Search results for: rapid and specific detection

Authors: Vaishnavi Shivde, Shrishti Sinha, Trapti Mishra

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Traffic light detection from a moving vehicle is an important technology both for driver safety assistance functions as well as for autonomous driving in the city. This paper proposed a deep-learning-based traffic light recognition method that consists of a pixel-wise image segmentation technique and a fully convolutional network i.e., UNET architecture. This paper has used a method for detecting the position and recognizing the state of the traffic lights in video sequences is presented and evaluated using Traffic Light Dataset which contains masked traffic light image data. The first stage is the detection, which is accomplished through image processing (image segmentation) techniques such as image cropping, color transformation, segmentation of possible traffic lights. The second stage is the recognition, which means identifying the color of the traffic light or knowing the state of traffic light which is achieved by using a Convolutional Neural Network (UNET architecture).

Keywords: traffic light detection, image segmentation, machine learning, classification, convolutional neural networks

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Authors: Devansh Jain, Shu Ting Goh

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With the aim of improving the quality of life of people of Singapore with provision of better transport services, Land and Transport Authority Singapore recently published its Master Plan 2013. The major objectives mentioned in the plan were to make a comprehensive public transport network with better quality Mass Rapid Transit, bus services along with cycling and walking. MRT is the backbone of the transport system in Singapore, and to promote and increase the MRT ridership, good accessibility to access the MRT stations is a necessity. The aim of this paper is to investigate the relationship between MRT ridership and the provision of feeder bus services in Singapore planning areas and also to understand the hub and spoke model adopted by Singapore for provision of transport services. The findings of the study will lead to conclusions made from the Regression model developed by the various factors affecting MRT ridership, and hence will benefit to enhance the services provided by the system.

Keywords: quality of life, public transport, mass rapid transit, ridership

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Authors: Deeman Yousif Mahmood, Mohammed Abdullah Hussein

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The goal of a network-based intrusion detection system is to classify activities of network traffics into two major categories: normal and attack (intrusive) activities. Nowadays, data mining and machine learning plays an important role in many sciences; including intrusion detection system (IDS) using both supervised and unsupervised techniques. However, one of the essential steps of data mining is feature selection that helps in improving the efficiency, performance and prediction rate of proposed approach. This paper applies unsupervised K-means clustering algorithm with information gain (IG) for feature selection and reduction to build a network intrusion detection system. For our experimental analysis, we have used the new NSL-KDD dataset, which is a modified dataset for KDDCup 1999 intrusion detection benchmark dataset. With a split of 60.0% for the training set and the remainder for the testing set, a 2 class classifications have been implemented (Normal, Attack). Weka framework which is a java based open source software consists of a collection of machine learning algorithms for data mining tasks has been used in the testing process. The experimental results show that the proposed approach is very accurate with low false positive rate and high true positive rate and it takes less learning time in comparison with using the full features of the dataset with the same algorithm.

Keywords: information gain (IG), intrusion detection system (IDS), k-means clustering, Weka

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Authors: M. Kamel, A. Hoayek, M. Batton-Hubert

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With the increase of network virtualization and the disparity of vendors, the continuous monitoring and detection of anomalies cannot rely on static rules. An advanced analytical methodology is needed to discriminate between ordinary events and unusual anomalies. In this paper, we focus on log data (textual data), which is a crucial source of information for network performance. Then, we introduce an algorithm used as a pipeline to help with the pretreatment of such data, group it into patterns, and dynamically label each pattern as an anomaly or not. Such tools will provide users and experts with continuous real-time logs monitoring capability to detect anomalies and failures in the underlying system that can affect performance. An application of real-world data illustrates the algorithm.

Keywords: logs, anomaly detection, ML, scoring, NLP

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Authors: Azam Fazelpour, Saeed Reza Dehghani, Vlastimil Masek, Yuri S. Muzychka

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The study uses a novel image analysis based on thermal imaging to detect temperature contours created on salt ice surface during transient phenomena. Thermal cameras detect objects by using their emissivities and IR radiance. The ice surface temperature is not uniform during transient processes. The temperature starts to increase from the boundary of ice towards the center of that. Thermal cameras are able to report temperature changes on the ice surface at every individual moment. Various contours, which show different temperature areas, appear on the ice surface picture captured by a thermal camera. Identifying the exact boundary of these contours is valuable to facilitate ice surface temperature analysis. Image processing techniques are used to extract each contour area precisely. In this study, several pictures are recorded while the temperature is increasing throughout the ice surface. Some pictures are selected to be processed by a specific time interval. An image segmentation method is applied to images to determine the contour areas. Color thermal images are used to exploit the main information. Red, green and blue elements of color images are investigated to find the best contour boundaries. The algorithms of image enhancement and noise removal are applied to images to obtain a high contrast and clear image. A novel edge detection algorithm based on differences in the color of the pixels is established to determine contour boundaries. In this method, the edges of the contours are obtained according to properties of red, blue and green image elements. The color image elements are assessed considering their information. Useful elements proceed to process and useless elements are removed from the process to reduce the consuming time. Neighbor pixels with close intensities are assigned in one contour and differences in intensities determine boundaries. The results are then verified by conducting experimental tests. An experimental setup is performed using ice samples and a thermal camera. To observe the created ice contour by the thermal camera, the samples, which are initially at -20° C, are contacted with a warmer surface. Pictures are captured for 20 seconds. The method is applied to five images ,which are captured at the time intervals of 5 seconds. The study shows the green image element carries no useful information; therefore, the boundary detection method is applied on red and blue image elements. In this case study, the results indicate that proposed algorithm shows the boundaries more effective than other edges detection methods such as Sobel and Canny. Comparison between the contour detection in this method and temperature analysis, which states real boundaries, shows a good agreement. This color image edge detection method is applicable to other similar cases according to their image properties.

Keywords: color image processing, edge detection, ice contour boundary, salt ice, thermal image

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Authors: A. Shoiynbek, K. Kozhakhmet, P. Menezes, D. Kuanyshbay, D. Bayazitov

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Speech emotion recognition has received increasing research interest all through current years. There was used emotional speech that was collected under controlled conditions in most research work. Actors imitating and artificially producing emotions in front of a microphone noted those records. There are four issues related to that approach, namely, (1) emotions are not natural, and it means that machines are learning to recognize fake emotions. (2) Emotions are very limited by quantity and poor in their variety of speaking. (3) There is language dependency on SER. (4) Consequently, each time when researchers want to start work with SER, they need to find a good emotional database on their language. In this paper, we propose the approach to create an automatic tool for speech emotion extraction based on facial emotion recognition and describe the sequence of actions of the proposed approach. One of the first objectives of the sequence of actions is a speech detection issue. The paper gives a detailed description of the speech detection model based on a fully connected deep neural network for Kazakh and Russian languages. Despite the high results in speech detection for Kazakh and Russian, the described process is suitable for any language. To illustrate the working capacity of the developed model, we have performed an analysis of speech detection and extraction from real tasks.

Keywords: deep neural networks, speech detection, speech emotion recognition, Mel-frequency cepstrum coefficients, collecting speech emotion corpus, collecting speech emotion dataset, Kazakh speech dataset

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Authors: Muhammad Hassan Khalil, Xu Jiadong

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Early detection through screening is the best tool short of a perfect treatment against the malignant tumor inside the breast of a woman. By detecting cancer in its early stages, it can be recognized and treated before it has the opportunity to spread and change into potentially dangerous. Microwave tomography is a new imaging method based on contrast in dielectric properties of materials. The mathematical theory of microwave tomography involves solving an inverse problem for Maxwell’s equations. In this paper, we present designed antenna for breast cancer detection, which will use in microwave tomography configuration.

Keywords: microwave imaging, inverse scattering, breast cancer, malignant tumor detection

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Authors: Majid Farsadrooh, Najmeh Sabbaghi, Seyed Mohammad Mostashari, Abolhasan Moradi

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Phenolic compounds are chief environmental contaminants on account of their hazardous and toxic nature on human health. The preparation of sensitive and potent chemosensors to monitor emerging pollution in water and effluent samples has received great consideration. A novel and versatile nanocomposite sensor based on poly pyrogallol is presented for the first time in this study, and its electrochemical behavior for simultaneous detection of hydroquinone (HQ), catechol (CT), and resorcinol (RS) in the presence of nitrite is evaluated. The physicochemical characteristics of the fabricated nanocomposite were investigated by emission-scanning electron microscopy (FE-SEM), energy-dispersive X-ray spectroscopy (EDS), and Brunauer-Emmett-Teller (BET). The electrochemical response of the proposed sensor to the detection of HQ, CT, RS, and nitrite is studied using cyclic voltammetry (CV), chronoamperometry (CA), differential pulse voltammetry (DPV), and electrochemical impedance spectroscopy (EIS). The kinetic characterization of the prepared sensor showed that both adsorption and diffusion processes can control reactions at the electrode. In the optimized conditions, the new chemosensor provides a wide linear range of 0.5-236.3, 0.8-236.3, 0.9-236.3, and 1.2-236.3 μM with a low limit of detection of 21.1, 51.4, 98.9, and 110.8 nM (S/N = 3) for HQ, CT and RS, and nitrite, respectively. Remarkably, the electrochemical sensor has outstanding selectivity, repeatability, and stability and is successfully employed for the detection of RS, CT, HQ, and nitrite in real water samples with the recovery of 96.2%–102.4%, 97.8%-102.6%, 98.0%–102.4% and 98.4%–103.2% for RS, CT, HQ, and nitrite, respectively. These outcomes illustrate that poly pyrogallol is a promising candidate for effective electrochemical detection of dihydroxybenzene isomers in the presence of nitrite.

Keywords: electrochemical sensor, poly pyrogallol, phenolic compounds, simultaneous determination

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Authors: Samiul Islam Hazarika, Domngam Boje, Ananta Kumar Atta

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It is well familiar that solvents play a significant role in modern chemistry. Solvents can change the reactivity and physicochemical properties of molecules in a solution. Keeping this in mind, we have designed and synthesized a mono-triazolyl-linked pyrenyl-appended xylofuranose derivative for the detection of metal ions with changing solvent systems. The incorporation of a sugar backbone in the sensor increases the water solubility and biocompatibility. The experimental study revealed that the xylofuranose-based fluorescence probe did not exhibit any specific selectivity towards metal ions in acetonitrile (CH₃CN) solvent. Whereas, we revealed that triazole-linked pyrenyl-appended xylofuranose-based fluorescent sensor would exhibit high selectivity and sensitivity towards Au³⁺ ions in CH₃CN-H₂O (1/1, v/v) system. This observation might be explained by the viscosity and polarity differences of CH₃CN and CH₃CN-H₂O solvent systems. The formation of the sensor-Au³⁺ complex was also established by high-resolution mass spectrometry (HRMS) data of the complex.

Keywords: triazole, furanose, fluorometric, solvent dependent

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Authors: S. A. M. Sabrina, Gouget Lammel, Anne Chantal, Chazalviel, Jean Noël, Ozanam François, Etcheberry Arnaud, Tighlit Fatma Zohra, B. Samia, Gabouze Noureddine

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The elaboration and characterization of hybrid nano materials give rise to considerable interest due to the new properties that arising. They are considered as an important category of new materials having innovative characteristics by combining the specific intrinsic properties of inorganic compounds (semiconductors) with the grafted organic species. This open the way to improved properties and spectacular applications in various and important fields, especially in the environment. In this work, nano materials based-semiconductors were elaborated by chemical route. The obtained surfaces were grafted with organic functional groups. The functionalization process was optimized in order to confer to the hybrid nano material a good stability as well as the right properties required for the subsequent applications. Different characterization techniques were used to investigate the resulting nano structures, such as SEM, UV-Visible, FTIR, Contact angle and electro chemical measurements. Finally, applications were envisaged in environmental area. The elaborated nano structures were tested for the detection and the elimination of pollutants.

Keywords: hybrid materials, porous silicon, peptide, metal detection

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Authors: Ágota Ábrahám, Md Nurul Islam, Karimane Zeghbib, Gábor Kemenesi, Sazeda Akter

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Collecting palm sap as a food source is an everyday practice in some parts of the world. However, the consumption of palm juice has been associated with regular infections and epidemics in parts of Bangladesh. This is attributed to fruit-eating bats and other vertebrates or invertebrates native to the area, contaminating the food with their body secretions during the collection process. The frequent intake of palm juice, whether as a processed food product or in its unprocessed form, is a common phenomenon in large areas. The range of pathogens suitable for human infection resulting from this practice is not yet fully understood. Additionally, the high sugar content of the liquid makes it an ideal culture medium for certain bacteria, which can easily propagate and potentially harm consumers. Rapid diagnostics, especially in remote locations, could mitigate health risks associated with palm juice consumption. The primary objective of this research is the rapid genomic detection and risk assessment of bacteria that may cause infections in humans through the consumption of palm juice. Utilizing state-of-the-art third-generation Nanopore metagenomic sequencing technology based on 16S rRNA, and identified bacteria primarily involved in fermenting processes. The swift metagenomic analysis, coupled with the widespread availability and portability of Nanopore products (including real-time analysis options), proves advantageous for detecting harmful pathogens in food sources without relying on extensive industry resources and testing.

Keywords: raw date palm sap, NGS, metabarcoding, food safety

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Authors: Ehsan Khademi, Mahdi Jalili

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Despite the special features of bipartite networks, they are common in many systems. Real-world bipartite networks may show community structure, similar to what one can find in one-mode networks. However, the interpretation of the community structure in bipartite networks is different as compared to one-mode networks. In this manuscript, we compare a number of available methods that are frequently used to discover community structure of bipartite networks. These networks are categorized into two broad classes. One class is the methods that, first, transfer the network into a one-mode network, and then apply community detection algorithms. The other class is the algorithms that have been developed specifically for bipartite networks. These algorithms are applied on a model network with prescribed community structure.

Keywords: community detection, bipartite networks, co-clustering, modularity, network projection, complex networks

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Authors: N. Gayathri Devi, K. Batri

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The advent of technology has increased the traffic hazards and the road accidents take place. Collision detection system in automobile aims at reducing or mitigating the severity of an accident. This project aims at avoiding Vehicle head on collision by means of collision detection algorithm. This collision detection algorithm predicts the collision and the avoidance or minimization have to be done within few seconds on confirmation. Under critical situation collision minimization is made possible by turning the vehicle to the desired turn radius so that collision impact can be reduced. In order to avoid the collision completely, the turning of the vehicle should be achieved at reduced speed in order to maintain the stability.

Keywords: collision avoidance system, time to collision, time to turn, turn radius

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Authors: Anisha Mandal, Swambabu Varanasi

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Lead is one of the most prevalent toxic heavy metal ions, and its pollution poses a significant threat to the environment and human health. On the other hand, Ethylenediaminetetraacetic acid is a widely used metal chelating agent that, due to its poor biodegradability, is an incessant pollutant to the environment. For the first time, a green, simple, and cost-effective approach is used to hydrothermally synthesise photoluminescent carbon dots using Moringa Oleifera Gum in a single step. Then, using Moringa Oleifera Gum-derived carbon dots, a photoluminescent "ON-OFF-ON" mechanism for dual mode detection of trace Pb2+ and EDTA was proposed. MOG-CDs detect Pb2+ selectively and sensitively using a photoluminescence quenching mechanism, with a detection limit (LOD) of 0.000472 ppm. (1.24 nM). The quenched photoluminescence can be restored by adding EDTA to the MOG-CD+Pb2+ system; this strategy is used to quantify EDTA at a level of detection of 0.0026 ppm. (8.9 nM). The quantification of Pb2+ and EDTA in actual samples encapsulated the applicability and dependability of the proposed photoluminescent probe.

Keywords: carbon dots, photoluminescence, sensor, moringa oleifera gum

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Authors: Khalak Bin Khair, Saqib Jahir, Mohammed Ibrahim, Fahad Bin, Debajyoti Karmaker

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Object detection is a computer technology that deals with searching through digital images and videos for occurrences of semantic elements of a particular class. It is associated with image processing and computer vision. On top of object detection, we detect camouflage objects within an image using Deep Learning techniques. Deep learning may be a subset of machine learning that's essentially a three-layer neural network Over 6500 images that possess camouflage properties are gathered from various internet sources and divided into 4 categories to compare the result. Those images are labeled and then trained and tested using vgg16 architecture on the jupyter notebook using the TensorFlow platform. The architecture is further customized using Transfer Learning. Methods for transferring information from one or more of these source tasks to increase learning in a related target task are created through transfer learning. The purpose of this transfer of learning methodologies is to aid in the evolution of machine learning to the point where it is as efficient as human learning.

Keywords: deep learning, transfer learning, TensorFlow, camouflage, object detection, architecture, accuracy, model, VGG16

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Authors: Gaokai Liu

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The distribution of material grain sizes reflects the strength, fracture, corrosion and other properties, and the grain size can be acquired via the grain boundary. In recent years, the automatic grain boundary detection is widely required instead of complex experimental operations. In this paper, an effective solution is applied to acquire the grain boundary of material images. First, the initial superpixel segmentation result is obtained via a superpixel approach. Then, a region merging method is employed to merge adjacent regions based on certain similarity criterions, the experimental results show that the merging strategy improves the superpixel segmentation result on material datasets.

Keywords: grain boundary detection, image segmentation, material images, region merging

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Authors: S. Tehsin, S. Kausar

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The ultimate aim of machine intelligence is to explore and materialize the human capabilities, one of which is the ability to detect various text objects within one or more images displayed on any canvas including prints, videos or electronic displays. Multimedia data has increased rapidly in past years. Textual information present in multimedia contains important information about the image/video content. However, it needs to technologically testify the commonly used human intelligence of detecting and differentiating the text within an image, for computers. Hence in this paper feature set based on anatomical study of human text detection system is proposed. Subsequent examination bears testimony to the fact that the features extracted proved instrumental to text detection.

Keywords: biologically inspired vision, content based retrieval, document analysis, text extraction

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Authors: Shashank Bhatnagar, W. Wilfred Godfrey

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We develop in this paper, an approach to find the trendy topic, which not only considers the user-topic interaction but also considers the community, in which user belongs. This method modifies the previous approach of user-topic interaction to user-community-topic interaction with better speed-up in the range of [1.1-3]. We assume that trend detection in a social network is dependent on two things. The one is, broadcast of messages in social network governed by self-exciting point process, namely called Hawkes process and the second is, Community Rank. The influencer node links to others in the community and decides the community rank based on its PageRank and the number of users links to that community. The community rank decides the influence of one community over the other. Hence, the Hawkes process with the kernel of user-community-topic decides the trendy topic disseminated into the social network.

Keywords: community detection, community rank, Hawkes process, influencer node, pagerank, trend detection

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Authors: Evans Belly, Imdad Rizvi, M. M. Kadam

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Satellite imagery is one of the emerging technologies which are extensively utilized in various applications such as detection/extraction of man-made structures, monitoring of sensitive areas, creating graphic maps etc. The main approach here is the automated detection of buildings from very high resolution (VHR) optical satellite images. Initially, the shadow, the building and the non-building regions (roads, vegetation etc.) are investigated wherein building extraction is mainly focused. Once all the landscape is collected a trimming process is done so as to eliminate the landscapes that may occur due to non-building objects. Finally the label method is used to extract the building regions. The label method may be altered for efficient building extraction. The images used for the analysis are the ones which are extracted from the sensors having resolution less than 1 meter (VHR). This method provides an efficient way to produce good results. The additional overhead of mid processing is eliminated without compromising the quality of the output to ease the processing steps required and time consumed.

Keywords: building detection, shadow detection, landscape generation, label, partitioning, very high resolution (VHR) satellite imagery

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Authors: David X. Dong, Qingming Zhang, Meng Lu

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Nitrites enter waterways as runoff from croplands and are discharged from many industrial sites. Excessive nitrite inputs to water bodies lead to eutrophication. On-site rapid detection of nitrite is of increasing interest for managing fertilizer application and monitoring water source quality. Existing methods for detecting nitrites use spectrophotometry, ion chromatography, electrochemical sensors, ion-selective electrodes, chemiluminescence, and colorimetric methods. However, these methods either suffer from high cost or provide low measurement accuracy due to their poor selectivity to nitrites. Therefore, it is desired to develop an accurate and economical method to monitor nitrites in environments. We report a low-cost optical sensor, in conjunction with a machine learning (ML) approach to enable high-accuracy detection of nitrites in water sources. The sensor works under the principle of measuring molecular absorptions of nitrites at three narrowband wavelengths (295 nm, 310 nm, and 357 nm) in the ultraviolet (UV) region. These wavelengths are chosen because they have relatively high sensitivity to nitrites; low-cost light-emitting devices (LEDs) and photodetectors are also available at these wavelengths. A regression model is built, trained, and utilized to minimize cross-sensitivities of these wavelengths to the same analyte, thus achieving precise and reliable measurements with various interference ions. The measured absorbance data is input to the trained model that can provide nitrite concentration prediction for the sample. The sensor is built with i) a miniature quartz cuvette as the test cell that contains a liquid sample under test, ii) three low-cost UV LEDs placed on one side of the cell as light sources, with each LED providing a narrowband light, and iii) a photodetector with a built-in amplifier and an analog-to-digital converter placed on the other side of the test cell to measure the power of transmitted light. This simple optical design allows measuring the absorbance data of the sample at the three wavelengths. To train the regression model, absorbances of nitrite ions and their combination with various interference ions are first obtained at the three UV wavelengths using a conventional spectrophotometer. Then, the spectrophotometric data are inputs to different regression algorithm models for training and evaluating high-accuracy nitrite concentration prediction. Our experimental results show that the proposed approach enables instantaneous nitrite detection within several seconds. The sensor hardware costs about one hundred dollars, which is much cheaper than a commercial spectrophotometer. The ML algorithm helps to reduce the average relative errors to below 3.5% over a concentration range from 0.1 ppm to 100 ppm of nitrites. The sensor has been validated to measure nitrites at three sites in Ames, Iowa, USA. This work demonstrates an economical and effective approach to the rapid, reagent-free determination of nitrites with high accuracy. The integration of the low-cost optical sensor and ML data processing can find a wide range of applications in environmental monitoring and management.

Keywords: optical sensor, regression model, nitrites, water quality

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Authors: Salman Akrokayan

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Synthetic cDNA (ScDNA) of granulocyte colony-stimulating factor (G-CSF) was constructed using a DNA synthesizer with the aim to increase its expression level. 5' end of the ScDNA of G-CSF coding region was modified by decreasing the GC content without altering the predicted amino acids sequence. The identity of the resulting protein from ScDNA was confirmed by the highly specific enzyme-linked immunosorbent assay. In conclusion, a synthetic G-CSF cDNA in combination with the recombinant DNA protocol offers a rapid and reliable strategy for synthesizing the target protein. However, the commercial utilization of this methodology requires rigorous validation and quality control.

Keywords: synthetic cDNA, recombinant G-CSF, cloning, gene expression

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Authors: Mohammad Sarchami, Mohsen Zeinalkhani

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Always, financial reporting system faces some problems to win public ear. The increase in the number of fraud and representation, often combined with the bankruptcy of large companies, has raised concerns about the quality of financial statements. So, investors, legislators, managers, and auditors have focused on significant fraud detection or prevention in financial statements. This article aims to investigate the Sugeno fuzzy classification to consider fraud detection in financial reporting of accepted firms by Tehran stock exchange. The hypothesis is: Sugeno fuzzy classification may detect fraud in financial reporting by financial ratio. Hypothesis was tested using Matlab software. Accuracy average was 81/80 in Sugeno fuzzy classification; so the hypothesis was confirmed.

Keywords: fraud, financial reporting, Sugeno fuzzy classification, firm

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Authors: Adam Gushgari

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The SARS-CoV-2 pandemic has catalyzed the rapid adoption of wastewater-based epidemiology (WBE) methodologies both domestically and internationally. To support the rapid scale-up of pandemic-response wastewater surveillance systems, multiple federal agencies (i.e. US CDC), non-government organizations (i.e. Water Environment Federation), and private charities (i.e. Bill and Melinda Gates Foundation) have funded over $220 million USD supporting development and expanding equitable access of surveillance methods. Funds were primarily distributed directly to municipalities under the CARES Act (90.6%), followed by academic projects (7.6%), and initiatives developed by private companies (1.8%). In addition to federal funding for wastewater monitoring primarily conducted at wastewater treatment plants, state/local governments and private companies have leveraged wastewater sampling to obtain health and lifestyle data on student, prison inmate, and employee populations. We explore the viable paths for expansion of the WBE m1ethodology across a variety of analytical methods; the development of WBE-specific samplers and real-time wastewater sensors; and their application to various governments and private sector industries. Considerable investment in, and public acceptance of WBE suggests the methodology will be applied to other future notifiable diseases and health risks. Early research suggests that WBE methods can be applied to a host of additional “biological insults” including communicable diseases and pathogens, such as influenza, Cryptosporidium, Giardia, mycotoxin exposure, hepatitis, dengue, West Nile, Zika, and yellow fever. Interest in chemical insults is also likely, providing community health and lifestyle data on narcotics consumption, use of pharmaceutical and personal care products (PPCP), PFAS and hazardous chemical exposure, and microplastic exposure. Successful application of WBE to monitor analytes correlated with carcinogen exposure, community stress prevalence, and dietary indicators has also been shown. Additionally, technology developments of in situ wastewater sensors, WBE-specific wastewater samplers, and integration of artificial intelligence will drastically change the landscape of WBE through the development of “smart sewer” networks. The rapid expansion of the WBE field is creating significant business opportunities for professionals across the scientific, engineering, and technology industries ultimately focused on community health improvement.

Keywords: wastewater surveillance, wastewater-based epidemiology, smart cities, public health, pandemic management, substance abuse

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Authors: Rizka Ardhiyana, Liesbetini Haditjaroko, Sri Mulijani, Reki Ashadi Wicaksono, Raafqi Ranasasmita

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Designing a sensitive, specific and easy to use method to detect pork contamination in the food industry remains a major challenge. In the current study, we developed a sensitive thiol-bond AuNP-Probe biosensor that will change color when detecting pork DNA in the Cytochrome B region. The interaction between the biosensors and DNA sample is measured by spectrophotometer at 540 nm. The biosensor is made by reducing gold with trisodium citrate to produce gold nanoparticle with 39.05 nm diameter. The AuNP-Probe biosensor (gold nanoprobe) achieved 16.04 ng DNA/µl limit of detection and 53.48 ng DNA/µl limit of quantification. The linearity (R2) between color absorbance changes and DNA concentration is 0.9916. The biosensor has a good specificty as it does not cross-react with DNA of chicken and beef. To verify specificity towards the target sequence, PCR was tested to the target sequence and reacted to the PCR product with the biosensor. The PCR DNA isolate resulted in a 2.7 fold higher absorbance compared to pork-DNA isolate alone (without PCR). The sensitivity and specificity of the method show the promising application of the thiol-bond AuNP biosensor in pork-detection.

Keywords: biosensor, DNA probe, gold nanoparticle (AuNP), pork meat, qPCR

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Authors: Abdul Kadir, L. Meily Kurniawidjaja

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Batik Madura workers are exposed to many Musculoskeletal Disorders risk factors, particularly Low Back Pain (LBP). This study was conducted as an early detection of ergonomic risk level on Workers Industrial Sentra Batik Madura in Dusun Banyumas, Klampar Subdistrict, Proppo Pamekasan, Madura, East Java. This study includes 12 workers who 11 workers had pain in the upper and lower part of the neck, back, wrist right hand, also 10 workers had pain in the right shoulder. This is a descriptive observational study with cross-sectional approach. Qualitative research by observing workers activity such as draw and putting the wax motif, fabric dyeing, fabric painting, discoloration, washing, and drying. The results are workers have identified ergonomic hazards such as awkward postures, twisting movements, repetitive, and static work postures. Using the method of REBA and QEC, the results get a very high-risk level of activity in each of Madura batik making process is the draw and putting the wax motif, coloring, painting, discoloration, washing, and drying. The level of risk can be reduced by improvement of work equipment include the provision of seats, strut fabric, high settings furnaces, drums, coloring basin, and washing tub.

Keywords: activities of Madura's batik, ergonomic risk level, equipment, QEC (Quick Exposure Checklist), REBA (Rapid Entire Body Assessment)

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Authors: Xiang Yu, Chuntao Feng, Lu Yang, Meiyang Song, Wenhao Zhou

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The existing two-dimensional micro-Doppler features extraction ignores the correlation information between the spatial and temporal dimension features. For the range-Doppler map, the time dimension is introduced, and a frequency modulation continuous wave (FMCW) radar human fall detection algorithm based on time-varying range-Doppler features is proposed. Firstly, the range-Doppler sequence maps are generated from the echo signals of the continuous motion of the human body collected by the radar. Then the three-dimensional data cube composed of multiple frames of range-Doppler maps is input into the three-dimensional Convolutional Neural Network (3D CNN). The spatial and temporal features of time-varying range-Doppler are extracted by the convolution layer and pool layer at the same time. Finally, the extracted spatial and temporal features are input into the fully connected layer for classification. The experimental results show that the proposed fall detection algorithm has a detection accuracy of 95.66%.

Keywords: FMCW radar, fall detection, 3D CNN, time-varying range-doppler features

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Authors: Weitao Lin

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To quickly extract adequate information from large-scale unstructured text data, this paper studies the representation of events in Chinese scenarios and performs the regularized abstraction. It proposes a Chinese event detection technique based on dependency parsing and rule matching. The method first performs dependency parsing on the original utterance, then performs pattern matching at the word or phrase granularity based on the results of dependent syntactic analysis, filters out the utterances with prominent non-event characteristics, and obtains the final results. The experimental results show the effectiveness of the method.

Keywords: natural language processing, Chinese event detection, rules matching, dependency parsing

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Authors: Wei Kunkun, Cao Wanyang, Xu Yujie, Qiao Yuzhi, Liu Yingning

Abstract:

The reasonable design of bus parking spaces can improve the traffic capacity of the station and reduce traffic congestion. In order to reasonably determine the number of berths at BRT (Bus Rapid Transit) stops, it is based on the actual bus rapid transit station observation data, scheduling data, and passenger flow data. Optimize the number of station berths from the perspective of optimizing the balance of supply and demand at the site. Combined with the classical capacity calculation model, this paper first analyzes the important factors affecting the traffic capacity of BRT stops by using SPSS PRO and MATLAB programming software, namely the distribution of BRT stops and the distribution of BRT stop time. Secondly, the method of calculating the number of the classic human capital management (HCM) model is optimized based on the actual passenger demand of the station, and the method applicable to the actual number of station berths is proposed. Taking Gangding Station of Zhongshan Avenue Bus Rapid Transit Corridor in Guangzhou as an example, based on the calculation method proposed in this paper, the number of berths of sub-station 1, sub-station 2 and sub-station 3 is 2, which reduces the road space of the station by 33.3% compared with the previous berth 3 of each sub-station, and returns to social vehicles. Therefore, under the condition of ensuring the passenger flow demand of BRT stations, the road space of the station is reduced, and the road is returned to social vehicles, the traffic capacity of social vehicles is improved, and the traffic capacity and efficiency of the BRT corridor system are improved as a whole.

Keywords: urban transportation, bus rapid transit station, HCM model, capacity, number of berths

Procedia PDF Downloads 95

Authors: Xiaoyan Dai, Hsieh Yisan

Abstract:

Barcode processing of captured images is a huge challenge, as different shooting conditions can result in different barcode appearances. This paper proposes a deep learning-based barcode detection using synthetic-to-real data augmentation. We first augment barcodes themselves; we then augment images containing the barcodes to generate a large variety of data that is close to the actual shooting environments. Comparisons with previous works and evaluations with our original data show that this approach achieves state-of-the-art performance in various real images. In addition, the system uses hybrid resolution for barcode “scan” and is applicable to real-time applications.

Keywords: barcode detection, data augmentation, deep learning, image-based processing

Procedia PDF Downloads 174

Authors: Hoshang Kolivand, Mahyar Kolivand, Mohd Shahrizal Sunar, Mohd Azhar M. Arsad

Abstract:

Real-time shadow generation in virtual environments and Augmented Reality (AR) was always a hot topic in the last three decades. Lots of calculation for shadow generation among AR needs a fast algorithm to overcome this issue and to be capable of implementing in any real-time rendering. In this paper, a silhouette detection algorithm is presented to generate shadows for AR systems. Δ+ algorithm is presented based on extending edges of occluders to recognize which edges are silhouettes in the case of real-time rendering. An accurate comparison between the proposed algorithm and current algorithms in silhouette detection is done to show the reduction calculation by presented algorithm. The algorithm is tested in both virtual environments and AR systems. We think that this algorithm has the potential to be a fundamental algorithm for shadow generation in all complex environments.

Keywords: silhouette detection, shadow volumes, real-time shadows, rendering, augmented reality

Procedia PDF Downloads 443