Sensor Inputs Research Articles

Modeling and implementing a load management system for a solar home system based on Fuzzy Logic

Solar Home System is one of the technologies for utilizing solar power at home. To optimize the performance of PLTS, it is necessary to regulate the use of electrical energy. In this research, an effort is made to control the load using fuzzy logic to regulate the power consumption used by the load so that energy can be utilized effectively. The fuzzy logic method works based on the input given so that the desired results can be as expected. To test the effectiveness of the fuzzy logic method, this study was tested with two types of loads: lighting and fan loads. For lighting loads, it uses two light sensor inputs, and for air conditioning, it uses two temperature sensor inputs and a PIR sensor. The test results show that in the experiment of setting the light load using fuzzy logic, the average power usage at the load is 11.31 watts. In contrast, without fuzzy logic, the average power usage value for the load is 14.29 watts. In the fan load control experiment using fuzzy logic, the energy consumption setting was obtained according to the room temperature input and the number of people in the room. The test results received power usage without using fuzzy logic, where the average fan power usage value is 4.32 Watts, while without fuzzy logic, the average power usage value is 2.97 Watts. For one sensor input, the average power usage value of the average fan is 3.02 Watts; without fuzzy logic, the average power usage value is 2.93 Watts for two sensor inputs.

Heterogeneous Fusion of Camera and mmWave Radar Sensor of Optimizing Convolutional Neural Networks for Parking Meter System.

In this article, a novel heterogeneous fusion of convolutional neural networks that combined an RGB camera and an active mmWave radar sensor for the smart parking meter is proposed. In general, the parking fee collector on the street outdoor surroundings by traffic flows, shadows, and reflections makes it an exceedingly tough task to identify street parking regions. The proposed heterogeneous fusion convolutional neural networks combine an active radar sensor and image input with specific geometric area, allowing them to detect the parking region against different tough conditions such as rain, fog, dust, snow, glare, and traffic flow. They use convolutional neural networks to acquire output results along with the individual training and fusion of RGB camera and mmWave radar data. To achieve real-time performance, the proposed algorithm has been implemented on a GPU-accelerated embedded platform Jetson Nano with a heterogeneous hardware acceleration methodology. The experimental results exhibit that the accuracy of the heterogeneous fusion method can reach up to 99.33% on average.

Learning deep feature fusion for traffic light detection

Traffic light detection in real-world conditions is challenging because of the positioning of lights, variety in shapes and scales, and similarity with other objects. The paper presents a deep learning-based traffic light detection system by learning the fusion of handcrafted features. The handcrafted features for object detection focus on specific attributes such as shape, color, or texture. The objective of this work is to incorporate handcrafted features into the network learning process such that the resulting detector parameters are robust to input variations, sensor noise, and atmospheric noise. The proposed detection framework is based on the latest You only look once (YOLO) architecture, trained with the fusion of different information channels in the Integral Channel Features (ICF). The approach demonstrates a qualitative approach for identifying the optimal layer for additional feature injection in the network, and the selection of ICF channels to be applied for fusion. The validation of the proposed detector on the Bosch small traffic light dataset achieved the best mAP score of 55.70 % on the testing set. Further, a qualitative comparison of the proposed detector’s performance with that of other recent methods is presented, along with an analysis using auxiliary experiments.

SpectralMAE: Spectral Masked Autoencoder for Hyperspectral Remote Sensing Image Reconstruction

Accurate hyperspectral remote sensing information is essential for feature identification and detection. Nevertheless, the hyperspectral imaging mechanism poses challenges in balancing the trade-off between spatial and spectral resolution. Hardware improvements are cost-intensive and depend on strict environmental conditions and extra equipment. Recent spectral imaging methods have attempted to directly reconstruct hyperspectral information from widely available multispectral images. However, fixed mapping approaches used in previous spectral reconstruction models limit their reconstruction quality and generalizability, especially dealing with missing or contaminated bands. Moreover, data-hungry issues plague increasingly complex data-driven spectral reconstruction methods. This paper proposes SpectralMAE, a novel spectral reconstruction model that can take arbitrary combinations of bands as input and improve the utilization of data sources. In contrast to previous spectral reconstruction techniques, SpectralMAE explores the application of a self-supervised learning paradigm and proposes a masked autoencoder architecture for spectral dimensions. To further enhance the performance for specific sensor inputs, we propose a training strategy by combining random masking pre-training and fixed masking fine-tuning. Empirical evaluations on five remote sensing datasets demonstrate that SpectralMAE outperforms state-of-the-art methods in both qualitative and quantitative metrics.

Learning Type-2 Fuzzy Logic for Factor Graph Based-Robust Pose Estimation With Multi-Sensor Fusion

Although a wide variety of high-performance state estimation techniques have been introduced recently, the robustness and extension to actual conditions of the estimation systems have been challenging. This paper presents a robust adaptive state estimation framework based on the Type-2 fuzzy inference system and factor graph optimization for autonomous mobile robots. We use the hybrid solution to connect the advantages of the tightly and loosely coupled technique by providing an inertial sensor and other extrinsic sensors such as LiDARs and cameras. In order to tackle the uncertainty input covariance and sensor failures problems, a learnable observation model is introduced by joining the Type-2 FIS and factor graph optimization. In particular, the use of Type-2 Takagi-Sugeno FIS can learn the uncertainty by using particle swarm optimization before adding the observation model to the factor graph. The proposed design consists of four parts: sensor odometry, up-sampling, FIS based-learning observation model, and factor graph-based smoothing. We evaluate our system by using a mobile robot platform equipped with a sensor setup of multiple stereo cameras, an IMU, and a LiDAR sensor. We imitate the LiDAR odometry in structure environments without needing other bulky motion capture systems to learn the observation model of the visual-inertial estimators. The experimental results are deployed in real-world environments to present the accuracy and robustness of the algorithm.

A Zero-Shot Soft Sensor Modeling Approach Using Adversarial Learning for Robustness Against Sensor Fault

Soft sensors are widely used in many industrial systems to monitor key variables that are difficult to measure, using measurements from other available physical sensors. Because physical sensors are susceptible to faults, it is crucial for soft sensor models to be robust against them. Recently, deep learning has shown promising results in developing data-driven soft sensors for various applications. However, existing learning-based soft sensors are still vulnerable to sensor faults which could deteriorate the performance of the models. In this paper, we propose a deep learning-based modeling framework for developing soft sensor models that are robust to sensor faults. Due to the difficulty in obtaining datasets that cover all possible sensor fault characteristics, the proposed framework is developed to be zero-shot such that the model can be trained with only fault-free dataset without requiring any sensor fault patterns, thus greatly saving the time and resources needed to collect such data. Instead, adversarial examples are used as a proxy for faulty sensor inputs so that the model can learn to be adaptive through the proposed two-stage, uncertainty-aware recurrent neural network architecture. We demonstrate our approach on the TE benchmark process and a real industrial multiphase flow process, and show that robustness is achieved as the accuracy does not degrade significantly when sensor faults are present during model evaluation.

Low-order wavefront control using a Zernike sensor through Lyot coronagraphs for exoplanet imaging

Context. Wavefront sensing and control (WFSC) will play a key role in improving the stability of future large segmented space telescopes while relaxing the thermo-mechanical constraints on the observatory structure. Coupled with a coronagraph to reject the light of an observed bright star, WFSC enables the generation and stabilisation of a dark hole (DH) in the star image to perform planet observations. Aims. While WFSC traditionally relies on a single wavefront sensor (WFS) input to measure wavefront errors, the next generation of instruments will require several WFSs to address aberrations with different sets of spatial and temporal frequency contents. The multiple measurements produced in such a way will then have to be combined and converted to commands for deformable mirrors to modify the wavefront subsequently. Methods. We asynchronously operate a loop controlling the high-order modes digging a DH and a control loop that uses the rejected light by a Lyot coronagraph with a Zernike wavefront sensor to stabilize the low-order aberrations. Using the HiCAT testbed with a segmented telescope aperture, we implement concurrent operations and quantify the expected cross-talk between the two controllers. We then present experiments that alternate high-order and low-order control loops to identify and estimate their respective contributions. Results. We show an efficient combination of the high-order and low-order control loops, keeping a DH contrast better than 5 × 10−8 over a 30 min experiment and stability improvement by a factor of 1.5. In particular, we show a contrast gain of 1.5 at separations close to the DH inner working angle, thanks to the low-order controller contribution. Conclusions. Concurrently digging a DH and using the light rejected by a Lyot coronagraph to stabilize the wavefront is a promising path towards exoplanet imaging and spectroscopy with future large space observatories.

Automatic vehicle number plate recognition system

In simple terms, Automatic Vehicle Number Plate Recognition System (AVNPRS) can be described as a computer vision image processing technology. This technology captures images of vehicles and recognizes their number plates. As the world is becoming more sophisticated on each new day, security as well as management of transportation system have become a vital aspect in controlled places. Such places include modern campuses, shopping malls and other institutions. With today’s rate at which motor vehicles are increasing in numbers, there is need for an effective, affordable and efficient AVNPRS. Thus, this project proposes, outlines and discusses an efficient AVNPRS. The system under consideration in this project can be installed at main entrances of modern institutions. This is because the AVNPRS ensures that only authorized vehicles can automatically have access to such institutions. The AVNPRS ensures that it captures the image of the number plate once the input sensor detects the vehicle. The Sobel edge detection and Laplacian edge detection techniques are used at this critical stage. Thereafter, the Bounding box technique is used to find the number plate leading to character segmentation. After capturing, an image undergoes extraction and character investigation via the Optical Character Recognition (OCR). In addition, in achieving character recognition, matching between the computer template and segmented image is done via the OCR method. It is vital to mention that this system is sustainable as it successfully detects, recognizes and processes vehicle number plates on real images. The AVNPRS can be used for both traffic control and security. Therefore, the main aim of this piece of writing is to develop from a theoretical perspective an AVNPR system that can detect and capture vehicle number plate images.

Progress towards in-situ traceability and digitalization of temperature measurements

Autonomous control systems rely on input from sensors, so it is crucial that the sensor input is validated to ensure that it is ‘right’ and that the measurements are traceable to the International System of Units. The measurement and control of temperature is widespread, and its reliable measurement is key to maximising product quality, optimising efficiency, reducing waste and minimizing emissions such as CO2 and other harmful pollutants. Degradation of temperature sensors in harsh environments such as high temperature, contamination, vibration and ionising radiation causes a progressive loss of accuracy that is not apparent. Here we describe some new developments to overcome the problem of ‘calibration drift’, including self-validating thermocouples and embedded phase-change cells which self-calibrate in situ by means of a built-in temperature reference and practical primary thermometers such as the Johnson noise thermometer which measure temperature directly and do not suffer from calibration drift. All these developments will provide measurement assurance which is an essential part of digitalisation to ensure that sensor output is always ‘right’, as well as providing essential ‘points of truth’ in a sensor network. Some progress in digitalisation of calibrations to make them available to end-users via a website and/or an Application Programming Interface is also described.

Event-triggered adaptive fault-tolerant bounded control for a class of non-strict feedback nonlinear systems with input quantization

The issue of event-triggered adaptive fault-tolerant control (FTC) for a class of non-strict feedback nonlinear systems (NSs) with input quantization, and sensor and actuator faults (SAAF) is investigated in this paper, where SAAF can be both additive and multiplicative. By using backstepping method with fuzzy logic systems (FLSs), an event-triggered adaptive fault-tolerant controller is proposed. Accordingly, the event-triggered scheme and the quantization control are employed to change the signal transmission and alleviate the transmission burden. One feature of the proposed controller is that the control signal is bounded with a known bound in advance which can be ensured via tuning feedback control gains. Asymptotical stability analysis of the closed-loop systems is established. Furthermore, when the considered system subjects to additive and multiplicative sensor faults simultaneously, all the system states are bounded; in this situation the system output can converge to zero with multiplicative sensor faults. Finally, a single-link manipulator system is considered to illustrate the proposed control method and verity its effectiveness.

Steganography in IoT: Information Hiding with Joystick and Touch Sensors

This paper describes a multi-secret steganographic system for the Internet-of-Things. It uses two user-friendly sensors for data input: thumb joystick and touch sensor. These devices are not only easy to use, but also allow hidden data entry. The system conceals multiple messages into the same container, but with different algorithms. The embedding is realized with two methods of video steganography that work on mp4 files, namely, videostego and metastego. These methods were chosen because of their low complexity so that they may operate smoothly in environments with limited resources. It is possible to replace the suggested sensors with others that offer similar functionality.

Recognition of Lane Changing Maneuvers for Vehicle Driving Safety

The increasing number of vehicles has caused traffic conditions to become increasingly complicated in terms of safety. Emerging autonomous vehicles (AVs) have the potential to significantly reduce crashes. The advanced driver assistance system (ADAS) has received widespread attention. Lane keeping and lane changing are two basic driving maneuvers on highways. It is very important for ADAS technology to identify them effectively. The lane changing maneuver recognition has been used to study traffic safety for many years. Different models have been proposed. With the development of technology, machine learning has been introduced in this field with effective results. However, models which require a lot of physical data as input and unaffordable sensors lead to the high cost of AV platforms. This impedes the development of AVs. This study proposes a model of lane changing maneuver recognition based on a distinct set of physical data. The driving scenario from the natural vehicle trajectory dataset (i.e., HighD) is used for machine learning. Acceleration and velocity are extracted and labeled as physical data. The normalized features are then input into the k-nearest neighbor (KNN) classification model. The trained model was applied to another set of data and received good results. The results show that based on the acceleration features, the classification accuracy of lane keeping (LK), lane changing to the left (LCL) and lane changing to the right (LCR) is 100%, 97.89% and 96.19%.

Detection of The Lower Limb Asymmetries and Discomfort Considering Foot Pressure as an Attribute

A greater number of diseases and chronic conditions are making their presence since more than a decade. It has really taken its toll during the COVID-19 period. Our concern is totally the lower part of our body which includes the hip portion along with upper thigh, knees, lower limbs and ankles. Some diseases which we have surveyed has been taking place as such are Achilles’s tendonitis, arthritis in the ankle or ankles, bunions, hammertoes, plantar pressure diseases, et cetera. There are also a lot of conditions associated with lower limb muscles such as irregular muscle toning, muscle stressing, fatigue, et cetera which takes place in individuals of almost all kinds of ages. In order to nullify these conditions, we have decided to concentrate on various foot pressure points in our body. We will be analyzing the positions and intensity of these pressure points and conclude our experiment by chalking out a graphical representation of all the data accumulated with the help of certain equipment. We will be asking the subject by giving instructions to place his or her feet on the multiple sensors insole normally (with no extra pressure from the subject’s side). Each sole sensor will send analog input data as the values of the applied foot pressure. Voltage outputs are collected from each input sensor. We will then compare the different output from different sensors with predetermined standard data (collecting from those who have normal Body Mass Index range). The comparative graph of the current subject and the standard data will then be able to help us to analyze where the disease condition is taking place. Key Words: Foot pathology, foot pressure points, lower-limb asymmetry, sole sensor, body mass index range.

DC Motor Controller Using Full State Feedback

This paper discusses the implementation of a full state feedback control system on DC motors to stabilize the speed of DC motors and fight the disturbances given to DC motors. Modern controls such as full state feedback use 2 sensor inputs, namely the Hall effect speed sensor OH42E and the INA219 current sensor and use 3 parameters namely K1 (Constant 1), K2 (Constant 2), and KI (Integral Constant) in designing the controller, the goal is to get a good system response according to the desired design specifications. The test was carried out with a hardware-in-the-Loop (HIL) scheme which uses an Arduino microcontroller as a DC motor plant control device in the form of a control mathematical model entered in the Arduino IDE software and by trial and error to find the desired response value. The test results showed that at the values of K1=1, K2=1, KI=0.9, a stable system response was obtained with tr(s)=3, ts(s)=4, and Os(%)=7% The addition of an integral constant () value affects a short rising time but is inversely proportional to a high overshoot value as well. Varying the values of K1 and K2 as multipliers on the sensor values has an impact on the stability of the system response or oscillations. The stability of this system response indicates that full state feedback can be relied upon as a control system.

ECG-Based Semi-Supervised Anomaly Detection for Early Detection and Monitoring of Epileptic Seizures

Epilepsy is one of the most common brain diseases, characterized by frequent recurrent seizures or "ictal" states. A patient experiences uncontrollable muscular contractions, inducing loss of mobility and balance, which may result in injury or even death during these ictal states. Extensive investigation is vital to establish a systematic approach for predicting and informing patients about oncoming seizures ahead of time. Most methodologies developed are focused on the detection of abnormalities using mostly electroencephalogram (EEG) recordings. In this regard, research has indicated that certain pre-ictal alterations in the Autonomic Nervous System (ANS) can be detected in patient electrocardiogram (ECG) signals. The latter could potentially provide the basis for a robust seizure prediction approach. The recently proposed ECG-based seizure warning systems utilize machine learning models to classify a patient's condition. Such approaches require the incorporation of large, diverse, and thoroughly annotated ECG datasets, limiting their application potential. In this work, we investigate anomaly detection models in a patient-specific context with low supervision requirements. Specifically, we consider One-Class SVM (OCSVM), Minimum Covariance Determinant (MCD) Estimator, and Local Outlier Factor (LOF) models to quantify the novelty or abnormality of pre-ictal short-term (2-3 min) Heart Rate Variability (HRV) features of patients, trained on a reference interval considered to contain stable heart rate as the only form of supervision. Our models are evaluated against labels that were either hand-picked or automatically generated (weak labels) by a two-phase clustering procedure for samples of the "Post-Ictal Heart Rate Oscillations in Partial Epilepsy" (PIHROPE) dataset recorded by the Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, achieving detection in 9 out of 10 cases, with average AUCs of over 93% across all models and warning times ranging from 6 to 30 min prior to seizure. The proposed anomaly detection and monitoring approach can potentially pave the way for early detection and warning of seizure incidents based on body sensor inputs.

Data‐based learning control for optimization of nonlinear systems

Data‐based learning control for optimization of nonlinear systems

Spectrum recovery from multispectral ocean color data using system spectral shape factor

Data from ocean color monitoring sensors at different spectral channels are available for remote sensing of radiation as seen in the given spectral windows, which is used for deriving information on various atmospheric parameters. However, recent studies have demonstrated the potential of hyperspectral (HS) data over multispectral ocean color (MSOC) data in accurately estimating phytoplankton concentration and in monitoring the coastal dynamics. We propose system spectral shape factor (SSSF)-based approach to recover the embedded HS top-of-atmosphere (TOA) radiance (TOARAD) from the MSOC data. SSSF is defined as convolution of normalized input spectrum and sensor spectral response function (SRF). The advantage of SSSF is that it decouples magnitude and spectral shape part of sensor output and enables recovery of TOARAD. To test this method, the airborne visible/infrared imaging spectrometer-next generation data are used to simulate inputs to MSOC. SRF of ocean color monitor simulated MSOC. SSSF of TOARAD is estimated using SSSF of model-based path radiance spectrum of the pixel, which is similar in spectral shape. Methodology, developed using data from five stations, is validated with data from other five stations. The procedure is successfully repeated using SRFs of sea-viewing wide-field-of-view sensor. The recovered HS data are found to be consistent with the original spectra with very small deviations in spectral angle map (<0.012 rad) spectral information divergence (<5.8 × 10 − 5), mean percentage relative error (MPRE) of TOARAD (<0.7 % ), and MPRE of TOA water leaving radiance (<5.8 % ). This approach possibly opens up research for application of HS analysis on MSOC recovered spectra and for optimization of sensor configurations.

Design of an intelligent bean cultivation approach using computer vision, IoT and spatio-temporal deep learning structures

Crop health monitoring and weed removal are two crucial elements dictating efficient, productive and resilient cultivation. Due to frequent attacks by pest and pathogens, the crops become diseased resulting in degradation of the quality and quantity of the production. The process of continuous monitoring of crop health is challenging and requires the involvement of information and communication technologies (ICT). The outcome is precision agriculture where the Internet of Things (IoT) and Artificial Intelligence (AI) techniques are vital ingredients. The design of an integrated approach of precision agriculture based on IoT and AI is discussed here which is tailored for real time crop health monitoring and performs various other operations like weed detection, ambient air sensing, watering the vegetation automatically at regular intervals of time, spraying of pesticides etc. The proposed system is a combination of an IoT formed using sensors and devices, image processing and machine learning (ML)/ deep learning (DL) techniques confined to the cultivation of fifteen varieties of beans found in India. The work involves two intelligent learning models configured to capture spatio-temporal attributes of image samples and sensor inputs and for real time discrimination between healthy and diseased bean leaves, detection of weeds growing around the cultivation land and also for process control. The first approach employs a DL structure named EfficientNetB7 along with a Bidirectional Long Short Term Memory (BiLSTM) while the second method adopts a VGG16 with an integrated attention mechanism. Also experiments have been carried out using benchmark ML classifiers like Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbor (KNN), Multi-Layer Perceptron (MLP) and Time Delay Neural Network (TDNN) combined with feature extraction techniques. Segmentation methods have been used to separate out the diseased sections of the leaves which are then used as apriori labels for the classifiers to reinforce the previously known details of the bean varieties. Subsequently, the trained networks are tested with bean leaf samples collected from cultivation farms. Results show that our proposed DL models could accurately predict the health state of the bean leaves with less computation time. With an automated approach of bean leaf health discrimination, weed detection and process control, the cost effectiveness of the overall effort is enhanced. Further, the sensor pack also provides precise thresholds at which water sprinkling could be initiated resulting in water conservation.

Design a Visually Impaired Walker Using an Internet of Thing -Based Smart Bracelet

The eyes are one of the most important senses in the human body. They are used to understanding the surrounding situation so people can understand what is around them. Visual impairment is divided into two types, total blindness and low vision, depending on the degree of disability. A standard walker is a stick that helps you assess the condition of the road and the obstacles in front of you. The developing electric mobility aids are not only walking sticks. A glove-shaped walker was developed to detect objects before the tent and provide information from beeps and vibrations. Produce a walker using a visually impaired smart bracelet using sensor technology to help the blind's vigilance and mobility capable of detecting objects at a predetermined distance. The tool successfully outputs information in human voices recorded in DFPlayer Mini according to the conditions of ultrasonic sensor readings and monitoring of smart bracelets through the blynk application. Operation There are buttons used to turn the system on and off. All sensor inputs and outputs will be processed using the ESP-8266 NodeMCU. From the test results of the entire system, it can be concluded that the smart bracelet runs optimally according to the block diagram the author has compiled. Write down the conclusions of your paper and further research suggestions in the form of narratives and not in bullet or numeral form.

Fuzzy based Irrigation Control System for Indian Subcontinent

Water resource usage should be optimized as there is always a scarcity. This paper aims to provide an efficient way of water using sense and weather data and implementing a fuzzy decision model. An automated intelligent watering system is proposed in this paper using the internet of things and fuzzy logic. The weather data, coupled with Temperature, Relative Humidity, and soil moisture sensor data, is used to decide whether to switch on/off the motor. In-house-created prototypes of ground-moving robots have soil moisture, digital humidity, and temperature sensors implanted in them. The soil moisture sensor is attached to the Rack and pinion mechanism. The soil moisture sensor is pushed into the soil when the pinion rotates. It minimizes the use of sensors by using a distributed sensing method. Based on data obtained from sensors and meteorological information, the system will use this information to decide whether to Switch on/off the sprinkler motor. A fuzzy logic-based system decision is implemented on the input sensor and weather data, and the model will decide to switch on/off the actuator. An accuracy of 97% is achieved. The Android app is used to visualize sensor data, based on which the farmer can manually control the motor.