invasive device to measure BGCL is in demand. This paper presents an autonomous software module with a user-friendly graphical user interface (GUI) based on digital signal processing (DSP) and artificial neural network (ANN) to process, classify and recognize the BGL signature from captured ultra-wideband (UWB) signal through human blood medium. To capture the signal, a pair of UWB bio-antenna is placed in between the human earlobe. Received signals are captured and processed through GUI and undergo signal processing, ANN training, testing, and validation. An interface is developed to integrate the hardware (UWB transceiver, bio-antenna, etc.) and the developed software module to make a system. The initial system showed a consistent result with reliability and demonstrated 90.6% accuracy to detect the BGCL. The detection accuracy is 9.6% improved compared to existing work. Besides, this proposed system is cost-effective, user-friendly and suitable to be used by both doctors and home users.