Various Kinds Of Sensors Research Articles

Insight of Employing Molecular Junctions for Sensor Applications.

Molecular junctions (MJs) exhibit distinct charge transport properties and have the potential to become the next generation of electronic devices. Advancing molecular electronics for practical uses, such as sensors, is crucial to propel its progress to the next level. In this review, we discussed how MJs can serve as a sensor for detecting a wide range of analytes with exceptional sensitivity and specificity. The primary advances and potential of molecular junctions for the various kinds of sensors including photosensors, explosives (DNTs, TNTs), cancer biomarker detection (DNA, mRNA), COVID detection, biogases (CO, NO, NH), environmental pH, practical chemicals, and water pollutants are listed and examined here. The fundamental ideas of molecular junction formation as well as the sensing mechanism have been examined here. This review demonstrates that MJ-based sensors hold significant promise for real-time and on-site detection. It provides valuable insights into current research and outlines potential future directions for advancing molecular junction-based sensors for practical applications.

Fault Detection for Point Machines: A Review, Challenges, and Perspectives

Point machines are the actuators for railway switching and crossing systems that guide trains from one track to another. Hence, the safe and reliable behavior of point machines are pivotal for rail transportation. Recently, scholars and researchers have attempted to deploy various kinds of sensors on point machines for anomaly detection and/or incipient fault detection using date-driven algorithms. However, challenges arise when deploying condition monitoring and fault detection to trackside point machines in practical applications. This article begins by reviewing studies on fault and anomaly detection in point machines, encompassing employed methods and evaluation metrics. It subsequently conducts an in-depth analysis of point machines and outlines the envisioned intelligent fault detection system. Finally, it presents eight challenges and promising research directions along with a blueprint for intelligent point machine fault detection.

Factory Monitoring and Alert System

Abstract: The basic idea of our project is to make a Monitoring and Alert System using Embedded Systems and IOT. Our project deals with various kinds of sensors (like temperature sensors, gas sensors, etc.) and other peripherals along with an Arduino UNO Microcontroller and Wi-Fi module. Here all these sensors will collect data from the surrounding, and send it to the Arduino which will further send it to a website and will also send an alert if any sort of unwanted conditions is observed. Monitoring all the sensor data remotely with the help of Arduino UNO via the Internet of Things is the main purpose of our project. We believe that this will provide users with a simple, reliable, and speedy way to monitor a factory remotely and warn people in case of any emergency

On the Private Data Synthesis Through Deep Generative Models for Data Scarcity of Industrial Internet of Things

Due to the data-driven intelligence from the recent deep learning based approaches, the huge amount of data collected from various kinds of sensors from industrial devices have the potential to revolutionize the current technologies used in the industry. To improve the efficiency and quality of machines, the machine manufacturer needs to acquire the history of the machine operation process. However, due to the business secrecy, the factories are not willing to do so. One promising solution to the abovementioned difficulty is the synthetic dataset and an informatic network structure, both through deep generative models such as differentially private generative adversarial networks. Hence, this article initiates the study of the utility difference between the abovementioned two kinds. We carry out an empirical study and find that the classifier generated by private informatic network structure is more accurate than the classifier generated by private synthetic data, with approximately 0.31–7.66%.

Contribution of Human Augmentation in Creating Cognitive and Physical Improvements as an Integral Part of the Human Body

This article is based on the contribution of human augmentation in creating physical and cognitive improvement. Nowadays it makes a positive impact on the medical sector and technological advancement provides outstanding opportunities to lead a better life. It helps to increase the action of the people and also increase the level of activity. Artificial intelligence helps to improve the facilities of technologies and there are various kinds of sensors, devices, and another sensory mechanism that develop the action of the technologies. This study has focused on the construct between human enhancement and human augmentation. This study has also shed light on several kinds of human augmentation and also focused on the collaboration of humans and machines. It has been discussed that neuromorphic technologies achieved so much attention from scientists and it can be considered as a computing system that can mimic the “neurobiological architecture”. This study has focused on several kinds of several kinds of technologies that can improve the individuals’ difficulties such as artificial limbs, bionics, and other. On the other hand, this study has also elaborated the bonding between machine and collaboration. The “human augmentation” is the combination of biological applications and human beings in the field of medical science. This kind of advancement incorporates with medicines, gene editing, wearable devices and cellular implants. These technological experiments can be implemented on the body of human being and researchers have been working on the area of smart application of drugs to prohibit some serious health complications. These experiments can improve the society as well as make an impact on the people psychology as well. Keywords : Artificial limbs, genetic alteration, HUMAN AUGMENTATION, human-machine collaboration, implementation of chips, neurobiological architecture plastic surgery, Prosthetics.

Data-driven technology of fault diagnosis in railway point machines: review and challenges

Abstract Safety and reliability are absolutely vital for sophisticated Railway Point Machines (RPMs). Hence, various kinds of sensors and transducers are deployed on RPMs as much as possible to monitor their behaviour for detection of incipient faults and anticipation using data-driven technology. This paper firstly analyses and summarizes six RPMs’ characteristics and then reviews the data-driven algorithms applied to fault diagnosis in RPMs during the past decade. It provides not only the process and evaluation metrics but also the pros and cons of these different methods. Ultimately, regarding the characteristics of RPMs and the existing studies, eight challenging problems and promising research directions are pointed out.

Exponential Squirrel Search Algorithm-Based Deep Classifier for Intrusion Detection in Cloud Computing with Big Data Assisted Spark Framework

Intrusion detection systems (IDS) are extensively employed for detecting suspicious behaviors in hosts. The ability of distributed IDS solutions makes it viable to combine and handle various kinds of sensors and generate alerts to different hosts positioned in distributed platforms. However, to offer secure and feasible services in a cloud platform is an imperative issue due to the impacts of attacks. This paper devises a novel IDS framework using cloud data to counter the influence of attacks. Here, the spark architecture is employed for discovering the intrusions. The pre-processing is applied to the input data for removing artifacts and noise considering input data. Thereafter, the feature extraction and feature fusion are performed in slave nodes. The feature fusion is carried out with the proposed Exponential Squirrel Search Algorithm (ExpSSA) algorithm. The fused features are considered in a deep-stacked autoencoder (Deep SAE) for performing effective intrusion detection. The proposed ExpSSA is adapted to train Deep SAE for tuning optimum weights. The exponential weighted moving average (EWMA) and squirrel search algorithm (SSA) are combined to create the proposed ExpSSA. The proposed ExpSSA-based Deep SAE offered improved performance compared to other techniques with the highest accuracy, detection rate of 0.846, and minimal FPR.

Using Different Smartphone Sensors to Find the Speed of a Toy Car

There are many examples of using smartphones in physics experiments. Students and teachers can easily perform many physics experiments with the help of smartphone sensors, even experiments for which high-cost materials are needed. There are different types of such experiments in which various kinds of sensors are used. Many physical quantities can be reliably measured with smartphone sensors after some calibration. One of the physical quantities that can be determined with these sensors is object speed. For example, the angular velocities of rotating objects have been measured with a smartphone light as well as proximity, acceleration, and magnetic field sensors. The speeds of light-emitting objects traveling on a straight path and inclined plane have also been determined with a smartphone light sensor. Ball velocities have also been determined with a smartphone oscilloscope app. In this study, we determine the speed of a toy car over a certain distance using smartphone magnetic field, light, and proximity sensors simultaneously with just one experiment.

Experimental study of toxic properties of a lead-tin-selenium-tellurium solid solution

Introduction. Solid solutions are widely used in the manufacture of particularly sensitive and high-precision devices, various kinds of sensors and energy converters, which causes a large number of workers to come into contact with them. The purpose of the study: to conduct experimental research and estimate biological effect of a solid lead-tin-selenium-tellurium (LTST) solution on warm-blooded animals. Materials and methods. Solid solution (lead (52%), tin (8%), selenium (trace) and tellurium (40%) - powder insoluble in water. Experiments were carried out on mice, rats and rabbits. Acute and chronic toxicity, cumulative properties, fibrogenic activity, irritant effect on skin and mucous membranes were evaluated. Results. According to the parameters of acute toxicity LTST is a low-hazard compound (Hazard Class 4), it has an irritant effect on skin and mucous membranes, the expressed cumulative properties (Kcum - 2.58). General toxic effect of the alloy is characterized by changes in functional state of kidneys, hematopoietic function, blood coagulation system. The compound has a moderately pronounced fibrogenic effect. Limitations. The limitation of this study is the lack of literature data of toxicological studies on the biological effect of a solid solution of LTST was the basis for its study. Conclusions. The biological effect of a solid solution of LTST on the organism was studied for the first time on models of acute, subacute and chronic experiments. According to GOST 12.1.077-76 LTST belongs to Hazard Class 4 (low-hazardous compound).The solid solution has been established to belong to compounds which may cause acute inhalation poisoning (Limac - 46.73 mg/m3). LTST is dangerous in terms of the development of chronic intoxication, has pronounced cumulative properties (Kcum - 2.58). LTST has a general toxic effect, leads to disruption of hematopoietic function, increased content of coproporferin in urine, which is associated to a large extent with the presence of lead molecule in the solution. The solid solution causes the development of a fibrotic process of pulmonary tissue. LTST is less toxic than the lead in its composition. Control over the concentration of LTST in the air of the working area should be carried out according to the MPC = 0.05 mg/m3 (lead and its inorganic compounds).

LORA Based Wireless Weather Station

Abstract: In this Project we foster a model of "LoRa Based Wireless Weather Station". Where LoRa is a remote organization innovation supporting the web of things (IoT) framework. This innovation is an elective to other remote organization modules that have proactively been notable like GSM modules, Wi-Fi Modules and Bluetooth Modules. The use of the LoRa network expands the scope of remote cells that can arrive at distances of as much as 8 kilometres while as yet having low power usage. Climate boundaries that we are estimated in this task are Temperature, Pressure, Dew point, Altitude, Rainfall, Humidity and Light Intensity. In this model we use Arduino UNO, Barometric Sensor, Rain Sensor, Light Intensity Sensor and LoRa modules. What's more, Thingspeak Web application for sharing information to the clients. This framework can possibly be executed in metropolitan and provincial zones with various kinds of sensors related to it. This undertaking can be directed to shape a Low Power-Wide Area Network as a greater sensor organization.

Slope walking of humanoid robot without IMU sensor on an unknown slope

It is significantly challenging to develop biped robots, such as a humanoid robot, that can walk stably in complex environments, such as a rough terrain or slope. Additionally, various kinds of sensors are required. Force and torque (FT) and inertial measurement unit (IMU) sensors are necessary for a humanoid robot to achieve stable biped walking, and it is especially difficult to realize such locomotion without an IMU sensor in complex environments, such as a slope. Therefore, a new walking algorithm is needed so that a humanoid robot is able to walk well on a slope, even if the IMU sensor is damaged. This paper proposes a slope observer that can estimate the angle of the robot, angular velocity, and slope angle without an IMU sensor, as well as a controller for posture control and stabilization on a slope. The slope observer was developed based on a disturbance observer that can estimate the angle of the robot, angular velocity, and disturbance applied to the robot using only an FT sensor. The controller was designed as state feedback control based on the slope observer. Furthermore, the posture control was designed considering the slope angle such that the robot can always stand upright on the slope. The proposed slope observer and controller were verified with certain experiments performed in the lab, and the walking experiment of a real humanoid robot RoK-3 performed on the unknown slope. [https://youtu.be/x1PlzktlOpM]

Energy Efficient Low Latency Routing Design for Target Tracking Applications of Wireless Sensor Network

Target tracking is the greatest important applications in Wireless Sensor Networks (WSNs). The wireless sensor network applications have been increasing since the IoT has been established. Most of the applications have various kind of sensors to transmit the information from one source to another. The basic operation of a wireless sensor network is to sense the data, collect the data and transmit the data from time to time whenever the base station requires the data for evaluation. Improving the reliability, performance for the collection of the data is the main role of the wireless sensor device. Moreover, the objective of the wireless sensor network device is to minimize the latency and improve the energy efficiency in order to provide more reliability is a major performance metric for provisioning WSNs. In this paper, we have presented an Energy Efficient Low Latency Routing (EELLR) design for Target Tracking (TT) Applications of Wireless Sensor Network. This model provides reliability and has a better performance in terms of communication overhead, energy efficiency and packet processing latency reduction when compared with the existing routing-based models.

A Study into the Security Issues and Countermeasures for the Industrial Internet of Things (IIOT)

Industrial internet of things are essential factors for an organisation to maintain relevant work strategy. In this study, several types of security issues and countermeasures for IIOT are discussed critically. Various kinds of sensors, applications and devices are used by a company to identify security issues for IIOT. Visibility of work is overseen by a firm to gather several types of information related to work. Every company always tries to enhance troubleshooting and maintenance capabilities. Regulatory compliance is controlled by an organisation to understand issues of IIOT. Operational efficiency improves by these security issues for IIOT process. Quality assurance and artificial intelligence are controlled with help of these security issues. Digital transformation helps a company to mitigate those issues for IIOT. Lack of visibility, security integration and poor testing related issues are faced by a firm. Centralised connectivity helps an organisation to connect all networks in an organised way. This centralised connectivity plays an essential role to control performance. Secondary network system is managed by a company to mitigate security issues for IIOT. Several types of modern IoT devices are used for mitigating security issues. Keyword : Security issues, IIOT, IOT, centralised connectivity.

Residence Automation System Using Internet of Things

Home automation using Internet of Things is full of automation and the human being’s life is getting easier as almost everything is systematic by replacing the conventional/manual systems. Recent days, the internet is the mixed part of every human life to gather the knowledge of technology. Internet of things provides a source the devices to connect, sensed by sensors and controlled by remote across a grid infrastructure. Various kind of sensors are designed to meet the need of Home automation such as flame sensor, gas sensor, touch sensor, soil moisture sensor, Infrared sensor where it is connected with Arduino board and it automatically controls the environment. Infrared Sensor is used to find the movement of objects and is used in home automation for example we can use infrared sensor in car shed to monitor the car activities. Based on the car motion we can use infrared sensor. Infrared sensor is used to sense the car movement based on the movement light is automatically changed into on condition and if there is no movement the light turns into off condition. The Arduino board have microcontroller to control all the process occurred in home and it can be sensed by the various sensor and it can be alert to the owner by mail or any other process. The results are obtained with the help of Arduino Software and Arduino UNO for all the sensors.

Au/Pd Bimetallic Nanoparticles Decorated SnSe₂ Thin Films for NO₂ Detection.

In order to have a check and balance of the toxic gases in the environment, various kinds of sensors are currently being researched upon. As many of the toxic gases are also inflammable, therefore, there is a constant search for materials which can detect the gases at lower temperatures. Also, it is important that the sensor is selective for a particular gas. To meet such requirements, nanos-tructured materials are extensively being explored for such gas sensing applications, due to their large effective surface area. And, in order to further improve the gas sensing properties, metal catalysts are deposited over such nanomaterials. The smaller sized nanoparticles show better catalytic activity due to its effective larger surface area per unit volume. Depositing bimetallic materials is thus advantageous, since it can reduce the size of nanoparticles produced. In this work, ~7 nm thick Au/Pd thin film was sputter-coated over SnSe₂ nanostructured thin films. SnSe₂ thin film were deposited by thermally evaporating SnSe₂ powder. The materials were characterized for their structural, morphological and gas sensing properties. The ambient temperature response for 5 parts per million (ppm) NO₂ gas was measured to be 117%, with the response and recovery times being 10 and 19 seconds, respectively. The performance of the sensor improved with increase in the gas concentration and for 10 ppm gas, the recorded response was 137%, with the corresponding response and recovery times being 9 and 8 seconds, respectively. The limit of detection was 655 parts per billion (ppb). The mechanism of ambient temperature high response and low response/recovery times have been discussed based on physisorption, charge transfer, Au/Pd decoration and SnSe-SnSe₂ based p-n junction. In addition, an important aspect of this work worth pointing out is the deposition of a thin film consisting of nanostructured network using an industrially viable thermal evaporation method. Thus, this work opens a new dimension for 2D materials that can be used for selective gas detection at ambient temperature.

The future of autonomous underwater vehicle control

The future of autonomous underwater vehicle control

Research on Intelligent Management System of Meteorological Archives Based on Big Data Framework

The era of big data, analysis, and artificial intelligence is a new trend in intelligent big data analysis. The present stage of the geoscience progress allows the Earth to be analyzed as an extremely dynamic structure of multiple elements, such as the hydrosphere, lithosphere, and atmosphere, interacting in and with others. To derive useful information from them, large quantities of observation and simulation data provided by numeric models need to be analyzed. Visualization is a critical feature of data analytics since it is a simple and swift way to evaluate the data and consider the specific aspects and mistakes of the dataset. A geographic information system (GIS), the most efficient meteorological data visualization software class, provides excellent capabilities for geospatial data manipulation. The processing architecture that can efficiently be used as a back-end for GIS by providing quick access to the data stored at remote storage nodes is described in this paper. Weather departments use various kinds of sensors for data collection such as temperature, humidity, etc. The number and speed of the sensors in each sensor complicate the data processing time. This paper seeks to provide a weather-temperature analysis big data forecast architecture based on the MapReduce algorithm. The suggested intelligent management system of meteorological archives based on big data (IMS-MABD) framework methodology could promote research and advancement of intelligent big data analysis, large data analytics, business intelligence, artificial intelligence, and data science. Intelligent management systems for meteorological archive systems based on large data frameworks could be used. Experimental findings show that the architecture created allows real-time data access and can support many simultaneous applications successfully with a performance of 98.1%.

HarMI: Human Activity Recognition Via Multi-Modality Incremental Learning

Nowadays, with the development of various kinds of sensors in smartphones or wearable devices, human activity recognition (HAR) has been widely researched and has numerous applications in healthcare, smart city, etc. Many techniques based on hand-crafted feature engineering or deep neural network have been proposed for sensor based HAR. However, these existing methods usually recognize activities offline, which means the whole data should be collected before training, occupying large-capacity storage space. Moreover, once the offline model training finished, the trained model can't recognize new activities unless retraining from the start, thus with a high cost of time and space. In this paper, we propose a multi-modality incremental learning model, called HarMI, with continuous learning ability. The proposed HarMI model can start training quickly with little storage space and easily learn new activities without storing previous training data. In detail, we first adopt attention mechanism to align heterogeneous sensor data with different frequencies. In addition, to overcome catastrophic forgetting in incremental learning, HarMI utilizes the elastic weight consolidation and canonical correlation analysis from a multi-modality perspective. Extensive experiments based on two public datasets demonstrate that HarMI can achieve a superior performance compared with several state-of-the-arts.

Experimental System for Charging and Testing Batteries Specific to Multirotor Drones

The experimental stand created is made for charging, testing and monitoring the processes that take place at a drone, in a static and also dynamic manner. On this platform we have developed, created and tested various kinds of sensors, and charging methods, rechargeable batteries, multicopper drones, automatically without disconnecting or disassembling them from the drones they serve. Also, at this level we fitted a flat zone, that is polarized where the drone will land, in a controlled manner and after a preliminary testing of polarity and of tension level, the charging will begin, as its parameters will carefully monitored.

PENGEMBANGAN AUTOMATIC FEEDER PAKAN IKAN MENGGUNAKAN MICROCONTROLLER ESP8266

The Internet of Thing (IoT) is a combination of the internet and ubiquitoscomputing which has the function of controlling various kinds of electronic goods remotely and can be connected to various kinds of sensors. Technology can be used for the problem of feeding fish automatically which can be monitored from the blink application based on Android and IOS. The materials used in this study are iron and household furniture as construction, Mcu Esp8266 Node, Arduino, Loadcell, ultrasonic sensors, Servo Motors, and DC Motors. Tools that are made to work automatically run provide feed at predetermined hours and measure feed feed based on weight determined with a distance of ejection of feed up to 1 meter, and can be monitored using the blynk application via a local wifi network to find out the feed has been stocked and the amount of feed available in the main shelter.