Search results for: method detection limit

Authors: Ece Kök Yetimoğlu, Soner Çubuk, Neşe Taşci, M. Vezir Kahraman

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Lead(II) is one of the most toxic environmental pollutants in the world, due to its high toxicity and non-biodegradability. Lead exposure causes severe risks to human health such as central brain damages, convulsions, kidney damages, and even death. To determine lead(II) in environmental or biological samples, scientists use atomic absorption spectrometry (AAS), inductively coupled plasma mass spectrometry (ICPMS), fluorescence spectrometry and electrochemical techniques. Among these systems the fluorescence spectrometry and fluorescent chemical sensors have attracted considerable attention because of their good selectivity and high sensitivity. The fluorescent polymers usually contain covalently bonded fluorophores. In this study imidazole based UV cured polymeric film was prepared and designed to act as a fluorescence chemo sensor for lead (II) analysis. The optimum conditions such as influence of pH value and time on the fluorescence intensity of the sensor have also been investigated. The sensor was highly sensitive with a detection limit as low as 1.87 × 10−8 mol L-1 and it was successful in the determination of Pb(II) in water samples.

Keywords: fluorescence, lead(II), photopolymerization, polymeric sensor

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Authors: Marcus Walter, Norbert Nitzsche, Dirk Odenthal, Steffen Müller

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This contribution presents a friction estimator for industrial purposes which identifies Coulomb friction in a steering system. The estimator only needs a few, usually known, steering system parameters. Friction occurs on almost every mechanical system and has a negative influence on high-precision position control. This is demonstrated on a steering angle controller for highly automated driving. In this steering system the friction induces limit cycles which cause oscillating vehicle movement when the vehicle follows a given reference trajectory. When compensating the friction with the introduced estimator, limit cycles can be suppressed. This is demonstrated by measurements in a series vehicle.

Keywords: friction estimation, friction compensation, steering system, lateral vehicle guidance

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Authors: Taleb Hosni Abderrahmane, Berga Abdelmadjid

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The development of numerical analysis and its application to geomechanics problems have provided geotechnical engineers with extremely powerful tools. One of the most important problems in geotechnical engineering is the slope stability assessment. It is a very difficult task due to several aspects such the nature of the problem, experimental consideration, monitoring, controlling, and assessment. The main objective of this paper is to perform a comparative numerical study between the following methods: The Limit Equilibrium (LEM), Finite Element (FEM), Limit Analysis (LAM) and Distinct Element (DEM). The comparison is conducted in terms of the safety factors and the critical slip surfaces. Through the results, we see the feasibility to analyse slope stability by many methods.

Keywords: comparison, factor of safety, geomechanics, numerical methods, slope analysis, slip surfaces

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Authors: Oladapo Tolulope Ibitoye

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Neural networks have become prominent and widely engaged in algorithmic-based machine learning networks. They are perfect in solving day-to-day issues to a certain extent. Neural networks are computing systems with several interconnected nodes. One of the numerous areas of application of neural networks is object detection. This is a prominent area due to the coronavirus disease pandemic and the post-pandemic phases. Wearing a face mask in public slows the spread of the virus, according to experts’ submission. This calls for the development of a reliable and effective model for detecting face masks on people's faces during compliance checks. The existing neural network models for facemask detection are characterized by their black-box nature and large dataset requirement. The highlighted challenges have compromised the performance of the existing models. The proposed model utilized Faster R-CNN Model on Inception V3 backbone to reduce system complexity and dataset requirement. The model was trained and validated with very few datasets and evaluation results shows an overall accuracy of 96% regardless of skin tone.

Keywords: convolutional neural network, face detection, face mask, masked faces

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Authors: N. Larbi, F. Debbat

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Wireless communication systems have changed dramatically and shown spectacular evolution over the past two decades. These radio technologies are engaged in a quest endless high-speed transmission coupled to a constant need to improve transmission quality. Various radio communication systems being developed use code division multiple access (CDMA) technique. This work analyses a hybrid honey bees mating optimization algorithm (HBMO) applied to multiuser detection (MuD) in CDMA communication systems. The HBMO is a swarm-based optimization algorithm, which simulates the mating process of real honey bees. We apply a hybridization of HBMO with simulated annealing (SA) in order to improve the solution generated by the HBMO. Simulation results show that the detection based on Hybrid HBMO, in term of bit error rate (BER), is viable option when compared with the classic detectors from literature under Rayleigh flat fading channel.

Keywords: BER, DS-CDMA multiuser detection, genetic algorithm, hybrid HBMO, simulated annealing

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Authors: Priya Saha, Sourav Dey Roy Jr., Debotosh Bhattacharjee, Mita Nasipuri, Barin Kumar De, Mrinal Kanti Bhowmik

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Biometric face recognition technology (FRT) has gained a lot of attention due to its extensive variety of applications in both security and non-security perspectives. It has come into view to provide a secure solution in identification and verification of person identity. Although other biometric based methods like fingerprint scans, iris scans are available, FRT is verified as an efficient technology for its user-friendliness and contact freeness. Accurate facial feature localization plays an important role for many facial analysis applications including biometrics and emotion recognition. But, there are certain factors, which make facial feature localization a challenging task. On human face, expressions can be seen from the subtle movements of facial muscles and influenced by internal emotional states. These non-rigid facial movements cause noticeable alterations in locations of facial landmarks, their usual shapes, which sometimes create occlusions in facial feature areas making face recognition as a difficult problem. The paper proposes a new hybrid based technique for automatic landmark detection in both neutral and expressive frontal and near frontal face images. The method uses the concept of thresholding, sequential searching and other image processing techniques for locating the landmark points on the face. Also, a Graphical User Interface (GUI) based software is designed that could automatically detect 16 landmark points around eyes, nose and mouth that are mostly affected by the changes in facial muscles. The proposed system has been tested on widely used JAFFE and Cohn Kanade database. Also, the system is tested on DeitY-TU face database which is created in the Biometrics Laboratory of Tripura University under the research project funded by Department of Electronics & Information Technology, Govt. of India. The performance of the proposed method has been done in terms of error measure and accuracy. The method has detection rate of 98.82% on JAFFE database, 91.27% on Cohn Kanade database and 93.05% on DeitY-TU database. Also, we have done comparative study of our proposed method with other techniques developed by other researchers. This paper will put into focus emotion-oriented systems through AU detection in future based on the located features.

Keywords: biometrics, face recognition, facial landmarks, image processing

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Authors: Katsumi Hirata

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To develop the useful acoustic environmental recognition system, the method of estimating 3D-position of a stationary random acoustic source using bispectral analysis of 4-point detected signals is proposed. The method uses information about amplitude attenuation and propagation delay extracted from amplitude ratios and angles of auto- and cross-bispectra of the detected signals. It is expected that using bispectral analysis affects less influence of Gaussian noises than using conventional power spectral one. In this paper, the basic principle of the method is mentioned first, and its validity and features are considered from results of the fundamental experiments assumed ideal circumstances.

Keywords: 4-point detection, a stationary random acoustic source, auto- and cross-bispectra, estimation of 3D-position

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Authors: Radhia Toujani, Jalel Akaichi

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In recent years, the study of community detection in social networks has received great attention. The hierarchical structure of the network leads to the emergence of the convergence to a locally optimal community structure. In this paper, we aim to avoid this local optimum in the introduced hybrid hierarchical method. To achieve this purpose, we present an objective function where we incorporate the value of structural and semantic similarity based modularity and a metaheuristic namely bees colonies algorithm to optimize our objective function on both hierarchical level divisive and agglomerative. In order to assess the efficiency and the accuracy of the introduced hybrid bee colony model, we perform an extensive experimental evaluation on both synthetic and real networks.

Keywords: social network, community detection, agglomerative hierarchical clustering, divisive hierarchical clustering, similarity, modularity, metaheuristic, bee colony

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Authors: Shubhakar Gupta, Dhruv Prakash, Apoorv Mehta

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In this project, we design a modular robot capable of using and switching between multiple methods of propulsion and classifying terrain, based on an Inertial Measurement Unit (IMU) input. We wanted to make a robot that is not only intelligent in its functioning but also versatile in its physical design. The advantage of a modular robot is that it can be designed to hold several movement-apparatuses, such as wheels, legs for a hexapod or a quadpod setup, propellers for underwater locomotion, and any other solution that may be needed. The robot takes roughness input from a gyroscope and an accelerometer in the IMU, and based on the terrain classification from an artificial neural network; it decides which method of propulsion would best optimize its movement. This provides the bot with adaptability over a set of terrains, which means it can optimize its locomotion on a terrain based on its roughness. A feature like this would be a great asset to have in autonomous exploration or research drones.

Keywords: modular robotics, terrain detection, terrain classification, neural network

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Authors: Ali Hamza

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Accurate segmentation of breast ultrasound images is of paramount importance in enhancing the diagnostic capabilities of breast cancer detection. This study presents an approach utilizing the U-Net architecture for segmenting breast ultrasound images aimed at improving the accuracy and reliability of mass identification within the breast tissue. The proposed method encompasses a multi-stage process. Initially, preprocessing techniques are employed to refine image quality and diminish noise interference. Subsequently, the U-Net architecture, a deep learning convolutional neural network (CNN), is employed for pixel-wise segmentation of regions of interest corresponding to potential breast masses. The U-Net's distinctive architecture, characterized by a contracting and expansive pathway, enables accurate boundary delineation and detailed feature extraction. To evaluate the effectiveness of the proposed approach, an extensive dataset of breast ultrasound images is employed, encompassing diverse cases. Quantitative performance metrics such as the Dice coefficient, Jaccard index, sensitivity, specificity, and Hausdorff distance are employed to comprehensively assess the segmentation accuracy. Comparative analyses against traditional segmentation methods showcase the superiority of the U-Net architecture in capturing intricate details and accurately segmenting breast masses. The outcomes of this study emphasize the potential of the U-Net-based segmentation approach in bolstering breast ultrasound image analysis. The method's ability to reliably pinpoint mass boundaries holds promise for aiding radiologists in precise diagnosis and treatment planning. However, further validation and integration within clinical workflows are necessary to ascertain their practical clinical utility and facilitate seamless adoption by healthcare professionals. In conclusion, leveraging the U-Net architecture for breast ultrasound image segmentation showcases a robust framework that can significantly enhance diagnostic accuracy and advance the field of breast cancer detection. This approach represents a pivotal step towards empowering medical professionals with a more potent tool for early and accurate breast cancer diagnosis.

Keywords: mage segmentation, U-Net, deep learning, breast cancer detection, diagnostic accuracy, mass identification, convolutional neural network

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Authors: R. A. Mandour

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The physical, chemical, and microbiological analyses of fifty Quaternary water samples representing the different types of drinking water (surface and wells) in the governorate were carried-out. This paper aims to evaluate the drinking water in Dakahlia governorate in comparison with the national and international standards as a step to handle water pollutants affecting human health in this governorate. All investigated water samples were chemically considered suitable for drinking except two samples for iron, two samples for lead and one water sample for manganese having values higher than the permissible limit of EMH and WHO. Also microbiologically there were five water samples having a high total count of bacteria and three samples having high coli form than the permissible limit of EMH. Obviously, groundwater samples from Mit-Ghamr, El-Sinbillawin and Aga districts of Dakahlia governorate should have special attention for treatment.

Keywords: major ions, minor elements, microbiology, EMH, WHO

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Authors: Cristian Păuna

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In the first decades of the 21st century, in the electronic trading environment, algorithmic capital investments became the primary tool to make a profit by speculations in financial markets. A significant number of traders, private or institutional investors are participating in the capital markets every day using automated algorithms. The autonomous trading software is today a considerable part in the business intelligence system of any modern financial activity. The trading decisions and orders are made automatically by computers using different mathematical models. This paper will present one of these models called Price Prediction Line. A mathematical algorithm will be revealed to build a reliable trend line, which is the base for limit conditions and automated investment signals, the core for a computerized investment system. The paper will guide how to apply these tools to generate entry and exit investment signals, limit conditions to build a mathematical filter for the investment opportunities, and the methodology to integrate all of these in automated investment software. The paper will also present trading results obtained for the leading German financial market index with the presented methods to analyze and to compare different automated investment algorithms. It was found that a specific mathematical algorithm can be optimized and integrated into an automated trading system with good and sustained results for the leading German Market. Investment results will be compared in order to qualify the presented model. In conclusion, a 1:6.12 risk was obtained to reward ratio applying the trigonometric method to the DAX Deutscher Aktienindex on 24 months investment. These results are superior to those obtained with other similar models as this paper reveal. The general idea sustained by this paper is that the Price Prediction Line model presented is a reliable capital investment methodology that can be successfully applied to build an automated investment system with excellent results.

Keywords: algorithmic trading, automated trading systems, high-frequency trading, DAX Deutscher Aktienindex

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Authors: Nor Asrina Binti Ramlee

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Voltage sag, voltage swell, high-frequency noise and voltage transients are kinds of disturbances in power quality. They are also known as power quality events. Equipment used in the industry nowadays has become more sensitive to these events with the increasing complexity of equipment. This leads to the importance of distributing clean power quality to the consumer. To provide better service, the best analysis on power quality is very vital. Thus, this paper presents the events detection focusing on voltage sag and swell. The method is developed by applying time domain signal analysis using wavelet transform approach in MATLAB. Four types of mother wavelet namely Haar, Dmey, Daubechies, and Symlet are used to detect the events. This project analyzed real interrupted signal obtained from 22 kV transmission line in Skudai, Johor Bahru, Malaysia. The signals will be decomposed through the wavelet mothers. The best mother is the one that is capable to detect the time location of the event accurately.

Keywords: power quality, voltage sag, voltage swell, wavelet transform

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Authors: Amir Sasan Mozaffari Nejad

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This study was carried out to detect the presence of aflatoxin B1 (AFB1) in 36 samples of spices from Iran and India that was included of chilli powder (n=12), black pepper powder (n=12) and whole black pepper (n=12). Enzyme-linked immunosorbent assay (ELISA) method was used for analysing the samples. Aflatoxin B1 was found in all the spices samples, the concentration of AFB1 in Iranian samples was ranged from 63.16 to 626.81 ng/kg and in Indian samples was ranged from 31.15 to 245.94 ng/kg. The mean of AFB1 concentration in the chilli powder was significantly higher (P < 0.05) than the whole and powdered black pepper. However, none of the samples exceeded the maximum prescribed limit i.e. 5 µg/kg of European Union regulations for aflatoxin B1. The occurrence of AFB1 in spices samples could be a potential hazard for public health.

Keywords: Aflatoxin B1, chilli, black pepper, ELISA, Iran, India

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Authors: Avinash Malladhi

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Forensic accounting is a specialized field that involves the application of accounting principles, investigative skills, and legal knowledge to detect and prevent fraud. With the rise of big data and technological advancements, artificial intelligence (AI) and machine learning (ML) algorithms have emerged as powerful tools for forensic accountants to enhance their fraud detection capabilities. In this paper, we review and analyze various AI/ML algorithms that are commonly used in forensic accounting, including supervised and unsupervised learning, deep learning, natural language processing Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Support Vector Machines (SVMs), Decision Trees, and Random Forests. We discuss their underlying principles, strengths, and limitations and provide empirical evidence from existing research studies demonstrating their effectiveness in detecting financial fraud. We also highlight potential ethical considerations and challenges associated with using AI/ML in forensic accounting. Furthermore, we highlight the benefits of these technologies in improving fraud detection and prevention in forensic accounting.

Keywords: AI, machine learning, forensic accounting & fraud detection, anti money laundering, Benford's law, fraud triangle theory

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Authors: M. Van 'T Hof, R. V. Pasma, J. T. Bailly, H. W. Hoek, W. A. Ester

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Background: The transition into primary school can be challenging for children with an autism spectrum disorder (ASD). Due to the new demands that are made to children in this period, their limitations in social functioning and school achievements may manifest and appear faster. Detection of possible ASD signals mainly takes place by parents, teachers and during obligatory municipal maternal and child health centre visits. Physicians of municipal maternal and child health centres have limited education and instruments to detect ASD. Further education on detecting ASD is needed to optimally equip these doctors for this task. Most research aims to increase the early detection of ASD in children aged 0-3 years and shows positive results. However, there is a lack of research on educational interventions to detect ASD in children aged 4-6 years by municipal maternal and child health physicians. Aim: The aim of this study is to explore the effect of the online educational intervention: Detection of ASD in children aged 4-6 years for municipal maternal and child health physicians. This educational intervention is developed within The Reach-Aut Academic Centre for Autism; Transitions in education, and will be available throughout The Netherlands. Methods: Ninety-two participants will follow the educational intervention: Detection of ASD in children aged 4-6 years for municipal maternal and child health centre physicians. The educational intervention consists of three, one and a half hour sessions, which are offered through an online interactive classroom. The focus and content of the course has been developed in collaboration with three groups of stakeholders; autism scientists, clinical practitioners (municipal maternal and child health doctors and ASD experts) and parents of children with ASD. The primary outcome measure is knowledge about ASD: signals, early detection, communication with parents and referrals. The secondary outcome measures are the number of ASD related referrals, the attitude towards the mentally ill (CAMI), perceived competency about ASD knowledge and detection skills, and satisfaction about the educational intervention. Results and Conclusion: The study started in January 2016 and data collection will end mid 2017.

Keywords: ASD, child, detection, educational intervention, physicians

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Authors: J. Pazourek, K. Šmejkal, P. Kollár, J. Rajchard, J. Šinko, Z. Balounová, E. Vlková, H. Salmonová

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Pectinatella magnifica (Leidy, 1851) is an invasive freshwater animal that lives in colonies. A colony of Pectinatella magnifica (a gelatinous blob) can be up to several feet in diameter large and under favorable conditions it exhibits an extreme growth rate. Recently European countries around rivers of Elbe, Oder, Danube, Rhine and Vltava have confirmed invasion of Pectinatella magnifica, including freshwater reservoirs in South Bohemia (Czech Republic). Our project (Czech Science Foundation, GAČR P503/12/0337) is focused onto biology and chemistry of Pectinatella magnifica. We monitor the organism occurrence in selected South Bohemia ponds and sandpits during the last years, collecting information about physical properties of surrounding water, and sampling the colonies for various analyses (classification, maps of secondary metabolites, toxicity tests). Because the gelatinous matrix is during the colony lifetime also a host for algae, bacteria and cyanobacteria (co-habitants), in this contribution, we also applied a high performance liquid chromatography (HPLC) method for determination of potentially present cyanobacterial toxins (microcystin-LR, microcystin-RR, nodularin). Results from the last 3-year monitoring show that these toxins are under limit of detection (LOD), so that they do not represent a danger yet. The final goal of our study is to assess toxicity risks related to fresh water resources invaded by Pectinatella magnifica, and to understand the process of invasion, which can enable to control it.

Keywords: cyanobacteria, fresh water resources, Pectinatella magnifica invasion, toxicity monitoring

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Authors: Amira Amamou, Mnaouar Chouchane

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This paper investigates the bifurcation and nonlinear behavior of two degrees of freedom model of a symmetrical balanced rigid rotor supported by two identical journal bearings. The fluid film hydrodynamic reactions are modeled by applying both the short and the long bearing approximation and using half Sommerfeld solution. A numerical integration of equations of the journal centre motion is presented to predict the presence and the size of stable or unstable limit cycles in the neighborhood of the stability critical speed. For their stability margins, a continuation method based on the predictor-corrector mechanism is used. The numerical results of responses show that stability and bifurcation behaviors of periodic motions depend strongly on bearing parameters and its dynamic characteristics.

Keywords: hydrodynamic journal bearing, nonlinear stability, continuation method, bifurcations

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Authors: Kunal Das, Priya Sengupta, Abhishek K. Singh

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Pin assignment, scheduling, routing and error detection for arbitrary biochemical protocols in Digital Microfluidic Biochip have been reported in this paper. The research work is concentrating on pin assignment for 2 or 3 droplets routing in the arbitrary biochemical protocol, scheduling and routing in m × n biochip. The volumetric error arises due to droplet split in the biochip. The volumetric error detection is also addressed using biochip AND logic gate which is known as microfluidic AND or mAND gate. The algorithm for pin assignment for m × n biochip required m+n-1 numbers of pins. The basic principle of this algorithm is that no same pin will be allowed to be placed in the same column, same row and diagonal and adjacent cells. The same pin should be placed a distance apart such that interference becomes less. A case study also reported in this paper.

Keywords: digital microfludic biochip, cross-contamination, pin assignment, microfluidic AND gate

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Authors: Shuqi Zhang

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Federated learning allows users to participate in collaborative model training without sending data to third-party servers, reducing the risk of user data privacy leakage, and is widely used in smart finance and smart healthcare. However, the distributed architecture design of federation learning itself and the existence of secure aggregation protocols make it inherently vulnerable to backdoor attacks. To solve this problem, the federated learning backdoor defense framework FLEX based on group aggregation, cluster analysis, and neuron pruning is proposed, and inter-compatibility with secure aggregation protocols is achieved. The good performance of FLEX is verified by building a horizontal federated learning framework on the CIFAR-10 dataset for experiments, which achieves 98% success rate of backdoor detection and reduces the success rate of backdoor tasks to 0% ~ 10%.

Keywords: federated learning, secure aggregation, backdoor attack, cluster analysis, neuron pruning

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Authors: Kreso Ivandic, Filip Dodigovic, Damir Stuhec

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The paper deals with the proposed 'Material' and 'Action-resistance factor' design methods in designing the embedded retaining walls. The parametric analysis of evaluating the differences of the output values mutually and compared with classic approach computation was performed. There is a challenge with the criteria for choosing the proposed calculation design methods in Eurocode 7 with respect to current technical regulations and regular engineering practice. The basic criterion for applying a particular design method is to ensure minimum an equal degree of reliability in relation to the current practice. The procedure of combining the relevant partial coefficients according to design methods was carried out. The use of mentioned partial coefficients should result in the same level of safety, regardless of load combinations, material characteristics and problem geometry. This proposed approach of the partial coefficients related to the material and/or action-resistance should aimed at building a bridge between calculations used so far and pure probability analysis. The measure to compare the results was to determine an equivalent safety factor for each analysis. The results show a visible wide span of equivalent values of the classic safety factors.

Keywords: action-resistance factor design, classic approach, embedded retaining wall, Eurocode 7, limit states, material factor design

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Authors: Agampodi Promoda Perera, Yong Shin, Mi Kyoung Park

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The successful detection of pathogenic bacteria in blood provides important information for early detection, diagnosis and the prevention and treatment of infectious diseases. Silicon microring resonators are refractive-index-based optical biosensors that provide highly sensitive, label-free, real-time multiplexed detection of biomolecules. We demonstrate the technique of using GO (graphene oxide) to enhance the signal output of the silicon microring optical sensor. The activated carboxylic groups in GO molecules bind directly to single stranded DNA with an amino modified 5’ end. This conjugation amplifies the shift in resonant wavelength in a real-time manner. We designed a capture probe for strain Staphylococcus aureus of 21 bp and a longer complementary target sequence of 70 bp. The mismatched target sequence we used was of Streptococcus agalactiae of 70 bp. GO is added after the complementary binding of the probe and target. GO conjugates to the unbound single stranded segment of the target and increase the wavelength shift on the silicon microring resonator. Furthermore, our results show that GO could successfully differentiate between the mismatched DNA sequences from the complementary DNA sequence. Therefore, the proposed concept could effectively enhance sensitivity of pathogen detection sensors.

Keywords: label free biosensor, pathogenic bacteria, graphene oxide, diagnosis

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Authors: Hiranmoyee Das

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Aim. Due to late presentation and delayed diagnosis mortality rate of retinoblastoma is more than 50% in developing counties. So to facilitate the diagnosis, to decrease the disease and treatment burden and to increase the disease survival rate, an attempt was made for early diagnosis of Retinoblastoma by including fundus examination in routine immunization programs. Methods- A unique immunization card is followed in a tertiary health care center where examination of pupillary reflex is made mandatory in each visit of the child for routine immunization. In case of any abnormality, the child is referred to the ophthalmology department. Conclusion- Early detection is the key in the management of retinoblastoma. Every child is brought to the health care system at least five times before the age of 2 years for routine immunization. We should not miss this golden opportunity for early detection of retinoblastoma.

Keywords: retinoblastoma, immunization, unique, early

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Authors: Thanh Nga Thai

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A major issue in air transportation is in flight safety. Recent developments in control engineering have an attractive potential for resolving new issues related to guidance, navigation, and control of flying vehicles. Many future atmospheric missions will require increased on board autonomy including fault diagnosis and the subsequent control and guidance recovery actions. To improve designing system diagnostic, an efficient FDI- fault detection and identification- methodology is necessary to achieve. Contribute to characteristic of different faults in sensor and actuator in the view of mathematics brings a lot of profit in some condition changes in the system. This research finds some profit to reduce a trade-off to achieve between fault detection and performance of the closed loop system and cost and calculated in simulation.

Keywords: fault detection and identification, sensor faults, actuator faults, flight control system

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Authors: Zahow Muoftah

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Traditional manual visual grading of fruits has been one of the agricultural industry’s major challenges due to its laborious nature as well as inconsistency in the inspection and classification process. The main requirements for computer vision and visual processing are some effective techniques for identifying defects and estimating defect areas. Automated defect detection using computer vision and machine learning has emerged as a promising area of research with a high and direct impact on the visual inspection domain. Grading, sorting, and disease detection are important factors in determining the quality of fruits after harvest. Many studies have used computer vision to evaluate the quality level of fruits during post-harvest. Many studies have used computer vision to evaluate the quality level of fruits during post-harvest. Many studies have been conducted to identify diseases and pests that affect the fruits of agricultural crops. However, most previous studies concentrated solely on the diagnosis of a lesion or disease. This study focused on a comprehensive study to identify pests and diseases of apple and banana fruits using detection and classification defects on Banana and Apples by Computer Vision. As a result, the current article includes research from these domains as well. Finally, various pattern recognition techniques for detecting apple and banana defects are discussed.

Keywords: computer vision, banana, apple, detection, classification

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Authors: Sanjal Gavande, Thomas Mazzuchi, Shahram Sarkani

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In a complex aerospace operational environment, identifying failures in a procedure involving multiple human-machine interactions are difficult. These failures could lead to accidents causing loss of hardware or human life. The likelihood of failure further increases if operational procedures are tested for a novel system with multiple human-machine interfaces and with no prior performance data. The existing approach in the literature of reviewing complex operational tasks in a flowchart or tabular form doesn’t provide any insight into potential system failures due to human decision-making ability. To address these challenges, this research explores an agent-based simulation approach for reliability assessment and fault detection in complex human-machine systems while utilizing a human decision-making model. The simulation will predict the emergent behavior of the system due to the interaction between humans and their decision-making capability with the varying states of the machine and vice-versa. Overall system reliability will be evaluated based on a defined set of success-criteria conditions and the number of recorded failures over an assigned limit of Monte Carlo runs. The study also aims at identifying high-likelihood failure locations for the system. The research concludes that system reliability and failures can be effectively calculated when individual human and machine agent states are clearly defined. This research is limited to the operations phase of a system lifecycle process in an aerospace environment only. Further exploration of the proposed agent-based and human decision-making model will be required to allow for a greater understanding of this topic for application outside of the operations domain.

Keywords: agent-based model, complex human-machine system, human decision-making model, system reliability assessment

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Authors: G. Magalhães-Mota, C. Magro, S. Sério, E. Mateus, P. A. Ribeiro, A. B. Ribeiro, M. Raposo

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Triclosan [5-chloro-2-(2,4-dichlorophenoxy) phenol], belonging to the class of Pharmaceuticals and Personal Care Products (PPCPs), is a broad-spectrum antimicrobial agent and bactericide. Because of its antimicrobial efficacy, it is widely used in personal health and skin care products, such as soaps, detergents, hand cleansers, cosmetics, toothpastes, etc. However, it has been considered to disrupt the endocrine system, for instance, thyroid hormone homeostasis and possibly the reproductive system. Considering the widespread use of triclosan, it is expected that environmental and food safety problems regarding triclosan will increase dramatically. Triclosan has been found in river water samples in both North America and Europe and is likely widely distributed wherever triclosan-containing products are used. Although significant amounts are removed in sewage plants, considerable quantities remain in the sewage effluent, initiating widespread environmental contamination. Triclosan undergoes bioconversion to methyl-triclosan, which has been demonstrated to bio accumulate in fish. In addition, triclosan has been found in human urine samples from persons with no known industrial exposure and in significant amounts in samples of mother's milk, demonstrating its presence in humans. The action of sunlight in river water is known to turn triclosan into dioxin derivatives and raises the possibility of pharmacological dangers not envisioned when the compound was originally utilized. The aim of this work is to detect low concentrations of triclosan in an aqueous complex matrix through the use of a sensor array system, following the electronic tongue concept based on impedance spectroscopy. To achieve this goal, we selected the appropriate molecules to the sensor so that there is a high affinity for triclosan and whose sensitivity ensures the detection of concentrations of at least nano-molar. Thin films of organic molecules and oxides have been produced by the layer-by-layer (LbL) technique and sputtered onto glass solid supports already covered by gold interdigitated electrodes. By submerging the films in complex aqueous solutions with different concentrations of triclosan, resistance and capacitance values were obtained at different frequencies. The preliminary results showed that an array of interdigitated electrodes sensor coated or uncoated with different LbL and films, can be used to detect TCS traces in aqueous solutions in a wide range concentration, from 10⁻¹² to 10⁻⁶ M. The PCA method was applied to the measured data, in order to differentiate the solutions with different concentrations of TCS. Moreover, was also possible to trace a curve, the plot of the logarithm of resistance versus the logarithm of concentration, which allowed us to fit the plotted data points with a decreasing straight line with a slope of 0.022 ± 0.006 which corresponds to the best sensitivity of our sensor. To find the sensor resolution near of the smallest concentration (Cs) used, 1pM, the minimum measured value which can be measured with resolution is 0.006, so the ∆logC =0.006/0.022=0.273, and, therefore, C-Cs~0.9 pM. This leads to a sensor resolution of 0.9 pM for the smallest concentration used, 1pM. This attained detection limit is lower than the values obtained in the literature.

Keywords: triclosan, layer-by-layer, impedance spectroscopy, electronic tongue

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Authors: Rafika Bibi, Khaled Khaladi, Hind Mokran, Mohamed Salah Boukhechem

Abstract:

A high performance liquid chromatographic method with ultraviolet detection at 264nm was developed and validate for quantitative determination and separation of cefazolin and cefotaxime in urine, the mobile phase consisted of acetonitrile and phosphate buffer pH4,2(15 :85) (v/v) pumped through ODB 250× 4,6 mm, 5um column at a flow rate of 1ml/min, loop of 20ul. In this condition, the validation of this technique showed that it is linear in a range of 0,01 to 10ug/ml with a good correlation coefficient ( R>0,9997), retention time of cefotaxime, cefazolin was 9.0, 10.1 respectively, the statistical evaluation of the method was examined by means of within day (n=6) and day to day (n=5) and was found to be satisfactory with high accuracy and precision.

Keywords: cefazolin, cefotaxime, HPLC, bioscience, biochemistry, pharmaceutical

Procedia PDF Downloads 355

Authors: Helga Heylen

Abstract:

With dogs saving wildlife in New Zealand since 1890 and governments in Africa, Australia and Canada trusting them to give the best results, Conservation Dogs Ireland want to introduce more detection dogs to protect Europe's native wildlife. Conservation detection dogs are fast, portable and endlessly trainable. They are a cost-effective, highly sensitive and non-invasive way to detect protected and invasive species and wildlife disease. Conservation dogs find targets up to 40 times faster than any other method. They give results instantly, with near-perfect accuracy. They can search for multiple targets simultaneously, with no reduction in efficacy The European Red List indicates the decline in biodiversity has been most rapid in the past 50 years, and the risk of extinction never higher. Just two examples of major threats dogs are trained to tackle are: (I)Japanese Knotweed (Fallopia Japonica), not only a serious threat to ecosystems, crops, structures like bridges and roads - it can wipe out the entire value of a house. The property industry and homeowners are only just waking up to the full extent of the nightmare. When those working in construction on the roads move topsoil with a trace of Japanese Knotweed, it suffices to start a new colony. Japanese Knotweed grows up to 7cm a day. It can stay dormant and resprout after 20 years. In the UK, the cost of removing Japanese Knotweed from the London Olympic site in 2012 was around £70m (€83m). UK banks already no longer lend on a house that has Japanese Knotweed on-site. Legally, landowners are now obliged to excavate Japanese Knotweed and have it removed to a landfill. More and more, we see Japanese Knotweed grow where a new house has been constructed, and topsoil has been brought in. Conservation dogs are trained to detect small fragments of any part of the plant on sites and in topsoil. (II)Zebra mussels (Dreissena Polymorpha) are a threat to many waterways in the world. They colonize rivers, canals, docks, lakes, reservoirs, water pipes and cooling systems. They live up to 3 years and will release up to one million eggs each year. Zebra mussels attach to surfaces like rocks, anchors, boat hulls, intake pipes and boat engines. They cause changes in nutrient cycles, reduction of plankton and increased plant growth around lake edges, leading to the decline of Europe's native mussel and fish populations. There is no solution, only costly measures to keep it at bay. With many interconnected networks of waterways, they have spread uncontrollably. Conservation detection dogs detect the Zebra mussel from its early larvae stage, which is still invisible to the human eye. Detection dogs are more thorough and cost-effective than any other conservation method, and will greatly complement and speed up the work of biologists, surveyors, developers, ecologists and researchers.

Keywords: native biodiversity, conservation detection dogs, invasive species, Japanese Knotweed, zebra mussel

Procedia PDF Downloads 191

Authors: Hyun-Woo Cho

Abstract:

Automatic process monitoring schemes are designed to give early warnings for unusual process events or abnormalities as soon as possible. For this end, various techniques have been developed and utilized in various industrial processes. It includes multivariate statistical methods, representation skills in reduced spaces, kernel-based nonlinear techniques, etc. This work presents a nonlinear empirical monitoring scheme for batch type production processes with incomplete process measurement data. While normal operation data are easy to get, unusual fault data occurs infrequently and thus are difficult to collect. In this work, noise filtering steps are added in order to enhance monitoring performance by eliminating irrelevant information of the data. The performance of the monitoring scheme was demonstrated using batch process data. The results showed that the monitoring performance was improved significantly in terms of detection success rate of process fault.

Keywords: batch process, monitoring, measurement, kernel method

Procedia PDF Downloads 318