Search results for: autonomous sensors

Authors: Tapan Upadhyay, Aqib Siddiqui, Sameer Khan

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Railway crossing system in India is a manually operated level crossing system, either manned or unmanned. The main aim is to protect pedestrians and vehicles from colliding with trains, which pass at regular intervals, as India has the largest and busiest railway network. But because of human error and negligence, every year thousands of lives are lost due to accidents at railway crossings. To avoid this, we suggest a solution, by using Programmable Logical Controller (PLC) based automatic system, which will automatically control the barrier as well as roadblocks to stop people from crossing while security warning is given. Often people avoid security warning, and pass two-wheelers from beneath the barrier, while the train is at a distance away. This paper aims at reducing the fatality and accident rate by controlling barrier and roadblocks using sensors which sense the incoming train and vehicles and sends a signal to PLC. The PLC in return sends a signal to barrier and roadblocks. Once the train passes, the barrier and roadblocks retrieve back, and the passage is clear for vehicles and pedestrians to cross. PLC’s are used because they are very flexible, cost effective, space efficient, reduces complexity and minimises errors. Supervisory Control And Data Acquisition (SCADA) is used to monitor the functioning.

Keywords: level crossing, PLC, sensors, SCADA

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Authors: Cian O'Shea, Ross O'Halloran, Peter Haigh

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Wireless Sensor Networks (WSN) are Internet of Things (IoT) edge devices. They are becoming widely adopted in many industries, including health care, building energy management, and conditional monitoring. As the scale of WSN deployments increases, the cost and complexity of battery replacement and disposal become more significant and in time may become a barrier to adoption. Harvesting ambient energies provide a pathway to reducing dependence on batteries and in the future may lead to autonomously powered sensors. This work describes a simulation tool that enables the user to predict the battery life of a wireless sensor that utilizes energy harvesting to supplement the battery power. To create this simulator, all aspects of a typical WSN edge device were modelled including, sensors, transceiver, and microcontroller as well as the energy source components (batteries, solar cells, thermoelectric generators (TEG), supercapacitors and DC/DC converters). The tool allows the user to plug and play different pre characterized devices as well as add user-defined devices. The goal of this simulation tool is to predict the lifetime of a device and scope for extension using ambient energy sources.

Keywords: Wireless Sensor Network, IoT, edge device, simulation, solar cells, TEG, supercapacitor, energy harvesting

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Authors: Shinoy Vengaramkode Bhaskaran, Kaushik Sathupadi, Sandesh Achar

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Human locomotion recognition is important in a variety of sectors, such as robotics, security, healthcare, fitness tracking and cloud computing. With the increasing pervasiveness of peripheral devices, particularly Inertial Measurement Units (IMUs) sensors, researchers have attempted to exploit these advancements in order to precisely and efficiently identify and categorize human activities. This research paper introduces a state-of-the-art methodology for the recognition of human locomotion patterns in a cloud environment. The methodology is based on a publicly available benchmark dataset. The investigation implements a denoising and windowing strategy to deal with the unprocessed data. Next, feature extraction is adopted to abstract the main cues from the data. The SelectKBest strategy is used to abstract optimal features from the data. Furthermore, state-of-the-art ML classifiers are used to evaluate the performance of the system, including logistic regression, random forest, gradient boosting and SVM have been investigated to accomplish precise locomotion classification. Finally, a detailed comparative analysis of results is presented to reveal the performance of recognition models.

Keywords: artificial intelligence, cloud computing, IoT, human locomotion, gradient boosting, random forest, neural networks, body-worn sensors

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Authors: Moiz S. Vohra, Nagalingam Arun Prasanth, Wei L. Tan, S. H. Yeo

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The understanding of generation and collapse of acoustic cavitation bubbles are prerequisites for application of cavitation erosion. Microbubbles generated due to rapid fluctuation of pressure induced by propagation of ultrasonic wave lead to formation of high velocity microjets and or shock waves upon collapse. Due to vast application of ultrasonic, it is important to characterize and understand cavitation collapse pressure under the radiating surface at different conditions. A comparative investigation is carried out to determine impact load and dynamic pressure distribution exerted upon bubble collapse using thin film pressure sensors. Measurements were recorded at different input conditions such as amplitude, stand-off distance, insertion depth of the horn inside the liquid and pulse on-off time of acoustic vibrations. Impact force of 2.97 N is recorded at amplitude of 108 μm and stand-off distance of 1 mm from the sensor film, whereas impulsive force as low as 0.4 N is recorded at amplitude of 12 μm and stand-off distance of 5 mm from the sensor film. The results drawn from the investigation indicated that variety of impact loads can be achieved by controlling generation and collapse of bubbles, making it suitable to use for numerous application.

Keywords: ultrasonic cavitation, bubble collapse, pressure mapping sensor, impact load

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Authors: Dustin Schöder

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The potential benefits of the utilization of highly automated and autonomous trucks in logistics operations are the subject of interest to the entire logistics industry. The benefits of the use of these new technologies were scientifically investigated and implemented in roadmaps. So far, reliable data and experiences from real life use cases are still limited. A German research consortium of both academics and industry developed a highly automated (SAE level 4) vehicle for yard operations at railports and container depots. After development and testing, a several month field test at the DUSS Terminal in Ulm-Dornstadt (Germany) and the nearby DB Intermodal Services Container Depot in Ulm-Dornstadt was conducted. The truck was piloted in a shuttle service between both sites. In a holistic automation approach, the vehicle was integrated into a digital communication platform so that the truck could move autonomously without a driver and his manual interactions with a wide variety of stakeholders. The main goal is to investigate the effects of highly automated trucks in the key processes of container loading, unloading and container relocation on holistic railport yard operation. The field test data were used to investigate changes in process efficiency of key processes of railport and container yard operations. Moreover, effects on the capacity utilization and potentials for smothering peak workloads were analyzed. The results state that process efficiency in the piloted use case was significantly higher. The reason for that could be found in the digitalized data exchange and automated dispatch. However, the field test has shown that the effect is greatly varying depending on the ratio of highly automated and manual trucks in the yard as well as on the congestion level in the loading area. Furthermore, the data confirmed that under the right conditions, the capacity utilization of highly automated trucks could be increased. In regard to the potential for smothering peak workloads, no significant findings could be made based on the limited requirements and regulations of railway operation in Germany. In addition, an empirical survey among railport managers, operational supervisors, innovation managers and strategists (n=15) within the logistics industry in Germany was conducted. The goal was to identify key characteristics of future railports and terminals as well as requirements that railports will have to meet in the future. Furthermore, the railport processes where automation and autonomization make the greatest impact, as well as hurdles and challenges in the introduction of new technologies, have been surveyed. Hence, further potential use cases of highly automated and autonomous applications could be identified, and expectations have been mapped. As a result, a highly detailed and practice-based roadmap towards a ‘terminal 4.0’ was developed.

Keywords: highly automated driving, autonomous driving, SAE level 4, railport operations, container depot, intermodal logistics, potentials of autonomization

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Authors: Justin Tang

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Balance tests are frequently used to diagnose concussions on the sidelines of sporting events. Manual scoring, however, is labor intensive and subjective, and many concussions go undetected. This study institutes a novel approach to conducting the Balance Error Scoring System (BESS) more quantitatively using Microsoft’s gaming system Kinect, which uses a contactless sensor and several cameras to receive data and estimate body limb positions. Using a machine learning approach, Visual Gesture Builder, and a deterministic approach, MATLAB, we tested whether the Kinect can differentiate between “correct” and erroneous stances of the BESS. We created the two separate solutions by recording test videos to teach the Kinect correct stances and by developing a code using Java. Twenty-two subjects were asked to perform a series of BESS tests while the Kinect was collecting data. The Kinect recorded the subjects and mapped key joints onto their bodies to obtain angles and measurements that are interpreted by the software. Through VGB and MATLAB, the videos are analyzed to enumerate the number of errors committed during testing. The resulting statistics demonstrate a high correlation between manual scoring and the Kinect approaches, indicating the viability of the use of remote tracking devices in conducting concussion tests.

Keywords: automated, concussion detection, contactless sensors, microsoft kinect

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Authors: Stefano Zaninotto, Geoffrey Farrugia, Johan Debattista, Jason Gauci

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The need to reduce fuel and noise during taxi operations in the airports with a scenario of constantly increasing air traffic has resulted in an effort by the aerospace industry to move towards electric taxiing. In fact, this is one of the problems that is currently being addressed by SESAR JU and two main solutions are being proposed. With the first solution, electric motors are installed in the main (or nose) landing gear of the aircraft. With the second solution, manned or unmanned electric tow trucks are used to tow aircraft from the gate to the runway (or vice-versa). The presence of the tow trucks results in an increase in vehicle traffic inside the airport. Therefore, it is important to design the system in a way that the workload of Air Traffic Control (ATC) is not increased and the system assists ATC in managing all ground operations. The aim of this work is to develop an electric taxiing system, based on the use of autonomous tow trucks, which optimizes aircraft ground operations while keeping ATC in the loop. This system will consist of two components: an optimization tool and a Graphical User Interface (GUI). The optimization tool will be responsible for determining the optimal path for arriving and departing aircraft; allocating a tow truck to each taxiing aircraft; detecting conflicts between aircraft and/or tow trucks; and proposing solutions to resolve any conflicts. There are two main optimization strategies proposed in the literature. With centralized optimization, a central authority coordinates and makes the decision for all ground movements, in order to find a global optimum. With the second strategy, called decentralized optimization or multi-agent system, the decision authority is distributed among several agents. These agents could be the aircraft, the tow trucks, and taxiway or runway intersections. This approach finds local optima; however, it scales better with the number of ground movements and is more robust to external disturbances (such as taxi delays or unscheduled events). The strategy proposed in this work is a hybrid system combining aspects of these two approaches. The GUI will provide information on the movement and status of each aircraft and tow truck, and alert ATC about any impending conflicts. It will also enable ATC to give taxi clearances and to modify the routes proposed by the system. The complete system will be tested via computer simulation of various taxi scenarios at multiple airports, including Malta International Airport, a major international airport, and a fictitious airport. These tests will involve actual Air Traffic Controllers in order to evaluate the GUI and assess the impact of the system on ATC workload and situation awareness. It is expected that the proposed system will increase the efficiency of taxi operations while reducing their environmental impact. Furthermore, it is envisaged that the system will facilitate various controller tasks and improve ATC situation awareness.

Keywords: air traffic control, electric taxiing, autonomous tow trucks, graphical user interface, ground operations, multi-agent, route optimization

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Authors: Isomar Lima da Silva, Alcilene Batalha Pontes, Aristeu Jonatas Leite de Oliveira, Roberto Maia Augusto

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Context: Mechanical ventilators are medical devices that help provide oxygen and ventilation to patients with respiratory difficulties. This equipment consists of a manual breathing unit that can be operated by a doctor or nurse and a mechanical ventilator that controls the airflow and pressure in the patient's respiratory system. This type of ventilator is commonly used in emergencies and intensive care units where it is necessary to provide breathing support to critically ill or injured patients. Objective: In this context, this work aims to develop a reliable and low-cost mechanical ventilator to meet the demand of hospitals in treating people affected by Covid-19 and other severe respiratory diseases, offering a chance of treatment as an alternative to mechanical ventilators currently available in the market. Method: The project presents the development of a low-cost auxiliary ventilator with a controlled ventilatory system assisted by integrated hardware and firmware for respiratory cycle control in non-invasive mechanical ventilation treatments using a manual artificial respiration unit. The hardware includes pressure sensors capable of identifying positive expiratory pressure, peak inspiratory flow, and injected air volume. The embedded system controls the data sent by the sensors. It ensures efficient patient breathing through the operation of the sensors, microcontroller, and actuator, providing patient data information to the healthcare professional (system operator) through the graphical interface and enabling clinical parameter adjustments as needed. Results: The test data of the developed mechanical ventilator presented satisfactory results in terms of performance and reliability, showing that the equipment developed can be a viable alternative to commercial mechanical ventilators currently available, offering a low-cost solution to meet the increasing demand for respiratory support equipment.

Keywords: mechanical fans, breathing, medical equipment, COVID-19, intensive care units

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Authors: Zhihua Yu, Qi Zhang, Mingyu Zhang, Haolong Dai

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Distributed fiber-optic vibration sensors are gaining extensive attention, for the advantages of high sensitivity, accurate location, light weight, large-scale monitoring, good concealment, and etc. In this paper, a novel optical fiber distributed vibration sensing system is proposed, which is based on self-interference of Rayleigh backscattering with phase generated carrier (PGC) demodulation algorithm. Pulsed lights are sent into the sensing fiber and the Rayleigh backscattering light from a certain position along the sensing fiber would interfere through an unbalanced Michelson Interferometry (MI) to generate the interference light. An improved PGC demodulation algorithm is carried out to recover the phase information of the interference signal, which carries the sensing information. Three vibration events were applied simultaneously to different positions over 2000m sensing fiber and demodulated correctly. Experiments show that the spatial resolution of is 10 m, and the noise level of the Φ-OTDR system is about 10-3 rad/√Hz, and the signal to noise ratio (SNR) is about 30.34dB. This vibration measurement scheme can be applied at surface, seabed or downhole for vibration measurements or distributed acoustic sensing (DAS).

Keywords: fiber optics sensors, Michelson interferometry, MI, phase-sensitive optical time domain reflectometry, Φ-OTDR, phase generated carrier, PGC

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Authors: Ow Tze Weng, Suhaila Isaak, Yusmeeraz Yusof

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The trend of health care screening devices in the world is increasingly towards the favor of portability and wearability, especially in the most common electrocardiogram (ECG) monitoring system. This is because these wearable screening devices are not restricting the patient’s freedom and daily activities. While the demand of low power and low cost biomedical system on chip (SoC) is increasing in exponential way, the front end ECG sensors are still suffering from flicker noise for low frequency cardiac signal acquisition, 50 Hz power line electromagnetic interference, and the large unstable input offsets due to the electrode-skin interface is not attached properly. In this paper, a high performance CMOS amplifier for ECG sensors that suitable for low power wearable cardiac screening is proposed. The amplifier adopts the highly stable folded cascode topology and later being implemented into RC feedback circuit for low frequency DC offset cancellation. By using 0.13 µm CMOS technology from Silterra, the simulation results show that this front end circuit can achieve a very low input referred noise of 1 pV/√Hz and high common mode rejection ratio (CMRR) of 174.05 dB. It also gives voltage gain of 75.45 dB with good power supply rejection ratio (PSSR) of 92.12 dB. The total power consumption is only 3 µW and thus suitable to be implemented with further signal processing and classification back end for low power biomedical SoC.

Keywords: CMOS, ECG, amplifier, low power

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Authors: Kirolos Gerges Yakoub Gerges

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Self-sustaining agricultural machines act in stochastic surroundings and therefore, should be capable of perceive the surroundings in real time. This notion can be done using image sensors blended with superior device learning, mainly Deep mastering. Deep convolutional neural networks excel in labeling and perceiving colour pix and since the fee of RGB-cameras is low, the hardware cost of accurate notion relies upon heavily on memory and computation power. This paper investigates the opportunity of designing lightweight convolutional neural networks for semantic segmentation (pixel clever class) with reduced hardware requirements, to allow for embedded usage in self-reliant agricultural machines. The usage of compression techniques, a lightweight convolutional neural community is designed to carry out actual-time semantic segmentation on an embedded platform. The community is skilled on two big datasets, ImageNet and Pascal Context, to apprehend as much as four hundred man or woman instructions. The 400 training are remapped into agricultural superclasses (e.g. human, animal, sky, road, area, shelterbelt and impediment) and the capacity to provide correct actual-time perception of agricultural environment is studied. The network is carried out to the case of self-sufficient grass mowing the usage of the NVIDIA Tegra X1 embedded platform. Feeding case-unique pics to the community consequences in a fully segmented map of the superclasses within the picture. As the network remains being designed and optimized, handiest a qualitative analysis of the technique is entire on the abstract submission deadline. intending this cut-off date, the finalized layout is quantitatively evaluated on 20 annotated grass mowing pictures. Light-weight convolutional neural networks for semantic segmentation can be implemented on an embedded platform and show aggressive performance on the subject of accuracy and speed. It’s miles viable to offer value-efficient perceptive capabilities related to semantic segmentation for autonomous agricultural machines.

Keywords: centrifuge pump, hydraulic energy, agricultural applications, irrigationaxial flux machines, axial flux applications, coreless machines, PM machinesautonomous agricultural machines, deep learning, safety, visual perception

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Authors: Wei Zheng, Mao Ji, Zhe Hou, Meng Huang, Bo Qi

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The transformer is the key equipment of the power system. Winding deformation is one of the main transformer defects, and timely and effective detection of the transformer winding deformation can ensure the safe and stable operation of the transformer to the maximum extent. When winding deformation occurs, the size, shape and spatial position of the winding will change, which directly leads to the change of magnetic flux leakage distribution. Therefore, it is promising to study the online detection method of the transformer winding deformation based on magnetic flux leakage characteristics, in which the key step is to study the optimal placement method of magnetic flux leakage sensors inside the transformer. In this paper, a simulation model of the transformer winding deformation is established to obtain the internal magnetic flux leakage distribution of the transformer under normal operation and different winding deformation conditions, and the law of change of magnetic flux leakage distribution due to winding deformation is analyzed. The results show that different winding deformation leads to different characteristics of the magnetic flux leakage distribution. On this basis, an optimized placement of magnetic flux leakage sensors inside the transformer is proposed to provide a basis for the online detection method of transformer winding deformation based on the magnetic flux leakage characteristics.

Keywords: magnetic flux leakage, sensor placement method, transformer, winding deformation

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Authors: Juzhong Tan, William Kerr

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Roasting is one critical procedure in chocolate processing, where special favors are developed, moisture content is decreased, and better processing properties are developed. Therefore, determination of roasting degree of cocoa bean is important for chocolate manufacturers to ensure the quality of chocolate products, and it also decides the commercial value of cocoa beans collected from cocoa farmers. The roasting degree of cocoa beans currently relies on human specialists, who sometimes are biased, and chemical analysis, which take long time and are inaccessible to many manufacturers and farmers. In this study, a self-made electronic nose system consists of gas sensors (TGS 800 and 2000 series) was used to detecting the gas generated by cocoa beans with a different roasting degree (0min, 20min, 30min, and 40min) and the signals collected by gas sensors were used to train a three-layers ANN. Chemical analysis of the graded beans was operated by traditional GC-MS system and the contents of volatile chemical compounds were used to train another ANN as a reference to electronic nosed signals trained ANN. Both trained ANN were used to predict cocoa beans with a different roasting degree for validation. The best accuracy of grading achieved by electronic nose signals trained ANN (using signals from TGS 813 826 820 880 830 2620 2602 2610) turned out to be 96.7%, however, the GC trained ANN got the accuracy of 83.8%.

Keywords: artificial neutron network, cocoa bean, electronic nose, roasting

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Authors: Mahmoud Mohammad Salem Al-Suod, Ushkarenko O. Alexander, Dorogan I. Olga

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New algorithms using microprocessor systems have been proposed for protection the diesel-generator unit in autonomous power systems. The software structure is designed to enhance the control automata of the system, in which every protection module of diesel-generator encapsulates the finite state machine.

Keywords: diesel-generator unit, protection, state diagram, control system, algorithm, software components

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Authors: Ahmed Elaksher, Islam Omar

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Accurate geometric relation algorithms are imperative in Earth and planetary satellite and aerial image processing, particularly for high-resolution images that are used for topographic mapping. Most of these satellites carry push-broom sensors. These sensors are optical scanners equipped with linear arrays of CCDs. These sensors have been deployed on most EOSs. In addition, the LROC is equipped with two push NACs that provide 0.5 meter-scale panchromatic images over a 5 km swath of the Moon. The HiRISE carried by the MRO and the HRSC carried by MEX are examples of push-broom sensor that produces images of the surface of Mars. Sensor models developed in photogrammetry relate image space coordinates in two or more images with the 3D coordinates of ground features. Rigorous sensor models use the actual interior orientation parameters and exterior orientation parameters of the camera, unlike approximate models. In this research, we generate a generic push-broom sensor model to process imageries acquired through linear array cameras and investigate its performance, advantages, and disadvantages in generating topographic models for the Earth, Mars, and the Moon. We also compare and contrast the utilization, effectiveness, and applicability of available photogrammetric techniques and softcopies with the developed model. We start by defining an image reference coordinate system to unify image coordinates from all three arrays. The transformation from an image coordinate system to a reference coordinate system involves a translation and three rotations. For any image point within the linear array, its image reference coordinates, the coordinates of the exposure center of the array in the ground coordinate system at the imaging epoch (t), and the corresponding ground point coordinates are related through the collinearity condition that states that all these three points must be on the same line. The rotation angles for each CCD array at the epoch t are defined and included in the transformation model. The exterior orientation parameters of an image line, i.e., coordinates of exposure station and rotation angles, are computed by a polynomial interpolation function in time (t). The parameter (t) is the time at a certain epoch from a certain orbit position. Depending on the types of observations, coordinates, and parameters may be treated as knowns or unknowns differently in various situations. The unknown coefficients are determined in a bundle adjustment. The orientation process starts by extracting the sensor position and, orientation and raw images from the PDS. The parameters of each image line are then estimated and imported into the push-broom sensor model. We also define tie points between image pairs to aid the bundle adjustment model, determine the refined camera parameters, and generate highly accurate topographic maps. The model was tested on different satellite images such as IKONOS, QuickBird, and WorldView-2, HiRISE. It was found that the accuracy of our model is comparable to those of commercial and open-source software, the computational efficiency of the developed model is high, the model could be used in different environments with various sensors, and the implementation process is much more cost-and effort-consuming.

Keywords: photogrammetry, push-broom sensors, IKONOS, HiRISE, collinearity condition

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Authors: Zixian Zhang, Shanliang Yao, Zile Huang, Zhaodong Wu, Xiaohui Zhu, Yong Yue, Jieming Ma

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Unmanned Surface Vehicles (USVs) are valuable due to their ability to perform dangerous and time-consuming tasks on the water. Object detection tasks are significant in these applications. However, inherent challenges, such as the complex distribution of obstacles, reflections from shore structures, water surface fog, etc., hinder the performance of object detection of USVs. To address these problems, this paper provides a fusion method for USVs to effectively detect objects in the inland surface environment, utilizing vision sensors and 3+1D Millimeter-wave radar. MMW radar is complementary to vision sensors, providing robust environmental information. The radar 3D point cloud is transferred to 2D radar pseudo image to unify radar and vision information format by utilizing the point transformer. We propose a multi-source object detection network (RV-YOLOX )based on radar-vision fusion for inland waterways environment. The performance is evaluated on our self-recording waterways dataset. Compared with the YOLOX network, our fusion network significantly improves detection accuracy, especially for objects with bad light conditions.

Keywords: inland waterways, YOLO, sensor fusion, self-attention

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Authors: Mehdi Modabberifar, Sanaz Jabary, Mojtaba Ghodsi

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Stiffness sensing is an important issue in medical diagnostic, robotics surgery, safe handling, and safe grasping of objects in production lines. Detecting and obtaining the characteristics in dwelling lumps embedded in a soft tissue and safe removing and handling of detected lumps is needed in surgery. Also in industry, grasping and handling an object without damaging in a place where it is not possible to access a human operator is very important. In this paper, a method for object handling is presented. It is based on the use of an intelligent gripper to detect the object stiffness and then setting a programmable force for grasping the object to move it. The main components of this system includes sensors (sensors for measuring force and displacement), electrical (electrical and electronic circuits, tactile data processing and force control system), mechanical (gripper mechanism and driving system for the gripper) and the display unit. The system uses a rotary potentiometer for measuring gripper displacement. A microcontroller using the feedback received by the load cell, mounted on the finger of the gripper, calculates the amount of stiffness, and then commands the gripper motor to apply a certain force on the object. Results of Experiments on some samples with different stiffness show that the gripper works successfully. The gripper can be used in haptic interfaces or robotic systems used for object handling.

Keywords: gripper, haptic, stiffness, robotic

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Authors: Victor Breux, Jérôme Boutet, Alain Goret, Viviane Cattin

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Early detection of anomalies in data centers is important to reduce downtimes and the costs of periodic maintenance. However, there is little research on this topic and even fewer on the fusion of sensor data for the detection of abnormal events. The goal of this paper is to propose a method for anomaly detection in data centers by combining sensor data (temperature, humidity, power) and deep learning models. The model described in the paper uses one autoencoder per sensor to reconstruct the inputs. The auto-encoders contain Long-Short Term Memory (LSTM) layers and are trained using the normal samples of the relevant sensors selected by correlation analysis. The difference signal between the input and its reconstruction is then used to classify the samples using feature extraction and a random forest classifier. The data measured by the sensors of a data center between January 2019 and May 2020 are used to train the model, while the data between June 2020 and May 2021 are used to assess it. Performances of the model are assessed a posteriori through F1-score by comparing detected anomalies with the data center’s history. The proposed model outperforms the state-of-the-art reconstruction method, which uses only one autoencoder taking multivariate sequences and detects an anomaly with a threshold on the reconstruction error, with an F1-score of 83.60% compared to 24.16%.

Keywords: anomaly detection, autoencoder, data centers, deep learning

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Authors: W. J. Zhang, Y. Q. Du, M. L. Wang

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Noninvasive diagnostics of diseases via breath analysis has attracted considerable scientific and clinical interest for many years and become more and more promising with the rapid advancement in nanotechnology and biotechnology. The volatile organic compounds (VOCs) in exhaled breath, which are mainly blood borne, particularly provide highly valuable information about individuals’ physiological and pathophysiological conditions. Additionally, breath analysis is noninvasive, real-time, painless and agreeable to patients. We have developed a wireless sensor array based on single-stranded DNA (ssDNA)-decorated single-walled carbon nanotubes (SWNT) for the detection of a number of physiological indicators in breath. Eight DNA sequences were used to functionalize SWNT sensors to detect trace amount of methanol, benzene, dimethyl sulfide, hydrogen sulfide, acetone and ethanol, which are indicators of heavy smoking, excessive drinking, and diseases such as lung cancer, breast cancer, cirrhosis and diabetes. Our tests indicated that DNA functionalized SWNT sensors exhibit great selectivity, sensitivity, reproducibility, and repeatability. Furthermore, different molecules can be distinguished through pattern recognition enabled by this sensor array. Thus, the DNA-SWNT sensor array has great potential to be applied in chemical or bimolecular detection for the noninvasive diagnostics of diseases and health monitoring.

Keywords: breath analysis, diagnosis, DNA-SWNT sensor array, noninvasive

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Authors: Wabinyai Fidel Raja, Gideon Lubisa

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Air pollution is becoming a growing concern in Africa due to rapid industrialization and urbanization, leading to implications for public health and the environment. Establishing a comprehensive air quality monitoring network is crucial to combat this issue. However, conventional methods of monitoring are insufficient in African cities due to the high cost of setup and maintenance. To address this, low-cost sensors (LCS) can be deployed in various urban areas through the use of participatory air quality network siting (PAQNS). PAQNS involves stakeholders from the community, local government, and private sector working together to determine the most appropriate locations for air quality monitoring stations. This approach improves the accuracy and representativeness of air quality monitoring data, engages and empowers community members, and reflects the actual exposure of the population. Implementing PAQNS in African cities can build trust, promote accountability, and increase transparency in the air quality management process. However, challenges to implementing this approach must be addressed. Nonetheless, improving air quality is essential for protecting public health and promoting a sustainable environment. Implementing participatory and data-informed air quality monitoring can take a significant step toward achieving these important goals in African cities and beyond.

Keywords: low-cost sensors, participatory air quality network siting, air pollution, air quality management

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Authors: Vahid Bayrami Rad

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In this research, common methods and techniques have been investigated with a focus on intelligent fault finding and monitoring systems in the oil industry. In fact, remote and intelligent control methods are considered a necessity for implementing various operations in the oil industry, but benefiting from the knowledge extracted from countless data generated with the help of data mining algorithms. It is a avoid way to speed up the operational process for monitoring and troubleshooting in today's big oil companies. Therefore, by comparing data mining algorithms and checking the efficiency and structure and how these algorithms respond in different conditions, The proposed (Bayesian) algorithm using data clustering and their analysis and data evaluation using a colored Petri net has provided an applicable and dynamic model from the point of view of reliability and response time. Therefore, by using this method, it is possible to achieve a dynamic and consistent model of the remote control system and prevent the occurrence of leakage in oil pipelines and refineries and reduce costs and human and financial errors. Statistical data The data obtained from the evaluation process shows an increase in reliability, availability and high speed compared to other previous methods in this proposed method.

Keywords: wireless sensors, petroleum equipment troubleshooting, Bayesian algorithm, colored Petri net, rapid miner, data mining-reliability

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Authors: Ben Morrow, Patrick O'Hara, Natalie Ban, Tunai Marques, Molly Fraser, Christopher Bone

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Relative to commercial fisheries, recreational fisheries are often poorly understood and pose various challenges for monitoring frameworks. In British Columbia (BC), Canada, Pacific salmon are heavily targeted by recreational fishers while also being a key source of nutrient flow and crucial prey for a variety of marine and terrestrial fauna, including endangered Southern Resident killer whales (Orcinus orca). Although commercial fisheries were historically responsible for the majority of salmon retention, recreational fishing now comprises both greater effort and retention. The current monitoring scheme for recreational salmon fisheries involves aerial-roving creel surveys. However, this method has been identified as costly and having low predictive power as it is often limited to sampling fragments of fluid and temporally dynamic fisheries. This study used imagery from two shore-based autonomous cameras in a highly active recreational fishery around Sooke, BC, and evaluated their efficacy in supplementing existing aerial-roving surveys for monitoring a recreational salmon fishery. This study involved continuous monitoring and high temporal resolution (over one million images analyzed in a single fishing season), using a deep learning-based vessel detection algorithm and a custom image annotation tool to efficiently thin datasets. This allowed for the quantification of peak-season effort from a busy harbour, species-specific retention estimates, high levels of detected fishing events at a nearby popular fishing location, as well as the proportion of the fishery management area represented by cameras. Then, this study demonstrated how it could substantially enhance the temporal resolution of a fishery through diel activity pattern analyses, scaled monthly to visualize clusters of activity. This work also highlighted considerable off-season fishing detection, currently unaccounted for in the existing monitoring framework. These results demonstrate several distinct applications of autonomous cameras for providing enhanced detail currently unavailable in the current monitoring framework, each of which has important considerations for the managerial allocation of resources. Further, the approach and methodology can benefit other studies that apply shore-based camera monitoring, supplement aerial-roving creel surveys to improve fine-scale temporal understanding, inform the optimal timing of creel surveys, and improve the predictive power of recreational stock assessments to preserve important and endangered fish species.

Keywords: cameras, monitoring, recreational fishing, stock assessment

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Authors: Balgaisha G. Mukanova, Yelbek B. Utepov, Aida G. Nazarova, Alisher Z. Imanov

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The problem of identifying local noise sources on a construction site using a sensor system is considered. Mathematical modeling of detected signals on sensors was carried out, considering signal decay and signal delay time between the source and detector. Recordings of noises produced by construction tools were used as a dependence of noise on time. Synthetic sensor data was constructed based on these data, and a model of the propagation of acoustic waves from a point source in the three-dimensional space was applied. All sensors and sources are assumed to be located in the same plane. A source localization method is checked based on the signal time delay between two adjacent detectors and plotting the direction of the source. Based on the two direct lines' crossline, the noise source's position is determined. Cases of one dominant source and the case of two sources in the presence of several other sources of lower intensity are considered. The number of detectors varies from three to eight detectors. The intensity of the noise field in the assessed area is plotted. The signal of a two-second duration is considered. The source is located for subsequent parts of the signal with a duration above 0.04 sec; the final result is obtained by computing the average value.

Keywords: acoustic model, direction of arrival, inverse source problem, sound localization, urban noises

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Authors: Mylena McCoggle, Shyra Wilson, Andrea Rivera, Rocio Alba-Flores

Abstract:

This work introduces the use of EMGs (electromyography) from muscle sensors to develop an Artificial Neural Network (ANN) for pattern recognition to control a small unmanned aerial vehicle. The objective of this endeavor exhibits interfacing drone applications beyond manual control directly. MyoWare Muscle sensor contains three EMG electrodes (dual and single type) used to collect signals from the posterior (extensor) and anterior (flexor) forearm and the bicep. Collection of raw voltages from each sensor were connected to an Arduino Uno and a data processing algorithm was developed with the purpose of interpreting the voltage signals given when performing flexing, resting, and motion of the arm. Each sensor collected eight values over a two-second period for the duration of one minute, per assessment. During each two-second interval, the movements were alternating between a resting reference class and an active motion class, resulting in controlling the motion of the drone with left and right movements. This paper further investigated adding up to three sensors to differentiate between hand gestures to control the principal motions of the drone (left, right, up, and land). The hand gestures chosen to execute these movements were: a resting position, a thumbs up, a hand swipe right motion, and a flexing position. The MATLAB software was utilized to collect, process, and analyze the signals from the sensors. The protocol (machine learning tool) was used to classify the hand gestures. To generate the input vector to the ANN, the mean, root means squared, and standard deviation was processed for every two-second interval of the hand gestures. The neuromuscular information was then trained using an artificial neural network with one hidden layer of 10 neurons to categorize the four targets, one for each hand gesture. Once the machine learning training was completed, the resulting network interpreted the processed inputs and returned the probabilities of each class. Based on the resultant probability of the application process, once an output was greater or equal to 80% of matching a specific target class, the drone would perform the motion expected. Afterward, each movement was sent from the computer to the drone through a Wi-Fi network connection. These procedures have been successfully tested and integrated into trial flights, where the drone has responded successfully in real-time to predefined command inputs with the machine learning algorithm through the MyoWare sensor interface. The full paper will describe in detail the database of the hand gestures, the details of the ANN architecture, and confusion matrices results.

Keywords: artificial neural network, biosensors, electromyography, machine learning, MyoWare muscle sensors, Arduino

Procedia PDF Downloads 174

Authors: Elyas Palantei, Dewiani, Farid Armin, Ardiansyah

Abstract:

High precision of printed microwave sensor utilized for sensing and monitoring the potential breast cancer existed in women breast tissue was optimally computed. The single element of UWB printed sensor that successfully modeled through several numerical optimizations was multiple fabricated and incorporated with woman bra to form the spherical sensors array. One sample of UWB microwave sensor obtained through the numerical computation and optimization was chosen to be fabricated. In overall, the spherical sensors array consists of twelve stair patch structures, and each element was individually measured to characterize its electrical properties, especially the return loss parameter. The comparison of S11 profiles of all UWB sensor elements is discussed. The constructed UWB sensor is well verified using HFSS programming, CST programming, and experimental measurement. Numerically, both HFSS and CST confirmed the potential operation bandwidth of UWB sensor is more or less 4.5 GHz. However, the measured bandwidth provided is about 1.2 GHz due to the technical difficulties existed during the manufacturing step. The configuration of UWB microwave sensing and monitoring system implemented consists of 12 element UWB printed sensors, vector network analyzer (VNA) to perform as the transceiver and signal processing part, the PC Desktop/Laptop acting as the image processing and displaying unit. In practice, all the reflected power collected from whole surface of artificial breast model are grouped into several numbers of pixel color classes positioned on the corresponding row and column (pixel number). The total number of power pixels applied in 2D-imaging process was specified to 100 pixels (or the power distribution pixels dimension 10x10). This was determined by considering the total area of breast phantom of average Asian women breast size and synchronizing with the single UWB sensor physical dimension. The interesting microwave imaging results were plotted and together with some technical problems arisen on developing the breast sensing and monitoring system are examined in the paper.

Keywords: UWB sensor, UWB microwave imaging, spherical array, breast cancer monitoring, 2D-medical imaging

Procedia PDF Downloads 195

Authors: Arkadiusz Urzędowski, Dorota Wójcicka-Migasiuk, Andrzej Sachajdak, Magdalena Paśnikowska-Łukaszuk

Abstract:

Due to the impact of climate changes and inevitability to reduce greenhouse gases, the need to use low-carbon and sustainable construction has increased. In this work, it is investigated how texture of the surface building materials and radiative heat transfer phenomenon in flat multilayer can be correlated. Attempts to test the surface emissivity are taken however, the trustworthiness of measurement results remains a concern since sensor size and thickness are common problems. This paper presents an experimental method to studies surface emissivity with use self constructed thermal sensors and thermal imaging technique. The surface of building materials was modified by mechanical and chemical treatment affecting the reduction of the emissivity. For testing the shaping surface of materials and mapping its three-dimensional structure, scanning profilometry were used in a laboratory. By comparing the results of laboratory tests and performed analysis of 3D computer fluid dynamics software, it can be shown that a change in the surface coverage of materials affects the heat transport by radiation between layers. Motivated by recent advancements in variational inference, this publication evaluates the potential use a dedicated data processing approach, and properly constructed temperature sensors, the influence of the surface emissivity on the phenomenon of radiation and heat transport in the entire partition can be determined.

Keywords: heat transfer, surface roughness, surface emissivity, radiation

Procedia PDF Downloads 97

Authors: Manvelidze irakli, Iashvili Genadi

Abstract:

Research has shown that public institutions in Autonomous Republic of Ajara try their best to make their official electronic data (web-pages, social websites) more informative and improve them. Part of public institutions offer interesting electronic services and initiatives to the public although they are seldom used in communication process. The statistical analysis of the use of web-pages and social websites of public institutions for example their facebook page show lack of activity. The reason could be the fact that public institutions give people less possibility of interaction in official web-pages. Second reason could be the fact that these web-pages are less known to the public and the third reason could be the fact that heads of these institutions lack awareness about the necessity of strengthening citizens’ involvement. In order to increase people’s involvement in this process it is necessary to have at least 23 e-services in one web-page. The research has shown that 11 of the 16 public institutions have only 5 services which are contact, social networks and hotline. Besides introducing innovative services government institutions should evaluate them and make them popular and easily accessible for the public. It would be easy to solve this problem if public institutions had concrete strategic plan of public relations which involved matters connected with maximum usage of electronic services while interaction with citizens. For this moment only one governmental body has a functioning action plan of public relations. As a result of the research organizational, social, methodological and technical problems have been revealed. It should be considered that there are many feedback possibilities like forum, RSS, blogs, wiki, twitter, social networks, etc. usage of only one or three of such instruments indicate that there is no strategy of regional electronic governance. It is necessary to develop more mechanisms of feedback which will increase electronic interaction, discussions and it is necessary to introduce the service of online petitions. It is important to reduce the so-called “digital inequality” and increase internet access for the public. State actions should decrease such problems. In the end if such shortcomings will be improved the role of electronic interactions in democratic processes will increase.

Keywords: e-Government, electronic services, information technology, regional government, regional government

Procedia PDF Downloads 310

Authors: Tran Thi Anh Dao, M. Arnold, L. Schacher, D. C. Adolphe, G. Reys

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During tooth extraction or implant surgery, dentists are in contact with numerous infectious germs from patients' saliva and blood. For that reason, dentist's clothes have to play their role of protection from contamination. In addition, dentist's apparels should be not only protective but also comfortable and breathable because dentists have to perform many operations and treatments on patients throughout the day with high concentration and intensity. However, this type of protective garments has not been studied scientifically, whereas dentists are facing new risks and eager for looking for a comfortable personal protective equipment. For that reason, we have proposed some new designs of dentist's gown. They were expected to diminish heat accumulation that are considered as an important factor in reducing the level of comfort experienced by users. Experiments using infra-red technology were carried out in order to compare the breathable properties between a traditional gown and a new design with open zones. Another experiment using pressure sensors was also carried out to study ergonomic aspects trough the flexibility of movements of sleeves. The sleeves-design which is considered comfortable and flexible will be chosen for the further step. The results from the two experiments provide valuable information for the development of a new design of dentists' gowns in order to achieve maximum levels of cooling and comfort for the human body.

Keywords: garment, dentists, comfort, design, protection, thermal

Procedia PDF Downloads 221

Authors: Shahnam Behnam Malekzadeh, Ian Kerr, Tyson Kaempffer, Teague Harper, Andrew Watson

Abstract:

The Site C Hydroelectric dam is currently being constructed in north-eastern British Columbia on sub-horizontal sedimentary strata that dip approximately 15 meters from one bank of the Peace River to the other. More than 615 pressure sensors (Vibrating Wire Piezometers) have been installed on bedding planes (BPs) since construction began, with over 80 more planned before project completion. These pressure measurements are essential to monitor the stability of the rock foundation during and after construction and for dam safety purposes. BPs are identified by their clay gouge infilling, which varies in thickness from less than 1 to 20 mm and can be challenging to identify as the core drilling process often disturbs or washes away the gouge material. Without the use of depth predictions from nearby boreholes, stratigraphic markers, and downhole geophysical data, it is difficult to confidently identify BP targets for the sensors. In this paper, a Case-Based Reasoning (CBR) method was used to develop an empirical model called the Bedding Plane Elevation Prediction (BPEP) to help geologists and geotechnical engineers to predict geological features and bedding planes at new locations in a fast and accurate manner. To develop CBR, a database was developed based on 64 pressure sensors already installed on key bedding planes BP25, BP28, and BP31 on the Right Bank, including bedding plane elevations and coordinates. Thirteen (20%) of the most recent cases were selected to validate and evaluate the accuracy of the developed model, while the similarity was defined as the distance between previous cases and recent cases to predict the depth of significant BPs. The average difference between actual BP elevations and predicted elevations for above BPs was ±55cm, while the actual results showed that 69% of predicted elevations were within ±79 cm of actual BP elevations while 100% of predicted elevations for new cases were within ±99cm range. Eventually, the actual results will be used to develop the database and improve BPEP to perform as a learning machine to predict more accurate BP elevations for future sensor installations.

Keywords: case-based reasoning, geological feature, geology, piezometer, pressure sensor, core logging, dam construction

Procedia PDF Downloads 80

Authors: João Madureira, Ricardo Lagido, Inês Sousa, Fraunhofer Portugal

Abstract:

Surf is an increasingly popular sport and its performance evaluation is often qualitative. This work aims at using a smartphone to collect and analyze the GPS and inertial sensors data in order to obtain quantitative metrics of the surfing performance. Two approaches are compared for detection of wave rides, computing the number of waves rode in a surfing session, the starting time of each wave and its duration. The first approach is based on computing the velocity from the Global Positioning System (GPS) signal and finding the velocity thresholds that allow identifying the start and end of each wave ride. The second approach adds information from the Inertial Measurement Unit (IMU) of the smartphone, to the velocity thresholds obtained from the GPS unit, to determine the start and end of each wave ride. The two methods were evaluated using GPS and IMU data from two surfing sessions and validated with similar metrics extracted from video data collected from the beach. The second method, combining GPS and IMU data, was found to be more accurate in determining the number of waves, start time and duration. This paper shows that it is feasible to use smartphones for quantification of performance metrics during surfing. In particular, detection of the waves rode and their duration can be accurately determined using the smartphone GPS and IMU.

Keywords: inertial measurement unit (IMU), global positioning system (GPS), smartphone, surfing performance

Procedia PDF Downloads 401