Search results for: cluster detection

Authors: Koushlendra Kumar Singh, Manish Kumar Bajpai, Rajesh K. Pandey

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The edges of low contrast images are not clearly distinguishable to the human eye. It is difficult to find the edges and boundaries in it. The present work encompasses a new approach for low contrast images. The Chebyshev polynomial based fractional order filter has been used for filtering operation on an image. The preprocessing has been performed by this filter on the input image. Laplacian of Gaussian method has been applied on preprocessed image for edge detection. The algorithm has been tested on two test images.

Keywords: low contrast image, fractional order differentiator, Laplacian of Gaussian (LoG) method, chebyshev polynomial

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Authors: Jacintha Menezes, Ramalingam Dharmalingam, Palaiahnakote Shivakumara

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In the recent years, Omani acid lime cultivation and production has been affected by Citrus greening or Huanglongbing (HLB) disease. HLB disease is one of the most destructive diseases for citrus, with no remedies or countermeasures to stop the disease. Currently used Polymerase chain reaction (PCR) and enzyme-linked immunosorbent assay (ELISA) HLB detection tests require lengthy and labor-intensive laboratory procedures. Furthermore, the equipment and staff needed to carry out the laboratory procedures are frequently specialized hence making them a less optimal solution for the detection of the disease. The current research uses hyperspectral imaging technology for automatic detection of citrus trees with HLB disease. Omani citrus tree leaf images were captured through portable Specim IQ hyperspectral camera. The research considered healthy, nutrition deficient, and HLB infected leaf samples based on the Polymerase chain reaction (PCR) test. The highresolution image samples were sliced to into sub cubes. The sub cubes were further processed to obtain RGB images with spatial features. Similarly, RGB spectral slices were obtained through a moving window on the wavelength. The resized spectral-Spatial RGB images were given to Convolution Neural Networks for deep features extraction. The current research was able to classify a given sample to the appropriate class with 92.86% accuracy indicating the effectiveness of the proposed techniques. The significant bands with a difference in three types of leaves are found to be 560nm, 678nm, 726 nm and 750nm.

Keywords: huanglongbing (HLB), hyperspectral imaging (HSI), · omani citrus, CNN

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Authors: Arvind Srivastav, Tarun Kanti Bhattacharyya

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The north-eastern, Himalayan, and Eastern Ghats Belt of India comprise of earthquake-prone, remote, and hilly terrains. Earthquakes have caused enormous damages in these regions in the past. A wireless sensor network based earthquake early warning system (EEWS) is being developed to mitigate the damages caused by earthquakes. It consists of sensor nodes, distributed over the region, that perform majority voting of the output of the seismic sensors in the vicinity, and relay a message to a base station to alert the residents when an earthquake is detected. At the heart of the EEWS is a low-power two-stage seismic sensor that continuously tracks seismic events from incoming three-axis accelerometer signal at the first-stage, and, in the presence of a seismic event, triggers the second-stage P-wave detector that detects the onset of P-wave in an earthquake event. The parameters of the P-wave detector have been optimized for minimizing detection time and maximizing the accuracy of detection.Working of the sensor scheme has been verified with seven earthquakes data retrieved from IRIS. In all test cases, the scheme detected the onset of P-wave accurately. Also, it has been established that the P-wave onset detection time reduces linearly with the sampling rate. It has been verified with test data; the detection time for data sampled at 10Hz was around 2 seconds which reduced to 0.3 second for the data sampled at 100Hz.

Keywords: earthquake early warning system, EEWS, STA/LTA, polarization, wavelet, event detector, P-wave detector

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Authors: K. Shiba, T. Kaburagi, Y. Kurihara

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With global aging, people who require care, such as people with dementia (PwD), are increasing within many developed countries. And PwDs may wander and unconsciously set foot outdoors, it may lead serious accidents, such as, traffic accidents. Here, round-the-clock monitoring by caregivers is necessary, which can be a burden for the caregivers. Therefore, an automatic wandering detection system is required when an elderly person wanders outdoors, in which case the detection system transmits a ‘moving’ followed by an ‘absence’ state. In this paper, we focus on the transition from the ‘resting’ to the ‘absence’ state, via the ‘moving’ state as one of the wandering transitions. To capture the transition of the three states, our method based on the hidden Markov model (HMM) is built. Using our method, the restraint where the ‘resting’ state and ‘absence’ state cannot be transmitted to each other is applied. To validate our method, we conducted the experiment with 10 subjects. Our results show that the method can classify three states with 0.92 accuracy.

Keywords: wander, microwave Doppler sensor, respiratory frequency band, the state transition, hidden Markov model (HMM).

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Authors: A. A. M. A. S. S. Perera, L. A. Ranasinghe, T. K. H. Nimeshika, D. M. Dhanushka Dissanayake, Namalie Walgampaya

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Nowadays, skin fungal diseases are mostly found in people of tropical countries like Sri Lanka. A skin fungal disease is a particular kind of illness caused by fungus. These diseases have various dangerous effects on the skin and keep on spreading over time. It becomes important to identify these diseases at their initial stage to control it from spreading. This paper presents an automated skin fungal disease identification system implemented to speed up the diagnosis process by identifying skin fungal infections in digital images. An image of the diseased skin lesion is acquired and a comprehensive computer vision and image processing scheme is used to process the image for the disease identification. This includes colour analysis using RGB and HSV colour models, texture classification using Grey Level Run Length Matrix, Grey Level Co-Occurrence Matrix and Local Binary Pattern, Object detection, Shape Identification and many more. This paper presents the approach and its outcome for identification of four most common skin fungal infections, namely, Tinea Corporis, Sporotrichosis, Malassezia and Onychomycosis. The main intention of this research is to provide an automated skin fungal disease identification system that increase the diagnostic quality, shorten the time-to-diagnosis and improve the efficiency of detection and successful treatment for skin fungal diseases.

Keywords: Circularity Index, Grey Level Run Length Matrix, Grey Level Co-Occurrence Matrix, Local Binary Pattern, Object detection, Ring Detection, Shape Identification

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Authors: Mahmoud Mabrok, Channarong Rodkhum

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Flavobacterium columanre is one of the devastating pathogen that causes noticeable economic losses in freshwater cultured fish. Like other filamentous bacteria, F. columanre tends to aggregate and fluctuate to all kind of media, thus revealing obstacles in recognition of its colonies. Since the molecular typing is the only fundamental tool for rapid and precise detection of this pathgen. The present study developed a species-specific PCR assay based on cslA unique gene of F. columnare. The cslA gene sequences of 13 F. columnare, strains retrieved from gene bank database, were aligned to identify a conserved homologous segment prior to primers design. The new primers yielded amplicons of 287 bp from F. columnare strains but not from relevant or other pathogens, unlike to other published set that showed no specificity and cross-reactivity with F. indicum. The primers were sensitive and detected as few as 7 CFUs of bacteria and 3 pg of gDNA template. The sensitivity was reduced ten times when using tissue samples. These primers precisely defined all field isolates in a double-blind study, proposing their applicable use for field detection.

Keywords: Columnaris infection, cslA gene, Flavobacterium columnare, PCR

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Authors: Francielle A. Vargas, Thiago A. S. Pardo

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According to the standard philosophical definition, lying is saying something that you believe to be false with the intent to deceive. For deception detection, the FBI trains its agents in a technique named statement analysis, which attempts to detect deception based on parts of speech (i.e., linguistics style). This method is employed in interrogations, where the suspects are first asked to make a written statement. In this poster, we model false statements using linguistics style. In order to achieve this, we methodically analyze linguistic features in a corpus of fake news in the Portuguese language. The results show that they present substantial lexical, syntactic and semantic variations, as well as punctuation and emotion distinctions.

Keywords: deception detection, linguistics style, computational linguistics, natural language processing

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Authors: V. Nagammai

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Very-large-scale integration (VLSI) is the process of creating an integrated circuit (IC) by combining thousands of transistors into a single chip. Development of technology increases the complexity in IC manufacturing which may vary the power consumption, increase the size and latency period. Topology defines a number of connections between network. In this project, NoC topology is generated using atlas tool which will increase performance in turn determination of constraints are effective. The routing is performed by XY routing algorithm and wormhole flow control. In NoC topology generation, the value of power, area and latency are predetermined. In previous work, placement, routing and shortest path evaluation is performed using an algorithm called floor planning with cluster reconstruction and path allocation algorithm (FCRPA) with the account of 4 3x3 switch, 6 4x4 switch, and 2 5x5 switches. The usage of the 4x4 and 5x5 switch will increase the power consumption and area of the block. In order to avoid the problem, this paper has used one 8x8 switch and 4 3x3 switches. This paper uses IPRCA which of 3 steps they are placement, clustering, and shortest path evaluation. The placement is performed using min – cut placement and clustering are performed using an algorithm called cluster generation. The shortest path is evaluated using an algorithm called Dijkstra's algorithm. The power consumption of each block is determined. The experimental result shows that the area, power, and wire length improved simultaneously.

Keywords: application specific noc, b* tree representation, floor planning, t tree representation

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Authors: Babak Bashari Rad

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A metamorphic computer virus uses different code transformation techniques to mutate its body in duplicated instances. Characteristics and function of new instances are mostly similar to their parents, but they cannot be easily detected by the majority of antivirus in market, as they depend on string signature-based detection techniques. The purpose of this research is to propose a Hidden Markov Model for classification of metamorphic viruses in executable files. In the proposed solution, portable executable files are inspected to extract the instructions opcodes needed for the examination of code. A Hidden Markov Model trained on portable executable files is employed to classify the metamorphic viruses of the same family. The proposed model is able to generate and recognize common statistical features of mutated code. The model has been evaluated by examining the model on a test data set. The performance of the model has been practically tested and evaluated based on False Positive Rate, Detection Rate and Overall Accuracy. The result showed an acceptable performance with high average of 99.7% Detection Rate.

Keywords: malware classification, computer virus classification, metamorphic virus, metamorphic malware, Hidden Markov Model

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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

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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: 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: Edemealem Desalegn Kingawa, Kafte Tasew Timkete, Mekashaw Girmaw Abebe, Terefe Feyisa, Abiyot Bitew Mihretie, Senait Teklemarkos Haile

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Currently, hate speech is a growing challenge for society, individuals, policymakers, and researchers, as social media platforms make it easy to anonymously create and grow online friends and followers and provide an online forum for debate about specific issues of community life, culture, politics, and others. Despite this, research on identifying and detecting hate speech is not satisfactory performance, and this is why future research on this issue is constantly called for. This paper provides a systematic review of the literature in this field, with a focus on approaches like word embedding techniques, machine learning, deep learning technologies, hate speech terminology, and other state-of-the-art technologies with challenges. In this paper, we have made a systematic review of the last six years of literature from Research Gate and Google Scholar. Furthermore, limitations, along with algorithm selection and use challenges, data collection, and cleaning challenges, and future research directions, are discussed in detail.

Keywords: Amharic hate speech, deep learning approach, hate speech detection review, Afaan Oromo hate speech detection

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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: Saman Ghaffarian, Ilgin Gökaşar

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This paper presents a new automatic vehicle detection method from very high resolution aerial images to measure traffic density. The proposed method starts by extracting road regions from image using road vector data. Then, the road image is divided into equal sections considering resolution of the images. Gradient vectors of the road image are computed from edge map of the corresponding image. Gradient vectors on the each boundary of the sections are divided where the gradient vectors significantly change their directions. Finally, number of vehicles in each section is carried out by calculating the standard deviation of the gradient vectors in each group and accepting the group as vehicle that has standard deviation above predefined threshold value. The proposed method was tested in four very high resolution aerial images acquired from Istanbul, Turkey which illustrate roads and vehicles with diverse characteristics. The results show the reliability of the proposed method in detecting vehicles by producing 86% overall F1 accuracy value.

Keywords: aerial images, intelligent transportation systems, traffic density measurement, vehicle detection

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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: 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: Igor Belykh

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Anti-scatter grids used in radiographic imaging for the contrast enhancement leave specific artifacts. Those artifacts may be visible or may cause Moiré effect when a digital image is resized on a diagnostic monitor. In this paper, we propose an automated grid artifacts detection and suppression algorithm which is still an actual problem. Grid artifacts detection is based on statistical approach in spatial domain. Grid artifacts suppression is based on Kaiser bandstop filter transfer function design and application avoiding ringing artifacts. Experimental results are discussed and concluded with description of advantages over existing approaches.

Keywords: grid, computed radiography, pattern recognition, image processing, filtering

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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: Marit D. Murry, Amy K. Marks

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The transition to college is a critical period that affords abundant opportunities for growth in conjunction with novel challenges for emerging adults. During this time, emerging adults are garnering experiences and acquiring hosts of new information that they are required to synthesize and use to inform life-shaping decisions. This stage is characterized by instability and exploration, which necessitates a diverse set of coping skills to successfully navigate and positively adapt to their evolving environment. However, important sociocultural factors result in differences that occur developmentally for minority emerging adults (i.e., emerging adults with an identity that has been or is marginalized). While the transition to college holds vast potential, not all are afforded the same chances, and many individuals enter into this stage at varying degrees of readiness. Understanding the nuance and diversity of student preparedness for college and contextualizing these factors will better equip systems to support incoming students. Emerging adulthood for ethnic, racial minority students presents itself as an opportunity for growth and resiliency in the face of systemic adversity. Ethnic, racial identity (ERI) is defined as an identity that develops as a function of one’s ethnic-racial group membership. Research continues to demonstrate ERI as a resilience factor that promotes positive adjustment in young adulthood. Adaptive coping responses (e.g., engaging in help-seeking behavior, drawing on personal and community resources) have been identified as possible mechanisms through which ERI buffers youth against stressful life events, including discrimination. Additionally, trait mindfulness has been identified as a significant predictor of general psychological health, and mindfulness practice has been shown to be a self-regulatory strategy that promotes healthy stress responses and adaptive coping strategy selection. The current study employed a person-centered approach to explore emerging patterns across ethnic identity development and psychological well-being criterion variables among college freshmen. Data from 283 incoming college freshmen at Northeastern University were analyzed. The Brief COPE Acceptance and Emotional Support scales, the Five Factor Mindfulness Questionnaire, and MIEM Exploration and Affirmation measures were used to inform the cluster profiles. The TwoStep auto-clustering algorithm revealed an optimal three-cluster solution (BIC = 848.49), which classified 92.6% (n = 262) of participants in the sample into one of the three clusters. The clusters were characterized as ‘Mixed Adjustment’, ‘Lowest Adjustment’, and ‘Moderate Adjustment.’ Cluster composition varied significantly by ethnicity X² (2, N = 262) = 7.74 (p = .021) and gender X² (2, N = 259) = 10.40 (p = .034). The ‘Lowest Adjustment’ cluster contained the highest proportion of students of color, 41% (n = 32), and male-identifying students, 44.2% (n = 34). Follow-up analyses showed higher ERI exploration in ‘Moderate Adjustment’ cluster members, also reported higher levels of psychological distress, with significantly elevated depression scores (p = .011), psychological diagnoses of depression (p = .013), anxiety (p = .005) and psychiatric disorders (p = .025). Supporting prior research, students engaging with identity exploration processes often endure more psychological distress. These results indicate that students undergoing identity development may require more socialization and different services beyond normal strategies.

Keywords: adjustment, coping, college, emerging adulthood, ethnic-racial identity, psychological well-being, resilience

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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: 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: Gurkirat Kaur, Manoj Kumar, Manju Bala

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Ethernet is the most influential and widely used technology in the world. With the growing demand of low latency and high throughput technologies like InfiniBand and RoCE, unique features viz. RDMA (Remote Direct Memory Access) have evolved. RDMA is an effective technology which is used for reducing system load and improving performance. InfiniBand is a well known technology which provides high-bandwidth and low-latency and makes optimal use of in-built features like RDMA. With the rapid evolution of InfiniBand technology and Ethernet lacking the RDMA and zero copy protocol, the Ethernet community has came out with a new enhancements that bridges the gap between InfiniBand and Ethernet. By adding the RDMA and zero copy protocol to the Ethernet a new networking technology is evolved, called RDMA over Converged Ethernet (RoCE). RoCE is a standard released by the IBTA standardization body to define RDMA protocol over Ethernet. With the emergence of lossless Ethernet, RoCE uses InfiniBand’s efficient transport to provide the platform for deploying RDMA technology in mainstream data centres over 10GigE, 40GigE and beyond. RoCE provide all of the InfiniBand benefits transport benefits and well established RDMA ecosystem combined with converged Ethernet. In this paper, we evaluate the heterogeneous Linux cluster, having multi nodes with fast interconnects i.e. gigabit Ethernet and Soft RoCE. This paper presents the heterogeneous Linux cluster configuration and evaluates its performance using Intel’s MPI Benchmarks. Our result shows that Soft RoCE is performing better than Ethernet in various performance metrics like bandwidth, latency and throughput.

Keywords: ethernet, InfiniBand, RoCE, RDMA, MPI, Soft RoCE

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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

Abstract:

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: M. Dasgupta, S. Banerjee

Abstract:

Content Based Image Retrieval (CBIR) coupled with Case Based Reasoning (CBR) is a paradigm that is becoming increasingly popular in the diagnosis and therapy planning of medical ailments utilizing the digital content of medical images. This paper presents a survey of some of the promising approaches used in the detection of abnormalities in retina images as well in mammographic screening and detection of regions of interest in MRI scans of the brain. We also describe our proposed algorithm to detect hard exudates in fundus images of the retina of Diabetic Retinopathy patients.

Keywords: case based reasoning, exudates, retina image, similarity based retrieval

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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: Forrest Kaatz, Adhemar Bultheel

Abstract:

We use an informational statistical mechanics approach to study the catalytic thermodynamics of platinum and palladium cuboctahedral nanoclusters. Nanoclusters and their adatoms are viewed as chemical graphs with a nearest neighbor adjacency matrix. We use the Morse potential to determine bond energies between cluster atoms in a coordination type calculation. We use adsorbate energies calculated from density functional theory (DFT) to study the adatom effects on the thermodynamic quantities, which are derived from a Hamiltonian. Oxygen radical and molecular adsorbates are studied on platinum clusters and hydrogen on palladium clusters. We calculate the entropy, free energy, and total energy as the coverage of adsorbates increases from bridge and hollow sites on the surface. Thermodynamic behavior versus adatom coverage is related to the structural distribution of adatoms on the nanocluster surfaces. The thermodynamic functions are characterized using a simple adsorption model, with linear trends as the coverage of adatoms increases. The data exhibits size effects for the measured thermodynamic properties with cluster diameters between 2 and 5 nm. Entropy and enthalpy calculations of Pt-O2 compare well with previous theoretical data for Pt(111)-O2, and our Pd-H results show similar trends as experimental measurements for Pd-H2 nanoclusters. Our methods are general and may be applied to wide variety of nanocluster adsorbate systems.

Keywords: catalytic thermodynamics, palladium nanocluster absorbates, platinum nanocluster absorbates, statistical mechanics

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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 296