Search results for: speed hump detection

Authors: Guanqiao Wang, Hongyang Yu

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There is a lot of repetitive work in the traditional construction industry. These repetitive tasks can significantly improve production efficiency by replacing manual tasks with robots. There- fore, robots appear more and more frequently in the construction industry. Navigation and positioning are very important tasks for construction robots, and the requirements for accuracy of positioning are very high. Traditional indoor robots mainly use radiofrequency or vision methods for positioning. Compared with ordinary robots, the indoor plastering robot needs to be positioned closer to the wall for wall plastering, so the requirements for construction positioning accuracy are higher, and the traditional navigation positioning method has a large error, which will cause the robot to move. Without the exact position, the wall cannot be plastered, or the error of plastering the wall is large. A new positioning method is proposed, which is assisted by line lasers and uses image processing-based positioning to perform more accurate positioning on the traditional positioning work. In actual work, filter, edge detection, Hough transform and other operations are performed on the images captured by the camera. Each time the position of the laser line is found, it is compared with the standard value, and the position of the robot is moved or rotated to complete the positioning work. The experimental results show that the actual positioning error is reduced to less than 0.5 mm by this accurate positioning method.

Keywords: indoor plastering robot, navigation, precise positioning, line laser, image processing

Procedia PDF Downloads 138

Authors: Ojin Kwon, Yong-Jin Yoon, Liu Xin, Zhang Hongbao

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Wireless Body Area Network (WBAN) is a short-range wireless communication around human body for various applications such as wearable devices, entertainment, military, and especially medical devices. WBAN attracts the attention of continuous health monitoring system including diagnostic procedure, early detection of abnormal conditions, and prevention of emergency situations. Compared to cellular network, WBAN system is more difficult to control inter- and inner-cell interference due to the limited power, limited calculation capability, mobility of patient, and non-cooperation among WBANs. In this paper, we compare the performance of resource allocation scheme based on several Pseudo Orthogonal Codewords (POCs) to mitigate inter-WBAN interference. Previously, the POCs are widely exploited for a protocol sequence and optical orthogonal code. Each POCs have different properties of auto- and cross-correlation and spectral efficiency according to its construction of POCs. To identify different WBANs, several different pseudo orthogonal patterns based on POCs exploits for resource allocation of WBANs. By simulating these pseudo orthogonal resource allocations of WBANs on MATLAB, we obtain the performance of WBANs according to different POCs and can analyze and evaluate the suitability of POCs for the resource allocation in the WBANs system.

Keywords: wireless body area network, body sensor network, resource allocation without feedback, interference mitigation, pseudo orthogonal pattern

Procedia PDF Downloads 343

Authors: Atish Bagchi, Siva Chandrasekaran

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Background: According to World Bank national accounts data, the estimated global manufacturing value-added output in 2020 was 13.74 trillion USD. These manufacturing processes are monitored, modelled, and controlled by advanced, real-time, computer-based systems, e.g., Industrial IoT, PLC, SCADA, etc. These systems measure and manipulate a set of physical variables, e.g., temperature, pressure, etc. Despite the use of IoT, SCADA etc., in manufacturing, studies suggest that unplanned downtime leads to economic losses of approximately 864 billion USD each year. Therefore, real-time, accurate detection, classification and prediction of machine behaviour are needed to minimise financial losses. Although vast literature exists on time-series data processing using machine learning, the challenges faced by the industries that lead to unplanned downtimes are: The current algorithms do not efficiently handle the high-volume streaming data from industrial IoTsensors and were tested on static and simulated datasets. While the existing algorithms can detect significant 'point' outliers, most do not handle contextual outliers (e.g., values within normal range but happening at an unexpected time of day) or subtle changes in machine behaviour. Machines are revamped periodically as part of planned maintenance programmes, which change the assumptions on which original AI models were created and trained. Aim: This research study aims to deliver a Graph Neural Network(GNN)based hybrid forecasting model that interfaces with the real-time machine control systemand can detect, predict machine behaviour and behavioural changes (anomalies) in real-time. This research will help manufacturing industries and utilities, e.g., water, electricity etc., reduce unplanned downtimes and consequential financial losses. Method: The data stored within a process control system, e.g., Industrial-IoT, Data Historian, is generally sampled during data acquisition from the sensor (source) and whenpersistingin the Data Historian to optimise storage and query performance. The sampling may inadvertently discard values that might contain subtle aspects of behavioural changes in machines. This research proposed a hybrid forecasting and classification model which combines the expressive and extrapolation capability of GNN enhanced with the estimates of entropy and spectral changes in the sampled data and additional temporal contexts to reconstruct the likely temporal trajectory of machine behavioural changes. The proposed real-time model belongs to the Deep Learning category of machine learning and interfaces with the sensors directly or through 'Process Data Historian', SCADA etc., to perform forecasting and classification tasks. Results: The model was interfaced with a Data Historianholding time-series data from 4flow sensors within a water treatment plantfor45 days. The recorded sampling interval for a sensor varied from 10 sec to 30 min. Approximately 65% of the available data was used for training the model, 20% for validation, and the rest for testing. The model identified the anomalies within the water treatment plant and predicted the plant's performance. These results were compared with the data reported by the plant SCADA-Historian system and the official data reported by the plant authorities. The model's accuracy was much higher (20%) than that reported by the SCADA-Historian system and matched the validated results declared by the plant auditors. Conclusions: The research demonstrates that a hybrid GNN based approach enhanced with entropy calculation and spectral information can effectively detect and predict a machine's behavioural changes. The model can interface with a plant's 'process control system' in real-time to perform forecasting and classification tasks to aid the asset management engineers to operate their machines more efficiently and reduce unplanned downtimes. A series of trialsare planned for this model in the future in other manufacturing industries.

Keywords: GNN, Entropy, anomaly detection, industrial time-series, AI, IoT, Industry 4.0, Machine Learning

Procedia PDF Downloads 136

Authors: M. Akhlaque Ahmed, Sidra A. Shaikh, Maliha Afshan Siddiqui

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Solar radiation studies of Sindh province have been studied to evaluate the solar energy potential of the area. Global and diffuse solar radiation on horizontal surface over five cities namely Karachi, Hyderabad, Nawabshah, Chore and Padidan of Sindh province were carried out using sun shine hour data of the area to assess the feasibility of solar energy utilization. The result obtained shows a large variation of direct and diffuse component of solar radiation in winter and summer months. 50% direct and 50% diffuse solar radiation for Karachi and Hyderabad were observed and for Chore in summer month July and August the diffuse radiation is about 33 to 39%. For other areas of Sindh such as Nawabshah and Patidan the contribution of direct solar radiation is high throughout the year. The Kt values for Nawabshah and Patidan indicates a clear sky almost throughout the year. In Nawabshah area the percentage of diffuse radiation does not exceed more than 29%. The appearance of cloud is rare even in the monsoon months July and August whereas Karachi and Hyderabad and Chore has low solar potential during the monsoon months. During the monsoon period Karachi and Hyderabad can utilize hybrid system with wind power as wind speed is higher. From the point of view of power generation the estimated values indicate that Karachi and Hyderabad and chore has low solar potential for July and August while Nawabshah, and Padidan has high solar potential Throughout the year.

Keywords: global and diffuse solar radiation, province of Sindh, solar energy potential, solar radiation studies for power generation

Procedia PDF Downloads 247

Authors: Zain Ul Abdin, Mirza Aizaz Asim, Rao Sohail Ahmad Khan, Luqman Amrao, Fiaz Hussain, Hasooba Hira, Saqi Kosar Abbas

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The success of Integrated vector management programmes mainly depends on the correct identification of insect vector species involved in vector borne diseases. Based on molecular data the most important insect species involved as vectors for Zoonotic diseases in Pakistan were identified. The precise and accurate identification of such type of organism is only possible through molecular based techniques like “DNA barcoding”. Morphological species identification in insects at any life stage, is very challenging, therefore, DNA barcoding was used as a tool for rapid and accurate species identification in a wide variety of taxa across the globe and parallel studies revealed that DNA barcoding data can be effectively used in resolving taxonomic ambiguities, detection of cryptic diversity, invasion biology, description of new species etc. A comprehensive survey was carried out for the collection of insects (both adult and immature stages) in district Faisalabad, Pakistan and their DNA was extracted and mitochondrial cytochrome oxidase subunit I (COI-59) barcode sequences was used for molecular identification of immature and adult life stage.This preliminary research work opens new frontiers for developing sustainable insect vectors management programmes for saving lives of mankind from fatal diseases.

Keywords: zoonotic diseases, cytochrome oxidase, and insect vectors, CO1

Procedia PDF Downloads 155

Authors: Alireza Barari, Maryam Firoozi, Maryam Ebrahimzadeh, Romina Roohan Ardeshiri, Maryam Kamarloeei

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Background: The The purpose of this study was to evaluate the effect of six-week aerobic training and taxol consumption on interleukin 8 and Plasminogen Activator Inhibitor-1 (PAI-1) in mice with cervical cancer. Material and method: In this experimental study, 40 female C57 mice, eight weeks old, were randomly divided into 4 groups: cancer, cancer-taxol complement, cancer-training and cancer-training - taxol complement with 10 mice in each group. The implantation of cancerous tumors was performed under the skin of the upper pelvis. The training group completed the endurance training protocol, which included 3 sessions per week, 50 minutes per session, at a speed of 14-18 m/s for six weeks. A dose of 60 mg/ kg/day, a pure extract of Taxol was injected peritoneal Data were analyzed by t-test, One-way ANOVA and post hoc Bonferron's at the significant level P<0. 05. Results: The results showed that there was a significant difference between mean values of interleukin-8 (P < 0.05, F = 12.25) and the plasminogen activator inhibitor-1 (P < 0.05, P=0.10737) in four groups. A significance level of less than 0.05 in Tukey test for both variables also showed a significant difference between the "control" group and the complementary "exercise" group. Namely, six weeks of aerobic training, along with taxol, have a significant effect on the level of plasminogen activator inhibitor-1 and interleukin-8 mice with cervical cancer. Conclusion: Considering the effect of training on these variables, this type of exercise can be used as a complementary therapeutic approach along with other therapies for cervical cancer.

Keywords: cervical cancer, taxol, endurance training, interleukin 8, plasminogen activator inhibitor-1

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Authors: Rana Praween Kumar

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Preterm-low birth weight delivery is a major cause of infant morbidity and mortality in developing countries and has been linked to poor periodontal health during pregnancy. Gingivitis and chronic periodontitis are highly prevalent chronic inflammatory oral diseases. The detection and diagnosis of these common diseases is a fundamentally important component of oral health care. This study is intended to investigate predisposing and enabling factors as determinants of oral health indicators in pregnancy as well as the association between periodontal problems during pregnancy with age and socio economic status of the individual. A community –based prospective cohort study will be conducted in Jamshedpur, Jharkhand, India among pregnant women using completed interviews and a full mouth oral clinical examination using the CPITN (Community Periodontal Index of Treatment Need) and OHI-S (Simplified Oral Hygiene) indices with adequate sample size and informed consent to the patient following proper inclusion and exclusion criteria. Multiple logistic regression analyses will be used to identify independent determinants of periodontal problems and use of dental services during pregnancy. Analysis of covariance (ANCOVA) will be used to investigate the relationship between periodontal problems with the age and socioeconomic status. The result will help in proper monitoring of periodontal health during pregnancy encouraging the delivery of healthy child and the maintenance of proper health of the mother.

Keywords: infant, periodontal problems, pregnancy, pre-term-low birth weight delivery

Procedia PDF Downloads 149

Authors: Saad M. Darwish, Adel A. El-Zoghabi, Moustafa F. Ashry

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The popularity of the Internet and the availability of powerful computers and high-speed networks as low-cost commodity components are changing the way we use computers today. These technical opportunities have led to the possibility of using geographically distributed and multi-owner resources to solve large-scale problems in science, engineering, and commerce. Recent research on these topics has led to the emergence of a new paradigm known as Grid computing. To achieve the promising potentials of tremendous distributed resources, effective and efficient load balancing algorithms are fundamentally important. Unfortunately, load balancing algorithms in traditional parallel and distributed systems, which usually run on homogeneous and dedicated resources, cannot work well in the new circumstances. In this paper, the concept of a fast fractal transform in heterogeneous grid computing based on R-tree and the domain-range entropy is proposed to improve fault tolerance and load balancing algorithm by improve connectivity, communication delay, network bandwidth, resource availability, and resource unpredictability. A novel two-dimension figure of merit is suggested to describe the network effects on load balance and fault tolerance estimation. Fault tolerance is enhanced by adaptively decrease replication time and message cost while load balance is enhanced by adaptively decrease mean job response time. Experimental results show that the proposed method yields superior performance over other methods.

Keywords: Grid computing, load balancing, fault tolerance, R-tree, heterogeneous systems

Procedia PDF Downloads 478

Authors: Sikiru Oluwarotimi Ismail, Hom Nath Dhakal, Anish Roy, Dong Wang, Ivan Popov

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The ultrasonically-assisted drilling (UAD) is a non-traditional technique which involves the superimposition of a high frequency and low amplitude vibration, usually greater than 18kHz and less than 20µm respectively, on a drill bit along the feed direction. UAD has remarkable advantages over the conventional drilling (CD), especially the high drilling-force reduction. Force reduction improves the quality of the drilled holes, reduces power consumption rate and cost of production. Nevertheless, in addition to the setbacks of UAD including expensiveness of set-up, unpredicted results and chipping effects, this paper presents the problems of insignificant force reduction and poor surface quality during UAD of hemp fibre-reinforced composites (HFRCs), a natural composite, with polycaprolactone (PCL) matrix. The experimental results obtained depict that HFRCs/PCL samples have more burnt chip-materials attached on the drilled holes during UAD than CD. This effect produced a very high surface roughness (Ra), up to 13µm. In a bid to reduce these challenges, different drilling parameters (feed rates and cutting speeds, frequencies and amplitudes for UAD), conditions (dry machining and airflow cooling) and diameters of drill bits (3mm and 6mm of high speed steel), as well as HFRCs/PCL samples of various fibre aspect ratios, including 0 (neat), 19, 26, 30 and 38 have been used. However, the setbacks still persisted. Evidently, the benefits of UAD are not obtainable for the drilling of the HFRCs/PCL laminates. These problems occurred due to the 60 °C melting temperature of PCL, quite lower than 56-90.2 °C and 265–290.8 °C composite-tool interface temperature during CD and UAD respectively.

Keywords: force reduction, hemp fibre-reinforced composites, ultrasonically-assisted drilling, surface quality

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Authors: Mohammad Bakhshandeh

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Structural Health Monitoring (SHM) is a crucial and necessary practice that plays a vital role in ensuring the safety and integrity of critical structures, and in particular, bridges. The continuous monitoring of bridges for signs of damage or degradation through Bridge Health Monitoring (BHM) enables early detection of potential problems, allowing for prompt corrective action to be taken before significant damage occurs. Although all monitoring techniques aim to provide accurate and decisive information regarding the remaining useful life, safety, integrity, and serviceability of bridges, understanding the development and propagation of damage is vital for maintaining uninterrupted bridge operation. Over the years, extensive research has been conducted on BHM methods, and experts in the field have increasingly adopted new methodologies. In this article, we provide a comprehensive exploration of the various BHM approaches, including sensor-based, non-destructive testing (NDT), model-based, and artificial intelligence (AI)-based methods. We also discuss the challenges associated with BHM, including sensor placement and data acquisition, data analysis and interpretation, cost and complexity, and environmental effects, through an extensive review of relevant literature and research studies. Additionally, we examine potential solutions to these challenges and propose future research ideas to address critical gaps in BHM.

Keywords: structural health monitoring (SHM), bridge health monitoring (BHM), sensor-based methods, machine-learning algorithms, and model-based techniques, sensor placement, data acquisition, data analysis

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Authors: Huawei Bai, Jianguo Yao, Fellow, IEEE

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Atrial fibrillation (AF) is the most common form of heart arrhythmia and is closely associated with mortality and morbidity in heart failure, stroke, and coronary artery disease. The development of single spot optical sensors enables widespread photoplethysmography (PPG) screening, especially for AF, since it represents a more convenient and noninvasive approach. To our knowledge, most existing studies based on public and unbalanced datasets can barely handle the multiple noises sources in the real world and, also, lack interpretability. In this paper, we construct a large- scale PPG dataset using measurements collected from PPG wrist- watch devices worn by volunteers and propose an attention-based two-stream heterogeneous neural network (TSHNN). The first stream is a hybrid neural network consisting of a three-layer one-dimensional convolutional neural network (1D-CNN) and two-layer attention- based bidirectional long short-term memory (Bi-LSTM) network to learn representations from temporally sampled signals. The second stream extracts latent representations from the PPG time-frequency spectrogram using a five-layer CNN. The outputs from both streams are fed into a fusion layer for the outcome. Visualization of the attention weights learned demonstrates the effectiveness of the attention mechanism against noise. The experimental results show that the TSHNN outperforms all the competitive baseline approaches and with 98.09% accuracy, achieves state-of-the-art performance.

Keywords: PPG wearables, atrial fibrillation, feature fusion, attention mechanism, hyber network

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Authors: Zegrar Ahmed, Ghabi Mohamed

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The degradation of arid and semi-arid ecosystems in Algeria has become a palpable fact that only hinders progress and rural development. In these exceptionally fragile environments, the decline of vegetation is done according to an alarming increase and wind erosion dominates. The ecosystem is subjected to a long hot dry season and low annual average rainfall. The urgency of the fight against desertification is imposed by the very nature of the process that tends to self-accelerate, resulting when human intervention is not forthcoming the irreversibility situations, preventing any possibility of restoration state of these zones. These phenomena have led to different degradation processes, such as the destruction of vegetation, soil erosion, and deterioration of the physical environment. In this study, the work is mainly based on the criteria for classification and identification of physical parameters for spatial analysis and multi-sources to determine the vulnerability of major steppe formations and their impact on desertification. we used Landsat data with two different dates March 2010 and November 2014 in order to determine the changes in land cover, sand moving and land degradation for the diagnosis of the desertification Phenomenon. The application, through specific processes, including the supervised classification was used to characterize the main steppe formations. An analysis of the vulnerability of plant communities was conducted to assign weights and identify areas most susceptible to desertification. Vegetation indices are used to characterize the steppe formations to determine changes in land use.

Keywords: remote sensing, SIG, ecosystem, degradation, desertification

Procedia PDF Downloads 332

Authors: S. Vadivukkarasi, K. Karthi, M. Karthick, C. Dinesh, S. Santhosh, A. Yogaraj

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The appointment system was designed to minimize patient’s idle time overlooking patients waiting time in hospitals. This is no longer valid in today’s consumer oriented society. Long waiting times for treatment in the outpatient department followed by short consultations has long been a complaint. Nowadays, customers use waiting time as a decisive factor in choosing a service provider. Queuing theory constitutes a very powerful tool because queuing models require relatively little data and are simple and fast to use. Because of this simplicity and speed, modelers can be used to quickly evaluate and compare various alternatives for providing service. The application of queuing models in the analysis of health care systems is increasingly accepted by health care decision makers. Timely access to care is a key component of high-quality health care. However, patient delays are prevalent throughout health care systems, resulting in dissatisfaction and adverse clinical consequences for patients as well as potentially higher costs and wasted capacity for providers. Arguably, the most critical delays for health care are the ones associated with health care emergencies. The allocation of resources can be divided into three general areas: bed management, staff management, and room facility management. Effective and efficient patient flow is indicated by high patient throughput, low patient waiting times, a short length of stay at the hospital and overtime, while simultaneously maintaining adequate staff utilization rates and low patient’s idle times.

Keywords: appointment system, patient scheduling, bed management, queueing calculation, system analysis

Procedia PDF Downloads 292

Authors: Saad Chahba, Rabia Sehab, Ahmad Akrad, Cristina Morel

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Nowadays, the electric machines used in urban electric vehicles are, in most cases, three-phase electric machines with or without a magnet in the rotor. Permanent Magnet Synchronous Machine (PMSM) and Induction Machine (IM) are the main components of drive trains of electric and hybrid vehicles. These machines have very good performance in healthy operation mode, but they are not redundant to ensure safety in faulty operation mode. Faced with the continued growth in the demand for electric vehicles in the automotive market, improving the reliability of electric vehicles is necessary over the lifecycle of the electric vehicle. Multiphase electric machines respond well to this constraint because, on the one hand, they have better robustness in the event of a breakdown (opening of a phase, opening of an arm of the power stage, intern-turn short circuit) and, on the other hand, better power density. In this work, a diagnosis approach using a neural network for an open circuit fault or more of a five-phase induction machine is developed. Validation on the simulator of the vehicle drivetrain, at reduced power, is carried out, creating one and more open circuit stator phases showing the efficiency and the reliability of the new approach to detect and to locate on-line one or more open phases of a five-induction machine.

Keywords: electric vehicle drivetrain, multiphase drives, induction machine, control, open circuit (OC) fault diagnosis, artificial neural network

Procedia PDF Downloads 189

Authors: Sheetal Sharma

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Runoff,decreasing water levels and recharge in urban areas have been a complex issue now a days pointing defective urban design and increasing demography as cause. Very less has been discussed or analysed for water sensitive Urban Master Plans or local area plans. Land use planning deals with land transformation from natural areas into developed ones, which lead to changes in natural environment. Elaborated knowledge of relationship between the existing patterns of land use-land cover and recharge with respect to prevailing soil below is less as compared to speed of development. The parameters of incompatibility between urban functions and the functions of the natural environment are becoming various. Changes in land patterns due to built up, pavements, roads and similar land cover affects surface water flow seriously. It also changes permeability and absorption characteristics of the soil. Urban planners need to know natural processes along with modern means and best technologies available,as there is a huge gap between basic knowledge of natural processes and its requirement for balanced development planning leading to minimum impact on water recharge. The present paper analyzes the variations in land use land cover and their impacts on surface flows and sub-surface recharge in study area. The methodology adopted was to analyse the changes in land use and land cover using GIS and Civil 3d auto cad. The variations were used in  computer modeling using Storm-water Management Model to find out the runoff for various soil groups and resulting recharge observing water levels in POW data for last 40 years of the study area. Results were anlayzed again to find best correlations for sustainable recharge in urban areas.

Keywords: geology, runoff, urban planning, land use-land cover

Procedia PDF Downloads 306

Authors: Syed Ahsan, Saleh Alshomrani, Ishtiaq Rasool, Ali Hassan

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In this oral presentation, we will provide an overview of the technical and semantic architecture of a desert border security and critical infrastructure protection security system. Modern border security systems are designed to reduce the dependability and intrusion of human operators. To achieve this, different types of sensors are use along with video surveillance technologies. Application of these technologies in a harsh desert environment of Saudi Arabia poses unique challenges. Environmental and geographical factors including high temperatures, desert storms, temperature variations and remoteness adversely affect the reliability of surveillance systems. To successfully implement a reliable, effective system in a harsh desert environment, the following must be achieved: i) Selection of technology including sensors, video cameras, and communication infrastructure that suit desert environments. ii) Reduced power consumption and efficient usage of equipment to increase the battery life of the equipment. iii) A reliable and robust communication network with efficient usage of bandwidth. Also, to reduce the expert bottleneck, an ontology-based intelligent information systems needs to be developed. Domain knowledge unique and peculiar to Saudi Arabia needs to be formalized to develop an expert system that can detect abnormal activities and any intrusion.

Keywords: border security, sensors, abnormal activity detection, ontologies

Procedia PDF Downloads 473

Authors: Alexander N. Pisarchik, Parth Chholak

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Intrinsic brain noise was estimated via magneto-encephalograms (MEG) recorded during perception of flickering visual stimuli with frequencies of 6.67 and 8.57 Hz. First, we measured the mean phase difference between the flicker signal and steady-state event-related field (SSERF) in the occipital area where the brain response at the flicker frequencies and their harmonics appeared in the power spectrum. Then, we calculated the probability distribution of the phase fluctuations in the regions of frequency locking and computed its kurtosis. Since kurtosis is a measure of the distribution’s sharpness, we suppose that inverse kurtosis is related to intrinsic brain noise. In our experiments, the kurtosis value varied among subjects from K = 3 to K = 5 for 6.67 Hz and from 2.6 to 4 for 8.57 Hz. The majority of subjects demonstrated leptokurtic kurtosis (K < 3), i.e., the distribution tails approached zero more slowly than Gaussian. In addition, we found a strong correlation between kurtosis and brain complexity measured as the correlation dimension, so that the MEGs of subjects with higher kurtosis exhibited lower complexity. The obtained results are discussed in the framework of nonlinear dynamics and complex network theories. Specifically, in a network of coupled oscillators, phase synchronization is mainly determined by two antagonistic factors, noise, and the coupling strength. While noise worsens phase synchronization, the coupling improves it. If we assume that each neuron and each synapse contribute to brain noise, the larger neuronal network should have stronger noise, and therefore phase synchronization should be worse, that results in smaller kurtosis. The described method for brain noise estimation can be useful for diagnostics of some brain pathologies associated with abnormal brain noise.

Keywords: brain, flickering, magnetoencephalography, MEG, visual perception, perception time

Procedia PDF Downloads 136

Authors: M. Devaki, K. B. Jayanthi

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The identification of water bodies from satellite images has recently received a great deal of attention. Different methods have been developed to distinguish water bodies from various satellite images that vary in terms of time and space. Urban water identification issues body manifests in numerous applications with a great deal of certainty. There has been a sharp rise in the usage of satellite images to map natural resources, including urban water bodies and forests, during the past several years. This is because water and forest resources depend on each other so heavily that ongoing monitoring of both is essential to their sustainable management. The relevant elements from satellite pictures have been chosen using a variety of techniques, including machine learning. Then, a convolution neural network (CNN) architecture is created that can identify a superpixel as either one of two classes, one that includes water or doesn't from input data in a complex metropolitan scene. The deep learning technique, CNN, has advanced tremendously in a variety of visual-related tasks. CNN can improve classification performance by reducing the spectral-spatial regularities of the input data and extracting deep features hierarchically from raw pictures. Calculate the water body using the satellite image's resolution. Experimental results demonstrate that the suggested method outperformed conventional approaches in terms of water extraction accuracy from remote-sensing images, with an average overall accuracy of 97%.

Keywords: water body, Deep learning, satellite images, convolution neural network

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Authors: Riaan Kleyn

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Mass wine production companies across the globe are provided with grapes from winegrowers that predominantly utilize mechanical harvesting machines to harvest wine grapes. Mechanical harvesting accelerates the rate at which grapes are harvested, allowing grapes to be delivered faster to meet the demands of wine cellars. The disadvantage of the mechanical harvesting method is the inclusion of material-other-than-grapes (MOG) in the harvested wine grape loads arriving at the cellar which degrades the quality of wine that can be produced. Currently, wine cellars do not have a method to determine the amount of MOG present within wine grape loads. This paper seeks to find an optimal computer vision method capable of detecting the amount of MOG within a wine grape load. A MOG detection method will encourage winegrowers to deliver MOG-free wine grape loads to avoid penalties which will indirectly enhance the quality of the wine to be produced. Traditional image segmentation methods were compared to deep learning segmentation methods based on images of wine grape loads that were captured at a wine cellar. The Mask R-CNN model with a ResNet-50 convolutional neural network backbone emerged as the optimal method for this study to determine the amount of MOG in an image of a wine grape load. Furthermore, a statistical analysis was conducted to determine how the MOG on the surface of a grape load relates to the mass of MOG within the corresponding grape load.

Keywords: computer vision, wine grapes, machine learning, machine harvested grapes

Procedia PDF Downloads 77

Authors: Raana Babadi Fathipour

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Street foods hold a significant place in the tapestry of socioeconomic and cultural norms, beloved across the globe. Serving as a convenient and affordable option for city dwellers seeking nourishment, these culinary delights also serve as a vital source of income for vendors, particularly women. Additionally, street food acts as a mirror reflecting traditional local customs and practices, an element that draws tourists to experience the authenticity of a culture firsthand. Despite its many virtues, concerns have emerged regarding the microbiological safety of street food worldwide. Often prepared and sold in subpar conditions without proper oversight or regulation, street food has become synonymous with potential health risks. The presence of elevated levels of fecal indicator bacteria and various pathogens in these unregulated delicacies further perpetuates anxieties surrounding their consumption. This analysis delves into the intricate microbiological intricacies inherent in street food, shedding light on the pertinent safety concerns and prevalent pathogens. Additionally, it elaborates on the worldwide standing of this vital economic endeavor. Moreover, it advocates for the adoption of molecular detection techniques over conventional culture-based methods to gain a more comprehensive grasp of the true microbial risks posed by street cuisine. Acknowledgment marks the initial step towards resolving any given issue.

Keywords: foodborne pathogens, microbiological safety, street food, viruses

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Authors: Manendra Singh Parihar, Sandip Ghosh Chowdhury

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Austenitic stainless steels are widely used and give a good combination of properties. When this steel is plastically deformed, a phase transformation of the metastable Face Centred Cubic Austenite to the stable Body Centred Cubic (α’) or to the Hexagonal close packed (ԑ) martensite may occur, leading to the enhancement in the mechanical properties like strength. The work was based on variant selection and corresponding texture analysis for the strain induced martensitic transformation during deformation of the parent austenite FCC phase to form the product HCP and the BCC martensite phases separately, obeying their respective orientation relationships. The automated method for reconstruction of the parent phase orientation using the EBSD data of the product phase orientation is done using the MATLAB and TSL-OIM software. The method of triplets was used which involves the formation of a triplet of neighboring product grains having a common variant and linking them using a misorientation-based criterion. This led to the proper reconstruction of the pre-transformation phase orientation data and thus to its microstructure and texture. The computational speed of current method is better compared to the previously used methods of reconstruction. The reconstruction of austenite from ԑ and α’ martensite was carried out for multiple samples and their IPF images, pole figures, inverse pole figures and ODFs were compared. Similar type of results was observed for all samples. The comparison gives the idea for estimating the correct sequence of the transformation i.e. γ → ε → α’ or γ → α’, during deformation of AISI 304 austenitic stainless steel.

Keywords: variant selection, reconstruction, EBSD, austenitic stainless steel, martensitic transformation

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Authors: Messaoud Eljamai, Sami Hidouri

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Massive multiple-input multiple-output (MIMO) is an emerging technology for new cellular networks such as 5G systems. Its principle is to use many antennas per cell in order to maximize the network's spectral efficiency. Inter-cellular interference remains a fundamental problem. The use of massive MIMO will not derogate from the rule. It improves performances only when the number of antennas is significantly greater than the number of users. This, considerably, limits the networks spectral efficiency. In this paper, a coordinated detector for an uplink massive MIMO system is proposed in order to mitigate the inter-cellular interference. The proposed scheme combines the coordinated multipoint technique with an interference-cancelling algorithm. It requires the serving cell to send their received symbols, after processing, decision and error detection, to the interfered cells via a backhaul link. Each interfered cell is capable of eliminating intercellular interferences by generating and subtracting the user’s contribution from the received signal. The resulting signal is more reliable than the original received signal. This allows the uplink massive MIMO system to improve their performances dramatically. Simulation results show that the proposed detector improves system spectral efficiency compared to classical linear detectors.

Keywords: massive MIMO, COMP, interference canceling algorithm, spectral efficiency

Procedia PDF Downloads 137

Authors: Mihai-Alexandru Florea, Veronica Drumea, Roxana Nita, Cerasela Gird, Laura Olariu

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The aim of this study was to investigate the residues of pesticide found in tea water infusions. A multi-residues method to determine 147 pesticides has been developed using the QuEChERS (Quick, Easy, Cheap, Effective, Rugged, Safe) procedure and dispersive solid phase extraction (d-SPE) for the cleanup the pesticides from complex matrices such as plants and tea. Sample preparation was carefully optimized for the efficient removal of coextracted matrix components by testing more solvent systems. Determination of pesticides was performed using GC-MS/MS (100 of pesticides) and LC-MS/MS (47 of pesticides). The selected reaction monitoring (SRM) mode was chosen to achieve low detection limits and high compounds selectivity and sensitivity. Overall performance was evaluated and validated according to DG-SANTE Guidelines. To assess the pesticide residue transfer rate (qualitative) from dried tea in infusions the samples (tea) were spiked with a mixture of pesticides at the maximum residues level accepted for teas and herbal infusions. In order to investigate the release of the pesticides in tea preparations, the medicinal plants were prepared in four ways by variation of water temperature and the infusion time. The pesticides from infusions were extracted using two methods: QuEChERS versus solid-phase extraction (SPE). More that 90 % of the pesticides studied was identified in infusion.

Keywords: tea, solid-phase extraction (SPE), selected reaction monitoring (SRM), QuEChERS

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Authors: Rajkumar Kolangarakandy

Abstract:

Feature extraction and selection is the primary part of any mammogram classification algorithms. The choice of feature, attribute or measurements have an important influence in any classification system. Discrete Wavelet Transformation (DWT) coefficients are one of the prominent features for representing images in frequency domain. The features obtained after the decomposition of the mammogram images using wavelet transformations have higher dimension. Even though the features are higher in dimension, they were highly correlated and redundant in nature. The dimensionality reduction techniques play an important role in selecting the optimum number of features from the higher dimension data, which are highly correlated. PCA is a mathematical tool that reduces the dimensionality of the data while retaining most of the variation in the dataset. In this paper, a multilevel classification of mammogram images using reduced discrete wavelet transformation coefficients and lazy classifiers is proposed. The classification is accomplished in two different levels. In the first level, mammogram ROIs extracted from the dataset is classified as normal and abnormal types. In the second level, all the abnormal mammogram ROIs is classified into benign and malignant too. A further classification is also accomplished based on the variation in structure and intensity distribution of the images in the dataset. The Lazy classifiers called Kstar, IBL and LWL are used for classification. The classification results obtained with the reduced feature set is highly promising and the result is also compared with the performance obtained without dimension reduction.

Keywords: PCA, wavelet transformation, lazy classifiers, Kstar, IBL, LWL

Procedia PDF Downloads 329

Authors: Sikander Nawaz Khan

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Natural disasters like flood, earthquake, cyclone, volcanic eruption and others are causing immense losses to the property and lives every year. Current status and actual loss information of natural hazards can be determined and also prediction for next probable disasters can be made using different remote sensing and mapping technologies. Global Positioning System (GPS) calculates the exact position of damage. It can also communicate with wireless sensor nodes embedded in potentially dangerous places. GPS provide precise and accurate locations and other related information like speed, track, direction and distance of target object to emergency responders. Remote Sensing facilitates to map damages without having physical contact with target area. Now with the addition of more remote sensing satellites and other advancements, early warning system is used very efficiently. Remote sensing is being used both at local and global scale. High Resolution Satellite Imagery (HRSI), airborne remote sensing and space-borne remote sensing is playing vital role in disaster management. Early on Geographic Information System (GIS) was used to collect, arrange, and map the spatial information but now it has capability to analyze spatial data. This analytical ability of GIS is the main cause of its adaption by different emergency services providers like police and ambulance service. Full potential of these so called 3S technologies cannot be used in alone. Integration of GPS and other remote sensing techniques with GIS has pointed new horizons in modeling of earth science activities. Many remote sensing cases including Asian Ocean Tsunami in 2004, Mount Mangart landslides and Pakistan-India earthquake in 2005 are described in this paper.

Keywords: disaster mitigation, GIS, GPS, remote sensing

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Authors: Mokoena Oratilwe Penwell, Seeletse Solly Matshonisa

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The demand for quality products is a vital factor determining the success of a producing company, and the reality of this demand influences customer satisfaction. In Sefako Makgatho Health Sciences University (SMU), concerns over the quality of food being sold have been raised by mostly students and staff who are primary consumers of food being sold by the cafeteria. Suspicions of food poisoning and the occurrence of diarrhea-related to food from the cafeteria, amongst others, have been raised. However, minimal measures have been taken to resolve the issue of food quality. New service providers have been appointed, and still, the same trends are being observed, the quality of food seems to depreciate continuously. This paper uses multi-attribute failure mode analysis (MAFMA) for failure detection and minimization on the machines used for food production by SMU catering company before being sold to both staff, and students so as to improve production plant reliability, and performance. Analytical Hierarchy Process (AHP) will be used for the severity ranking of the weight criterions and development of the hierarchical structure for the cafeteria company. Amongst other potential issues detected, maintenance of the machines and equipment used for food preparations was of concern. Also, the staff lacked sufficient hospitality skills, supervision, and management in the cafeteria needed greater attention to mitigate some of the failures occurring in the food production plant.

Keywords: MAFMA, food quality, maintenance, supervision

Procedia PDF Downloads 123

Authors: Chongtham Jiten, K. Chandramani Singh, Radhapiyari Laishram

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Nanocrystalline powders of the lead-free piezoelectric material, tantalum-substituted potassium sodium niobate (K0.5Na0.5)(Nb0.9Ta0.1)O3 (KNNT), were produced using a Retsch PM100 planetary ball mill by setting the milling time to 15h, 20h, 25h, 30h, 35h and 40h, at a fixed speed of 250rpm. The average particle size of the milled powders was found to decrease from 12nm to 3nm as the milling time increases from 15h to 25h, which is in agreement with the existing theoretical model. An anomalous increase to 98nm and then a drop to 3nm in the particle size were observed as the milling time further increases to 30h and 40h respectively. Various sizes of these starting KNNT powders were used to investigate the effect of milling time on the microstructure, dielectric properties, phase transitions and piezoelectric properties of the resulting KNNT ceramics. The particle size of starting KNNT was somewhat proportional to the grain size. As the milling time increases from 15h to 25h, the resulting ceramics exhibit enhancement in the values of relative density from 94.8% to 95.8%, room temperature dielectric constant (εRT) from 878 to 1213, and piezoelectric charge coefficient (d33) from 108pC/N to 128pC/N. For this range of ceramic samples, grain size refinement suppresses the maximum dielectric constant (εmax), shifts the Curie temperature (Tc) to a lower temperature and the orthorhombic-tetragonal phase transition (Tot) to a higher temperature. Further increase of milling time from 25h to 40h produces a gradual degradation in the values of relative density, εRT, and d33 of the resulting ceramics.

Keywords: perovskite, dielectric, ceramics, high-energy milling

Procedia PDF Downloads 310

Authors: Abu Salim Mustafa

Abstract:

Water plays a major role in maintaining life on earth, but it can also serve as a matrix for pathogenic organisms, posing substantial health threats to humans. Although, outbreaks of diseases attributable to drinking water may not be common in industrialized countries, they still occur and can lead to serious acute, chronic, or sometimes fatal health consequences. The analysis of drinking water samples from different regions of Kuwait was performed in this study for bacterial and viral contaminations. Drinking tap water samples were collected from 15 different locations of the six Kuwait governorates. All samples were analyzed by confocal microscopy for the presence of bacteria. The samples were cultured in vitro to detect cultivable organisms. DNA was isolated from the cultured organisms and the identity of the bacteria was determined by sequencing the bacterial 16S rRNA genes, followed by BLAST analysis in the database of NCBI, USA. RNA was extracted from water samples and analyzed by real-time PCR for the detection of viruses with potential health risks, i.e. Astrovirus, Enterovirus, Norovirus, Rotavirus, and Hepatitis A. Confocal microscopy showed the presence of bacteria in some water samples. The 16S rRNA gene sequencing of culture grown organisms, followed by BLAST analysis, identified the presence of several non-pathogenic bacterial species. However, one sample had Acinetobacter baumannii, which often causes opportunistic infections in immunocompromised people, but none of the studied viruses could be detected in the drinking water samples analyzed. The results indicate that drinking water samples analyzed from various locations in Kuwait are relatively safe for drinking and do not contain many harmful pathogens.

Keywords: drinking water, microbial contaminant, 16S rDNA, Kuwait

Procedia PDF Downloads 143

Authors: Lucia Salvioni, Pietro Giorgio Lovaglio, Valerio Leoni, Miriam Colombo, Luisa Fiandra

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The involvement of plasmatic surfactant protein-D (SP-D) in pulmonary diseases has been long investigated, and over the last two years, more interest has been directed to determine its role as a marker of COVID-19. In this direction, several studies aimed to correlate pulmonary surfactant proteins with the clinical manifestations of the virus indicated SP-D as a prognostic biomarker of COVID-19 pneumonia severity. The present work has performed a retrospective study on a relatively large cohort of patients of Hospital Pio XI of Desio (Lombardia, Italy) with the aim to assess differences in the hematic SP-D concentrations among COVID-19 patients and healthy donors and the role of SP-D as a prognostic marker of severity and/or of mortality risk. The obtained results showed a significant difference in the mean of log SP-D levels between COVID-19 patients and healthy donors, so as between dead and survived patients. SP-D values were significantly higher for both hospitalized COVID-19 and dead patients, with threshold values of 150 and 250 ng/mL, respectively. SP-D levels at admission and increasing differences among follow-up and admission values resulted in the strongest significant risk factors of mortality. Therefore, this study demonstrated the role of SP-D as a predictive marker of SARS-CoV-2 infection and its outcome. A significant correlation of SP-D with patient mortality indicated that it is also a prognostic factor in terms of mortality, and its early detection should be considered to design adequate preventive treatments for COVID-19 patients.

Keywords: SARS-CoV-2 infection, COVID-19, surfactant protein-D (SP-D), mortality, biomarker

Procedia PDF Downloads 65

Authors: Miriam Palomo-Nieto, Adrian Agricola, Rudolf Psotta, Reza Abdollahipour, Ludvik Valtr

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The importance of vision and the visual control of movement have been labeled in the literature related to motor control and many studies have demonstrated that children with low motor competence may rely more heavily on vision to perform movements than their typically developing peers. The aim of the study was to highlight the effects of different visual conditions on motor performance during walking in children with different levels of motor coordination. Participants (n = 32, mean age = 8.5 years sd. ± 0.5) were divided into two groups: typical development (TD) and low motor coordination (LMC) based on the scores of the Movement Assessment Battery for Children (MABC-2). They were asked to walk along a 10 meters walkway where the Optojump-Next instrument was installed in a portable laboratory (15 x 3 m), which allows that all participants had the same visual information. They walked in self-selected speed under four visual conditions: full vision (FV), limited vision 100 ms (LV-100), limited vision 150 ms (LV-150) and non-vision (NV). For visual occlusion participants were equipped with Plato Goggles that shut for 100 and 150 ms, respectively, within each 2 sec. Data were analyzed in a two-way mixed-effect ANOVA including 2 (TD vs. LMC) x 4 (FV, LV-100, LV-150 & NV) with repeated-measures on the last factor (p ≤.05). Results indicated that TD children walked faster and with longer normalized steps length and strides than LMC children. For TD children the percentage of the single support and swing time were higher than for low motor competence children. However, the percentage of load response and pre swing was higher in the low motor competence children rather than the TD children. These findings indicated that through walking we could be able to identify different levels of motor coordination in children. Likewise, LMC children showed shorter percentages in those parameters regarding only one leg support, supporting the idea of balance problems.

Keywords: visual information, motor performance, walking pattern, optojump

Procedia PDF Downloads 565