Neural network controller for two-degree-freedom helicopter control system

Now a day automatic flight control is a crucial issue especially for emergency services. One of the fittest candidates for such services is two-degree-freedom (2DOF) helicopter. Automatic control features of 2-DOF helicopter are usually approximated using the linear quadratic regulator (LQR), which can be further enhanced in-terms of Neural Network (NN). Hence, this paper presents the nonlinear flight control of 2DOF helicopter using NN. A back propagation, feed forward NN model is developer and employed to approximate the nonlinear control features of 2DOF helicopter using Matlab software. The effectiveness of the basic 2DOF helicopter NN controller is apparent (~ 2% pitch and 14% yaw improvement) compared to the conventional (LQR) methods

Non-invasive blood glucose concentration level estimation accuracy using ultra-wide band and artificial intelligence

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

Non-Invasive Diabetes Level Monitoring System using Artificial Intelligence and UWB

Diabetes is a silent-killer disease throughout the world. It is not cura-ble, therefore, regular blood glucose concentration levels (BGCL) monitoring is necessary to be healthy in a long run. The traditional way of BGCL measurement is invasive by pricking and collecting blood sample from human arm (or finger-tip), then measuring the level either using a glucometer or sending to laboratory. This blood collecting process produces significant discomfort to the patients, es-pecially to the children with type-A diabetes, resulting increased undetected-cases and health-complications. To overcome this drawbacks, a non-invasive ul-tra-wideband (UWB) BGCL measurement system is proposed here with en-hanced software module. The hardware can be controlled through the graphical user interface (GUI) of software and can execute signal processing, feature ex-traction, and feature classification using artificial intelligence (AI). As AI, cas-cade forward neural network (CFNN) and naïve bayes (NB) algorithms are in-vestigated, then CFNN with four independent features (skewness, kurtosis, vari-ance, mean-absolute-deviation) are found to be best-suited for BGCL estimation. A transmit (Tx) antenna was placed at one side of left-earlobe to Tx UWB sig-nals, and a receive (Rx) antenna at opposite side to Rx transmitted signals with BGCL marker. These signals are saved and used for AI training, validation and testing. The system with CFNN shows approximately 86.62% accuracy for BGCL measurement, which is 5.62% improved compared to other methods by showing its superiority. This enhanced system is affordable, effective and easy-to-use for all users (home and hospital), to reduce undetected diabetes cases and related mortality rate in near future

Software module development for non-invasive blood glucose measurement using an ultra-wide band and machine learning

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

Multi-hop file transfer in WiFi direct based cognitive radio network for cloud back-up

In this chapter, an application for Android WiFi Direct multi-hop communications with log-file generation and cloud-based back-up have been proposed. WiFi Direct technology is used to peer-to-peer files transfer between neighboring devices without going through any access point. Distributed file systems for the cloud is a system that enables users to have access to the same data or file remotely (any-time any-where). The proposed custom WiFi Direct based Cognitive Radio (CR) application is able to create an ad-hoc network for multi-hop file transfer wirelessly using WiFi between two or more devices. Be-sides, to customize the channel according to the user demand, CR technique is used. An application (apps) is developed and used in mobile devices (smart phones, note book, etc) in a testbed to verify the system performances. This ap-plication detects and saves all the network activities information (in terms of log file) to keep track of the user activity and connection details in the network. The generated log files are stored in the cloud for further processing and security purpose. The performance of WiFi Direct based CR discovery service, channel detection, log file generation, multi-hop communication and WiFi Direct applications were successfully tested intensively with ~ 93% efficiency. Based on experimental data, an empirical model for multi-hop communication is proposed and validated. This shows, multi-hop file transfer and cloud back-up of log-file are possible through neighbor nodes having WiFi direct connection in a network. This can be helpful for data safety, recovery and connection status monitoring/analysis for possible intrusion detection