Tracking Unit Research Articles

Laser wireless energy supply system of high-potential monitoring equipment based on machine vision

Real-time monitoring of high-voltage power equipment is an important link to ensure the safe and stable operation of the power system. The independent power supply of the monitoring equipment can ensure that the monitoring data is stable and effective without being affected by the power grid. This paper designs and builds a laser wireless power supply system based on machine vision and tracking and targeting technology for the actual application needs of high-potential monitoring equipment in power grids. The article introduces the overall architecture of the laser wireless power supply system and elaborates on the control method and image processing strategy of the tracking and targeting unit. The article proves the feasibility of the scheme through experiments, and realizes the accurate matching of the position of the photovoltaic cell in the presence of background interference. The research results of this article can provide technical support for the energy supply of high-potential monitoring equipment of the power grid.

Towards Turning Smartphones Into mmWave Scanners

This paper aims to assess the possibilities of using current and incoming fifth generation (5G) devices as millimeter wave (mmWave) imagers. For this purpose, previous works about freehand scanning with compact devices working at mmWave frequencies are herein extended. In particular, a new tracking unit is now attached to the scanning device so the main parts of the freehand imager, namely the radiofrequency (RF) and tracking subsystems, become portable and not depend on external systems. Specifically, this work illustrates the possibilities of 5G devices working at the mmWave band as imagers. Results supporting that the embedded tracking unit provides enough accuracy are described and compared with an accurate reference system. The impact of the final image is also considered revealing that the shape of imaged objects can be easily identified.

Fault Identification of Photovoltaic Array Based on Machine Learning Classifiers

Fault identification in Photovoltaic (PV) array is a contemporary research topic motivated by the higher penetration levels of PV systems in recent electrical grids. Therefore, this work aims to define an optimal Machine learning (ML) structure of automatic detection and diagnosis algorithm for common PV array faults, namely, permanent (Arc Fault, Line-to-Line, Maximum Power Point Tracking unit failure, and Open-Circuit faults), and temporary (Shading) under a wide range of climate datasets, fault impedances, and shading scenarios. To achieve the best-fit ML structure, three distinct ML classifiers are compared, namely, Decision Tree (DT) based on different splitting criteria, K-Nearest Neighbors (KNN) based on the different metrics of distance and weighting functions, and Support Vector Machine (SVM) based on different Kernel functions and multi-classification approaches. Also, Bayesian Optimization is adopted to assign the optimal hyperparameters to the fault classifiers. To investigate the performance of classifiers reported, both simulation and experimental case studies are carried out and presented.

The Effect of Maximum Power Point Tracking on Efficiency in Photovoltaic Solar Panel Systems

Güneş enerjisi sistemlerinde, enerji dönüşümündeki verimliliğin düşük olması nedeniyle, günümüzde verimi arttırmaya yönelik çalışmalar önem kazanmıştır. Güneş enerjisi sistemlerini maksimum verimde kullanmak için, Maksimum Güç Noktası İzleyicisi (MGNİ) sistemleri geliştirilmiştir. Bu çalışmada, sık kullanılan bir MGNİ algoritmasının verime etkisi incelenmiştir. Matlab/Simulink ortamında güneş pili modülü ve yükselten DA–DA dönüştürücü içeren, değişken ortam koşullarında maksimum güç noktasını tespit eden bir sistem tasarlanmıştır. Bu sistem, MGNİ birimi olmadan ve MGNİ birimi olması durumda çalıştırılarak elde edilen sonuçlar ve verime etkileri karşılaştırılmıştır. Sistem üç farklı durumda incelenmiştir. Elde edilen çıkarımlarda maksimum güç noktası izleyicisi varken birinci çalışma koşulunda verim %72.6’dan %92.95’e, ikinci çalışma durumunda verim %90.59’dan %96.43’e ve üçüncü çalışma durumunda verim %92.27’den %94.46’ya yükseldiği gözlemlenmiştir.

Incorporation of a solar tracking system for enhancing the performance of solar air heaters in drying apple slices

The main aim of this study was to develop an integrated solar-tracking system to maximize the efficiency of solar heaters manufactured from recyclable aluminum cans (RAC) for optimum drying of apple slices. The results revealed that the thermal efficiency of the solar air heater incorporated with a tracking unit was significantly improved by about 45% compared with the conventional fixed heaters at all tested air flow rates. The highest thermal efficiency of 87.1% of the solar air heater equipped with a tracking unit was achieved at the highest air flow rate of 44 m3 h-1. The effective moisture diffusivity (Deff) increased at the high levels of drying air temperature and flow rate and the highest value of Deff (5.43 × 10−10 m2s−1) was obtained in the dryer with a tracking system at the highest air flow rate of 44 m3 h-1. The drying rate of apple slices under such a tracking module was considerably higher than that of either the traditional fixed system or the ambient sun drying.

Modeling the evolution of interconnected processes: It is the song and the singers: Tracking units of selection with interaction networks.

Recently, Doolittle and Inkpen formulated a thought provoking theory, asserting that evolution by natural selection was responsible for the sideways evolution of two radically different kinds of selective units (also called Domains). The former entities, termed singers, correspond to the usual objects studied by evolutionary biologists (gene, genomes, individuals, species, etc.), whereas the later, termed songs, correspond to re-produced biological and ecosystemic functions, processes, information, and memes. Singers perform songs through selected patterns of interactions, meaning that a wealth of critical phenomena might receive novel evolutionary explanations. However, this theory did not provide an empirical approach to study evolution in such a broadened context. Here, we show that analyzing songs and singers, using patterns of interaction networks as a common ontology for both, offers a novel, actionable, inclusive and mathematical way to analyze not only the re-production but also the evolution and fitness of biological and ecosystemic interconnected processes.

Secure vehicle to vehicle voice chat based MQTT and CoAP internet of things protocol

The congestion of road traffic is one of the most problems facing the ambulance transportation to provide fast healthcare service for patient. In this work, ambulance tracking with messages transfer system has been designed and implemented such that a central monitoring and tracking unit can observe ambulance using MQTT IoT protocol. Where each vehicle is occupied with an intelligent embedded system (Raspberry Pi) unit. When an ambulance is being in the road, it will communicate with other vehicle or road traffic by means of CoAP IoT protocol as a direct device to device communication. The proposed system has been designed such that driver use voice chat and the system are completely hand free. The voice message is being transfer into text by using speech recognition based Google API library, and then the received text message is converted again to speech by using text to speech algorithm. An encryption–decryption process-based stream cipher has been used. The message between IoT nodes has been encrypted using One Time Pad (OTP) and DNA computing. Furthermore, the required key sequence was generated using a linear feedback shift register (LFSR) as a pseudo number key generator. This key sequence was combined to generate a unique key for each message.

The Design of the Vehicle Tracker tools using Raspberry

In this paper, it is explained about the design of the Vehicle Tracker tool which is useful in tracking the location of movable assets, by utilizing a GPS receiver USB receiver with a built in antenna, to get coordinates, a Raspberry as a controller and a cellular modem as a media information channel connected to the internet. The controller combines these coordinate information with digital maps obtained from Google Map servers on the internet. When the client accesses the Vehicle Tracker unit via the internet, the controller (Raspberry) provides 'real time' coordinate data along with a digital map. Testing is done by comparing it with GPS Standards with results that are still within the tolerance limit required by GPS International. Latency / delay testing to find out how long the information is sent from the device to the client results in latency / delay under 1 second.
 
 Keywords : Vehicle Tracker, GPS, latency, delay, Raspberry

Spatial Equivalent Circuit Model for Simulation of On-Chip Thermoelectric Harvesters.

Interest in autonomous low-power energy sources has risen with the development and widespread use of devices with very low energy consumption. Interest in thermoelectric harvesters has increased against this background. Thermoelectric harvesters, especially harvesters on-chip, have peculiar properties related to the thermal route, thermal transients, and spatial temperature distribution within the chip. A behavioral model of the harvester is required for engineers to successfully develop voltage converters with maximum power point tracking and energy storage units. There are accurate models based on the finite element method, but these models are usually not compatible with simulators of electrical circuits, and therefore are not convenient for designers. Existing equivalent circuit models fit this requirement, but usually do not consider many parameters. This article proposes an original method that allows simulating spatial thermoelectric processes by analogy with the finite difference method, using electrical circuits simulations software. The study proposes a complete methodology for building the model and examples of simulations of one-, two- and three-dimensional problems, as well as examples of simulation of macro problems in the presence of external thermal and electrical devices, such as heatsink and electrical load.

A comprehensive study of techno-economic and environmental features of different solar tracking systems for residential photovoltaic installations

The ever increasing rate of energy consumption, limited fossil fuels and pollution have made the expansion of renewable resources essential. Due to the suitable solar potential available in Iran, the use of solar energy has been developed more than other renewable resources. In this paper, photovoltaic (PV) panels are hybridized with battery banks and the power grid to provide electricity for 100 residential units and by performing a techno-economic-environmental analysis on the use of different solar tracking systems PV power generation is maximized. The final aim of this paper is to find the optimum scenario for reaching the highest efficiency along with emissions that are within the standard range. Also, the effect of grid sell back on the results is estimated and a sensitivity analysis is performed to generalize the results for other economic and climate conditions. Results show that the use of the solar tracking system significantly reduces the number of needed panels, but this size reduction is not always cost-effective due to the high cost of tracking units. The vertical tracking system indicated the best cost-efficiency with 0.241 $/kWh cost of energy and an average of 23% improvement on the output power. Although the dual-axis tracker improved power production by 32%, it was less cost-efficient. Sensitivity analysis indicated that with 33% renewable fraction and slightly different initial costs, the optimum cost of energy will vary from 0.227 to 0.255 $/kWh.

Low-cost DIY GPS trackers improve upland game bird monitoring

We tested the possibility and feasibility of assembling Arduino GPS trackers without previous engineering experience and modified them for upland game birds under extreme environmental conditions. Low-cost GPS trackers were assembled and deployed on ring-necked pheasants Phasianus colchicus in conjunction with an ongoing winter survival study. To assess GPS receiver accuracy, we deployed trackers in a static test. The static test fix rate was 1.0, median error was 2.5 m and mean error was 13.3 m (SD = 39.5). During the mobile test, wild pheasants were captured using walk-in funnel traps baited with corn from January to March 2019. During winter, 407 VHF locations and 1574 GPS locations of 35 individuals were collected, resulting in a 287% increase in data density at only 23% increase in cost. The fix rate during the mobile test averaged to 0.83. To determine if trackers were low-cost, we calculated cumulative costs of equipment and supplies required to recreate the GPS tracking unit. GPS costs were $47.60 per unit with an additional $202.00 for the supplemental VHF transmitter.

Enhancing Passenger Comfort in Autonomous Vehicles Through Vehicle Handling Analysis and Optimization

Autonomous Vehicles (AVs) offer significant advantages in terms of traffic and fuel efficiency accident prevention, and reduced travel time. `Sense-plan-act' cycle of the AV has been designed and implemented to achieve a high level of active safety that considerably reduces the risk to passengers and pedestrians. However, within existing path planning and tracking algorithms, the subject of passenger comfort has received less attention compared to other AV topics. This paper is aimed at characterizing vehicle handling criteria and path profiles configuration in order to identify self-driving requirements for passenger comfort. The emphasis is given to the efficacy and usability of the classical handling analysis methods from a self-driving perspective. To these aims, we initially establish a framework which is defined by comfortable path profiles and a versatile handling model of an AV. Then, we introduce a directional path tracking unit which optimally implements AV's trajectories using a controller and a speed regulator. The path tracking unit includes a multi-objective optimizer enabling improvement the handling behavior of the AV. The integration of the proposed methodology into the state-of-the-art AVs' control system can lead to an enhanced comfort level.

Within-field variability of turfgrass surface properties and athlete performance: Modeling their relationship using GPS and GIS technologies

Surface properties of turfgrass athletic fields exhibit within-field variability. Wearable global positioning system athlete performance tracking units allow for the investigation of field impact on athlete performance. The purpose of this technical note is to present a case study introducing a methodology to model the effect of within-field variability on athlete performance using global positioning system and geographic information system technologies. Fifteen male collegiate club rugby athletes wore a global positioning system unit (10 Hz frequency sampling locational data) during two home games. Only athlete speed (m/s) was considered because measurements were georeferenced. Soil moisture, soil compaction, turfgrass quality, and surface hardness measurements were taken and georeferenced from the field prior to the games. The field’s boundary was digitized in a geographic information system and divided into 3 m2 grid cells. Georeferenced data were imported to the geographic information system and underwent processes to calculate the team’s weighted mean speed and surface property variability scores in each grid cell for both games. Linear regressions were conducted with the data sets to determine the effect of within-field variability on team mean speed. Depending on the game, within-field variability of each measured surface property, as well as a few interactions, did significantly influence team speed. Future larger-scale studies can build upon the reported methodology to further investigate and validate these types of relationships. Coaches, trainers, athletes, and field managers could use this information to prepare for, or manage, turfgrass athletic fields in a way that better meets expectations and maximizes performance.

P679 What is the best strategy after failure to anti-TNF? Ustekinumab or other anti-TNF?

Abstract Background After the appearance of new biologics are more treatment options for first and second-line individualising according to the characteristics of each patient. The efficacy of a second anti-TNF is lower than the initial treatment so a more efficient strategy could change the therapeutic target. Methods We included patients with an established diagnosis of Crohn’s disease who were on second-line biological treatment (ustekinumab or anti-TNF) after failure of a first anti-TNF (adalimumab or infliximab) treatment. Only patients in whom the indication was the ‘induction of remission of inflammatory activity’ so those patients in whom the indication was different from the intestinal activity (intestinal manifestations, perianal disease etc.) were selected were not considered valid for analysis. Patients who had undergone intestinal resection alone were included in the study if the indication of second-line biologic treatment was already established and no recurrence prophylaxis thereof. Results A total of 56 patients with an established diagnosis of Crohn’s disease stable tracking unit EII La Paz Hospital who were treated with second-line ustekinumab (21 patients, 37.5%) or anti-TNF (35 patients were included, 62.5%) for remission induction. Baseline characteristics of patients and their IBD are summarised in Table 1. The duration of biological treatment at the time of analysis was 1.48 years (SD: 1.2) for ustekinumab group and 3.55 years (SD 3.3) for the anti-TNF group. Ustekinumab group in 16/21 (76.2%) achieved clinical remission, 3/21 (14.3%) had no response while achieving remission (note that in one patient occurred exitus otherwise cause adenocarcinoma páncreas- unable to properly assess drug response), 2/21 patients (9.5%) did not answer. In the group of anti-TNF, 15/35 patients (42.8%) achieved clinical remission, 12/35 patients (34.3%) had response without remission, 8/35 patients (22.9%) No response. These differences between groups reached statistical significance (p = 0.015). Among patients who achieved a response / clinical remission included those wherein assessed by endoscopy identifying intestinal activity no statistically significant differences between groups vs. anti-TNF ustekinumab both SESCD (average 2 vs. 6.3 p = 0, 39) to the Ruttgerts in (mean 2.5 vs. 2, p = 0.67) postsurgical stage. Regarding the response/biological remission found no significant differences in CRP between ustekinumab and anti-TNF (4.55 vs. 5.79, p = 0.68) or with fecal calprotectin (276.25 vs. 268.82, p = 0.94). Conclusion In our experience, the second-line treatment with ustekinumab after the failure of a first anti-TNF has better remission rates and response to treatment with a second anti-TNF.

Heterogeneous Parallelization for Object Detection and Tracking in UAVs

Recent technical advancements in both fields of unmanned aerial vehicles (UAV) control and artificial intelligence (AI) have made a certain realm of applications possible. However, one of the main problems in integration of these two areas is the bottle-neck of computing AI applications on UAV's resource limited platform. One of the main solution for this problem is that AI and control software from one side and computing hardware mounted on UAV from the other side be adopted together based on the main constraints of the resource limited computing platform on UAV. Basically, the target constraints of such adaptation are performance, energy efficiency, and accuracy. In this paper, we propose a strategy to integrate and adopt the commonly used object detection and tracking algorithm and UAV control software to be executed on a heterogeneous resource limited computing units on a UAV. For object detection, a convolutional neural network (CNN) algorithm is used. For object tracking, a novel algorithm is proposed that can execute along with object tracking via sequential stream data. For UAV control, a Gain-Scheduled PID controller is designed that steers the UAV by continuously manipulation of the actuators based on the stream data from the tracking unit and dynamics of the UAV. All the algorithms are adopted to be executed on a heterogeneous platform including NVIDIA Jetson TX2 embedded computer and an ARM Cortex M4. The observation from real-time operation of the platform shows that using the proposed platform reduces the power consumption by 53.69% in contrast with other existing methods while having marginal penalty for object detection and tracking parts.

Performance Analysis of Acquisition Algorithms for Navic

Indian Regional Navigation Satellite System (IRNSS), is an indigenous navigation system designed and developed by ISRO (Indian Space Research Organization).It is named as NavIC, Navigation with Indian Constellation by Indian Prime Minister. NavIC is designed to have seven satellite constellation that provides reliable position, navigation and timing services over India. The focal modules of NavIC receiver are acquisition, tracking and navigation unit. Among them, acquisition is the data processing unit for detecting satellite signals and their corresponding code phase and carrier frequency. In this paper, various acquisition algorithms like Serial search and Parallel Code Phase search algorithms are analyzed and compared with Cooley-Tukey FFT algorithm and sub-sampled Fast Fourier transform (ssFFT).The results obtained in MATLAB shows that the acquisition computation time for ssFFT based NavIC receiver is faster than parallel FFT acquisition and the Cooley-Tukey FFT IRNSS acquisition algorithm is faster and provides better code phase and carrier frequency values compared to serial search acquisition algorithm.

The modified method for position estimation of human body joints in video sequences

Reliable recognition of human movements has a wide range of applications, including games, human-computer interaction, security and healthcare. In recent years, computer graphics and computer vision researchers have developed plenty of new motion-capture algorithms that operate on simpler hardware and with far fewer limitations than before. The objective of this paper is to improve the accuracy of estimation of human skeleton joints positions in video sequences. Particularly the proposed method in this paper consists of five blocks. The input sequence of images is fed to the tracking and motion compensation unit where the tracking algorithm determines the object displacement and centers it within the frame. The motion information is also propagated to the additional unit of point-of-view estimation. This unit calculates the motion angles in the frame and monitors the object size, thus determining whether the object is approaching or moving away from the camera, and then feeds these data to the neural network. The network consists of three convolutional layers. Each convolutional layer is followed by a pooling layer. The last pooling layer connects to the cascade of three fully connected layers. All activation functions in these layers are the ReLU ones, except the last layer, where the linear activation is used. The HOG3D features treated as the input of the first convolutional layer. The data from the point-of-view, tracking and motion compensation unit goes directly to the input of fully connected layers. To cope with inaccurate or undetected joints positions, the method uses the additional procedure, which determines unreliable joints and extrapolates their new positions from the previous ones using the additional neural network. It is assumed that this structure of the method improves the position prediction accuracy due to the following reasons: taking into account the information about motion angles and zooming allows to distinguish movements that are similar in centered frames but different in displacement; using of adaptive window size for HOG3D features; using the neural network to extrapolate the positions of joints in case of absence of the prediction or in case of its low accuracy. Experiments on the HumanEva-1 dataset confirmed that the suggested modifications permit achieving higher accuracies, and thus the prospect of the use of proposed modified method to predict the body position in motion recognition systems.

Solar Tracker Platform Development for Energy Efficiency Improvement of Photovoltaic Panels

Solar energy systems have emerged over the last decades as the cleanest and most abundant renewable energy resources available worldwide. Solar trackers are devices specially developed to enhance the energy efficiency of solar energy systems. This paper presents the design and implementation stages of a reconfigurable hardware technology-based two-axis solar tracker platform, specially conceived to improve the energy efficiency of photovoltaic (PV) panels. The main module of this platform is the NI-MyRIO ready-to-use development system built upon a high-performance Field Programmable Gate Array (FPGA) processor that controls the entire solar tracker unit. Optimal tracking of the sun movement and obtaining the maximal energy efficiency rate is achieved by simultaneous real-time controlling both the captured sunlight intensity and PV cell temperature magnitudes. In this way, a robust and versatile positioning system has been developed that performs a high precision and accurate tracking pathway. All the control algorithms are implemented there under the LabView graphical programming software toolkit. The final solution boosts in a useful and modularized tracking system that looks useful in a wide range of applications both in industrial and domestic project sites with different power scales.

Solar tracker platform development for energy efficiency improvement of photovoltaic panels

Solar energy systems have emerged over the last decades as the cleanest and most abundant renewable energy resources available worldwide. Solar trackers are devices specially developed to enhance the energy efficiency of solar energy systems. This paper presents the design and implementation stages of a reconfigurable hardware technology-based two-axis solar tracker platform, specially conceived to improve the energy efficiency of photovoltaic (PV) panels. The main module of this platform is the NI MyRIO ready-to-use development system built upon a high-performance Field Programmable Gate Array (FPGA) processor that controls the entire solar tracker unit. Optimal tracking of the sun movement and obtaining the maximal energy efficiency rate is achieved by simultaneous real-time controlling both the captured sunlight intensity and PV cell temperature magnitudes. In this way, a robust and versatile positioning system has been developed that performs a high precision and accurate tracking pathway. All the control algorithms are implemented there under the LabView graphical programming software toolkit. The final solution boosts in a useful and modularized tracking system that looks useful in a wide range of applications both in industrial and domestic project sites with different power scales.

Improved Accuracy Test Method for Mobile Eye Tracking in Usability Scenarios

Eye tracking has been used in usability testing for many years to gain objective measurements to inform label, instruction, and product design. With many different testing environments, possible participants, hardware, and software, key metrics can vary greatly. These metrics can also vary for different studies, so a standardized test method, metrics, and calculation method are proposed in this study. The Tobii Pro Glasses 2 is a mobile eye tracker that does not significantly affect user mobility compared to other eye-trackers. This study aims to build a testing method, which can modify to better fit the varying conditions found in usability testing. This study was performed with Tobii Pro Glasses 2; however, this test method can be used with any mobile eye tracking units. Even with poor testing conditions, this test method results in reliable metrics which can be utilized to inform expectation and decisions with eye tracking. These methods are recommended to be performed prior to eye tracking testing to determine testing-specific performance.