Search results for: FMCW radar sensors

Authors: M. Kessi

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We investigated the effect of the magnetic field on carrier transport phenomena in the transistor channel region of Double-Gate Metal-Oxide-Semiconductor Field-Effect Transistor (MOSFET). This explores the Lorentz force and basic physical properties of solids exposed to a constant external magnetic field. The magnetic field modulates the electrons and potential distribution in the case of silicon Tunnel FETs. This modulation shows up in the device's external electrical characteristics such as ON current (ION), subthreshold leakage current (IOF), the threshold voltage (VTH), the magneto-transconductance (gm) and the output magneto-conductance (gDS) of Tunnel FET. Moreover, the channel doping concentration and potential distribution are obtained using the numerical method by solving Poisson’s transport equation in 3D modules semiconductor magnetic sensors available in Silvaco TCAD tools. The numerical simulations of the magnetic nano-sensors are relatively new. In this work, we present the results of numerical simulations based on 3D magnetic sensors. The results show excellent accuracy comportment and good agreement compared with that obtained in the experimental study of MOSFETs technology.

Keywords: single-gate MOSFET, magnetic field, hall field, Lorentz force

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Authors: Bruk Gebregziabher

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The safe and efficient operation of Autonomous Mobile Robots (AMRs) in complex environments, such as manufacturing, logistics, and agriculture, necessitates accurate multiobject tracking and predictive collision avoidance. This paper presents algorithms and techniques for addressing these challenges using Lidar sensor data, emphasizing ensemble Kalman filter. The developed predictive collision avoidance algorithm employs the data provided by lidar sensors to track multiple objects and predict their velocities and future positions, enabling the AMR to navigate safely and effectively. A modification to the dynamic windowing approach is introduced to enhance the performance of the collision avoidance system. The overall system architecture encompasses object detection, multi-object tracking, and predictive collision avoidance control. The experimental results, obtained from both simulation and real-world data, demonstrate the effectiveness of the proposed methods in various scenarios, which lays the foundation for future research on global planners, other controllers, and the integration of additional sensors. This thesis contributes to the ongoing development of safe and efficient autonomous systems in complex and dynamic environments.

Keywords: autonomous mobile robots, multi-object tracking, predictive collision avoidance, ensemble Kalman filter, lidar sensors

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Authors: Fethi Bouksila, Magnus Persson, Ronny Berndtsson, Akissa Bahri

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Under salinity conditions, we evaluate the performance of Hilhost (2000) model to predict pore electrical conductivity ECp from dielectric permittivity and bulk electrical conductivity (ECa) using Time and Frequency Domain Reflectometry sensors (TDR, FDR). Using FDR_WET sensor, RMSE of ECp was 4.15 dS m-1. By replacing the standard soil parameter (K0) in Hilhost model by K0-ECa relationship, the RMSE of ECp decreased to 0.68 dS m-1. WET sensor could give similar accuracy to estimate ECp than TDR if calibrated values of K0 were used instead of standard values in Hilhost model.

Keywords: hilhost model, soil salinity, time domain reflectometry, frequency domain reflectometry, dielectric methods

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Authors: Eric A. Morris, Xia Liu, Yee Ka Wong, Greg J. Evans, Jeff R. Brook

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Air quality monitoring stations tend to be widely distributed and are often located far from major roadways, thus, determining where, when, and which traffic-related air pollutants (TRAPs) have the greatest impact on public health becomes a matter of extrapolation. Compact, multipollutant sensor systems are an effective solution as they enable several TRAPs to be monitored in a geospatially dense network, thus filling in the gaps between conventional monitoring stations. This work describes two applications of one such system named AirSENCE for gathering actionable air quality data relevant to smart city infrastructures. In the first application, four AirSENCE devices were co-located with traffic monitors around the perimeter of a city block in Oshawa, Ontario. This study, which coincided with the COVID-19 outbreak of 2020 and subsequent lockdown measures, demonstrated a direct relationship between decreased traffic volumes and TRAP concentrations. Conversely, road construction was observed to cause elevated TRAP levels while reducing traffic volumes, illustrating that conventional smart city sensors such as traffic counters provide inadequate data for inferring air quality conditions. The second application used two AirSENCE sensors on opposite sides of a major 2-way commuter road in Toronto. Clear correlations of TRAP concentrations with wind direction were observed, which shows that impacted areas are not necessarily static and may exhibit high day-to-day variability in air quality conditions despite consistent traffic volumes. Both of these applications provide compelling evidence favouring the inclusion of air quality sensors in current and future smart city infrastructure planning. Such sensors provide direct measurements that are useful for public health alerting as well as decision-making for projects involving traffic mitigation, heavy construction, and urban renewal efforts.

Keywords: distributed sensor network, continuous ambient air quality monitoring, Smart city sensors, Internet of Things, traffic-related air pollutants

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Authors: Rahmat Ali, Beenish Khan, Aftabullah, Abid A. Shah

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This research is an attempt to investigate the wide range of events using acoustic emission (AE) sensors of the concrete cubes subjected to different stress condition loading and unloading of concrete cubes. A total of 27 specimens were prepared and tested including 18 cubic (6”x6”x6”) and nine cylindrical (4”x8”) specimens were molded from three batches of concrete using w/c of 0.40, 0.50, and 0.60. The compressive strength of concrete was determined from concrete cylinder specimens. The deterioration of concrete was evaluated using the occurrence of felicity and Kaiser effects at each stress condition. It was found that acoustic emission hits usually exceeded when damage increases. Additionally, the correlation between AE techniques and the load applied were determined by plotting the normalized values. The influence of w/c on sensitivity of the AE technique in detecting concrete damages was also investigated.

Keywords: acoustic emission, concrete, felicity ratio, sensors

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Authors: Mohd Ruzlin Mohd Mokhtar

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At present, most operators who are working on projects for utilities such as power, water, oil, gas, telecommunication and sewerage are using technologies e.g. Total station, Global Positioning System (GPS), Electromagnetic Locator (EML) and Ground Penetrating Radar (GPR) to perform underground utility mapping. With the increase in popularity of Horizontal Directional Drilling (HDD) method among the local authorities and asset owners, most of newly installed underground utilities need to use the HDD method. HDD method is seen as simple and create not much disturbance to the public and traffic. Thus, it was the preferred utilities installation method in most of areas especially in urban areas. HDDs were installed much deeper than exiting utilities (some reports saying that HDD is averaging 5 meter in depth). However, this impacts the accuracy or ability of existing underground utility mapping technologies. In most of Malaysia underground soil condition, those technologies were limited to maximum of 3 meter depth. Thus, those utilities which were installed much deeper than 3 meter depth could not be detected by using existing detection tools. The accuracy and reliability of existing underground utility mapping technologies or work procedure were in doubt. Thus, a mitigation action plan is required. While installing new utility using Horizontal Directional Drilling (HDD) method, a more accurate underground utility mapping can be achieved by using Gyro OMU compared to existing practice using e.g. EML and GPR. Gyro OMU is a method to accurately identify the location of HDD thus this mapping can be used or referred to avoid those cost of breakdown due to future HDD works which can be caused by inaccurate underground utility mapping.

Keywords: Gyro Orientation Measurement Unit (Gyro OMU), Horizontal Directional Drilling (HDD), Ground Penetrating Radar (GPR), Electromagnetic Locator (EML)

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Authors: Mohamed Shawki Elamir, Heinrich Gotzig, Raoul Zoellner, Patrick Maeder

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In this work, a methodology is presented for identifying the optimal digitization parameters for the analog signal of ultrasonic sensors. These digitization parameters are the resolution of the analog to digital conversion and the sampling rate. This is accomplished through the derivation of characteristic curves based on Fano inequality and the calculation of the mutual information content over a given dataset. The mutual information is calculated between the examples in the dataset and the corresponding variation in the feature that needs to be estimated. The optimal parameters are identified in a manner that ensures optimal estimation performance while preventing inefficiency in using unnecessarily powerful analog to digital converters.

Keywords: analog to digital conversion, digitization, sampling rate, ultrasonic

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Authors: Prodipta Bhowmik, Poulami Saha, Preety Mehra, Yogesh Soni, Triloki Nath Jha

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In India, the smart city concept is growing day by day. So, for smart city development, a better traffic management and monitoring system is a very important requirement. Nowadays, road accidents increase due to more vehicles on the road. Reckless driving is mainly responsible for a huge number of accidents. So, an efficient traffic management system is required for all kinds of roads to control the traffic speed. The speed limit varies from road to road basis. Previously, there was a radar system but due to high cost and less precision, the radar system is unable to become favorable in a traffic management system. Traffic management system faces different types of problems every day and it has become a researchable topic on how to solve this problem. This paper proposed a computer vision and machine learning-based automated system for multiple vehicle detection, tracking, and speed estimation of vehicles using image processing. Detection of vehicles and estimating their speed from a real-time video is tough work to do. The objective of this paper is to detect vehicles and estimate their speed as accurately as possible. So for this, a real-time video is first captured, then the frames are extracted from that video, then from that frames, the vehicles are detected, and thereafter, the tracking of vehicles starts, and finally, the speed of the moving vehicles is estimated. The goal of this method is to develop a cost-friendly system that can able to detect multiple types of vehicles at the same time.

Keywords: OpenCV, Haar Cascade classifier, DLIB, YOLOV3, centroid tracker, vehicle detection, vehicle tracking, vehicle speed estimation, computer vision

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Authors: Ali H. Daraji, Ye Jianqiao

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The micro electrical devices of the wireless sensor network are continuously developed and become very small and compact with low electric power requirements using limited period life conventional batteries. The low power requirement for these devices, cost of conventional batteries and its replacement have encouraged researcher to find alternative power supply represented by energy harvesting system to provide an electric power supply with infinite period life. In the last few years, the investigation of energy harvesting for structure health monitoring has increased to powering wireless sensor network by converting waste mechanical vibration into electricity using piezoelectric sensors. Optimisation of energy harvesting is an important research topic to ensure a flowing of efficient electric power from structural vibration. The harvesting power is mainly based on the properties of piezoelectric material, dimensions of piezoelectric sensor, its position on a structure and value of an external electric load connected between sensor electrodes. Larger surface area of sensor is not granted larger power harvesting when the sensor area is covered positive and negative mechanical strain at the same time. Thus lead to reduction or cancellation of piezoelectric output power. Optimisation of energy harvesting is achieved by locating these sensors precisely and efficiently on the structure. Limited published work has investigated the energy harvesting for aircraft wing. However, most of the published studies have simplified the aircraft wing structure by a cantilever flat plate or beam. In these studies, the optimisation of energy harvesting was investigated by determination optimal value of an external electric load connected between sensor electrode terminals or by an external electric circuit or by randomly splitting piezoelectric sensor to two segments. However, the aircraft wing structures are complex than beam or flat plate and mostly constructed from flat and curved skins stiffened by stringers and ribs with more complex mechanical strain induced on the wing surfaces. This aircraft wing structure bonded with discrete macro fibre composite sensors was modelled using multiphysics finite element to optimise the energy harvesting by determination of the optimal number of sensors, location and the output resistance load. The optimal number and location of macro fibre sensors were determined based on the maximization of the open and close loop sensor output voltage using frequency response analysis. It was found different optimal distribution, locations and number of sensors bounded on the top and the bottom surfaces of the aircraft wing.

Keywords: energy harvesting, optimisation, sensor, wing

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Authors: Muhammad Zikril Hakim Md. Azizi, Norhafizan Ahmad, Raja Ariffin Raja Ghazilla

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Postural stability, attention level of the archer and particularly the vibrations of the bow itself plays a prominent role in determining the athletes performance. Many techniques and systems had been developing to monitor the parameters of the archers during training. In Malaysia, archery coaches tend to use non-scientific ways that they are familiar with, to evaluate archer performance. An approach that provides more affordable yet accurate systems to the masses and relatively easy system deployment procedure need to be proposed. Hence, this project will address to fulfil the needs. Three area of the archer parameter were included for data monitoring sensors. Attention level can be measured using EEG sensor, centre of mass linked to the postural stability can be measured by foot pressure sensor, and the bow vibrations in three axis will be relayed by the vibrations sensors placed directly on the bow using wireless sensors. Arduino based microcontroller used to relay all the data back to the interfacing systems. Interface systems will be using Python language and C++ framework for user interface and hardware interfacing systems. All sensor data can be observed in real time using the in-house applications, and each sessions can be saved to common files so that coach and the team can have a further discussion and comparisons.

Keywords: archery, graphical user interface, microcontroller, wireless sensor, monitoring system

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Authors: Hsyi-En Cheng, Ying-Yi Liou

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Metal-oxide semiconductor (MOS) gas sensors are widely used in the gas-detection market due to their high sensitivity, fast response, and simple device structures. However, the high working temperature of MOS gas sensors makes them difficult to integrate with the appliance or consumer goods. One-dimensional (1-D) nanostructures are considered to have the potential to lower their working temperature due to their large surface-to-volume ratio, confined electrical conduction channels, and small feature sizes. Unfortunately, the difficulty of fabricating 1-D nanostructure electrodes has hindered the development of low-temperature MOS gas sensors. In this work, we proposed a method to fabricate nanotube-arrays, and the SnO₂ nanotube-array sensors with different wall thickness were successfully prepared and examined. The fabrication of SnO₂ nanotube arrays incorporates the techniques of barrier-free anodic aluminum oxide (AAO) template and atomic layer deposition (ALD) of SnO₂. First, 1.0 µm Al film was deposited on ITO glass substrate by electron beam evaporation and then anodically oxidized by five wt% phosphoric acid solution at 5°C under a constant voltage of 100 V to form porous aluminum oxide. As the Al film was fully oxidized, a 15 min over anodization and a 30 min post chemical dissolution were used to remove the barrier oxide at the bottom end of pores to generate a barrier-free AAO template. The ALD using reactants of TiCl4 and H₂O was followed to grow a thin layer of SnO₂ on the template to form SnO₂ nanotube arrays. After removing the surface layer of SnO₂ by H₂ plasma and dissolving the template by 5 wt% phosphoric acid solution at 50°C, upright standing SnO₂ nanotube arrays on ITO glass were produced. Finally, Ag top electrode with line width of 5 μm was printed on the nanotube arrays to form SnO₂ nanotube-array sensor. Two SnO₂ nanotube-arrays with wall thickness of 30 and 60 nm were produced in this experiment for the evaluation of gas sensing ability. The flat SnO₂ films with thickness of 30 and 60 nm were also examined for comparison. The results show that the properties of ALD SnO₂ films were related to the deposition temperature. The films grown at 350°C had a low electrical resistivity of 3.6×10-3 Ω-cm and were, therefore, used for the nanotube-array sensors. The carrier concentration and mobility of the SnO₂ films were characterized by Ecopia HMS-3000 Hall-effect measurement system and were 1.1×1020 cm-3 and 16 cm3/V-s, respectively. The electrical resistance of SnO₂ film and nanotube-array sensors in air and in a 5% H₂-95% N₂ mixture gas was monitored by Pico text M3510A 6 1/2 Digits Multimeter. It was found that, at 200 °C, the 30-nm-wall SnO₂ nanotube-array sensor performs the highest responsivity to 5% H₂, followed by the 30-nm SnO₂ film sensor, the 60-nm SnO₂ film sensor, and the 60-nm-wall SnO₂ nanotube-array sensor. However, at temperatures below 100°C, all the samples were insensitive to the 5% H₂ gas. Further investigation on the sensors with thinner SnO₂ is necessary for improving the sensing ability at temperatures below 100 °C.

Keywords: atomic layer deposition, nanotube arrays, gas sensor, tin dioxide

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Authors: Gaurav Bhanjana, Ganga Ram Chaudhary, Sandeep Kumar, Neeraj Dilbaghi

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Main sources of endocrine disrupting compounds in the ecosystem are hormones, pesticides, phthalates, flame retardants, dioxins, personal-care products, coplanar polychlorinated biphenyls (PCBs), bisphenol A, and parabens. These endocrine disrupting compounds are responsible for learning disabilities, brain development problems, deformations of the body, cancer, reproductive abnormalities in females and decreased sperm count in human males. Although discharge of these chemical compounds into the environment cannot be stopped, yet their amount can be retarded through proper evaluation and detection techniques. The available techniques for determination of these endocrine disrupting compounds mainly include high performance liquid chromatography (HPLC), mass spectroscopy (MS) and gas chromatography-mass spectrometry (GC–MS). These techniques are accurate and reliable but have certain limitations like need of skilled personnel, time consuming, interference and requirement of pretreatment steps. Moreover, these techniques are laboratory bound and sample is required in large amount for analysis. In view of above facts, new methods for detection of endocrine disrupting compounds should be devised that promise high specificity, ultra sensitivity, cost effective, efficient and easy-to-operate procedure. Nowadays, electrochemical sensors/biosensors modified with nanomaterials are gaining high attention among researchers. Bioelement present in this system makes the developed sensors selective towards analyte of interest. Nanomaterials provide large surface area, high electron communication feature, enhanced catalytic activity and possibilities of chemical modifications. In most of the cases, nanomaterials also serve as an electron mediator or electrocatalyst for some analytes.

Keywords: electrochemical, endocrine disruptors, microscopy, nanoparticles, sensors

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Authors: Y. Fekir, K. Mederbal, M. A. Hammadouche, D. Anteur

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The cultural park of the Tassili, occupying a large area of Algeria, is characterized by a rich vegetative biodiversity to be preserved and managed both in time and space. The management of a large area (case of Tassili), by its complexity, needs large amounts of data, which for the most part, are spatially localized (DEM, satellite images and socio-economic information etc.), where the use of conventional and traditional methods is quite difficult. The remote sensing, by its efficiency in environmental applications, became an indispensable solution for this kind of studies. Multispectral imaging sensors have been very useful in the last decade in very interesting applications of remote sensing. They can aid in several domains such as the de¬tection and identification of diverse surface targets, topographical details, and geological features. In this work, we try to extract vegetative areas using fusion techniques between data acquired from sensor on-board the Earth Observing 1 (EO-1) satellite and Landsat ETM+ and TM sensors. We have used images acquired over the Oasis of Djanet in the National Park of Tassili in the south of Algeria. Fusion technqiues were applied on the obtained image to extract the vegetative fraction of the different classes of land use. We compare the obtained results in vegetation end member extraction with vegetation indices calculated from both Hyperion and other multispectral sensors.

Keywords: Landsat ETM+, EO1, data fusion, vegetation, Tassili, Algeria

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Authors: Endalamaw Ewnu Kassa, Ade Kurniawan, Ya-Fen Wu, Sajal Biring

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Perovskite-based gas sensors represent a highly promising materials within the realm of gas sensing technology, with a particular focus on detecting ammonia (NH3) due to its potential hazards. Our work conducted thorough comparison of various solvents, including dimethylformamide (DMF), DMF-dimethyl sulfoxide (DMSO), dimethylacetamide (DMAC), and DMAC-DMSO, for the preparation of our perovskite solution (MAPbI3). Significantly, we achieved an exceptional response at 10 ppm of ammonia gas by employing a binary solvent mixture of DMAC-DMSO. In contrast to prior reports that relied on single solvents for MAPbI3 precursor preparation, our approach using mixed solvents demonstrated a marked improvement in gas sensing performance. We attained enhanced surface coverage, a reduction in pinhole occurrences, and precise control over grain size in our perovskite films through the careful selection and mixtures of appropriate solvents. This study shows a promising potential of employing binary and multi-solvent mixture strategies as a means to propel advancements in gas sensor technology, opening up new opportunities for practical applications in environmental monitoring and industrial safety.

Keywords: sensors, binary solvents, ammonia, sensitivity, grain size, pinholes, surface coverage

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Authors: Tien-Li Chang, Huang-Chi Huang, Zhao-Chi Chen, Wun-Yi Chen

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The droplet-based microfluidic are used as micro-reactors for chemical and biological assays. Hence, the precise addition of reagents into the droplets is essential for this function in the scope of lab-on-a-chip applications. To obtain the characteristics (size, velocity, pressure, and frequency of production) of droplets, this study describes an integrated on-chip method of real-time signal detection. By controlling and manipulating the fluids, the flow behavior can be obtained in the droplet-based microfluidics. The detection method is used a type of infrared sensor. Through the varieties of droplets in the microfluidic devices, the real-time conditions of velocity and pressure are gained from the sensors. Here the microfluidic devices are fabricated by polydimethylsiloxane (PDMS). To measure the droplets, the signal acquisition of sensor and LabVIEW program control must be established in the microchannel devices. The devices can generate the different size droplets where the flow rate of oil phase is fixed 30 μl/hr and the flow rates of water phase range are from 20 μl/hr to 80 μl/hr. The experimental results demonstrate that the sensors are able to measure the time difference of droplets under the different velocity at the voltage from 0 V to 2 V. Consequently, the droplets are measured the fastest speed of 1.6 mm/s and related flow behaviors that can be helpful to develop and integrate the practical microfluidic applications.

Keywords: microfluidic, droplets, sensors, single detection

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Authors: K. Chethana, A. S. Guru Prasad, H. N. Vikranth, H. Varun, S. N. Omkar, S. Asokan

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This paper describes a novel application of Fiber Braggs Grating (FBG) sensors on an unstable platform to assess human postural stability and balance. The FBG sensor based Stability Analyzing Device (FBGSAD) developed demonstrates the applicability of FBG sensors in the measurement of plantar strain to assess the postural stability of subjects on unstable platforms during different stances in eyes open and eyes closed conditions on a rocker board. Comparing the Centre of Gravity (CG) variations measured on the lumbar vertebra of subjects using a commercial accelerometer along with FBGSAD validates the study. The results obtained depict qualitative similarities between the data recorded by both FBGSAD and accelerometer, illustrating the reliability and consistency of FBG sensors in biomechanical applications for both young and geriatric population. The developed FBGSAD simultaneously measures plantar strain distribution and postural stability and can serve as a tool/yardstick to mitigate space motion sickness, identify individuals who are susceptible to falls and to qualify subjects for balance and stability, which are important factors in the selection of certain unique professionals such as aircraft pilots, astronauts, cosmonauts etc.

Keywords: biomechanics, fiber bragg gratings, plantar strain measurement, postural stability analysis

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Authors: Aneesh Babu, S. P. Anusha

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A dynamic signal control system combines traditional traffic lights with an array of sensors to intelligently control vehicle and pedestrian traffic. The present study focus on evaluating the performance of dynamic signal control systems for mixed traffic conditions. Data collected from four different approaches to a typical four-legged signalized intersection at Trivandrum city in the Kerala state of India is used for the study. Performance of three other dynamic signal control methods, namely (i) Non-sequential method (ii) Webster design for consecutive signal cycle using flow as input, and (iii) dynamic signal control using RFID delay as input, were evaluated. The evaluation of the dynamic signal control systems was carried out using a calibrated VISSIM microsimulation model. Python programming was used to integrate the dynamic signal control algorithm through the COM interface in VISSIM. The intersection delay obtained from different dynamic signal control methods was compared with the delay obtained from fixed signal control. Based on the study results, it was observed that the intersection delay was reduced significantly by using dynamic signal control methods. The dynamic signal control method using delay from RFID sensors resulted in a higher percentage reduction in delay and hence is a suitable choice for implementation under mixed traffic conditions. The developed dynamic signal control strategies can be implemented in ITS applications under mixed traffic conditions.

Keywords: dynamic signal control, intersection delay, mixed traffic conditions, RFID sensors

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Authors: Andrew Hwang

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The average American worker sits for thirteen hours a day, often with poor posture and infrequent breaks, which can lead to health issues and back problems. The Smart Cushion was created to alert individuals of their poor postures, and may potentially alleviate back problems and correct poor posture. The Smart Cushion is a portable, rectangular, foam cushion, with five strategically placed pressure sensors, that utilizes an Arduino Uno circuit board and specifically designed software, allowing it to collect data from the five pressure sensors and store the data on an SD card. The data is then compiled into graphs and compared to controlled postures. Before volunteers sat on the cushion, their levels of back pain were recorded on a scale from 1-10. Data was recorded for an hour during sitting, and then a new, corrected posture was suggested. After using the suggested posture for an hour, the volunteers described their level of discomfort on a scale from 1-10. Different patterns of sitting postures were generated that were able to serve as early warnings of potential back problems. By using the Smart Cushion, the areas where different volunteers were applying the most pressure while sitting could be identified, and the sitting postures could be corrected. Further studies regarding the relationships between posture and specific regions of the body are necessary to better understand the origins of back pain; however, the Smart Cushion is sufficient for correcting sitting posture and preventing the development of additional back pain.

Keywords: Arduino Sketch Algorithm, biomedical technology, pressure sensors, Smart Cushion

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Authors: Aqsa Ali, Aleem Mushtaq, Attaullah Memon, Monna

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In this paper, we present a low cost design for a smart glove that can perform sign language recognition to assist the speech impaired people. Specifically, we have designed and developed an Assistive Hand Gesture Interpreter that recognizes hand movements relevant to the American Sign Language (ASL) and translates them into text for display on a Thin-Film-Transistor Liquid Crystal Display (TFT LCD) screen as well as synthetic speech. Linear Bayes Classifiers and Multilayer Neural Networks have been used to classify 11 feature vectors obtained from the sensors on the glove into one of the 27 ASL alphabets and a predefined gesture for space. Three types of features are used; bending using six bend sensors, orientation in three dimensions using accelerometers and contacts at vital points using contact sensors. To gauge the performance of the presented design, the training database was prepared using five volunteers. The accuracy of the current version on the prepared dataset was found to be up to 99.3% for target user. The solution combines electronics, e-textile technology, sensor technology, embedded system and machine learning techniques to build a low cost wearable glove that is scrupulous, elegant and portable.

Keywords: American sign language, assistive hand gesture interpreter, human-machine interface, machine learning, sensing glove

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Authors: Yudo Prasetyo, Hana Sugiastu Firdaus, Diyanah Diyanah

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The phenomenon of the lack of clean water supply in several big cities in Indonesia is a major problem in the development of urban areas. Moreover, in the city of Semarang, the population density and growth of physical development is very high. Continuous and large amounts of underground water (aquifer) exposure can result in a drastically aquifer supply declining in year by year. Especially, the intensity of aquifer use in the fulfilment of household needs and industrial activities. This is worsening by the land subsidence phenomenon in some areas in the Semarang city. Therefore, special research is needed to know the spatial correlation of the impact of decreasing aquifer capacity on the land subsidence phenomenon. This is necessary to give approve that the occurrence of land subsidence can be caused by loss of balance of pressure on below the land surface. One method to observe the correlation pattern between the two phenomena is the application of remote sensing technology based on radar and optical satellites. Implementation of Differential Interferometric Synthetic Aperture Radar (DINSAR) or Small Baseline Area Subset (SBAS) method in SENTINEL-1A satellite image acquisition in 2014-2017 period will give a proper pattern of land subsidence. These results will be spatially correlated with the aquifer-declining pattern in the same time period. Utilization of survey results to 8 monitoring wells with depth in above 100 m to observe the multi-temporal pattern of aquifer change capacity. In addition, the pattern of aquifer capacity will be validated with 2 underground water cavity maps from observation of ministries of energy and natural resources (ESDM) in Semarang city. Spatial correlation studies will be conducted on the pattern of land subsidence and aquifer capacity using overlapping and statistical methods. The results of this correlation will show how big the correlation of decrease in underground water capacity in influencing the distribution and intensity of land subsidence in Semarang city. In addition, the results of this study will also be analyzed based on geological aspects related to hydrogeological parameters, soil types, aquifer species and geological structures. The results of this study will be a correlation map of the aquifer capacity on the decrease in the face of the land in the city of Semarang within the period 2014-2017. So hopefully the results can help the authorities in spatial planning and the city of Semarang in the future.

Keywords: aquifer, differential interferometric synthetic aperture radar (DINSAR), land subsidence, small baseline area subset (SBAS)

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Authors: Hui Zhang, Sherif Beskhyroun

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Structural Health monitoring systems (SHM) are commonly used to identify and assess structural damage. In terms of damage detection, SHM needs to periodically collect data from sensors placed in the structure as damage-sensitive features. This includes abnormal changes caused by the strain field and abnormal symptoms of the structure, such as damage and deterioration. Currently, deploying sensors on a large scale in a building structure is a challenge. In this study, a highly stretchable strain sensors are used in this study to collect data sets of strain generated on the surface of full-size reinforced concrete (RC) frames under extreme cyclic load application. This sensing sheet can be switched freely between the test bending strain and the axial strain to achieve two different configurations. On this basis, the deep neural network prediction model of the frame beam and frame column is established. The training results show that the method can accurately predict the strain value and has good generalization ability. The two deep neural network prediction models will also be deployed in the SHM system in the future as part of the intelligent strain sensor system.

Keywords: strain sensing sheets, deep neural networks, strain measurement, SHM system, RC frames

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Authors: Marjila Burhanzoi, Kenta Onohara, Tomoaki Ikegami

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Faults in photovoltaic (PV) modules should be detected to the greatest extent as early as possible. For that conventional fault detection methods such as electrical characterization, visual inspection, infrared (IR) imaging, ultraviolet fluorescence and electroluminescence (EL) imaging are used, but they either fail to detect the location or category of fault, or they require expensive equipment and are not convenient for onsite application. Hence, these methods are not convenient to use for monitoring small-scale PV systems. Therefore, low cost and efficient inspection techniques with the ability of onsite application are indispensable for PV modules. In this study in order to establish efficient inspection technique, correlation between faults and magnetic flux density on the surface is of crystalline PV modules are investigated. Magnetic flux on the surface of normal and faulted PV modules is measured under the short circuit and illuminated conditions using two different sensor devices. One device is made of small integrated sensors namely 9-axis motion tracking sensor with a 3-axis electronic compass embedded, an IR temperature sensor, an optical laser position sensor and a microcontroller. This device measures the X, Y and Z components of the magnetic flux density (Bx, By and Bz) few mm above the surface of a PV module and outputs the data as line graphs in LabVIEW program. The second device is made of a laser optical sensor and two magnetic line sensor modules consisting 16 pieces of magnetic sensors. This device scans the magnetic field on the surface of PV module and outputs the data as a 3D surface plot of the magnetic flux intensity in a LabVIEW program. A PC equipped with LabVIEW software is used for data acquisition and analysis for both devices. To show the effectiveness of this method, measured results are compared to those of a normal reference module and their EL images. Through the experiments it was confirmed that the magnetic field in the faulted areas have different profiles which can be clearly identified in the measured plots. Measurement results showed a perfect correlation with the EL images and using position sensors it identified the exact location of faults. This method was applied on different modules and various faults were detected using it. The proposed method owns the ability of on-site measurement and real-time diagnosis. Since simple sensors are used to make the device, it is low cost and convenient to be sued by small-scale or residential PV system owners.

Keywords: fault diagnosis, fault location, integrated sensors, PV modules

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Authors: Chinmay Shah

Abstract:

The project is all about development of a close loop traffic light control system using PLC (Programmable Logic Controller). This project is divided into two parts which are hardware and software. The hardware part for this project is a model of four way junction of a traffic light. Three indicator lamps (Red, Yellow and Green) are installed at each lane for represents as traffic light signal. This traffic control model is a replica of actuated traffic control. Actuated traffic control system is a close loop traffic control system which controls the timing of the indicator lamps depending on the fluidity of traffic for a particular lane. To make it autonomous, in each lane three IR sensors are placed which helps to sense the percentage of traffic present on any particular lane. The IR Sensors and Indicator lamps are connected to LG PLC XGB series. The PLC controls every signal which is coming from the inputs (IR Sensors) to software and display to the outputs (Indicator lamps). Default timing for the indicator lamps is 30 seconds for each lane. But depending on the percentage of traffic present, if the traffic is nearly 30-35%, green lamp will be on for 10 seconds, for 65-70% traffic it will be 20 seconds, for full 100% traffic it will be on for full 30 seconds. The software part that operates with LG PLC is “XG 5000” Programmer. Using this software, the ladder logic diagram is programmed to control the traffic light base on the flow chart. At the end of this project, the traffic light system is actuated successfully by PLC.

Keywords: close loop, IR sensor, PLC, light control system

Procedia PDF Downloads 537

Authors: Rabbia Idrees, Ananda Maiti, Saurabh Garg, Muhammad Bilal Amin

Abstract:

IoT devices are the basic building blocks of IoT network that generate enormous volume of real-time and high-speed data to help organizations and companies to take intelligent decisions. To integrate this enormous data from multisource and transfer it to the appropriate client is the fundamental of IoT development. The handling of this huge quantity of devices along with the huge volume of data is very challenging. The IoT devices are battery-powered and resource-constrained and to provide energy efficient communication, these IoT devices go sleep or online/wakeup periodically and a-periodically depending on the traffic loads to reduce energy consumption. Sometime these devices get disconnected due to device battery depletion. If the node is not available in the network, then the IoT network provides incomplete, missing, and inaccurate data. Moreover, many IoT applications, like vehicle tracking and patient tracking require the IoT devices to be mobile. Due to this mobility, If the distance of the device from the sink node become greater than required, the connection is lost. Due to this disconnection other devices join the network for replacing the broken-down and left devices. This make IoT devices dynamic in nature which brings uncertainty and unreliability in the IoT network and hence produce bad quality of data. Due to this dynamic nature of IoT devices we do not know the actual reason of abnormal data. If data are of poor-quality decisions are likely to be unsound. It is highly important to process data and estimate data quality before bringing it to use in IoT applications. In the past many researchers tried to estimate data quality and provided several Machine Learning (ML), stochastic and statistical methods to perform analysis on stored data in the data processing layer, without focusing the challenges and issues arises from the dynamic nature of IoT devices and how it is impacting data quality. A comprehensive review on determining the impact of dynamic nature of IoT devices on data quality is done in this research and presented a data quality model that can deal with this challenge and produce good quality of data. This research presents the data quality model for the sensors monitoring water quality. DBSCAN clustering and weather sensors are used in this research to make data quality model for the sensors monitoring water quality. An extensive study has been done in this research on finding the relationship between the data of weather sensors and sensors monitoring water quality of the lakes and beaches. The detailed theoretical analysis has been presented in this research mentioning correlation between independent data streams of the two sets of sensors. With the help of the analysis and DBSCAN, a data quality model is prepared. This model encompasses five dimensions of data quality: outliers’ detection and removal, completeness, patterns of missing values and checks the accuracy of the data with the help of cluster’s position. At the end, the statistical analysis has been done on the clusters formed as the result of DBSCAN, and consistency is evaluated through Coefficient of Variation (CoV).

Keywords: clustering, data quality, DBSCAN, and Internet of things (IoT)

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Authors: Caner Koc, Dilara Gerdan Koc, Mustafa Vatandas

Abstract:

The fundamental components of autonomous agricultural robot design, such as having a working understanding of coordinates, correctly constructing the desired route, and sensing environmental elements, are the most important. A variety of sensors, hardware, and software are employed by agricultural robots to find these systems.These enable the fully automated driving system of an autonomous vehicle to simulate how a human-driven vehicle would respond to changing environmental conditions. To calculate the vehicle's motion trajectory using data from the sensors, this automation system typically consists of a sophisticated software architecture based on object detection and driving decisions. In this study, the software architecture of an autonomous agricultural vehicle is compared to the trajectory planning techniques.

Keywords: agriculture 5.0, computational intelligence, motion planning, trajectory planning

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Authors: Milca F. Coelho, K. Bousson, Kawser Ahmed

Abstract:

To accurately track an aerospace vehicle in a time-critical situation and in a highly nonlinear environment, is one of the strongest interests within the aerospace community. The tracking is achieved by estimating accurately the state of a moving target, which is composed of a set of variables that can provide a complete status of the system at a given time. One of the main ingredients for a good estimation performance is the use of efficient estimation algorithms. A well-known framework is the Kalman filtering methods, designed for prediction and estimation problems. The success of the Kalman Filter (KF) in engineering applications is mostly due to the Extended Kalman Filter (EKF), which is based on local linearization. Besides its popularity, the EKF presents several limitations. To address these limitations and as a possible solution to tracking problems, this paper proposes the use of the Ensemble Kalman Filter (EnKF). Although the EnKF is being extensively used in the context of weather forecasting and it is being recognized for producing accurate and computationally effective estimation on systems with a very high dimension, it is almost unknown by the tracking community. The EnKF was initially proposed as an attempt to improve the error covariance calculation, which on the classic Kalman Filter is difficult to implement. Also, in the EnKF method the prediction and analysis error covariances have ensemble representations. These ensembles have sizes which limit the number of degrees of freedom, in a way that the filter error covariance calculations are a lot more practical for modest ensemble sizes. In this paper, a realistic simulation of a radar tracking was performed, where the EnKF was applied and compared with the Extended Kalman Filter. The results suggested that the EnKF is a promising tool for tracking applications, offering more advantages in terms of performance.

Keywords: Kalman filter, nonlinear state estimation, optimal tracking, stochastic environment

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Authors: Chhabi Nigam, S. Ramakrishnan

Abstract:

This paper brings out the analysis of the airborne Synthetic Aperture Radar (SAR) data to study the impact of Doppler centroid on Image quality and geocoding accuracy from the perspective of Stripmap mode of data acquisition. Although in Stripmap mode of data acquisition radar beam points at 90 degrees broad side (side looking), shift in the Doppler centroid is invariable due to platform motion. In-accurate estimation of Doppler centroid leads to poor image quality and image miss-registration. The effect of Doppler centroid is analyzed in this paper using multiple sets of data collected from airborne platform. Occurrences of ghost (ambiguous) targets and their power levels have been analyzed that impacts appropriate choice of PRF. Effect of aircraft attitudes (roll, pitch and yaw) on the Doppler centroid is also analyzed with the collected data sets. Various stages of the RDA (Range Doppler Algorithm) algorithm used for image formation in Stripmap mode, range compression, Doppler centroid estimation, azimuth compression, range cell migration correction are analyzed to find the performance limits and the dependence of the imaging geometry on the final image. The ability of Doppler centroid estimation to enhance the imaging accuracy for registration are also illustrated in this paper. The paper also tries to bring out the processing of low squint SAR data, the challenges and the performance limits imposed by the imaging geometry and the platform dynamics on the final image quality metrics. Finally, the effect on various terrain types, including land, water and bright scatters is also presented.

Keywords: ambiguous target, Doppler Centroid, image registration, Airborne SAR

Procedia PDF Downloads 188

Authors: Tuğçe Yücel

Abstract:

A Wireless Sensor Network(WSN) is a collection of sensor nodes which are deployed randomly in an area for surveillance. Efficient utilization of limited battery energy of sensors for increased network lifetime as well as data security are major design objectives for WSN. Moreover secure transmission of data sensed to a base station for further processing. Producing multiple copies of data packets and sending them on different paths is one of the strategies for this purpose, which leads to redundant energy consumption and hence reduced network lifetime. In this work we develop a restricted multi-copy multipath strategy where data move through ‘frequently’ or ‘heavily’ used sensors is copied by the sensor incident to such central nodes and sent on node-disjoint paths. We develop a mixed integer programing(MIP) model and heuristic approach present some preleminary test results.

Keywords: MIP, sensor, telecommunications, WSN

Procedia PDF Downloads 479

Authors: Ch. Sridevi, A. Chalapathi Rao, P. Srinivasulu

Abstract:

The location accuracy of data products is a critical parameter in assessing the geometric performance of satellite sensors. This study focuses on reliability analysis of onboard sensors to evaluate their performance in terms of location accuracy performance over time. The analysis utilizes field failure data and employs the weibull distribution to determine the reliability and in turn to understand the improvements or degradations over a period of time. The analysis begins by scrutinizing the location accuracy error which is the root mean square (RMS) error of differences between ground control point coordinates observed on the product and the map and identifying the failure data with reference to time. A significant challenge in this study is to thoroughly analyze the possibility of an infant mortality phase in the data. To address this, the Weibull distribution is utilized to determine if the data exhibits an infant stage or if it has transitioned into the operational phase. The shape parameter beta plays a crucial role in identifying this stage. Additionally, determining the exact start of the operational phase and the end of the infant stage poses another challenge as it is crucial to eliminate residual infant mortality or wear-out from the model, as it can significantly increase the total failure rate. To address this, an approach utilizing the well-established statistical Laplace test is applied to infer the behavior of sensors and to accurately ascertain the duration of different phases in the lifetime and the time required for stabilization. This approach also helps in understanding if the bathtub curve model, which accounts for the different phases in the lifetime of a product, is appropriate for the data and whether the thresholds for the infant period and wear-out phase are accurately estimated by validating the data in individual phases with Weibull distribution curve fitting analysis. Once the operational phase is determined, reliability is assessed using Weibull analysis. This analysis not only provides insights into the reliability of individual sensors with regards to location accuracy over the required period of time, but also establishes a model that can be applied to automate similar analyses for various sensors and parameters using field failure data. Furthermore, the identification of the best-performing sensor through this analysis serves as a benchmark for future missions and designs, ensuring continuous improvement in sensor performance and reliability. Overall, this study provides a methodology to accurately determine the duration of different phases in the life data of individual sensors. It enables an assessment of the time required for stabilization and provides insights into the reliability during the operational phase and the commencement of the wear-out phase. By employing this methodology, designers can make informed decisions regarding sensor performance with regards to location accuracy, contributing to enhanced accuracy in satellite-based applications.

Keywords: bathtub curve, geometric performance, Laplace test, location accuracy, reliability analysis, Weibull analysis

Procedia PDF Downloads 44

Authors: Sayed Isahaq Hossain, K. T. Chang, Moustapha Ndour

Abstract:

Inference of the phreatic line through earth dams is of paramount importance because it could be directly associated with piping phenomena which may lead to the dam failure. Normally in the field, the instrumentations such as ‘diver’ and ‘standpipe’ are to be used to identify the seepage conditions which only provide point data with a fair amount of interpolation or assumption. Here in this paper, we employed high-resolution fiber optic cable sensors (FOCS) based on Raman Scattering in order to obtain a very accurate phreatic line and seepage profile. Unlike the above-mention devices which pinpoint the water level location, this kind of Distributed Fiber Optics Sensing gives us more reliable information due to its inherent characteristics of continuous measurement.

Keywords: standpipe, diver, FOCS, monitoring, Raman scattering

Procedia PDF Downloads 326