Array Of Different Sensors Research Articles

ПРОГРАМНЕ ЗАБЕЗПЕЧЕННЯ РОЗУМНОГО ГОДИННИКА ДЛЯ САМОСТІЙНИХ ЗАНЯТЬ СПОРТОМ ТА ФІТНЕСОМ

According to available statistics, the most popular smart watch among users is the Apple Watch. Today, there are more than 100 million unique users of this device, 75% of whom use it for sports. With a wide array of different sensors to track a user's physical parameters, neither Apple nor third-party developers have yet developed software to systematize all the collected data to improve an athlete's physical pa-rameters and achieve personal athletic goals. The purpose of the research is to find the possibility of improving the physical parameters of a novice athlete by means of a comprehensive analysis of his activity data collected by a smart watch and creating more personalized recommendations during training based on this data. There are many fitness apps available for the Apple Watch, each with its own unique features and features. Unfortunately, none of the analyzed applications pro-vide sufficient information regarding the correct execution of training and the collec-tion of indicators, which is a disadvantage for users who want to do sports without risks to their health. One of the main challenges of software development for smartwatches is the lack of extensive monetization opportunities. This led to the lack of interest of large companies and teams in this type of software. Small screen size and limited process-ing power compared to desktop or mobile devices are also issues. This means that developers must optimize both the user interface and the algorithms of their software product to increase the speed of the application. Another challenge is the diversity of the smartwatch market, where different devices run on different operating systems and have different hardware specifications. You have to consider the specifics and limitations of each device and platform and make sure that your apps are optimized for each of them, or focus on building your software products for only a limited num-ber of devices. Due to the lack of tools for developing applications for several platforms at once, the path of developing an application for only one platform - for WatchOS - was chosen. Also, one of the goals is to create an application that is completely autono-mous from a smartphone. For this, a simple and minimalistic interface and a simple algorithm for analyzing training data have been developed. The proposed application is focused on running. Statistics will be collected dur-ing the first few training sessions. After starting the activity, the application will start displaying all the standard information, such as pace, heart rate, activity time, dis-tance traveled. This data comes from the sensors and modules of the smart watch. When basic training statistics are collected, the app will begin to display tips on how to continue training.

PIPELINE DEFECT PREDICTION USING SUPPORT VECTOR MACHINES

Oil and gas pipeline condition monitoring is a potentially challenging process due to varying temperature conditions, harshness of the flowing commodity and unpredictable terrains. Pipeline breakdown can potentially cost millions of dollars worth of loss, not to mention the serious environmental damage caused by the leaking commodity. The proposed techniques, although implemented on a lab scale experimental rig, ultimately aim at providing a continuous monitoring system using an array of different sensors strategically positioned on the surface of the pipeline. The sensors used are piezoelectric ultrasonic sensors. The raw sensor signal will be first processed using the discrete wavelet transform (DWT) as a feature extractor and then classified using the powerful learning machine called the support vector machine (SVM). Preliminary tests show that the sensors can detect the presence of wall thinning in a steel pipe by classifying the attenuation and frequency changes of the propagating lamb waves. The SVM algorithm was able to classify the signals as abnormal in the presence of wall thinning.

Continued Neurogenesis in Adult Drosophila as a Mechanism for Recruiting Environmental Cue-Dependent Variants

BackgroundThe skills used by winged insects to explore their environment are strongly dependent upon the integration of neurosensory information comprising visual, acoustic and olfactory signals. The neuronal architecture of the wing contains a vast array of different sensors which might convey information to the brain in order to guide the trajectories during flight. In Drosophila, the wing sensory cells are either chemoreceptors or mechanoreceptors and some of these sensors have as yet unknown functions. The axons of these two functionally distinct types of neurons are entangled, generating a single nerve. This simple and accessible coincidental signaling circuitry in Drosophila constitutes an excellent model system to investigate the developmental variability in relation to natural behavioral polymorphisms.Methodology/Principal FindingsA fluorescent marker was generated in neurons at all stages of the Drosophila life cycle using a highly efficient and controlled genetic recombination system that can be induced in dividing precursor cells (MARCM system, flybase web site). It allows fluorescent signals in axons only when the neuroblasts and/or neuronal cell precursors like SOP (sensory organ precursors) undergo division during the precedent steps. We first show that a robust neurogenesis continues in the wing after the adults emerge from the pupae followed by an extensive axonal growth. Arguments are presented to suggest that this wing neurogenesis in the newborn adult flies was influenced by genetic determinants such as the frequency dependent for gene and by environmental cues such as population density.ConclusionsWe demonstrate that the neuronal architecture in the adult Drosophila wing is unfinished when the flies emerge from their pupae. This unexpected developmental step might be crucial for generating non-heritable variants and phenotypic plasticity. This might therefore constitute an advantage in an unstable ecological system and explain much regarding the ability of Drosophila to robustly adapt to their environment.

Surface acoustic wave sensors to detect volatile gases by measuring output phase shift

This paper presents properties of saw acoustic wave (SAW) gas sensors to detect volatile gases such as acetone, methanol, and ethanol by measuring phase shift. A dual-delay-line saw sensors with a center frequency of 100 MHz were fabricated on 128∘ Y-Z LiNbO3 piezoelectric substrate. In order to improve sensitivity of SAW sensors, a thin titanium (Ti) film as mass sensitive layer was deposited using e-beam evaporation on the surface of the SAW sensors. In our investigation the response time and sensitivity of SAW sensors were measured. The response time and sensitivity of SAW sensor with thin Ti film were strongly improved because of changing electrical and mechanical properties in the mass sensitive layer. As a result, high sensitivity and fast response time could be achieved by deposition of thin Ti film as mass sensitive layer on the surface of SAW sensor. It can be applied for high performance electronic nose system by assembling an array of different sensors.

Development of Modular Ozone Sensor System for application in practical use

A computerized Modular Ozone Sensor System (MOSS) for evaluating the sensitivity and reliability of different sensor/transducer combinations is presented. The system consists of a compact and portable apparatus that can be used to compare various sensors. The sensor types bench-marked using MOSS are electrochemical cells, various semiconducting gas sensors based on metal oxides (In 2O 3, SnO 2), and Thickness Shear Mode Resonator (TSMR) sensors, which were mounted on separate modules. The gas mixtures containing known amounts of ozone were analyzed using this modular array of different sensors. All electronic sensor modules, including standardized connections are home-built and optimized as a part of this investigation. Computerized data acquisition and evaluation was performed using a standard hardware and an analysis software developed during this work. In order to test the setup, a set of different sensors was employed. Their sensitivity to ozone and their cross-sensitivity to other gases in ambient condition and to humidity were evaluated. Sensors, based on indium oxides, performed best in detecting ozone. They had the largest sensor sensitivity as well as the smallest cross-sensitivities.

Resolving combustible gas mixtures using gas sensitive resistors with arrays of electrodes

Gas sensitive resistors using semiconducting oxides operating at elevated temperatures and having an array of different electrode spacings on a single device are functionally equivalent to an array of different sensors with differing patterns of response to different gases. Here, a single device with two different electrode spacings is used successfully to distinguish between gases having different combustion rates, and to analyse binary mixtures of combustible gases at low concentrations in air. Furthermore it is shown that devices of this type provide considerable insight into the mechanisms of both the surface-catalysed combustion and the gas response. The kinetic law for the surface catalysed combustion of the target gas is derived by comparison with the results of numerical simulations using an iterative procedure based on the Newton–Raphson method. The accurate analysis of the gas mixtures forced a reformulation of the response model which, for a mixture of n gases is shown to be (R/R0)1/β– 1 =∑nj= 1AjPj where R denotes the resistance (R0 in air), Aj the response coefficient and Pj the partial pressure of gas j. The response order, β, is the same for all gases.