バイオセンサーとバイオエレクトロニクス

The development of various new kinds of sensors for the accurate detection of biomarkers in biological fluids and environmental samples are of greatest importance for the early diagnosis of diseases and to avoid the contamination of environment through pollutants, toxic and biohazardous materials. Sensitivity limits of biosensor have increased due to developments of new biological methods like tagging of fluorescence molecule with nanomaterials. Moreover, usage of peptide arrays, aptamers, antibodies, nucleotides and molecule fixed polymers, facilitate to improve advanced biosensors over conventional approaches. Several biosensors ranging from nanomaterials, polymers to microbes have broader potential applications. Generally, biosensor has been organized into several categories containing diverse sensing arrangements such as mechanical, optical and electrical transducers and modern biosensors use micro- and nanofabrication tools, as either label-free or labeled. This review provides an overview of recent developments and applications of biosensors in the fields of biomedical sciences and environmental monitoring, along with the better detection limit and improved sensitivity of the biosensors.

This project discusses about a brain controlled wheel chair based on Brain–computer interfaces (BCI). BCI’s are systems that can bypass conventional channels of communication (i.e., muscles and thoughts) to provide direct communication and control between the human brain and physical devices by translating different patterns of brain activity into commands in real time. The intention of the project work is to develop a robot that can assist the disabled people in their daily life to do some work independent of others. Here, we analyze the brain wave signals. Human brain consists of millions of interconnected neurons, the pattern of interaction between these neurons are represented as thoughts and emotional states. According to the human thoughts, this pattern will be changing which in turn produce different electrical waves. A muscle contraction will also be generate a unique electrical signal. All this electrical waves will be sensed by the brain wave sensor it will convert the data into packets and transmit through Bluetooth medium. Level analyzer unit (LAU) will receive the brain wave raw data and it will extract and process the signal using MATLAB platform. Then the control commands will be transmitted to the robot module to process. With this entire system, we can move a robot according to the human thoughts and it can be turned by blink muscle contraction.

This article reviews the recent progress in optical sensors using photonic crystal fiber (PCF) technology, which is newly adopted beyond conventional optical fibers (OFs) due to their unique geometric structures. PCFs have some exceptional properties. It will focus the most conversant sensing application areas with their consistent parameters and perception. Numerous PCF sensors are available in day-to-day world. Researchers and technologists are working on PCFs with more than 15-20 application areas. In this article, sensing applications in physical and bio- chemical with their diverse types are reviewed recent times. Physical sensors as temperature, curvature, torsion, vibration, pressure, refractive index, evanescent; where the bio-chemical sensors as humidity, gas, pH, liquid etc. has been reviewed in this article. The review shows that, most physical sensing applications are varied with their different structures and parameters.

The fiber-optic biosensor with encapsuled bioreporters use a special optical element (OE) from pure silica with an active layer contents bioluminescent bioreporters was developed as a real in-situ detector for on-line measurement in remote localities. The active layer of biosensor contents bioluminescent bioreporters – genetic modified cells, which are sensitive to its surrounding environment-immobilized in silica matrix on the end of OE. Bioreporters are able to react by emission of visible light (≈500 nm) – bioluminiscence reaction (BL) – in presence of specific analyte.

The genetic modified bacterial stain Pseudomonas putida TVA8 was chosen as biorecognition part of biosensor producing BL in the presence of benzene, toluene, ethylbenzene and xylens (BTEX). Very low level of BL signal was detected by high-sensitive photon-counter. The sensitivity of biosensor depends on a value of detected BL and it can be very low. The signal is also affected by number of cells (light sources) immobilized into the active layer and transmission through OE. Mathematical model of OE shape was developed based on geometric optics. The numerical model is built around the MATLAB scripting language. Simulation of ray transmission was calculated for OE with different shape.

Recently, many researchers studied learning through VR environment in various fields. Their assessment tools were based on tests, quizzes and/or statistical analysis of questionnaires. This study is based on the analysis of EEG signals collected from the students’ brains directly to capture their feelings and engagement during the lecture in both traditional and VR methods of teaching.

To recognize the emotions of the students, the fine K-Nearest Neighbor (KNN) algorithm is used. To calculate the engagement score for a student, a well-known engagement score formula issued.

The participants chosen are students of Anatomy and Physiology course. All participants were subject to three sessions of EEG signal acquisition for both Real Lecture and Virtual Reality, each session is five-minutes long. For better accuracy, EEG signals were captured three times for each student in each lecturing method. Based on the data-analyzing methods applied, which are Dependent Paired Samples T-Test and Independent Paired Samples T-Test, positive emotions in a real lecture are better than positive emotions in a VR-Lecture. However, the engagement score in both classes was approximately the same.