Search results for: range detection

Authors: Sebastian Töpfer, Jorge Fuenzalida, Marta Gilaberte Basset, Juan P. Torres, Markus Gräfe

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This work presents an experimental and theoretical study about the noise resilience of quantum holography with undetected photons. Quantum imaging has become an important research topic in the recent years after its first publication in 2014. Following this research, advances towards different spectral ranges in detection and different optical geometries have been made. Especially an interest in the field of near infrared to mid infrared measurements has developed, because of the unique characteristic, that allows to sample a probe with photons in a different wavelength than the photons arriving at the detector. This promising effect can be used for medical applications, to measure in the so-called molecule fingerprint region, while using broadly available detectors for the visible spectral range. Further advance the development of quantum imaging methods have been made by new measurement and detection schemes. One of which is quantum holography with undetected light. It combines digital phase shifting holography with quantum imaging to extent the obtainable sample information, by measuring not only the object transmission, but also its influence on the phase shift experienced by the transmitted light. This work will present extended research for the quantum holography with undetected light scheme regarding the influence of external noise. It is shown experimentally and theoretically that the samples information can still be at noise levels of 250 times higher than the signal level, because of its information being transmitted by the interferometric pattern. A detailed theoretic explanation is also provided.

Keywords: distillation, quantum holography, quantum imaging, quantum metrology

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Authors: Khin Mar Yee, Mu Mu Than, Kyi Lint, Aye Aye Oo, Chan Mya Hmway, Khin Zar Chi Winn

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The dynamic changes of Land Use and Land Cover (LULC) changes in Yangon have generally resulted the improvement of human welfare and economic development since the last twenty years. Making map of LULC is crucially important for the sustainable development of the environment. However, the exactly data on how environmental factors influence the LULC situation at the various scales because the nature of the natural environment is naturally composed of non-homogeneous surface features, so the features in the satellite data also have the mixed pixels. The main objective of this study is to the calculation of accuracy based on change detection of LULC changes by Support Vector Machines (SVMs). For this research work, the main data was satellite images of 1996, 2006 and 2015. Computing change detection statistics use change detection statistics to compile a detailed tabulation of changes between two classification images and Support Vector Machines (SVMs) process was applied with a soft approach at allocation as well as at a testing stage and to higher accuracy. The results of this paper showed that vegetation and cultivated area were decreased (average total 29 % from 1996 to 2015) because of conversion to the replacing over double of the built up area (average total 30 % from 1996 to 2015). The error matrix and confidence limits led to the validation of the result for LULC mapping.

Keywords: land use and land cover change, change detection, image processing, support vector machines

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Authors: Boudemagh Naime

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Automatic radar detection requires some methods of adapting to variations in the background clutter in order to control their false alarm rate. The problem becomes more complicated in non-Gaussian environment. In fact, the conventional approach in real time applications requires a complex statistical modeling and much computational operations. To overcome these constraints, we propose another approach based on artificial neural network (ANN-CMLD-CFAR) using a Back Propagation (BP) training algorithm. The considered environment follows a log-normal distribution in the presence of multiple Rayleigh-targets. To evaluate the performances of the considered detector, several situations, such as scale parameter and the number of interferes targets, have been investigated. The simulation results show that the ANN-CMLD-CFAR processor outperforms the conventional statistical one.

Keywords: radat detection, ANN-CMLD-CFAR, log-normal clutter, statistical modelling

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Authors: A. Lemzadmi

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Rotational and vibrational temperatures of the SF6-N2 gas mixture are spectroscopically measured over a pressure range of 2-14 bars. The spectra obtained of the light emission of the corona discharge were recorded with different values of pressure, voltage and current together with the variation of the position of the tip electrode. The emission of N2 is very dominant for different gas concentration and the second positive system 2S+ is the most important. The convolution method is used for the determination of the temperature. The Rotational temperature measurements of the plasma reveal gas temperatures in the range of 450-650°K and vibrational temperatures in the range of 1800-2200°K.

Keywords: rotational temperatures, corona discharges, SF6-N2 gas mixture, vibrational temperatures

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Authors: Sungsoo Na, Chanho Park

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Lung cancer is one of the most common severe diseases driving to the death of a human. Lung cancer can be divided into two cases of small-cell lung cancer (SCLC) and non-SCLC (NSCLC), and about 80% of lung cancers belong to the case of NSCLC. From several studies, the correlation between epidermal growth factor receptor (EGFR) and NSCLCs has been investigated. Therefore, EGFR inhibitor drugs such as gefitinib and erlotinib have been used as lung cancer treatments. However, the treatments result showed low response (10~20%) in clinical trials due to EGFR mutations that cause the drug resistance. Patients with resistance to EGFR inhibitor drugs usually are positive to KRAS mutation. Therefore, assessment of EGFR and KRAS mutation is essential for target therapies of NSCLC patient. In order to overcome the limitation of conventional therapies, overall EGFR and KRAS mutations have to be monitored. In this work, the only detection of EGFR will be presented. A variety of techniques has been presented for the detection of EGFR mutations. The standard detection method of EGFR mutation in ctDNA relies on real-time polymerase chain reaction (PCR). Real-time PCR method provides high sensitive detection performance. However, as the amplification step increases cost effect and complexity increase as well. Other types of technology such as BEAMing, next generation sequencing (NGS), an electrochemical sensor and silicon nanowire field-effect transistor have been presented. However, those technologies have limitations of low sensitivity, high cost and complexity of data analyzation. In this report, we propose a label-free and high-sensitive detection method of lung cancer using quartz crystal microbalance based platform. The proposed platform is able to sense lung cancer mutant DNA with a limit of detection of 1nM.

Keywords: cancer DNA, resonance frequency, quartz crystal microbalance, lung cancer

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Authors: P. Behera, K. K. Singh, D. K. Saini, M. De

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Implementation of 2D materials in the detection of bio analytes is highly advantageous in the field of sensing because of its high surface to volume ratio. We have designed our sensor array with different cationic two-dimensional MoS₂, where surface modification was achieved by cationic thiol ligands with different functionality. Green fluorescent protein (GFP) was chosen as signal transducers for its biocompatibility and anionic nature, which can bind to the cationic MoS₂ surface easily, followed by fluorescence quenching. The addition of bio-analyte to the sensor can decomplex the cationic MoS₂ and GFP conjugates, followed by the regeneration of GFP fluorescence. The fluorescence response pattern belongs to various analytes collected and transformed to linear discriminant analysis (LDA) for classification. At first, 15 different proteins having wide range of molecular weight and isoelectric points were successfully discriminated at 50 nM with detection limit of 1 nM. The sensor system was also executed in biofluids such as serum, where 10 different proteins at 2.5 μM were well separated. After successful discrimination of protein analytes, the sensor array was implemented for bacteria sensing. Six different bacteria were successfully classified at OD = 0.05 with a detection limit corresponding to OD = 0.005. The optimized sensor array was able to classify uropathogens from non-uropathogens in urine medium. Further, the technique was applied for discrimination of bacteria possessing resistance to different types and amounts of drugs. We found out the mechanism of sensing through optical and electrodynamic studies, which indicates the interaction between bacteria with the sensor system was mainly due to electrostatic force of interactions, but the separation of native bacteria from their drug resistant variant was due to Van der Waals forces. There are two ways bacteria can be detected, i.e., through bacterial cells and lysates. The bacterial lysates contain intracellular information and also safe to analysis as it does not contain live cells. Lysates of different drug resistant bacteria were patterned effectively from the native strain. From unknown sample analysis, we found that discrimination of bacterial cells is more sensitive than that of lysates. But the analyst can prefer bacterial lysates over live cells for safer analysis.

Keywords: array-based sensing, drug resistant bacteria, linear discriminant analysis, two-dimensional MoS₂

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Authors: Kunwar D. Yadav, Dayanand Sharma

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Surat City in India is famous for textile and dyeing industries which generate textile sludge in huge quantity. Textile sludge contains harmful chemicals which are poisonous and carcinogenic. The safe disposal and reuse of textile dyeing sludge are challenging for owner of textile industries and government of the state. The aim of present study was the vermicomposting of textile industries dyeing sludge with cow dung and Eisenia foetida as earthworm spices. The vermicompost reactor of 0.3 m³ capacity was used for vermicomposting. Textile dyeing sludge was mixed with cow dung in different proportion, i.e., 0:100 (C1), 10:90 (C2), 20:80 (C3), 30:70 (C4). Vermicomposting duration was 120 days. All the combinations of the feed mixture, the pH was increased to a range 7.45-7.78, percentage of total organic carbon was decreased to a range of 31-33.3%, total nitrogen was decreased to a range of 1.15-1.32%, total phosphorus was increased in the range of 6.2-7.9 (g/kg).

Keywords: cow dung, Eisenia foetida, textile sludge, vermicompost

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Authors: Neda Seyyedi, Reza Berangi

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Voice over IP (VOIP) network, also known as Internet telephony, is growing increasingly having occupied a large part of the communications market. With the growth of each technology, the related security issues become of particular importance. Taking advantage of this technology in different environments with numerous features put at our disposal, there arises an increasing need to address the security threats. Being IP-based and playing a signaling role in VOIP networks, Session Initiation Protocol (SIP) lets the invaders use weaknesses of the protocol to disable VOIP service. One of the most important threats is denial of service attack, a branch of which in this article we have discussed as flooding attacks. These attacks make server resources wasted and deprive it from delivering service to authorized users. Distributed denial of service attacks and attacks with a low rate can mislead many attack detection mechanisms. In this paper, we introduce a mechanism which not only detects distributed denial of service attacks and low rate attacks, but can also identify the attackers accurately. We detect and prevent flooding attacks in SIP protocol using Shannon (FDP-S), Renyi (FDP-R) and Tsallis (FDP-T) entropy. We conducted an experiment to compare the percentage of detection and rate of false alarm messages using any of the Shannon, Renyi and Tsallis entropy as a measure of disorder. Implementation results show that, according to the parametric nature of the Renyi and Tsallis entropy, by changing the parameters, different detection percentages and false alarm rates will be gained with the possibility to adjust the sensitivity of the detection mechanism.

Keywords: VOIP networks, flooding attacks, entropy, computer networks

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Authors: Mohsen Zahraei

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‎In this paper, ‎the notion of ‎rank-k numerical range of rectangular complex matrix polynomials‎ ‎are introduced. ‎Some algebraic and geometrical properties are investigated. ‎Moreover, ‎for ε>0 the notion of Birkhoff-James approximate orthogonality sets for ε-higher ‎rank numerical ranges of rectangular matrix polynomials is also introduced and studied. ‎The proposed definitions yield a natural generalization of the standard higher rank numerical ranges.

Keywords: ‎‎Rank-k numerical range‎, ‎isometry‎, ‎numerical range‎, ‎rectangular matrix polynomials

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Authors: Jae-Neung Lee, Keun-Chang Kwak

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This paper describes an analysis of Yacht Simulator international trends and also explains about Yacht. Examples of yacht Simulator using Yacht Simulator include image processing for totaling the total number of vehicles, edge/target detection, detection and evasion algorithm, image processing using SIFT (scale invariant features transform) matching, and application of median filter and thresholding.

Keywords: yacht simulator, simulator, trends analysis, SIFT

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Authors: Sangeeta Palekar, Mahima Rana, Jayu Kalambe

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In this paper, a microfluidic-based platform for the quantification of contaminants in the water is proposed. The proposed system uses microfluidic channels with an embedded environment for contaminants detection in water. Microfluidics-based platforms present an evident stage of innovation for fluid analysis, with different applications advancing minimal efforts and simplicity of fabrication. Polydimethylsiloxane (PDMS)-based microfluidics channel is fabricated using a soft lithography technique. Vertical and horizontal connections for fluid dispensing with the microfluidic channel are explored. The principle of colorimetry, which incorporates the use of Griess reagent for the detection of nitrite, has been adopted. Nitrite has high water solubility and water retention, due to which it has a greater potential to stay in groundwater, endangering aquatic life along with human health, hence taken as a case study in this work. The developed platform also compares the detection methodology, containing photodetectors for measuring absorbance and image sensors for measuring color change for quantification of contaminants like nitrite in water. The utilization of image processing techniques offers the advantage of operational flexibility, as the same system can be used to identify other contaminants present in water by introducing minor software changes.

Keywords: colorimetric, fluid contaminants, nitrite detection, microfluidics

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Authors: Samuel B. Dulay, Sandra Julich, Herbert Tomaso, Ciara K. O'Sullivan

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An early, rapid and definite detection for the presence of biowarfare agents, pathogens, viruses and toxins is required in different situations which include civil rescue and security units, homeland security, military operations, public transportation securities such as airports, metro and railway stations due to its harmful effect on the human population. In this work, an electrochemical genosensor array that allows simultaneous detection of different biowarfare agents within an integrated microsystem that provides an easy handling of the technology which combines a microfluidics setup with a multiplexing genosensor array has been developed and optimised for the following targets: Bacillus anthracis, Brucella abortis and melitensis, Bacteriophage lambda, Francisella tularensis, Burkholderia mallei and pseudomallei, Coxiella burnetii, Yersinia pestis, and Bacillus thuringiensis. The electrode array was modified via co-immobilisation of a 1:100 (mol/mol) mixture of a thiolated probe and an oligoethyleneglycol-terminated monopodal thiol. PCR products from these relevant biowarfare agents were detected reproducibly through a sandwich assay format with the target hybridised between a surface immobilised probe into the electrode and a horseradish peroxidase-labelled secondary reporter probe, which provided an enzyme based electrochemical signal. The potential of the designed microsystem for multiplexed genosensor detection and cross-reactivity studies over potential interfering DNA sequences has demonstrated high selectivity using the developed platform producing high-throughput.

Keywords: biowarfare agents, genosensors, multipled detection, microsystem

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Authors: T. Garba, Y. Y. Babanyara, T. O. Quddus, A. K. Mukatari

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Human activities make environmental parameters in order to keep on changing globally. While some changes are necessary and beneficial to flora and fauna, others have serious consequences threatening the survival of their natural habitat if these changes are not properly monitored and mitigated. In-situ assessments are characterized by many challenges due to the absence of time series data and sometimes areas to be observed or monitored are inaccessible. Satellites Remote Sensing provide us with the digital images of same geographic areas within a pre-defined interval. This makes it possible to monitor and detect changes of environmental phenomena. This paper, therefore, reviewed the commonly use changes detection techniques globally such as image differencing, image rationing, image regression, vegetation index difference, change vector analysis, principal components analysis, multidate classification, post-classification comparison, and visual interpretation. The paper concludes by suggesting the use of more than one technique.

Keywords: environmental phenomena, change detection, monitor, techniques

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Authors: Natalia Lukasik, Ewa Wagner-Wysiecka

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Development of selective, fast-responsive, and economical sensors for diverse ions detection and determination is one of the most extensively studied areas due to its importance in the field of clinical, environmental and industrial analysis. Among chemical sensors, vast popularity has gained ionophore-based optical sensors, where the generated analytical signal is a consequence of the molecular recognition of ion by the ionophore. Change of color occurring during host-guest interactions allows for quantitative analysis and for 'naked-eye' detection without the need of using sophisticated equipment. An example of application of such sensors is colorimetric detection of iron(III) cations. Iron as one of the most significant trace elements plays roles in many biochemical processes. For these reasons, the development of reliable, fast, and selective methods of iron ions determination is highly demanded. Taking all mentioned above into account a chromogenic amide derivative of 3,4-dihydroxybenzoic acid was synthesized, and its ability to iron(III) recognition was tested. To the best of authors knowledge (according to chemical abstracts) the obtained ligand has not been described in the literature so far. The catechol moiety was introduced to the ligand structure in order to mimic the action of naturally occurring siderophores-iron(III)-selective receptors. The ligand–ion interactions were studied using spectroscopic methods: UV-Vis spectrophotometry and infrared spectroscopy. The spectrophotometric measurements revealed that the amide exhibits affinity to iron(III) in dimethyl sulfoxide and fully aqueous solution, what is manifested by the change of color from yellow to green. Incorporation of the tested amide into a polymeric matrix (cellulose triacetate) ensured effective recognition of iron(III) at pH 3 with the detection limit 1.58×10⁻⁵ M. For the obtained sensor material parameters like linear response range, response time, selectivity, and possibility of regeneration were determined. In order to evaluate the effect of the size of the sensing material on iron(III) detection nanospheres (in the form of nanoemulsion) containing the tested amide were also prepared. According to DLS (dynamic light scattering) measurements, the size of the nanospheres is 308.02 ± 0.67 nm. Work parameters of the nanospheres were determined and compared with cellulose triacetate-based material. Additionally, for fast, qualitative experiments the test strips were prepared by adsorption of the amide solution on a glass microfiber material. Visual limit of detection of iron(III) at pH 3 by the test strips was estimated at the level 10⁻⁴ M. In conclusion, reported here amide derived from 3,4- dihydroxybenzoic acid proved to be an effective candidate for optical sensing of iron(III) in fully aqueous solutions. N. L. kindly acknowledges financial support from National Science Centre Poland the grant no. 2017/01/X/ST4/01680. Authors thank for financial support from Gdansk University of Technology grant no. 032406.

Keywords: ion-selective optode, iron(III) recognition, nanospheres, optical sensor

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Authors: Jocelyn B. Barbosa, Angeli L. Magbaril, Mariel T. Sabanal, John Paul T. Galario, Mikka P. Baldovino

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The use of technology has become ubiquitous in different areas of business today. With the advent of digital imaging and database technology, business owners have been motivated to integrate technology to their business operation ranging from small, medium to large enterprises. Technology has been found to have brought many benefits that can make a business grow. Hog or swine raising, for example, is a very popular enterprise in the Philippines, whose challenges in production monitoring can be addressed through technology integration. Swine production monitoring can become a tedious task as the enterprise goes larger. Specifically, problems like delayed and inconsistent reports are most likely to happen if counting of swine per pen of which building is done manually. In this study, we present iCount, which aims to ensure efficient swine detection and counting that hastens the swine production monitoring task. We develop a system that automatically detects and counts swine based on Sobel filter and ellipse fitting model, given the still photos of the group of swine captured in a pen. We improve the Sobel filter detection result through 8-neigbhorhood rule implementation. Ellipse fitting technique is then employed for proper swine detection. Furthermore, the system can generate periodic production reports and can identify the specific consumables to be served to the swine according to schedules. Experiments reveal that our algorithm provides an efficient way for detecting swine, thereby providing a significant amount of accuracy in production monitoring.

Keywords: automatic swine counting, swine detection, swine production monitoring, ellipse fitting model, sobel filter

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Authors: Nakarin Prempri

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Single-phase induction motors are widely used in industry. Most manufacturing processes use capacitor-run single-phase induction motors to drive mechanical loads. The selection of a suitable motor for driving is important. The optimum operating range of the motor can help the motor operate efficiently. Thus, this paper presents an operating range analysis of capacitor-run single-phase induction motors and a determination of suitability between motor and mechanical loads. an observational study found that the optimum operating range of the motor can be used to determine the suitability between the motor and the mechanical load. Such considerations ensure that the motor uses no more current than necessary and operates efficiently.

Keywords: single phase induction motor, operating range, torque curve, efficiency curve

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Authors: Chung-Yuan Huang, Yu-Hsiang Fu, Chuen-Tsai Sun

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Community detection represents an important data-mining tool for analyzing and understanding real-world complex network structures and functions. We believe that at least four criteria determine the appropriateness of a community detection algorithm: (a) it produces useable normalized mutual information (NMI) and modularity results for social networks, (b) it overcomes resolution limitation problems associated with synthetic networks, (c) it produces good NMI results and performance efficiency for Lancichinetti-Fortunato-Radicchi (LFR) benchmark networks, and (d) it produces good modularity and performance efficiency for large-scale real-world complex networks. To our knowledge, no existing community detection algorithm meets all four criteria. In this paper, we describe a simple hierarchical arc-merging (HAM) algorithm that uses network topologies and rule-based arc-merging strategies to identify community structures that satisfy the criteria. We used five well-studied social network datasets and eight sets of LFR benchmark networks to validate the ground-truth community correctness of HAM, eight large-scale real-world complex networks to measure its performance efficiency, and two synthetic networks to determine its susceptibility to resolution limitation problems. Our results indicate that the proposed HAM algorithm is capable of providing satisfactory performance efficiency and that HAM-identified communities were close to ground-truth communities in social and LFR benchmark networks while overcoming resolution limitation problems.

Keywords: complex network, social network, community detection, network hierarchy

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Authors: Peter Joseph Basil Morris, Chhabi Nigam, S. Ramakrishnan, P. Radhakrishna

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This paper brings out the analysis of the airborne Synthetic Aperture Radar (SAR) data using the Range Migration Algorithm (RMA) for the spotlight mode of operation. Unlike in polar format algorithm (PFA), space-variant defocusing and geometric distortion effects are mitigated in RMA since it does not assume that the illuminating wave-fronts are planar. This facilitates the use of RMA for imaging scenarios involving severe differential range curvatures enabling the imaging of larger scenes at fine resolution and at shorter ranges with low center frequencies. The RMA algorithm for the spotlight mode of SAR is analyzed in this paper using the airborne data. Pre-processing operations viz: - range de-skew and motion compensation to a line are performed on the raw data before being fed to the RMA component. Various stages of the RMA viz:- 2D Matched Filtering, Along Track Fourier Transform and Slot Interpolation are analyzed to find the performance limits and the dependence of the imaging geometry on the resolution of the final image. The ability of RMA to compensate for severe differential range curvatures in the two-dimensional spatial frequency domain are also illustrated in this paper.

Keywords: range migration algorithm, spotlight SAR, synthetic aperture radar, matched filtering, slot interpolation

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Authors: Salma Krafessi

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The financial markets have a multifaceted, intricate environment, and enormous volumes of data are produced every day. To find investment possibilities, possible fraudulent activity, and market oddities, accurate anomaly identification in this data is essential. Conventional methods for detecting anomalies frequently fail to capture the complex organization of financial data. In order to improve the identification of abnormalities in financial time series data, this study presents Tucker Decomposition as a reliable multi-way analysis approach. We start by gathering closing prices for the S&P 500 index across a number of decades. The information is converted to a three-dimensional tensor format, which contains internal characteristics and temporal sequences in a sliding window structure. The tensor is then broken down using Tucker Decomposition into a core tensor and matching factor matrices, allowing latent patterns and relationships in the data to be captured. A possible sign of abnormalities is the reconstruction error from Tucker's Decomposition. We are able to identify large deviations that indicate unusual behavior by setting a statistical threshold. A thorough examination that contrasts the Tucker-based method with traditional anomaly detection approaches validates our methodology. The outcomes demonstrate the superiority of Tucker's Decomposition in identifying intricate and subtle abnormalities that are otherwise missed. This work opens the door for more research into multi-way data analysis approaches across a range of disciplines and emphasizes the value of tensor-based methods in financial analysis.

Keywords: tucker decomposition, financial markets, financial engineering, artificial intelligence, decomposition models

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Authors: Hossein Arzani, Masoud Jafari Shalamzari, Z. Arzani

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This paper follows FAO model of suitability analysis. Influential factors affecting extensive grazing were determined and converted into a model. Taleghan rangelands were examined for common types of grazing animals as an example. Advantages and limitations were elicited. All range ecosystems’ components affect range suitability but due to the time and money restrictions, the most important and feasible elements were investigated. From which three sub-models including water accessibility, forage production and erosion sensitivity were considered. Suitable areas in four levels of suitability were calculated using GIS. This suitability modeling approach was adopted due to its simplicity and the minimal time that is required for transforming and analyzing the data sets. Managers could be benefited from the model to devise the measures more wisely to cope with the limitations and enhance the rangelands health and condition.

Keywords: range suitability, land-use, extensive grazing, modeling, land evaluation

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Authors: Z. Yousefniayejahr, S. Bolognini, A. Bonini, C. Campobasso, N. Poma, F. Vivaldi, M. Di Luca, A. Tavanti, F. Di Francesco

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Pathogenic bacteria represent a threat to healthcare systems and the food industry because their rapid detection remains challenging. Electrochemical biosensors are gaining prominence as a novel technology for the detection of pathogens due to intrinsic features such as low cost, rapid response time, and portability, which make them a valuable alternative to traditional methodologies. These sensors use biorecognition elements that are crucial for the identification of specific bacteria. In this context, bacteriophages are promising tools for their inherent high selectivity towards bacterial hosts, which is of fundamental importance when detecting bacterial pathogens in complex biological samples. In this study, we present the development of a low-cost and portable sensor based on the Zeno phage for the rapid detection of Staphylococcus aureus. Screen-printed gold electrodes functionalized with the Zeno phage were used, and electrochemical impedance spectroscopy was applied to evaluate the change of the charge transfer resistance (Rct) as a result of the interaction with S. aureus MRSA ATCC 43300. The phage-based biosensor showed a linear range from 101 to 104 CFU/mL with a 20-minute response time and a limit of detection (LOD) of 1.2 CFU/mL under physiological conditions. The biosensor’s ability to recognize various strains of staphylococci was also successfully demonstrated in the presence of clinical isolates collected from different geographic areas. Assays using S. epidermidis were also carried out to verify the species-specificity of the phage sensor. We only observed a remarkable change of the Rct in the presence of the target S. aureus bacteria, while no substantial binding to S. epidermidis occurred. This confirmed that the Zeno phage sensor only targets S. aureus species within the genus Staphylococcus. In addition, the biosensor's specificity with respect to other bacterial species, including gram-positive bacteria like Enterococcus faecium and the gram-negative bacterium Pseudomonas aeruginosa, was evaluated, and a non-significant impedimetric signal was observed. Notably, the biosensor successfully identified S. aureus bacterial cells in a complex matrix such as a nasal swab, opening the possibility of its use in a real-case scenario. We diluted different concentrations of S. aureus from 108 to 100 CFU/mL with a ratio of 1:10 in the nasal swap matrices collected from healthy donors. Three different sensors were applied to measure various concentrations of bacteria. Our sensor indicated high selectivity to detect S. aureus in biological matrices compared to time-consuming traditional methods, such as enzyme-linked immunosorbent assay (ELISA), polymerase chain reaction (PCR), and radioimmunoassay (RIA), etc. With the aim to study the possibility to use this biosensor to address the challenge associated to pathogen detection, ongoing research is focused on the assessment of the biosensor’s analytical performances in different biological samples and the discovery of new phage bioreceptors.

Keywords: electrochemical impedance spectroscopy, bacteriophage, biosensor, Staphylococcus aureus

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Authors: Noureddine Henka, Sami Tazi, Mohamad Assaad

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The electrical grid is a highly intricate system designed to transfer electricity from production areas to consumption areas. The Transmission System Operator (TSO) is responsible for ensuring the efficient distribution of electricity and maintaining the grid's safety and quality. However, due to the increasing integration of intermittent renewable energy sources, there is a growing level of uncertainty, which requires a faster responsive approach. A potential solution involves the use of electrical segmentation, which involves creating coherence zones where electrical disturbances mainly remain within the zone. Indeed, by means of coherent electrical zones, it becomes possible to focus solely on the sub-zone, reducing the range of possibilities and aiding in managing uncertainty. It allows faster execution of operational processes and easier learning for supervised machine learning algorithms. Electrical segmentation can be applied to various applications, such as electrical control, minimizing electrical loss, and ensuring voltage stability. Since the electrical grid can be modeled as a graph, where the vertices represent electrical buses and the edges represent electrical lines, identifying coherent electrical zones can be seen as a clustering task on graphs, generally called community detection. Nevertheless, a critical criterion for the zones is their ability to remain resilient to the electrical evolution of the grid over time. This evolution is due to the constant changes in electricity generation and consumption, which are reflected in graph structure variations as well as line flow changes. One approach to creating a resilient segmentation is to design robust zones under various circumstances. This issue can be represented through a multiplex graph, where each layer represents a specific situation that may arise on the grid. Consequently, resilient segmentation can be achieved by conducting community detection on this multiplex graph. The multiplex graph is composed of multiple graphs, and all the layers share the same set of vertices. Our proposal involves a model that utilizes a unified representation to compute a flattening of all layers. This unified situation can be penalized to obtain (K) connected components representing the robust electrical segmentation clusters. We compare our robust segmentation to the segmentation based on a single reference situation. The robust segmentation proves its relevance by producing clusters with high intra-electrical perturbation and low variance of electrical perturbation. We saw through the experiences when robust electrical segmentation has a benefit and in which context.

Keywords: community detection, electrical segmentation, multiplex graph, power grid

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Authors: S. Korkut, M. S. Kilic, E. Erhan

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In order to detect and quantify the phenolic contents of a wastewater with biosensors, two working electrodes based on modified Poly (Pyrrole) films were fabricated. Enzyme horseradish peroxidase was used as biomolecule of the prepared electrodes. Various phenolics were tested at the biosensor. Phenol detection was realized by electrochemical reduction of quinones produced by enzymatic activity. Analytical parameters were calculated and the results were compared with each other.

Keywords: carbon nanotube, phenol biosensor, polypyrrole, poly (glutaraldehyde)

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Authors: A. Nachour, L. Ouzizi, Y. Aoura

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Recent developments on multi-agent system have brought a new research field on image processing. Several algorithms are used simultaneously and improved in deferent applications while new methods are investigated. This paper presents a new automatic method for edge detection using several agents and many different actions. The proposed multi-agent system is based on parallel agents that locally perceive their environment, that is to say, pixels and additional environmental information. This environment is built using Vector Field Convolution that attract free agent to the edges. Problems of partial, hidden or edges linking are solved with the cooperation between agents. The presented method was implemented and evaluated using several examples on different synthetic and medical images. The obtained experimental results suggest that this approach confirm the efficiency and accuracy of detected edge.

Keywords: edge detection, medical MRImages, multi-agent systems, vector field convolution

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Authors: Retno Supriyanti, Ahmad Chuzaeri, Yogi Ramadhani, A. Haris Budi Widodo

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The use of ultrasonography in the medical world has been very popular including the diagnosis of pregnancy. In determining pregnancy, ultrasonography has many roles, such as to check the position of the fetus, abnormal pregnancy, fetal age and others. Unfortunately, all these things still need to analyze the role of the obstetrician in the sense of image raised by ultrasonography. One of the most striking is the determination of gestational age. Usually, it is done by measuring the length of the fetus manually by obstetricians. In this study, we developed a computer-aided diagnosis for the determination of gestational age by measuring the length of the fetus automatically using edge detection method and image morphology. Results showed that the system is sufficiently accurate in determining the gestational age based image processing.

Keywords: computer aided diagnosis, gestational age, and diameter of uterus, length of fetus, edge detection method, morphology image

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Authors: Niloofar Yousefi, Marie Alaghband, Ivan Garibay

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With the increase of credit card usage, the volume of credit card misuse also has significantly increased, which may cause appreciable financial losses for both credit card holders and financial organizations issuing credit cards. As a result, financial organizations are working hard on developing and deploying credit card fraud detection methods, in order to adapt to ever-evolving, increasingly sophisticated defrauding strategies and identifying illicit transactions as quickly as possible to protect themselves and their customers. Compounding on the complex nature of such adverse strategies, credit card fraudulent activities are rare events compared to the number of legitimate transactions. Hence, the challenge to develop fraud detection that are accurate and efficient is substantially intensified and, as a consequence, credit card fraud detection has lately become a very active area of research. In this work, we provide a survey of current techniques most relevant to the problem of credit card fraud detection. We carry out our survey in two main parts. In the first part, we focus on studies utilizing classical machine learning models, which mostly employ traditional transnational features to make fraud predictions. These models typically rely on some static physical characteristics, such as what the user knows (knowledge-based method), or what he/she has access to (object-based method). In the second part of our survey, we review more advanced techniques of user authentication, which use behavioral biometrics to identify an individual based on his/her unique behavior while he/she is interacting with his/her electronic devices. These approaches rely on how people behave (instead of what they do), which cannot be easily forged. By providing an overview of current approaches and the results reported in the literature, this survey aims to drive the future research agenda for the community in order to develop more accurate, reliable and scalable models of credit card fraud detection.

Keywords: Credit Card Fraud Detection, User Authentication, Behavioral Biometrics, Machine Learning, Literature Survey

Procedia PDF Downloads 109

Authors: Jooyoung Park, Junkyeong Kim, Aoqi Zhang, Seunghee Park

Abstract:

Non-destructive testing on cable is in great demand due to safety accidents at sites where many equipments using cables are installed. In this paper, the quantitative change of the obtained signal was analyzed using a magnetic flux leakage (MFL) method. A two-dimensional simulation was conducted with a FEM model replicating real elevator cables. The simulation data were compared for three parameters (depth of defect, width of defect and inspection velocity). Then, an experiment on same conditions was carried out to verify the results of the simulation. Signals obtained from both the simulation and the experiment were transformed to characterize the properties of the damage. Throughout the results, a cable damage detection based on an MFL method was confirmed to be feasible. In further study, it is expected that the MFL signals of an entire specimen will be gained and visualized as well.

Keywords: magnetic flux leakage (mfl), cable damage detection, non-destructive testing, numerical simulation

Procedia PDF Downloads 377

Authors: M. R. Ghasemi, R. Ghiasi, H. Varaee

Abstract:

Model updating method has received increasing attention in damage detection structures based on measured modal parameters. Therefore, a probability-based damage detection (PBDD) procedure based on a model updating procedure is presented in this paper, in which a one-stage model-based damage identification technique based on the dynamic features of a structure is investigated. The presented framework uses a finite element updating method with a Monte Carlo simulation that considers the uncertainty caused by measurement noise. Enhanced ideal gas molecular movement (EIGMM) is used as the main algorithm for model updating. Ideal gas molecular movement (IGMM) is a multiagent algorithm based on the ideal gas molecular movement. Ideal gas molecules disperse rapidly in different directions and cover all the space inside. This is embedded in the high speed of molecules, collisions between them and with the surrounding barriers. In IGMM algorithm to accomplish the optimal solutions, the initial population of gas molecules is randomly generated and the governing equations related to the velocity of gas molecules and collisions between those are utilized. In this paper, an enhanced version of IGMM, which removes unchanged variables after specified iterations, is developed. The proposed method is implemented on two numerical examples in the field of structural damage detection. The results show that the proposed method can perform well and competitive in PBDD of structures.

Keywords: enhanced ideal gas molecular movement (EIGMM), ideal gas molecular movement (IGMM), model updating method, probability-based damage detection (PBDD), uncertainty quantification

Procedia PDF Downloads 274

Authors: Foad Saadi

Abstract:

Aim of this work was to determine the approximate Epoxy forming composition range of Cr-Zn system for the composites produced by forming compositing. It was predicted by MI edema semi-empirical model that the composition had to be in the range of 30-60 wt. % tin, while Cr-32Zn had the most susceptibility to produce amorphous composite. In the next stage, some different compositions of Cr-Zn were foamingly composited, where one of them had the proper predicted composition. Products were characterized by SDM analysis. There was a good agreement between calculation and experiments, in which Cr-32Zn composite had the most amorphization degree.

Keywords: Cr-Zn system, forming compositing, amorphous composite, MI edema model

Procedia PDF Downloads 291

Authors: Hamidullah Binol, Abdullah Bal

Abstract:

Nowadays, food safety is a great public concern; therefore, robust and effective techniques are required for detecting the safety situation of goods. Hyperspectral Imaging (HSI) is an attractive material for researchers to inspect food quality and safety estimation such as meat quality assessment, automated poultry carcass inspection, quality evaluation of fish, bruise detection of apples, quality analysis and grading of citrus fruits, bruise detection of strawberry, visualization of sugar distribution of melons, measuring ripening of tomatoes, defect detection of pickling cucumber, and classification of wheat kernels. HSI can be used to concurrently collect large amounts of spatial and spectral data on the objects being observed. This technique yields with exceptional detection skills, which otherwise cannot be achieved with either imaging or spectroscopy alone. This paper presents a nonlinear technique based on kernel Fukunaga-Koontz transform (KFKT) for detection of fat content in ground meat using HSI. The KFKT which is the nonlinear version of FKT is one of the most effective techniques for solving problems involving two-pattern nature. The conventional FKT method has been improved with kernel machines for increasing the nonlinear discrimination ability and capturing higher order of statistics of data. The proposed approach in this paper aims to segment the fat content of the ground meat by regarding the fat as target class which is tried to be separated from the remaining classes (as clutter). We have applied the KFKT on visible and nearinfrared (VNIR) hyperspectral images of ground meat to determine fat percentage. The experimental studies indicate that the proposed technique produces high detection performance for fat ratio in ground meat.

Keywords: food (ground meat) inspection, Fukunaga-Koontz transform, hyperspectral imaging, kernel methods

Procedia PDF Downloads 427