Search results for: edge detection algorithm

Authors: Gurinder Singh, Kulbir Singh

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Digital multimedia contents such as image, video, and audio can be tampered easily due to the availability of powerful editing softwares. Multimedia forensics is devoted to analyze these contents by using various digital forensic techniques in order to validate their authenticity. Digital image forensics is dedicated to investigate the reliability of digital images by analyzing the integrity of data and by reconstructing the historical information of an image related to its acquisition phase. In this paper, a survey is carried out on the forgery detection by considering the most recent and promising digital image forensic techniques.

Keywords: Computer Forensics, Multimedia Forensics, Image Ballistics, Camera Source Identification, Forgery Detection

Procedia PDF Downloads 231

Authors: Qiu Chen, Koji Kotani, Feifei Lee, Tadahiro Ohmi

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In this paper, we propose an improved face recognition algorithm using histogram-based features in spatial and frequency domains. For adding spatial information of the face to improve recognition performance, a region-division (RD) method is utilized. The facial area is firstly divided into several regions, then feature vectors of each facial part are generated by Binary Vector Quantization (BVQ) histogram using DCT coefficients in low frequency domains, as well as Local Binary Pattern (LBP) histogram in spatial domain. Recognition results with different regions are first obtained separately and then fused by weighted averaging. Publicly available ORL database is used for the evaluation of our proposed algorithm, which is consisted of 40 subjects with 10 images per subject containing variations in lighting, posing, and expressions. It is demonstrated that face recognition using RD method can achieve much higher recognition rate.

Keywords: binary vector quantization (BVQ), DCT coefficients, face recognition, local binary patterns (LBP)

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Authors: Shivani Jha, Klaus Baier, Rafiq Azzam, Ramakar Jha

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In India, generally people migrate from rural area to the urban area for better infra-structural facilities, high standard of living, good job opportunities and advanced transport/communication availability. In fact, unplanned urban development due to migration of people causes seriou damage to the land use, water pollution and available water resources. In the present work, an attempt has been made to use satellite data of different years for urban change detection of Chennai metropolitan city along with pattern analysis to generate future scenario of urban development using buffer zoning in GIS environment. In the analysis, SRTM (30m) elevation data and IRS-1C satellite data for the years 1990, 2000, and 2014, are used. The flow accumulation, aspect, flow direction and slope maps developed using SRTM 30 m data are very useful for finding suitable urban locations for industrial setup and urban settlements. Normalized difference vegetation index (NDVI) and Principal Component Analysis (PCA) have been used in ERDAS imagine software for change detection in land use of Chennai metropolitan city. It has been observed that the urban area has increased exponentially in Chennai metropolitan city with significant decrease in agriculture and barren lands. However, the water bodies located in the study regions are protected and being used as freshwater for drinking purposes. Using buffer zone analysis in GIS environment, it has been observed that the development has taken place in south west direction significantly and will do so in future.

Keywords: urban change, satellite data, the Chennai metropolis, change detection

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Authors: Ferinar Moaidi, Mahdi Moaidi

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Environmental issues and the ever-increasing in demand of electrical energy make it necessary to have distributed generation (DG) resources in the power system. In this research, in order to realize the goals of reducing losses and improving the voltage profile in a microgrid, the allocation and sizing of DGs have been used. The proposed Genetic Algorithm (GA) is described from the array of artificial intelligence methods for solving the problem. The algorithm is implemented on the IEEE 33 buses network. This study is presented in two scenarios, primarily to illustrate the effect of location and determination of DGs has been done to reduce losses and improve the voltage profile. On the other hand, decisions made with the one-level assumptions of load are not universally accepted for all levels of load. Therefore, in this study, load modelling is performed and the results are presented for multi-levels load state.

Keywords: distributed generation, genetic algorithm, microgrid, load modelling, loss reduction, voltage improvement

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Authors: Yash Jain

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The US Center for Disease Control has recommended wearing masks to slow the spread of the virus. The research uses a video feed from a camera to conduct real-time classifications of whether or not a human is correctly wearing a mask, incorrectly wearing a mask, or not wearing a mask at all. Utilizing two distinct datasets from the open-source website Kaggle, a mask detection network had been trained. The first dataset that was used to train the model was titled 'Face Mask Detection' on Kaggle, where the dataset was retrieved from and the second dataset was titled 'Face Mask Dataset, which provided the data in a (YOLO Format)' so that the TinyYoloV3 model could be trained. Based on the data from Kaggle, two machine learning models were implemented and trained: a Tiny YoloV3 Real-time model and a two-stage neural network classifier. The two-stage neural network classifier had a first step of identifying distinct faces within the image, and the second step was a classifier to detect the state of the mask on the face and whether it was worn correctly, incorrectly, or no mask at all. The TinyYoloV3 was used for the live feed as well as for a comparison standpoint against the previous two-stage classifier and was trained using the darknet neural network framework. The two-stage classifier attained a mean average precision (MAP) of 80%, while the model trained using TinyYoloV3 real-time detection had a mean average precision (MAP) of 59%. Overall, both models were able to correctly classify stages/scenarios of no mask, mask, and incorrectly worn masks.

Keywords: datasets, classifier, mask-detection, real-time, TinyYoloV3, two-stage neural network classifier

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Authors: Mohammad Tarek Al Muallim, Ozhan Duzenli, Ceyhun Ilguy

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Passive sonar is a method for detecting acoustic signals in the ocean. It detects the acoustic signals emanating from external sources. With passive sonar, we can determine the bearing of the target only, no information about the range of the target. Target Motion Analysis (TMA) is a process to estimate the position and speed of a target using passive sonar information. Since bearing is the only available information, the TMA technique called Bearing-only TMA. Many TMA techniques have been developed. However, until now, there is not a very effective method that could be used to always track an unknown target and extract its moving trace. In this work, a design of effective Bearing-only TMA Algorithm is done. The measured bearing angles are very noisy. Moreover, for multi-beam sonar, the measurements is quantized due to the sonar beam width. To deal with this, modified gain extended Kalman filter algorithm is used. The algorithm is fine-tuned, and many modules are added to improve the performance. A special validation gate module is used to insure stability of the algorithm. Many indicators of the performance and confidence level measurement are designed and tested. A new method to detect if the target is maneuvering is proposed. Moreover, a reactive optimal observer maneuver based on bearing measurements is proposed, which insure converging to the right solution all of the times. To test the performance of the proposed TMA algorithm a simulation is done with a MATLAB program. The simulator program tries to model a discrete scenario for an observer and a target. The simulator takes into consideration all the practical aspects of the problem such as a smooth transition in the speed, a circular turn of the ship, noisy measurements, and a quantized bearing measurement come for multi-beam sonar. The tests are done for a lot of given test scenarios. For all the tests, full tracking is achieved within 10 minutes with very little error. The range estimation error was less than 5%, speed error less than 5% and heading error less than 2 degree. For the online performance estimator, it is mostly aligned with the real performance. The range estimation confidence level gives a value equal to 90% when the range error less than 10%. The experiments show that the proposed TMA algorithm is very robust and has low estimation error. However, the converging time of the algorithm is needed to be improved.

Keywords: target motion analysis, Kalman filter, passive sonar, bearing-only tracking

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Authors: Miroslav Popelka, Jakub Nožička

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In this paper we deal with using Lego Mindstorms in simulation of robotic systems with respect to cost reduction. Lego Mindstorms kit contains broad variety of hardware components which are required to simulate, program and test the robotics systems in practice. Algorithm programming went in development environment supplied together with Lego kit as in programming language C# as well. Algorithm following the line, which we dealt with in this paper, uses theoretical findings from area of controlling circuits. PID controller has been chosen as controlling circuit whose individual components were experimentally adjusted for optimal motion of robot tracking the line. Data which are determined to process by algorithm are collected by sensors which scan the interface between black and white surfaces followed by robot. Based on discovered facts Lego Mindstorms can be considered for low-cost and capable kit to simulate real robotics systems.

Keywords: LEGO Mindstorms, PID controller, low-cost robotics systems, line follower, sensors, programming language C#, EV3 Home Edition Software

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Authors: Behrooz Nikahval, Mohammad Saeed Ahrari-Khafi, Sakineh Behroozpoor, Saeed Nazifi

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Osteoarthritis (OA) is a progressive and degenerative condition of the articular cartilage and other joints’ structures. It is essential to diagnose this condition as early as possible. The present research was performed to measure the Osteoprotegrin (OPG) and Tumor Necrosis Factor α (TNF-α) in synovial fluid and blood serum of dogs with surgically transected cruciate ligament as a model of OA, to evaluate if measuring of these parameters can be used as a way of early diagnosis of OA. In the present study, four mature, clinically healthy dogs were selected to investigate the effect of experimental OA, on OPG and TNF-α as a way of early detection of OA. OPG and TNF-α were measured in synovial fluid and blood serum on days 0, 14, 28, 90 and 180 after surgical transaction of cranial cruciate ligament in one stifle joint. Statistical analysis of the results showed that there was a significant increase in TNF-α in both synovial fluid and blood serum. OPG showed a decrease two weeks after OA induction. However, it fluctuated afterward. In conclusion, TNF-α could be used in both synovial fluid and blood serum as a way of early detection of OA; however, further research still needs to be conducted on OPG values in OA detection.

Keywords: osteoarthritis, osteoprotegrin, tumor necrosis factor α, synovial fluid, serum, dog

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Authors: Shashank Gahlaut, Chandrashekhar Sharan, J. P. Singh

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Increasing incidence of antibiotic-resistant bacteria is a big concern for the treatment of pathogenic diseases. The effect of treatment of patients with antibiotics often leads to the evolution of antibiotic resistance in the pathogens. The detection of antibiotic or antimicrobial resistant bacteria (microbes) is quite essential as it is becoming one of the big threats globally. Here we propose a novel technique to tackle this problem. We are taking a step forward to prevent the infections and diseases due to drug resistant microbes. This detection is based on some unique features of silver (a noble metal) nanorods (AgNRs) which are fabricated by a physical deposition method called thermal glancing angle deposition (GLAD). Silver nanorods are found to be highly sensitive and selective for hydrogen sulfide (H2S) gas. Color and water wetting (contact angle) of AgNRs are two parameters what are effected in the presence of this gas. H₂S is one of the major gaseous products evolved in the bacterial metabolic process. It is also known as gasotransmitter that transmits some biological singles in living systems. Nitric Oxide (NO) and Carbon mono oxide (CO) are two another members of this family. Orlowski (1895) observed the emission of H₂S by the bacteria for the first time. Most of the microorganism produce these gases. Here we are focusing on H₂S gas evolution to determine live/dead and antibiotic-resistant bacteria. AgNRs array has been used for the detection of H₂S from micro-organisms. A mobile app is also developed to make it easy, portable, user-friendly, and cost-effective.

Keywords: antibiotic resistance, hydrogen sulfide, live and dead bacteria, mobile app

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Authors: Yuta Nakano, Kozo Kajiwara, Atsushi Hori, Takeshi Fujita

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In association with the wave of electric vehicles (EV), low energy consumption systems have become more and more important. One of the key technologies to realize low energy consumption is a dynamic vision sensor (DVS), or we can call it an event sensor, neuromorphic vision sensor and so on. This sensor has several features, such as high temporal resolution, which can achieve 1 Mframe/s, and a high dynamic range (120 DB). However, the point that can contribute to low energy consumption the most is its sparsity; to be more specific, this sensor only captures the pixels that have intensity change. In other words, there is no signal in the area that does not have any intensity change. That is to say, this sensor is more energy efficient than conventional sensors such as RGB cameras because we can remove redundant data. On the other side of the advantages, it is difficult to handle the data because the data format is completely different from RGB image; for example, acquired signals are asynchronous and sparse, and each signal is composed of x-y coordinate, polarity (two values: +1 or -1) and time stamp, it does not include intensity such as RGB values. Therefore, as we cannot use existing algorithms straightforwardly, we have to design a new processing algorithm to cope with DVS data. In order to solve difficulties caused by data format differences, most of the prior arts make a frame data and feed it to deep learning such as Convolutional Neural Networks (CNN) for object detection and recognition purposes. However, even though we can feed the data, it is still difficult to achieve good performance due to a lack of intensity information. Although polarity is often used as intensity instead of RGB pixel value, it is apparent that polarity information is not rich enough. Considering this context, we proposed to use the timestamp information as a data representation that is fed to deep learning. Concretely, at first, we also make frame data divided by a certain time period, then give intensity value in response to the timestamp in each frame; for example, a high value is given on a recent signal. We expected that this data representation could capture the features, especially of moving objects, because timestamp represents the movement direction and speed. By using this proposal method, we made our own dataset by DVS fixed on a parked car to develop an application for a surveillance system that can detect persons around the car. We think DVS is one of the ideal sensors for surveillance purposes because this sensor can run for a long time with low energy consumption in a NOT dynamic situation. For comparison purposes, we reproduced state of the art method as a benchmark, which makes frames the same as us and feeds polarity information to CNN. Then, we measured the object detection performances of the benchmark and ours on the same dataset. As a result, our method achieved a maximum of 7 points greater than the benchmark in the F1 score.

Keywords: event camera, dynamic vision sensor, deep learning, data representation, object recognition, low energy consumption

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Authors: Hosuk Shin, Young-Hyun Seo, Eunhak Lee, Seung-Young Kho

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Currently, in Seoul, users have the privilege to avoid riding crowded buses with the installation of Bus Information System (BIS). BIS has three levels of on-board bus ridership level information (spacious, normal, and crowded). However, there are flaws in the system due to it being real time which could provide incomplete information to the user. For example, a bus comes to the station, and on the BIS it shows that the bus is crowded, but on the stop that the user is waiting many people get off, which would mean that this station the information should show as normal or spacious. To fix this problem, this study predicts the bus ridership level using smart card data to provide more accurate information about the passenger ridership level on the bus. An Artificial Neural Network (ANN) is an interconnected group of nodes, that was created based on the human brain. Forecasting has been one of the major applications of ANN due to the data-driven self-adaptive methods of the algorithm itself. According to the results, the ANN algorithm was stable and robust with somewhat small error ratio, so the results were rational and reasonable.

Keywords: smartcard data, ANN, bus, ridership

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Authors: Ainagul Yermekova, Liudmila Goncharenko, Ali Baghirzade, Sergey Sybachin

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In this article, we address the problem of Kazakhstan's transition to the Latin alphabet. The transition process started in 2017 and is scheduled to be completed in 2025. In connection with these events, the problem of recognizing the characters of the new alphabet is raised. Well-known character recognition programs such as ABBYY FineReader, FormReader, MyScript Stylus did not recognize specific Kazakh letters that were used in Cyrillic. The author tries to give an assessment of the well-known method of character recognition that could be in demand as part of the country's transition to the Latin alphabet. Three methods of character recognition: template, structured, and feature-based, are considered through the algorithms of operation. At the end of the article, a general conclusion is made about the possibility of applying a certain method to a particular recognition process: for example, in the process of population census, recognition of typographic text in Latin, or recognition of photos of car numbers, store signs, etc.

Keywords: text detection, template method, recognition algorithm, structured method, feature method

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Authors: Mini P. Singh, Manasi Majumdar, Kapil Goyal, Pvm Lakshmi, Deepak Bhatia, Radha Kanta Ratho

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India is endemic for Hepatitis E virus and frequent water borne outbreaks are reported. The conventional diagnosis rests on the detection of serum anti-HEV IgM antibodies which may take 7-10 days to develop. Early diagnosis in such a situation is desirable for the initiation of prompt control measures. The present study compared three diagnostic methods in 60 samples collected during two suspected HEV outbreaks in the vicinity of Chandigarh, India. The anti-HEV IgM, HEV antigen and HEV-RNA could be detected in serum samples of 52 (86.66%), 16 (26.66%) and 18 (30%) patients respectively. The suitability of saliva samples for antibody detection was also evaluated in 21 paired serum- saliva samples. A total of 15 serum samples showed the presence of anti HEV IgM antibodies, out of which 10 (10/15; 66.6%) were also positive for these antibodies in saliva samples (χ2 = 7.636, p < 0.0057), thus showing a concordance of 76.91%. The positivity of reverse transcriptase PCR and HEV antigen detection was 100% within one week of illness which declined to 5-10% thereafter. The outbreak was attributed to HEV Genotype 1, Subtype 1a and the clinical and environmental strains clustered together. HEV antigen and RNA were found to be an early diagnostic marker with 96.66% concordance. The results indicate that the saliva samples can be used as an alternative to serum samples in an outbreak situation.

Keywords: HEV-antigen, outbreak, phylogenetic analysis, saliva

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Authors: Mayssa Bensalah, Atef Boujelben, Mouna Baklouti, Mohamed Abid

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Image segmentation has a fundamental role in analysis and interpretation for many applications. The automated segmentation of organs and tissues throughout the body using computed imaging has been rapidly increasing. Indeed, it represents one of the most important parts of clinical diagnostic tools. In this paper, we discuss a thorough literature review of recent methods of tumour segmentation from medical images which are briefly explained with the recent contribution of various researchers. This study was followed by comparing these methods in order to define new directions to develop and improve the performance of the segmentation of the tumour area from medical images.

Keywords: features extraction, image segmentation, medical images, tumor detection

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Authors: Samuele Viaro, Pierre Ricco

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Görtler instabilities generate in boundary layers from an unbalance between pressure and centrifugal forces caused by concave surfaces. Their spatial streamwise evolution influences transition to turbulence. It is therefore important to understand even the early stages where perturbations, still small, grow linearly and could be controlled more easily. This work presents a rigorous theoretical framework for compressible flows using the linearized unsteady boundary region equations, where only the streamwise pressure gradient and streamwise diffusion terms are neglected from the full governing equations of fluid motion. Boundary and initial conditions are imposed through an asymptotic analysis in order to account for the interaction of the boundary layer with free-stream turbulence. The resulting parabolic system is discretize with a second-order finite difference scheme. Realistic flow parameters are chosen from wind tunnel studies performed at supersonic and subsonic conditions. The Mach number ranges from 0.5 to 8, with two different radii of curvature, 5 m and 10 m, frequencies up to 2000 Hz, and vortex spanwise wavelengths from 5 mm to 20 mm. The evolution of the perturbation flow is shown through velocity, temperature, pressure profiles relatively close to the leading edge, where non-linear effects can still be neglected, and growth rate. Results show that a global stabilizing effect exists with the increase of Mach number, frequency, spanwise wavenumber and radius of curvature. In particular, at high Mach numbers curvature effects are less pronounced and thermal streaks become stronger than velocity streaks. This increase of temperature perturbations saturates at approximately Mach 4 flows, and is limited in the early stage of growth, near the leading edge. In general, Görtler vortices evolve closer to the surface with respect to a flat plate scenario but their location shifts toward the edge of the boundary layer as the Mach number increases. In fact, a jet-like behavior appears for steady vortices having small spanwise wavelengths (less than 10 mm) at Mach 8, creating a region of unperturbed flow close to the wall. A similar response is also found at the highest frequency considered for a Mach 3 flow. Larger vortices are found to have a higher growth rate but are less influenced by the Mach number. An eigenvalue approach is also employed to study the amplification of the perturbations sufficiently downstream from the leading edge. These eigenvalue results are compared with the ones obtained through the initial value approach with inhomogeneous free-stream boundary conditions. All of the parameters here studied have a significant influence on the evolution of the instabilities for the Görtler problem which is indeed highly dependent on initial conditions.

Keywords: compressible boundary layers, Görtler instabilities, receptivity, turbulence transition

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Authors: Zahra Khan

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Surface-Enhanced Raman Spectroscopy (SERS) has received increased attention in recent years, focusing on biological and medical applications due to its great sensitivity as well as molecular specificity. In the context of biological samples, there are generally two methodologies for SERS based applications: label-free detection and the use of SERS tags. The necessity of tagging can make the process slower and limits the use for real life. Label-free detection offers the advantage that it reports direct spectroscopic evidence associated with the target molecule rather than the label. Reproducible, highly monodisperse gold nanoparticles (Au NPs) were synthesized using a relatively facile seed-mediated growth method. Different capping agents (TRIS, citrate, and CTAB) were used during synthesis, and characterization was performed. They were then mixed with different analyte solutions before drop-casting onto a glass slide prior to Raman measurements to see which NPs displayed the highest SERS activity as well as their stability. A host of different analytes were tested, both non-biomolecules and biomolecules, which were all successfully detected using this method at concentrations as low as 10-3M with salicylic acid reaching a detection limit in the nanomolar range. SERS was also performed on samples with a mixture of analytes present, whereby peaks from both target molecules were distinctly observed. This is a fast and effective rapid way of testing samples and offers potential applications in the biomedical field as a tool for diagnostic and treatment purposes.

Keywords: gold nanoparticles, label free, seed-mediated growth, SERS

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Authors: Siobhan O’Shea, Sangeetha Vijaysri Nair, Hee Cheol Kim, Charles Thomas Nugent, Cheuk Yan William Tong, Sam Douthwaite, Andrew Worlock

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The Aptima® HIV-1 Quant Dx Assay is a fully automated assay on the Panther system. It is based on Transcription-Mediated Amplification and real time detection technologies. This assay is intended for monitoring HIV-1 viral load in plasma specimens and for the detection of HIV-1 in plasma and serum specimens. Nine-hundred and seventy nine specimens selected at random from routine testing at St Thomas’ Hospital, London were anonymised and used to compare the performance of the Aptima HIV-1 Quant Dx assay and Roche COBAS® AmpliPrep/COBAS® TaqMan® HIV-1 Test, v2.0. Two-hundred and thirty four specimens gave quantitative HIV-1 viral load results in both assays. The quantitative results reported by the Aptima Assay were comparable those reported by the Roche COBAS AmpliPrep/COBAS TaqMan HIV-1 Test, v2.0 with a linear regression slope of 1.04 and an intercept on -0.097. The Aptima assay detected HIV-1 in more samples than the Roche assay. This was not due to lack of specificity of the Aptima assay because this assay gave 99.83% specificity on testing plasma specimens from 600 HIV-1 negative individuals. To understand the reason for this higher detection rate a side-by-side comparison of low level panels made from the HIV-1 3rd international standard (NIBSC10/152) and clinical samples of various subtypes were tested in both assays. The Aptima assay was more sensitive than the Roche assay. The good sensitivity, specificity and agreement with other commercial assays make the HIV-1 Quant Dx Assay appropriate for both viral load monitoring and detection of HIV-1 infections.

Keywords: HIV viral load, Aptima, Roche, Panther system

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Authors: Yao-Hong Tsai

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This paper presents a weighted approach to unconstrained iris recognition. Nowadays, commercial systems are usually characterized by strong acquisition constraints based on the subject’s cooperation. However, it is not always achievable for real scenarios in our daily life. Researchers have been focused on reducing these constraints and maintaining the performance of the system by new techniques at the same time. With large variation in the environment, there are two main improvements to develop the proposed iris recognition system. For solving extremely uneven lighting condition, statistic based illumination normalization is first used on eye region to increase the accuracy of iris feature. The detection of the iris image is based on Adaboost algorithm. Secondly, the weighted approach is designed by Gaussian functions according to the distance to the center of the iris. Furthermore, local binary pattern (LBP) histogram is then applied to texture classification with the weight. Experiment showed that the proposed system provided users a more flexible and feasible way to interact with the verification system through iris recognition.

Keywords: authentication, iris recognition, adaboost, local binary pattern

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Authors: Aniela Pop, Ianina Birsan, Corina Orha, Rodica Pode, Florica Manea

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Carbon nanofiber-epoxy composite electrode has been investigated through voltammetric and amperometric techniques in order to detect parabens from aqueous solutions. The occurrence into environment as emerging pollutants of these preservative compounds has been extensively studied in the last decades, and consequently, a rapid and reliable method for their quantitative quantification is required. In this study, methylparaben (MP) and propylparaben (PP) were chosen as representatives for paraben class. The individual electrochemical detection of each paraben has been successfully performed. Their electrochemical oxidation occurred at the same potential value. Their simultaneous quantification should be assessed electrochemically only as general index of paraben class as a cumulative signal corresponding to both MP and PP from solution. The influence of pH on the electrochemical signal was studied. pH ranged between 1.3 and 9.0 allowed shifting the detection potential value to smaller value, which is very desired for the electroanalysis. Also, the signal is better-defined and higher sensitivity is achieved. Differential-pulsed voltammetry and square-wave voltammetry were exploited under the optimum pH conditions to improve the electroanalytical performance for the paraben detection. Also, the operation conditions were selected, i.e., the step potential, modulation amplitude and the frequency. Chronomaprometry application as the easiest electrochemical detection method led to worse sensitivity, probably due to a possible fouling effect of the electrode surface. The best electroanalytical performance was achieved by pulsed voltammetric technique but the selection of the electrochemical technique is related to the concrete practical application. A good reproducibility of the voltammetric-based method using carbon nanofiber-epoxy composite electrode was determined and no interference effect was found for the cation and anion species that are common in the water matrix. Besides these characteristics, the long life-time of the electrode give to carbon nanofiber-epoxy composite electrode a great potential for practical applications.

Keywords: carbon nanofiber-epoxy composite electrode, electroanalysis, methylparaben, propylparaben

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Authors: Gaspar J. Machado, Stéphane Clain, Raphael Loubère

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The goal of the present work is to numerically study steady-state nonlinear hyperbolic equations in the context of the finite volume framework. We will consider the unidimensional Burgers' equation as the reference case for the scalar situation and the unidimensional Euler equations for the vectorial situation. We consider two approaches to solve the nonlinear equations: a time marching algorithm and a direct steady-state approach. We first develop the necessary and sufficient conditions to obtain the existence and unicity of the solution. We treat regular examples and solutions with a steady shock and to provide very-high-order finite volume approximations we implement a method based on the MOOD technology (Multi-dimensional Optimal Order Detection). The main ingredient consists in using an 'a posteriori' limiting strategy to eliminate non physical oscillations deriving from the Gibbs phenomenon while keeping a high accuracy for the smooth part.

Keywords: Euler equations, finite volume, MOOD, steady-state

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Authors: L. I. Izhar, T. Stathaki, K. Howell

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Analyzing and interpreting of thermograms have been increasingly employed in the diagnosis and monitoring of diseases thanks to its non-invasive, non-harmful nature and low cost. In this paper, a novel system is proposed to improve diagnosis and monitoring of morphoea skin disorder based on integration with the published lines of Blaschko. In the proposed system, image registration based on global and local registration methods are found inevitable. This paper presents a modified normalized gradient cross-correlation (NGC) method to reduce large geometrical differences between two multimodal images that are represented by smooth gray edge maps is proposed for the global registration approach. This method is improved further by incorporating an iterative-based normalized cross-correlation coefficient (NCC) method. It is found that by replacing the final registration part of the NGC method where translational differences are solved in the spatial Fourier domain with the NCC method performed in the spatial domain, the performance and robustness of the NGC method can be greatly improved. It is shown in this paper that the hybrid NGC method not only outperforms phase correlation (PC) method but also improved misregistration due to translation, suffered by the modified NGC method alone for thermograms with ill-defined jawline. This also demonstrates that by using the gradients of the gray edge maps and a hybrid technique, the performance of the PC based image registration method can be greatly improved.

Keywords: Blaschko’s lines, image registration, morphoea, thermal imaging

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Authors: Milad Yousefi, Moslem Yousefi, Ricarpo Poley, Amer Nordin Darus

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Design optimization of heat exchangers is a very complicated task that has been traditionally carried out based on a trial-and-error procedure. To overcome the difficulties of the conventional design approaches especially when a large number of variables, constraints and objectives are involved, a new method based on a well-stablished evolutionary algorithm, particle swarm optimization (PSO), weighted sum approach and a novel constraint handling strategy is presented in this study. Since, the conventional constraint handling strategies are not effective and easy-to-implement in multi-objective algorithms, a novel feasibility-based ranking strategy is introduced which is both extremely user-friendly and effective. A case study from industry has been investigated to illustrate the performance of the presented approach. The results show that the proposed algorithm can find the near pareto-optimal with higher accuracy when it is compared to conventional non-dominated sorting genetic algorithm II (NSGA-II). Moreover, the difficulties of a trial-and-error process for setting the penalty parameters is solved in this algorithm.

Keywords: Heat exchanger, Multi-objective optimization, Particle swarm optimization, NSGA-II Constraints handling.

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Authors: Sheema Shuja Khattak, Gule Saman, Imran Khan, Abdus Salam

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Image segmentation plays an important role in medical imaging applications. Therefore, accurate methods are needed for the successful segmentation of medical images for diagnosis and detection of various diseases. In this paper, we have used maximum entropy to achieve image segmentation. Maximum entropy has been calculated using Shannon, Renyi, and Tsallis entropies. This work has novelty based on the detection of skin lesion caused by the bite of a parasite called Sand Fly causing the disease is called Cutaneous Leishmaniasis.

Keywords: shannon, maximum entropy, Renyi, Tsallis entropy

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Authors: Abdul Ghaffar Memon, Xiao Hong Zhou, Yunpeng Xing, Ruoyu Wang, Miao He

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Due to deleterious environmental and health effects of the Hg²⁺ ions, various online, detection methods apart from the traditional analytical tools have been developed by researchers. Biosensors especially, label, label-free, colorimetric and optical sensors have advanced with sensitive detection. However, there remains a gap of ultrasensitive quantification as noise interact significantly especially in the AuNP based label-free colorimetry. This study reported an amplification strategy using Exo-III enzyme for target recycling of Hg²⁺ ions in a T-rich hairpin loop metallobase label-free colorimetric nanosensor with an improved sensitivity using unmodified gold nanoparticles (uGNPs) as an indicator. The two T-rich metallobase hairpin loop structures as 5’- CTT TCA TAC ATA GAA AAT GTA TGT TTG -3 (HgS1), and 5’- GGC TTT GAG CGC TAA GAA A TA GCG CTC TTT G -3’ (HgS2) were tested in the study. The thermodynamic properties of HgS1 and HgS2 were calculated using online tools (http://biophysics.idtdna.com/cgi-bin/meltCalculator.cgi). The lab scale synthesized uGNPs were utilized in the analysis. The DNA sequence had T-rich bases on both tails end, which in the presence of Hg²⁺ forms a T-Hg²⁺-T mismatch, promoting the formation of dsDNA. Later, the Exo-III incubation enable the enzyme to cleave stepwise mononucleotides from the 3’ end until the structure become single-stranded. These ssDNA fragments then adsorb on the surface of AuNPs in their presence and protect AuNPs from the induced salt aggregation. The visible change in color from blue (aggregation stage in the absence of Hg²⁺) and pink (dispersion state in the presence of Hg²⁺ and adsorption of ssDNA fragments) can be observed and analyzed through UV spectrometry. An ultrasensitive quantitative nanosensor employing Exo-III assisted target recycling of mercury ions through label-free colorimetry with nanomolar detection using uGNPs have been achieved and is further under the optimization to achieve picomolar range by avoiding the influence of the environmental matrix. The proposed strategy will supplement in the direction of uGNP based ultrasensitive, rapid, onsite, label-free colorimetric detection.

Keywords: colorimetric, Exo-III, gold nanoparticles, Hg²⁺ detection, label-free, signal amplification

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Authors: Vaishali D. Khairnar

Abstract:

The main aim is to provide lower-latency and highly reliable communication facilities for vehicles in the automobile industry; vehicle-to-everything (V2X) communication basically intends to increase expressway road security and its effectiveness. The Ultra-Reliable Low-Latency Communications (URLLC) algorithm and cellular networks are applied in combination with Mobile Broadband (MBB). This is particularly used in express way safety-based driving applications. Expressway vehicle drivers (humans) will communicate in V2X systems using the sixth-generation (6G) communication systems which have very high-speed mobility features. As a result, we need to determine how to ensure reliable and consistent wireless communication links and improve the quality to increase channel gain, which is becoming a challenge that needs to be addressed. To overcome this challenge, we proposed a unique multi-user scheduling algorithm for ultra-massive multiple-input multiple-output (MIMO) systems using 6G. In wideband wireless network access in case of high traffic and also in medium traffic conditions, moreover offering quality-of-service (QoS) to distinct service groups with synchronized contemporaneous traffic on the highway like the Mumbai-Pune expressway becomes a critical problem. Opportunist MAC (OMAC) is a way of proposing communication across a wireless communication link that can change in space and time and might overcome the above-mentioned challenge. Therefore, a multi-user scheduling algorithm is proposed for MIMO systems using a cross-layered MAC protocol to achieve URLLC and high reliability in V2X communication.

Keywords: ultra-reliable low latency communications, vehicle-to-everything communication, multiple-input multiple-output systems, multi-user scheduling algorithm

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Authors: Hossein Edrisi

Abstract:

To study the impact of the amount and volume of power of isolation transformer on the lamps in airfield Ground Lighting Systems. A test was conducted in Persian Gulf International Airport, This airport is situated in the south of Iran and it is one of the most cutting-edge airports, the same one that owns modern devices. Iran uses materials and auxiliary equipment which are made by ADB Company from Belgium. Airfield ground lighting (AGL) systems are responsible for providing visual issue to aircrafts and helicopters in the runways. In an AGL system a great deal of lamps are connected in serial circuits to each other and each ring has its individual constant current regulators (CCR), which through that provide energy to the lamps. Control of lamps is crucial for maintenance and operation in the AGL systems. Thanks to the Programmable Logic Controller (PLC) that is a cutting-edge technology can help the system to connect the elements from substations and ATC (TOWER). For this purpose, a test in real conditions of the airport done for all element that used in the airport such as isolation transformer in different power capacity and different consuming power and brightness of the lamps. The data were analyzed with Lux meter and Multimeter. The results had shown that the increase in the power of transformer caused a significant increase in brightness. According to the Ohm’s law and voltage division, without changing the characteristics of the light bulb, it is not possible to change the voltage, just need to change the amount of transformer with which it connects to the lamps. When the voltage is increased, the current through the bulb has to increase as well, because of Ohm's law: I=V/R and I=V/R which means that if V increases, so do I increase. The output voltage on the constant current regulator emerges between the lamps and the transformers.

Keywords: AGL, CCR, lamps, transformer, Ohm’s law

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Authors: Deepak Kumar, Debasish Deb, Reena Mamgain

Abstract:

Radar signal processing requires high number crunching capability. Most often this is achieved using multiprocessor platform. Though multiprocessor platform provides the capability of meeting the real time computational challenges, the architecture of the same along with mapping of the algorithm on the architecture plays a vital role in efficiently using the platform. Towards this, along with standard performance metrics, few additional metrics are defined which helps in evaluating the multiprocessor platform along with the algorithm mapping. A generic multiprocessor architecture can not suit all the processing requirements. Depending on the system requirement and type of algorithms used, the most suitable architecture for the given problem is decided. In the paper, we study different architectures and quantify the different performance metrics which enables comparison of different architectures for their merit. We also carried out case study of different architectures and their efficiency depending on parallelism exploited on algorithm or data or both.

Keywords: radar signal processing, multiprocessor architecture, efficiency, load imbalance, buffer requirement, pipeline, parallel, hybrid, cluster of processors (COPs)

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Authors: Hatim Laalej, Salvador Sumohano-Verdeja, Thomas McLeay

Abstract:

Monitoring of tool wear in milling operations is essential for achieving the desired dimensional accuracy and surface finish of a machined workpiece. Although there are numerous statistical models and artificial intelligence techniques available for monitoring the wear of cutting tools, these techniques cannot pin point which cutting edge of the tool, or which insert in the case of indexable tooling, is worn or broken. Currently, the task of monitoring the wear on the tool cutting edges is carried out by the operator who performs a manual inspection, causing undesirable stoppages of machine tools and consequently resulting in costs incurred from lost productivity. The present study is concerned with the development of a flute tracking system to segment signals related to each physical flute of a cutter with three flutes used in an end milling operation. The purpose of the system is to monitor the cutting condition for individual flutes separately in order to determine their progressive wear rates and to predict imminent tool failure. The results of this study clearly show that signals associated with each flute can be effectively segmented using the proposed flute tracking system. Furthermore, the results illustrate that by segmenting the sensor signal by flutes it is possible to investigate the wear in each physical cutting edge of the cutting tool. These findings are significant in that they facilitate the online condition monitoring of a cutting tool for each specific flute without the need for operators/engineers to perform manual inspections of the tool.

Keywords: machining, milling operation, tool condition monitoring, tool wear prediction

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Authors: David G. Maxinez, Andrés Ferreyra Ramírez, Liliana Castillo Sánchez, Nancy Adán Mendoza, Carlos Aviles Cruz

Abstract:

This research describes the use of fuzzy logic in detection, assessment, analysis and evaluation of children with behavioral disorders. It shows how to acquire and analyze ambiguous, vague and full of uncertainty data coming from the input variables to get an accurate assessment result for each of the typologies presented by children with behavior problems. Behavior disorders analyzed in this paper are: hyperactivity (H), attention deficit with hyperactivity (DAH), conduct disorder (TD) and attention deficit (AD).

Keywords: alteration, behavior, centroid, detection, disorders, economic, fuzzy logic, hyperactivity, impulsivity, social

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Authors: Shekher Kummari, V. Sunil Kumar, K. Vengatajalabathy Gobi

Abstract:

A simple, cost-effective, reusable and reagent-free electrochemical biosensor is developed with functionalized multiwall carbon nanotube paste electrode (f-CNTPE) for the sensitive and selective determination of the important chemotherapeutic drug methotrexate (MTX), which is widely used for the treatment of various cancer and autoimmune diseases. The electrochemical response of the fabricated electrode towards the detection of MTX is examined by cyclic voltammetry (CV), differential pulse voltammetry (DPV) and square wave voltammetry (SWV). CV studies have shown that f-CNTPE electrode system exhibited an excellent electrocatalytic activity towards the oxidation of MTX in phosphate buffer (0.2 M) compared with a conventional carbon paste electrode (CPE). The oxidation peak current is enhanced by nearly two times in magnitude. Applying the DPV method under optimized conditions, a linear calibration plot is achieved over a wide range of concentration from 4.0×10⁻⁷ M to 5.5×10⁻⁶ M with the detection limit 1.6×10⁻⁷ M. further, by applying the SWV method a parabolic calibration plot was achieved starting from a very low concentration of 1.0×10⁻⁸ M, and the sensor could detect as low as 2.9×10⁻⁹ M MTX in 10 s and 10 nM were detected in steady state current-time analysis. The f-CNTPE shows very good selectivity towards the specific recognition of MTX in the presence of important biological interference. The electrochemical biosensor detects MTX in-vitro directly from pharmaceutical sample, undiluted urine and human blood serum samples at a concentration range 5.0×10⁻⁷ M with good recovery limits.

Keywords: amperometry, electrochemical detection, human blood serum, methotrexate, MWCNT, SWV

Procedia PDF Downloads 298