Search results for: rapid and specific detection

Authors: V. Mantey, N. Findlay, I. Maddox

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The quality and quantity of global satellite data have been increasing exponentially in recent years as spaceborne systems become more affordable and the sensors themselves become more sophisticated. This is a valuable resource for many applications, including disaster management and relief. However, while more information can be valuable, the volume of data available is impossible to manually examine. Therefore, the question becomes how to extract as much information as possible from the data with limited manpower. Buildings are a key feature of interest in satellite imagery with applications including telecommunications, population models, and disaster relief. Machine learning tools are fast becoming one of the key resources to solve this problem, and models have been developed to detect buildings in optical satellite imagery. However, by and large, most models focus on affluent regions where buildings are generally larger and constructed further apart. This work is focused on the more difficult problem of detection in populated regions. The primary challenge with detecting small buildings in densely populated regions is both the spatial and spectral resolution of the optical sensor. Densely packed buildings with similar construction materials will be difficult to separate due to a similarity in color and because the physical separation between structures is either non-existent or smaller than the spatial resolution. This study finds that training models until they are overfitting the input sample can perform better in these areas than a more robust, generalized model. An overfitted model takes less time to fine-tune from a generalized pre-trained model and requires fewer input data. The model developed for this study has also been fine-tuned using existing, open-source, building vector datasets. This is particularly valuable in the context of disaster relief, where information is required in a very short time span. Leveraging existing datasets means that little to no manpower or time is required to collect data in the region of interest. The training period itself is also shorter for smaller datasets. Requiring less data means that only a few quality areas are necessary, and so any weaknesses or underpopulated regions in the data can be skipped over in favor of areas with higher quality vectors. In this study, a landcover classification model was developed in conjunction with the building detection tool to provide a secondary source to quality check the detected buildings. This has greatly reduced the false positive rate. The proposed methodologies have been implemented and integrated into a configurable production environment and have been employed for a number of large-scale commercial projects, including continent-wide DEM production, where the extracted building footprints are being used to enhance digital elevation models. Overfitted machine learning models are often considered too specific to have any predictive capacity. However, this study demonstrates that, in cases where input data is scarce, overfitted models can be judiciously applied to solve time-sensitive problems.

Keywords: building detection, disaster relief, mask-RCNN, satellite mapping

Procedia PDF Downloads 166

Authors: Jaeyoung Kim, Heeju Lee, Huijin Jung, Heesoo Pyo, Seungki Kim, Joonseok Lee

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Avian influenza viruses (AIV) are the primary cause of highly contagious respiratory diseases caused by type A influenza viruses of the Orthomyxoviridae family. AIV are categorized on the basis of types of surface glycoproteins such as hemagglutinin and neuraminidase. Certain H5 and H7 subtypes of AIV have evolved to the high pathogenic avian influenza (HPAI) virus, which has caused considerable economic loss to the poultry industry and led to severe public health crisis. Several commercial kits have been developed for on-site detection of AIV. However, the sensitivity of these methods is too low to detect low virus concentrations in clinical samples and opaque stool samples. Here, we introduced a background-free near-infrared (NIR)-to-NIR upconversion nanoparticle-based lateral flow immunoassay (NNLFA) platform to yield a sensor that detects AIV within 20 minutes. Ca²⁺ ion in the shell was used to enhance the NIR-to-NIR upconversion photoluminescence (PL) emission as a heterogeneous dopant without inducing significant changes in the morphology and size of the UCNPs. In a mixture of opaque stool samples and gold nanoparticles (GNPs), which are components of commercial AIV LFA, the background signal of the stool samples mask the absorption peak of GNPs. However, UCNPs dispersed in the stool samples still show strong emission centered at 800 nm when excited at 980 nm, which enables the NNLFA platform to detect 10-times lower viral load than a commercial GNP-based AIV LFA. The detection limit of NNLFA for low pathogenic avian influenza (LPAI) H5N2 and HPAI H5N6 viruses was 10² EID₅₀/mL and 10³.⁵ EID₅₀/mL, respectively. Moreover, when opaque brown-colored samples were used as the target analytes, strong NIR emission signal from the test line in NNLFA confirmed the presence of AIV, whereas commercial AIV LFA detected AIV with difficulty. Therefore, we propose that this rapid and background-free NNLFA platform has the potential of detecting AIV in the field, which could effectively prevent the spread of these viruses at an early stage.

Keywords: avian influenza viruses, lateral flow immunoassay on-site detection, upconversion nanoparticles

Procedia PDF Downloads 158

Authors: Hanaa M. Abdelgawad, Mona Safar, Ayman M. Wahba

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Real-time image and video processing is a demand in many computer vision applications, e.g. video surveillance, traffic management and medical imaging. The processing of those video applications requires high computational power. Therefore, the optimal solution is the collaboration of CPU and hardware accelerators. In this paper, a Canny edge detection hardware accelerator is proposed. Canny edge detection is one of the common blocks in the pre-processing phase of image and video processing pipeline. Our presented approach targets offloading the Canny edge detection algorithm from processing system (PS) to programmable logic (PL) taking the advantage of High Level Synthesis (HLS) tool flow to accelerate the implementation on Zynq platform. The resulting implementation enables up to a 100x performance improvement through hardware acceleration. The CPU utilization drops down and the frame rate jumps to 60 fps of 1080p full HD input video stream.

Keywords: high level synthesis, canny edge detection, hardware accelerators, computer vision

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Authors: Xiaoxiao Li, Shuangcheng Jia, Qian Li

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Keypoint detection has always been a challenge in the field of image recognition. This paper proposes a novelty keypoint detection method which is called Multi-Scale Feature Fusion Convolutional Network with Attention (MFFCNA). We verified that the multi-scale features with the attention mechanism module have better feature expression capability. The feature fusion between different scales makes the information that the network model can express more abundant, and the network is easier to converge. On our self-made street sign corner dataset, we validate the MFFCNA model with an accuracy of 97.8% and a recall of 81%, which are 5 and 8 percentage points higher than the HRNet network, respectively. On the COCO dataset, the AP is 71.9%, and the AR is 75.3%, which are 3 points and 2 points higher than HRNet, respectively. Extensive experiments show that our method has a remarkable improvement in the keypoint recognition tasks, and the recognition effect is better than the existing methods. Moreover, our method can be applied not only to keypoint detection but also to image classification and semantic segmentation with good generality.

Keywords: keypoint detection, feature fusion, attention, semantic segmentation

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Authors: Ahmadu Maidorawa, Idrissa Djibo, Muhammad Tella

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This paper presents a method for detecting flow chart and figure plagiarism based on shape of image processing and multimedia retrieval. The method managed to retrieve flowcharts with ranked similarity according to different matching sets. Plagiarism detection is well known phenomenon in the academic arena. Copying other people is considered as serious offense that needs to be checked. There are many plagiarism detection systems such as turn-it-in that has been developed to provide these checks. Most, if not all, discard the figures and charts before checking for plagiarism. Discarding the figures and charts result in look holes that people can take advantage. That means people can plagiarize figures and charts easily without the current plagiarism systems detecting it. There are very few papers which talks about flowcharts plagiarism detection. Therefore, there is a need to develop a system that will detect plagiarism in figures and charts.

Keywords: flowchart, multimedia retrieval, figures similarity, image comparison, figure retrieval

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Authors: Ahmad H. Abdelgwad, Ahmed A. Nashat

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Landmine detection is an important and yet challenging problem remains to be solved. Ground Penetrating Radar (GPR) is a powerful and rapidly maturing technology for subsurface threat identification. The detection methodology of GPR depends mainly on the contrast of the dielectric properties of the searched target and its surrounding soil. This contrast produces a partial reflection of the electromagnetic pulses that are being transmitted into the soil and then being collected by the GPR.  One of the most critical hardware components for the performance of GPR is the antenna system. The current paper explores the design and simulation of a pyramidal horn antenna operating at L-band frequencies (1- 2 GHz) to detect a landmine. A prototype model of the GPR system setup is developed to simulate full wave analysis of the electromagnetic fields in different soil types. The contrast in the dielectric permittivity of the landmine and the sandy soil is the most important parameter to be considered for detecting the presence of landmine. L-band horn antenna is proved to be well-versed in the investigation of landmine detection.

Keywords: full wave analysis, ground penetrating radar, horn antenna design, landmine detection

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Authors: Renata Gerhardt, Detlev Belder

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Raman spectroscopy has attracted much attention as a structurally descriptive and label-free detection method. It is particularly suited for chemical analysis given as it is non-destructive and molecules can be identified via the fingerprint region of the spectra. In this work possibilities are investigated how to integrate Raman spectroscopy as a detection method for chip-based chromatography, making use of a droplet interface. A demanding task in lab-on-a-chip applications is the specific and sensitive detection of low concentrated analytes in small volumes. Fluorescence detection is frequently utilized but restricted to fluorescent molecules. Furthermore, no structural information is provided. Another often applied technique is mass spectrometry which enables the identification of molecules based on their mass to charge ratio. Additionally, the obtained fragmentation pattern gives insight into the chemical structure. However, it is only applicable as an end-of-the-line detection because analytes are destroyed during measurements. In contrast to mass spectrometry, Raman spectroscopy can be applied on-chip and substances can be processed further downstream after detection. A major drawback of Raman spectroscopy is the inherent weakness of the Raman signal, which is due to the small cross-sections associated with the scattering process. Enhancement techniques, such as surface enhanced Raman spectroscopy (SERS), are employed to overcome the poor sensitivity even allowing detection on a single molecule level. In SERS measurements, Raman signal intensity is improved by several orders of magnitude if the analyte is in close proximity to nanostructured metal surfaces or nanoparticles. The main gain of lab-on-a-chip technology is the building block-like ability to seamlessly integrate different functionalities, such as synthesis, separation, derivatization and detection on a single device. We intend to utilize this powerful toolbox to realize Raman detection in chip-based chromatography. By interfacing on-chip separations with a droplet generator, the separated analytes are encapsulated into numerous discrete containers. These droplets can then be injected with a silver nanoparticle solution and investigated via Raman spectroscopy. Droplet microfluidics is a sub-discipline of microfluidics which instead of a continuous flow operates with the segmented flow. Segmented flow is created by merging two immiscible phases (usually an aqueous phase and oil) thus forming small discrete volumes of one phase in the carrier phase. The study surveys different chip designs to realize coupling of chip-based chromatography with droplet microfluidics. With regards to maintaining a sufficient flow rate for chromatographic separation and ensuring stable eluent flow over the column different flow rates of eluent and oil phase are tested. Furthermore, the detection of analytes in droplets with surface enhanced Raman spectroscopy is examined. The compartmentalization of separated compounds preserves the analytical resolution since the continuous phase restricts dispersion between the droplets. The droplets are ideal vessels for the insertion of silver colloids thus making use of the surface enhancement effect and improving the sensitivity of the detection. The long-term goal of this work is the first realization of coupling chip based chromatography with droplets microfluidics to employ surface enhanced Raman spectroscopy as means of detection.

Keywords: chip-based separation, chip LC, droplets, Raman spectroscopy, SERS

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Authors: Muneer Abbad

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This study comes with a comprehensive analysis of specific factors affecting the adoption of e-commerce in Jordan. From the theoretical perspective, this study will make a contribution to the e-commerce by providing insights on the factors that seem to affect e-commerce’s adoption. The current study will provide managers information about the planning and formulating appropriate strategies to ensure rapid adoption of e-commerce in Jordan. It will offer marketing implications, conclusions, and suggestions for future research.

Keywords: e-commerce, Jordan, adoption, conceptual model

Procedia PDF Downloads 452

Authors: Kasra Majbouri Yazdi, Adel Majbouri Yazdi, Saeid Khodayi, Jingyu Hou, Wanlei Zhou, Saeed Saedy

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Fake news and false information are big challenges of all types of media, especially social media. There is a lot of false information, fake likes, views and duplicated accounts as big social networks such as Facebook and Twitter admitted. Most information appearing on social media is doubtful and in some cases misleading. They need to be detected as soon as possible to avoid a negative impact on society. The dimensions of the fake news datasets are growing rapidly, so to obtain a better result of detecting false information with less computation time and complexity, the dimensions need to be reduced. One of the best techniques of reducing data size is using feature selection method. The aim of this technique is to choose a feature subset from the original set to improve the classification performance. In this paper, a feature selection method is proposed with the integration of K-means clustering and Support Vector Machine (SVM) approaches which work in four steps. First, the similarities between all features are calculated. Then, features are divided into several clusters. Next, the final feature set is selected from all clusters, and finally, fake news is classified based on the final feature subset using the SVM method. The proposed method was evaluated by comparing its performance with other state-of-the-art methods on several specific benchmark datasets and the outcome showed a better classification of false information for our work. The detection performance was improved in two aspects. On the one hand, the detection runtime process decreased, and on the other hand, the classification accuracy increased because of the elimination of redundant features and the reduction of datasets dimensions.

Keywords: clustering, fake news detection, feature selection, machine learning, social media, support vector machine

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Authors: Yushen Dai, Tao Deng, Miaoying Chen, Baoqin Huang, Yan Ji, Yongshen Feng, Shaofei Liu, Dongmei Zhong, Tao Zhang, Lifeng Zhang

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Background: Detection and diagnosis are prerequisites for early interventions in the care of children with Autism Spectrum Disorder (ASD). However, few studies have focused on this topic. Aim: This study aims to characterize the timing from symptom detection to intervention in children with ASD and to identify the potential predictors of early detection, diagnosis, and intervention. Methods and procedures: A cross-sectional survey was conducted with 314 parents of children with ASD in Guangzhou, China. Outcomes and Results: This study found that most children (76.24%) were diagnosed within one year after detection, and 25.8% of them did not receive the intervention after diagnosis. Predictors to ASD diagnosis included ASD-related symptoms identified at a younger age, more serious symptoms, and initial symptoms with abnormal development and sensory anomalies. ASD-related symptoms observed at an older age, initial symptoms with the social deficit, sensory anomalies, and without language impairment, parents as the primary caregivers, family with lower income and less social support utilization increased the odds of the time lag between detection and diagnosis. Children whose fathers had a lower level of education were less likely to receive the intervention. Conclusions and Implications: The study described the time for detection, diagnosis, and interventions of children with ASD. Findings suggest that the ASD-related symptoms, the timing at which symptoms first become a concern, primary caregivers’ roles, father’s educational level, and the family economic status should be considered when offering support to improve early detection, diagnosis, and intervention. Helping children and their families take full advantage of support is also important.

Keywords: autism spectrum disorder, child, detection, diagnosis, intervention, social support

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Authors: Blessing Ojeme, Frederick Quinn, Russell Karls, Shannon Quinn

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The detection and segmentation of mitochondria from fluorescence microscopy are crucial for understanding the complex structure of the nervous system. However, the constant fission and fusion of mitochondria and image distortion in the background make the task of detection and segmentation challenging. In the literature, a number of open-source software tools and artificial intelligence (AI) methods have been described for analyzing mitochondrial images, achieving remarkable classification and quantitation results. However, the availability of combined expertise in the medical field and AI required to utilize these tools poses a challenge to its full adoption and use in clinical settings. Motivated by the advantages of automated methods in terms of good performance, minimum detection time, ease of implementation, and cross-platform compatibility, this study proposes a fully automated framework for the detection and segmentation of mitochondria using both image shape information and descriptive statistics. Using the low-cost, open-source python and openCV library, the algorithms are implemented in three stages: pre-processing, image binarization, and coarse-to-fine segmentation. The proposed model is validated using the mitochondrial fluorescence dataset. Ground truth labels generated using a Lab kit were also used to evaluate the performance of our detection and segmentation model. The study produces good detection and segmentation results and reports the challenges encountered during the image analysis of mitochondrial morphology from the fluorescence mitochondrial dataset. A discussion on the methods and future perspectives of fully automated frameworks conclude the paper.

Keywords: 2D, binarization, CLAHE, detection, fluorescence microscopy, mitochondria, segmentation

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Authors: J. Suwanprateeb, F. Thammarakcharoen

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Calcium phosphate coating (CaP) has been employed for protein delivery, but the typical direct protein adsorption on the coating led to low incorporation content and fast release of the protein from the coating. By using bovine serum albumin (BSA) as a model protein, rapid biomimetic co-precipitation between calcium phosphate and BSA was employed to control the distribution of BSA within calcium phosphate coating during biomimetic formation on titanium surface for only 6 h at 50 oC in an accelerated calcium phosphate solution. As a result, the amount of BSA incorporation and release duration could be increased by using a rapid biomimetic co-precipitation technique. Up to 43 fold increases in the BSA incorporation content and the increase from 6 h to more than 360 h in release duration compared to typical direct adsorption technique were observed depending on the initial BSA concentration used during co-precipitation (1, 10, and 100 microgram/ml). From X-ray diffraction and Fourier transform infrared spectroscopy studies, the coating composition was not altered with the incorporation of BSA by this rapid biomimetic co-precipitation and mainly comprised octacalcium phosphate and hydroxyapatite. However, the microstructure of calcium phosphate crystals changed from straight, plate-like units to curved, plate-like units with increasing BSA content.

Keywords: biomimetic, Calcium Phosphate Coating, protein, titanium

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Authors: Yousif Mohamed Y. Abdallah, Mommen A. Alkhir, Amel S. Algaddal

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This was experimental study conducted to study segmentation of brain in MRI images using edge detection and morphology filters. For brain MRI images each film scanned using digitizer scanner then treated by using image processing program (MatLab), where the segmentation was studied. The scanned image was saved in a TIFF file format to preserve the quality of the image. Brain tissue can be easily detected in MRI image if the object has sufficient contrast from the background. We use edge detection and basic morphology tools to detect a brain. The segmentation of MRI images steps using detection and morphology filters were image reading, detection entire brain, dilation of the image, filling interior gaps inside the image, removal connected objects on borders and smoothen the object (brain). The results of this study were that it showed an alternate method for displaying the segmented object would be to place an outline around the segmented brain. Those filters approaches can help in removal of unwanted background information and increase diagnostic information of Brain MRI.

Keywords: improvement, brain, matlab, markers, boundaries

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Authors: Sloka Bhushan

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Dark matter constitutes a majority of matter in the universe, yet very little is known about it due to its extreme lack of interaction with regular matter and the fundamental forces. Weakly Interacting Massive Particles, or WIMPs, have been contested to be one of the strongest candidates for dark matter due to their promising theoretical properties. However, various endeavors to detect these elusive particles have failed. This paper explores the various particles which may be WIMPs and the detection techniques being employed to detect WIMPs (such as underground detectors, LHC experiments, and so on). There is a special focus on the reasons for the lack of detection of WIMPs so far, and the possibility of limits in detection being a reason for the lack of physical evidence of the existence of WIMPs. This paper also explores possible inconsistencies within the WIMP particle theory as a reason for the lack of physical detection. There is a brief review on the possible solutions and alternatives to these inconsistencies. Additionally, this paper also reviews the supersymmetry theory and the possibility of the supersymmetric neutralino (A possible WIMP particle) being detectable. Lastly, a review on alternate candidates for dark matter such as axions and MACHOs has been conducted. The explorative study in this paper is conducted through a series of literature reviews.

Keywords: dark matter, particle detection, supersymmetry, weakly interacting massive particles

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Authors: Pradthana Sianglam, Wittaya Ngeontae

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A new approach for the fabrication of cadmium ion (Cd2+) sensor is demonstrated. The detection principle is based on the in-situ generation of cadmium sulfide quantum dots (CdS QDs) in the presence of chiral thiol containing compound and detection by the circular dichroism spectroscopy (CD). Basically, the generation of CdS QDs can be done in the presence of Cd2+, sulfide ion and suitable capping compounds. In addition, the strong CD signal can be recorded if the generated QDs possess chiral property (from chiral capping molecule). Thus, the degree of CD signal change depends on the number of the generated CdS QDs which can be related to the concentration of Cd2+ (excess of other components). In this work, we use the mixture of cysteamine (Cys) and L-Penicillamine (LPA) as the capping molecules. The strong CD signal can be observed when the solution contains sodium sulfide, Cys, LPA, and Cd2+. Moreover, the CD signal is linearly related to the concentration of Cd2+. This approach shows excellence selectivity towards the detection of Cd2+ when comparing to other cation. The proposed CD sensor provides low limit detection limits around 70 µM and can be used with real water samples with satisfactory results.

Keywords: circular dichroism sensor, quantum dots, enaniomer, in-situ generation, chemical sensor, heavy metal ion

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Authors: Rana Muhammad Armaghan Ayaz, Yigit Uysallı, Nima Bavili, Berna Morova, Alper Kiraz

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Traditional optical methods for example resonance wavelength shift and cavity ring-down spectroscopy used for material detection and sensing have disadvantages, for example, less resistance to laser noise, temperature fluctuations and extraction of the required information can be a difficult task like ring downtime in case of cavity ring-down spectroscopy. Phase shift cavity ring down spectroscopy is not only easy to use but is also capable of overcoming the said problems. This technique compares the phase difference between the signal coming out of the cavity with the reference signal. Detection of any material is made by the phase difference between them. By using this technique, air, water, and isopropyl alcohol can be recognized easily. This Methodology has far-reaching applications and can be used in air pollution detection, human breath analysis and many more.

Keywords: materials, noise, phase shift, resonance wavelength, sensitivity, time domain approach

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Authors: Lukumba Phiri, Simon Tembo, Kumbuso Joshua Nyoni

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In Zambia recent initiatives by various power operators like ZESCO, CEC, and consumers like the mines to upgrade power systems into smart grids target an even tighter integration with information technologies to enable the integration of renewable energy sources, local and bulk generation, and demand response. Thus, for the reliable operation of smart grids, its information infrastructure must be secure and reliable in the face of both failures and cyberattacks. Due to the nature of the systems, ICS/SCADA cybersecurity and governance face additional challenges compared to the corporate networks, and critical systems may be left exposed. There exist control frameworks internationally such as the NIST framework, however, there are generic and do not meet the domain-specific needs of the SCADA systems. Zambia is also lagging in cybersecurity awareness and adoption, therefore there is a concern about securing ICS controlling key infrastructure critical to the Zambian economy as there are few known facts about the true posture. In this paper, we introduce a stochastic Edged-based Anomaly Detection for SCADA systems (SEADS) framework for threat modeling and risk assessment. SEADS enables the calculation of steady-steady probabilities that are further applied to establish metrics like system availability, maintainability, and reliability.

Keywords: anomaly, availability, detection, edge, maintainability, reliability, stochastic

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Authors: Adewusi Adeoluwawale, Oronti Iyabosola Busola, Akinola Iretiayo, Komolafe Olusola Aderibigbe

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The complex and radial structure of the low voltage distribution network (415V) makes it vulnerable to faults which are due to system and the environmental related factors. Besides these, the protective scheme employed on the low voltage network which is the fuse cannot be monitored remotely such that in the event of sustained fault, the utility will have to rely solely on the complaint brought by customers for loss of supply and this tends to increase the length of outages. A microcontroller based fault detection scheme is hereby developed to detect low voltage and high voltage fault conditions which are common faults on this network. Voltages below 198V and above 242V on the distribution feeders are classified and detected as low voltage and high voltages respectively. Results shows that the developed scheme produced a good response time in the detection of these faults.

Keywords: fault detection, low voltage distribution feeders, outage times, sustained faults

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Authors: Nicole Virgili, Romolo Remetti

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The aim of this work is to characterize, from radiation protection point of view, the emission into the environment of air contaminated by argon-41. In this research work, 41Ar is produced by a TR19PET cyclotron, operated at 19 MeV, installed at 'A. Gemelli' University Hospital, Rome, Italy, for fluorine-18 production. The production rate of 41Ar has been calculated on the basis of the scheduled operation cycles of the cyclotron and by utilising proper production algorithms. Then extensive Monte Carlo calculations, carried out by MCNP code, have allowed to determine the absolute detection efficiency to 41Ar gamma rays of a Geiger Muller detector placed in the terminal part of the chimney. Results showed unsatisfactory detection efficiency values and the need for integrating the detection system with more efficient detectors.

Keywords: Cyclotron, Geiger Muller detector, MCNPX, argon-41, emission of radioactive gas, detection efficiency determination

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Authors: Nurhak Altın, Ayhan Kucukmanisa, Oguzhan Urhan

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It is important that highways are in good condition for traffic safety. Road crashes (road cracks, erosion of lane markings, etc.) can cause accidents by affecting driving. Image processing based methods for detecting road cracks are available in the literature. In this paper, a deep learning based road crack detection approach is proposed. YOLO (You Look Only Once) is adopted as core component of the road crack detection approach presented. The YOLO network structure, which is developed for object detection, is trained with road crack images as a new class that is not previously used in YOLO. The performance of the proposed method is compared using different training methods: using randomly generated weights and training their own pre-trained weights (transfer learning). A similar training approach is applied to the simplified version of the YOLO network model (tiny yolo) and the results of the performance are examined. The developed system is able to process 8 fps on NVIDIA Jetson TX1 development kit.

Keywords: deep learning, embedded platform, real-time processing, road crack detection

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Authors: Aria Vitkova, Hanna Sykulska-Lawrence

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In recent years, Europa has been one of the major focus points in astrobiology due to its high potential of harbouring life in the vast ocean underneath its icy crust. However, the detection of life on Europa faces many challenges due to the harsh environmental conditions and mission constraints. Raman spectroscopy is a highly capable and versatile in-situ characterisation technique that does not require any sample preparation. It has only been used on Earth to date; however, recent advances in optical and laser technology have also allowed it to be considered for extraterrestrial exploration. So far, most efforts have been focused on the exploration of Mars, the most imminent planetary target. However, as an emerging technology with high miniaturization potential, Raman spectroscopy also represents a promising tool for the exploration of Europa. In this study, the capabilities of Raman technology in terms of life detection on Europa are explored and assessed. Spectra of biosignatures identified as high priority molecular targets for life detection on Europa were acquired at various excitation wavelengths and conditions analogous to Europa. The effects of extremely low temperatures and low concentrations in water ice were explored and evaluated in terms of the effectiveness of various configurations of Raman instruments. Based on the findings, a design of a miniaturized Raman instrument optimized for in-situ detection of life on Europa is proposed.

Keywords: astrobiology, biosignatures, Europa, life detection, Raman Spectroscopy

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Authors: Mohammad Besharatloo

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Intrusion detection is an important research topic in network security because of increasing growth in the use of computer network services. Intrusion detection is done with the aim of detecting the unauthorized use or abuse in the networks and systems by the intruders. Therefore, the intrusion detection system is an efficient tool to control the user's access through some predefined regulations. Since, the data used in intrusion detection system has high dimension, a proper representation is required to show the basis structure of this data. Therefore, it is necessary to eliminate the redundant features to create the best representation subset. In the proposed method, a hybrid model of differential evolution and firefly algorithms was employed to choose the best subset of properties. In addition, decision tree and support vector machine (SVM) are adopted to determine the quality of the selected properties. In the first, the sorted population is divided into two sub-populations. These optimization algorithms were implemented on these sub-populations, respectively. Then, these sub-populations are merged to create next repetition population. The performance evaluation of the proposed method is done based on KDD Cup99. The simulation results show that the proposed method has better performance than the other methods in this context.

Keywords: intrusion detection system, differential evolution, firefly algorithm, support vector machine, decision tree

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Authors: Mahmoud E. Mohamed, Ahmed F. Shalash, Hanan A. Kamal

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In communication systems, frequency jump is a serious problem caused by the oscillators used. Kalman filters are used to detect that jump, Despite the tradeoff between the noise level and the speed of the detection. In this paper, An improvement is introduced in the Kalman filter, Through a nonlinear change in the bandwidth of the filter. Simulation results show a considerable improvement in the filter speed with a very low noise level. Additionally, The effect on the response to false alarms is also presented and false alarm rate show improvement.

Keywords: Kalman filter, innovation, false detection, improvement

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Authors: Sukhleen Kaur

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In security groundwork, Intrusion Detection System (IDS) has become an important component. The IDS has received increasing attention in recent years. IDS is one of the effective way to detect different kinds of attacks and malicious codes in a network and help us to secure the network. Data mining techniques can be implemented to IDS, which analyses the large amount of data and gives better results. Data mining can contribute to improving intrusion detection by adding a level of focus to anomaly detection. So far the study has been carried out on finding the attacks but this paper detects the malicious files. Some intruders do not attack directly, but they hide some harmful code inside the files or may corrupt those file and attack the system. These files are detected according to some defined parameters which will form two lists of files as normal files and harmful files. After that data mining will be performed. In this paper a hybrid classifier has been used via Naive Bayes and Ripper classification methods. The results show how the uploaded file in the database will be tested against the parameters and then it is characterised as either normal or harmful file and after that the mining is performed. Moreover, when a user tries to mine on harmful file it will generate an exception that mining cannot be made on corrupted or harmful files.

Keywords: data mining, association, classification, clustering, decision tree, intrusion detection system, misuse detection, anomaly detection, naive Bayes, ripper

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Authors: Omar Elharrouss, Youssef Hmamouche, Assia Kamal Idrissi, Btissam El Khamlichi, Amal El Fallah-Seghrouchni

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Edge detection is represented as one of the most challenging tasks in computer vision, due to the complexity of detecting the edges or boundaries in real-world images that contains objects of different types and scales like trees, building as well as various backgrounds. Edge detection is represented also as a key task for many computer vision applications. Using a set of backbones as well as attention modules, deep-learning-based methods improved the detection of edges compared with the traditional methods like Sobel and Canny. However, images of complex scenes still represent a challenge for these methods. Also, the detected edges using the existing approaches suffer from non-refined results while the image output contains many erroneous edges. To overcome this, n this paper, by using the mechanism of residual learning, a refined edge detection network is proposed (RED-Net). By maintaining the high resolution of edges during the training process, and conserving the resolution of the edge image during the network stage, we make the pooling outputs at each stage connected with the output of the previous layer. Also, after each layer, we use an affined batch normalization layer as an erosion operation for the homogeneous region in the image. The proposed methods are evaluated using the most challenging datasets including BSDS500, NYUD, and Multicue. The obtained results outperform the designed edge detection networks in terms of performance metrics and quality of output images.

Keywords: edge detection, convolutional neural networks, deep learning, scale-representation, backbone

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Authors: Stelios A. Mitilineos, Nicolas-Alexander Tatlas, Georgia Korompili, Lampros Kokkalas, Stelios M. Potirakis

Abstract:

Obstructive Sleep Apnea Hypopnea Syndrome (OSAHS) is a widespread chronic disease that mostly remains undetected, mainly due to the fact that it is diagnosed via polysomnography which is a time and resource-intensive procedure. Screening the disease’s symptoms at home could be used as an alternative approach in order to alert individuals that potentially suffer from OSAHS without compromising their everyday routine. Since snoring is usually linked to OSAHS, developing a snore detector is appealing as an enabling technology for screening OSAHS at home using ubiquitous equipment like commodity microphones (included in, e.g., smartphones). In this context, this study developed a snore detection tool and herein present the approach and selection of specific sound features that discriminate snoring vs. environmental sounds, as well as the performance of the proposed tool. Furthermore, a Real-Time Snore Detector (RTSD) is built upon the snore detection tool and employed in whole-night sleep sound recordings resulting to a large dataset of snoring sound excerpts that are made freely available to the public. The RTSD may be used either as a stand-alone tool that offers insight to an individual’s sleep quality or as an independent component of OSAHS screening applications in future developments.

Keywords: obstructive sleep apnea hypopnea syndrome, apnea screening, snoring detection, machine learning, neural networks

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Authors: Abdelghani Chahmi

Abstract:

This article examines the detection and localization of faults in electrical systems, particularly those using asynchronous machines. First, the process of failure will be characterized, relevant symptoms will be defined and based on those processes and symptoms, a model of those malfunctions will be obtained. Second, the development of the diagnosis of the machine will be shown. As studies of malfunctions in electrical systems could only rely on a small amount of experimental data, it has been essential to provide ourselves with simulation tools which allowed us to characterize the faulty behavior. Fault detection uses signal processing techniques in known operating phases.

Keywords: induction motor, modeling, bearing damage, airgap eccentricity, torque variation

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Authors: Hussin K. Ragb, Vijayan K. Asari

Abstract:

In this paper, we present a pedestrian detection descriptor called Fused Structure and Texture (FST) features based on the combination of the local phase information with the texture features. Since the phase of the signal conveys more structural information than the magnitude, the phase congruency concept is used to capture the structural features. On the other hand, the Center-Symmetric Local Binary Pattern (CSLBP) approach is used to capture the texture information of the image. The dimension less quantity of the phase congruency and the robustness of the CSLBP operator on the flat images, as well as the blur and illumination changes, lead the proposed descriptor to be more robust and less sensitive to the light variations. The proposed descriptor can be formed by extracting the phase congruency and the CSLBP values of each pixel of the image with respect to its neighborhood. The histogram of the oriented phase and the histogram of the CSLBP values for the local regions in the image are computed and concatenated to construct the FST descriptor. Several experiments were conducted on INRIA and the low resolution DaimlerChrysler datasets to evaluate the detection performance of the pedestrian detection system that is based on the FST descriptor. A linear Support Vector Machine (SVM) is used to train the pedestrian classifier. These experiments showed that the proposed FST descriptor has better detection performance over a set of state of the art feature extraction methodologies.

Keywords: pedestrian detection, phase congruency, local phase, LBP features, CSLBP features, FST descriptor

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Authors: Mauricio George Miguel Jardini, Jose Antonio Jardini

Abstract:

Transmission power utilities participate with kilometers of circuits, many with particularities in terms of vegetation growth. To control these rights-of-way, maintenance teams perform ground, and air inspections, and the identification method is subjective (indirect). On a ground inspection, when identifying an irregularity, for example, high vegetation threatening contact with the conductor cable, pruning or suppression is performed immediately. In an aerial inspection, the suppression team is mobilized to the identified point. This work investigates the use of 3D modeling of a transmission line segment using RGB (red, blue, and green) images and LiDAR (Light Detection and Ranging) sensor data. Both sensors are coupled to unmanned aerial vehicle. The goal is the accurate and timely detection of vegetation along the right-of-way that can cause shutdowns.

Keywords: 3D modeling, LiDAR, right-of-way, transmission lines, vegetation

Procedia PDF Downloads 125

Authors: Aulanni’am Aulanni’am

Abstract:

Incidence of Diabetes Mellitus (DM) progressively increasing it became a serious problem in Indonesia and it is a disease that government is priority to be addressed. The longer a person is suffering from diabetes the more likely to develop complications particularly diabetic patients who are not well maintained. Therefore, Incidence of Diabetes Mellitus needs to be done in the early diagnosis of pre-phase of the disease. In this pre-phase disease, already happening destruction of pancreatic beta cells and declining in beta cell function and the sign autoimmunity reactions associated with beta cell destruction. Type 1 DM is a multifactorial disease triggered by genetic and environmental factors, which leads to the destruction of pancreatic beta cells. Early marker of "beta cell autoreactivity" is the synthesis of autoantibodies against 65-kDa protein, which can be a molecule that can be detected early in the disease pathomechanism. The importance of early diagnosis of diabetic patients held in the phase of pre-disease is to determine the progression towards the onset of pancreatic beta cell destruction and take precautions. However, the price for this examination is very expensive ($ 150/ test), the anti-GAD65 abs examination cannot be carried out routinely in most or even in all laboratories in Indonesia. Therefore, production-based Rapid Test Recombinant Human Protein GAD65 with "Reverse Flow Immunchromatography Technique" in Indonesia is believed to reduce costs and improve the quality of care of patients with diabetes in Indonesia. Rapid Test Product innovation is very simple and suitable for screening and routine inspection of GAD65 autoantibodies. In the blood serum of patients with diabetes caused by autoimmunity, autoantibody-GAD65 is a major serologic marker to detect autoimmune reaction because their concentration level of stability.GAD65 autoantibodies can be found 10 years before clinical symptoms of diabetes. Early diagnosis is more focused to detect the presence autontibodi-GAD65 given specification and high sensitivity. Autoantibodies- GAD65 that circulates in the blood is a major indicator of the destruction of the islet cells of the pancreas. Results of research in collaboration with Biofarma has produced GAD65 autoantibodies based Rapid Test had conducted the soft launch of products and has been tested with the results of a sensitivity of 100 percent and a specificity between 90 and 96% compared with the gold standard (import product) which worked based on ELISA method.

Keywords: diabetes mellitus, GAD65 autoantibodies, rapid test, sensitivity, specificity

Procedia PDF Downloads 263