Search results for: Edge detection

Authors: Amjad Alsulami, Farah Albishri, Kholod Alzubidi, Lama Almehemadi, Salma Elhag

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Food allergy is a common and rising problem among children. Many students have their first allergic reaction at school, one of these is anaphylaxis, which can be fatal. This study discovered that several schools' processes lacked safety regulations and information on how to handle allergy issues and chronic diseases like diabetes where students were not supervised or monitored during the cafeteria purchasing process. Academic institutions have no obvious prevention or effort when purchasing food containing allergens or negatively impacting the health status of students who suffer from chronic diseases. The stability of students' health must be maintained because it greatly affects their performance and educational achievement. To address this issue, this paper uses a business reengineering process to propose the automation of the whole food-purchasing process, which will aid in detecting and avoiding allergic occurrences and preventing any side effects from eating foods that are conflicting with students' health. This may be achieved by designing a smart card with an embedded QR code that reveals which foods cause an allergic reaction in a student. A survey was distributed to determine and examine how the cafeteria will handle allergic children and whether any management or policy is applied in the school. Also, the survey findings indicate that the integration of QR technology into the food purchasing process would improve health condition detection. The family supported that the suggested solution would be advantageous because it ensured their children avoided eating not allowed food. Moreover, by analyzing and simulating the as-is process and the suggested process, the results demonstrate that there is an improvement in quality and time.

Keywords: QR code, smart card, food allergies, Business Process reengineering, health condition detection.

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Authors: Adrien Derock, Pascal Veron

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Taking into account the link between the efficiency of a detector and the complexity of a stealth mechanism, we propose in this paper a new formalism for stealth using graph theory.

Keywords: Detection, eradication, graph, rootkit, stealth.

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Authors: Chia-Hung Lin, Mei-Sung Kang, Cong-Hui Huang, Chao-Lin Kuo

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This paper proposes fractal patterns for power quality (PQ) detection using color relational analysis (CRA) based classifier. Iterated function system (IFS) uses the non-linear interpolation in the map and uses similarity maps to construct various fractal patterns of power quality disturbances, including harmonics, voltage sag, voltage swell, voltage sag involving harmonics, voltage swell involving harmonics, and voltage interruption. The non-linear interpolation functions (NIFs) with fractal dimension (FD) make fractal patterns more distinguishing between normal and abnormal voltage signals. The classifier based on CRA discriminates the disturbance events in a power system. Compared with the wavelet neural networks, the test results will show accurate discrimination, good robustness, and faster processing time for detecting disturbing events.

Keywords: Power Quality (PQ), Color Relational Analysis(CRA), Iterated Function System (IFS), Non-linear InterpolationFunction (NIF), Fractal Dimension (FD).

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Authors: Emad S. Abouel Nasr, Abdulaziz M. El-Tamimi, Mustufa H. Abidi, Abdulrahman M. Al-Ahmari

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Virtual Assembly (VA) is one of the key technologies in advanced manufacturing field. It is a promising application of virtual reality in design and manufacturing field. It has drawn much interest from industries and research institutes in the last two decades. This paper describes a process for integrating an interactive Virtual Reality-based assembly simulation of a digital mockup with the CAD/CAM infrastructure. The necessary hardware and software preconditions for the process are explained so that it can easily be adopted by non VR experts. The article outlines how assembly simulation can improve the CAD/CAM procedures and structures; how CAD model preparations have to be carried out and which virtual environment requirements have to be fulfilled. The issue of data transfer is also explained in the paper. The other challenges and requirements like anti-aliasing and collision detection have also been explained. Finally, a VA simulation has been carried out for a ball valve assembly and a car door assembly with the help of Vizard virtual reality toolkit in a semi-immersive environment and their performance analysis has been done on different workstations to evaluate the importance of graphical processing unit (GPU) in the field of VA.

Keywords: Collision Detection, Graphical Processing Unit (GPU), Virtual Reality (VR), Virtual Assembly (VA).

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Authors: Norazah Yusof, Andi Besse Firdausiah Mansur

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Social learning network analysis has drawn attention for most researcher on e-learning research domain. This is due to the fact that it has the capability to identify the behavior of student during their social interaction inside e-learning. Normally, the social network analysis (SNA) is treating the students' interaction merely as node and edge with less meaning. This paper focuses on providing an ontology structure of e-learning Moodle that can enrich the relationships among students, as well as between the students and the teacher. This ontology structure brings great benefit to the future development of e-learning system.

Keywords: Ontology, e-learning, © Learning Network, Moodle.

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Authors: Rania Minkara, Jean-Pierre Dubois

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The major problem that wireless communication systems undergo is multipath fading caused by scattering of the transmitted signal. However, we can treat multipath propagation as multiple channels between the transmitter and receiver to improve the signal-to-scattering-noise ratio. While using Single Input Multiple Output (SIMO) systems, the diversity receivers extract multiple signal branches or copies of the same signal received from different channels and apply gain combining schemes such as Root Mean Square Gain Combining (RMSGC). RMSGC asymptotically yields an identical performance to that of the theoretically optimal Maximum Ratio Combining (MRC) for values of mean Signal-to- Noise-Ratio (SNR) above a certain threshold value without the need for SNR estimation. This paper introduces an improvement of RMSGC using two different issues. We found that post-detection and de-noising the received signals improve the performance of RMSGC and lower the threshold SNR.

Keywords: Bit error rate, de-noising, pre-detection, root-meansquare gain combining, single-input multiple-output channels.

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Authors: N. Nacereddine, M. Zelmat, S. S. Belaïfa, M. Tridi

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Industrial radiography is a famous technique for the identification and evaluation of discontinuities, or defects, such as cracks, porosity and foreign inclusions found in welded joints. Although this technique has been well developed, improving both the inspection process and operating time, it does suffer from several drawbacks. The poor quality of radiographic images is due to the physical nature of radiography as well as small size of the defects and their poor orientation relatively to the size and thickness of the evaluated parts. Digital image processing techniques allow the interpretation of the image to be automated, avoiding the presence of human operators making the inspection system more reliable, reproducible and faster. This paper describes our attempt to develop and implement digital image processing algorithms for the purpose of automatic defect detection in radiographic images. Because of the complex nature of the considered images, and in order that the detected defect region represents the most accurately possible the real defect, the choice of global and local preprocessing and segmentation methods must be appropriated.

Keywords: Digital image processing, global and localapproaches, radiographic film, weld defect.

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Authors: Abraham V. M., I. Sahul Hamid

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A decomposition of a graph G is a collection ψ of graphs H1,H2, . . . , Hr of G such that every edge of G belongs to exactly one Hi. If each Hi is either an induced path in G, then ψ is called an induced acyclic path decomposition of G and if each Hi is a (induced) cycle in G then ψ is called a (induced) cycle decomposition of G. The minimum cardinality of an induced acyclic path decomposition of G is called the induced acyclic path decomposition number of G and is denoted by ¤Çia(G). Similarly the cyclic decomposition number ¤Çc(G) is defined. In this paper we begin an investigation of these parameters.

Keywords: Cycle decomposition, Induced acyclic path decomposition, Induced acyclic path decomposition number.

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Authors: He Ronghui, Ma Guoqing, Wang Chunlei, Fang Lan

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This paper focuses on wormhole attacks detection in wireless sensor networks. The wormhole attack is particularly challenging to deal with since the adversary does not need to compromise any nodes and can use laptops or other wireless devices to send the packets on a low latency channel. This paper introduces an easy and effective method to detect and locate the wormholes: Since beacon nodes are assumed to know their coordinates, the straight line distance between each pair of them can be calculated and then compared with the corresponding hop distance, which in this paper equals hop counts × node-s transmission range R. Dramatic difference may emerge because of an existing wormhole. Our detection mechanism is based on this. The approximate location of the wormhole can also be derived in further steps based on this information. To the best of our knowledge, our method is much easier than other wormhole detecting schemes which also use beacon nodes, and to those have special requirements on each nodes (e.g., GPS receivers or tightly synchronized clocks or directional antennas), ours is more economical. Simulation results show that the algorithm is successful in detecting and locating wormholes when the density of beacon nodes reaches 0.008 per m2.

Keywords: Beacon node, wireless sensor network, worm hole attack.

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Authors: Nisar Ahmed Memon, Anwar Majid Mirza, S.A.M. Gilani

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Segmentation is an important step in medical image analysis and classification for radiological evaluation or computer aided diagnosis. The CAD (Computer Aided Diagnosis ) of lung CT generally first segment the area of interest (lung) and then analyze the separately obtained area for nodule detection in order to diagnosis the disease. For normal lung, segmentation can be performed by making use of excellent contrast between air and surrounding tissues. However this approach fails when lung is affected by high density pathology. Dense pathologies are present in approximately a fifth of clinical scans, and for computer analysis such as detection and quantification of abnormal areas it is vital that the entire and perfectly lung part of the image is provided and no part, as present in the original image be eradicated. In this paper we have proposed a lung segmentation technique which accurately segment the lung parenchyma from lung CT Scan images. The algorithm was tested against the 25 datasets of different patients received from Ackron Univeristy, USA and AGA Khan Medical University, Karachi, Pakistan.

Keywords: Computer Aided Diagnosis, Medical ImageProcessing, Region Growing, Segmentation, Thresholding,

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Authors: Anurag Sharma, Christian W. Omlin

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Self-organizing map (SOM) is a well known data reduction technique used in data mining. It can reveal structure in data sets through data visualization that is otherwise hard to detect from raw data alone. However, interpretation through visual inspection is prone to errors and can be very tedious. There are several techniques for the automatic detection of clusters of code vectors found by SOM, but they generally do not take into account the distribution of code vectors; this may lead to unsatisfactory clustering and poor definition of cluster boundaries, particularly where the density of data points is low. In this paper, we propose the use of an adaptive heuristic particle swarm optimization (PSO) algorithm for finding cluster boundaries directly from the code vectors obtained from SOM. The application of our method to several standard data sets demonstrates its feasibility. PSO algorithm utilizes a so-called U-matrix of SOM to determine cluster boundaries; the results of this novel automatic method compare very favorably to boundary detection through traditional algorithms namely k-means and hierarchical based approach which are normally used to interpret the output of SOM.

Keywords: cluster boundaries, clustering, code vectors, data mining, particle swarm optimization, self-organizing maps, U-matrix.

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Authors: Hyun-Koo Kim, Sagong Kuk, MinKwan Kim, Ho-Youl Jung

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This paper presents an effective method for detecting vehicles in front of the camera-assisted car during nighttime driving. The proposed method detects vehicles based on detecting vehicle headlights and taillights using techniques of image segmentation and clustering. First, to effectively extract spotlight of interest, a segmentation process based on automatic multi-level threshold method is applied on the road-scene images. Second, to spatial clustering vehicle of detecting lamps, a grouping process based on light tracking and locating vehicle lighting patterns. For simulation, we are implemented through Da-vinci 7437 DSP board with near infrared mono-camera and tested it in the urban and rural roads. Through the test, classification performances are above 97% of true positive rate evaluated on real-time environment. Our method also has good performance in the case of clear, fog and rain weather.

Keywords: Assistance Driving System, Multi-level Threshold Method, Near Infrared Mono Camera, Nighttime Vehicle Detection.

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Authors: Aqsa Jamil, Hiroshi Tamura, Hiroshi Katsuchi, Jiaqi Wang

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Yield point represents the upper limit of forces which can be applied on a specimen without causing any permanent deformation. After yielding, the behavior of specimen suddenly changes including the possibility of cracking or buckling. So, the accumulation of damage or type of fracture changes depending on this condition. As it is difficult to accurately detect yield points of the several stress concentration points in structural steel specimens, an effort has been made in this research work to develop a convenient technique using thermography (temperature-based detection) during tensile tests for the precise detection of yield point initiation. To verify the applicability of thermography camera, tests were conducted under different loading conditions and measuring the deformation by installing various strain gauges and monitoring the surface temperature with the help of thermography camera. The yield point of specimens was estimated by the help of temperature dip which occurs due to the thermoelastic effect during the plastic deformation. The scattering of the data has been checked by performing repeatability analysis. The effect of temperature imperfection and light source has been checked by carrying out the tests at daytime as well as midnight and by calculating the signal to noise ratio (SNR) of the noised data from the infrared thermography camera, it can be concluded that the camera is independent of testing time and the presence of a visible light source. Furthermore, a fully coupled thermal-stress analysis has been performed by using Abaqus/Standard exact implementation technique to validate the temperature profiles obtained from the thermography camera and to check the feasibility of numerical simulation for the prediction of results extracted with the help of thermographic technique.

Keywords: Signal to noise ratio, thermoelastic effect, thermography, yield point.

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Authors: T. Slimani, B. Ben Yaghlane, K. Mellouli

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In the field of concepts, the measure of Wu and Palmer [1] has the advantage of being simple to implement and have good performances compared to the other similarity measures [2]. Nevertheless, the Wu and Palmer measure present the following disadvantage: in some situations, the similarity of two elements of an IS-A ontology contained in the neighborhood exceeds the similarity value of two elements contained in the same hierarchy. This situation is inadequate within the information retrieval framework. To overcome this problem, we propose a new similarity measure based on the Wu and Palmer measure. Our objective is to obtain realistic results for concepts not located in the same way. The obtained results show that compared to the Wu and Palmer approach, our measure presents a profit in terms of relevance and execution time.

Keywords: Hierarchy, IS-A ontology, Semantic Web, Similarity Measure.

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Authors: Asim N., M. Mazhar Manzoor, Shariq A.

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Knowledge is renowned as a significant component for sustaining competitive advantage and gives leading edge in business. This study emphasizes towards proper and effectuate utilization of internal and external (both either explicit or tacit) knowledge comes from stakeholders, highly supportive to combat with the challenges and enhance organizational productivity. Furthermore, it proposed a model under context of IRSA framework which facilitates the organization including flow of knowledge and experience sharing among employees. In discussion section an innovative model which indulges all functionality as mentioned in analysis section.

Keywords: Effective Decision-Making, Internal & ExternalKnowledge, Knowledge Management, Tacit & Explicit Knowledge.

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Authors: Masoud Sadeghian, Alireza Fatehi

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In this paper, we use nonlinear system identification method to predict and detect process fault of a cement rotary kiln. After selecting proper inputs and output, an input-output model is identified for the plant. To identify the various operation points in the kiln, Locally Linear Neuro-Fuzzy (LLNF) model is used. This model is trained by LOLIMOT algorithm which is an incremental treestructure algorithm. Then, by using this method, we obtained 3 distinct models for the normal and faulty situations in the kiln. One of the models is for normal condition of the kiln with 15 minutes prediction horizon. The other two models are for the two faulty situations in the kiln with 7 minutes prediction horizon are presented. At the end, we detect these faults in validation data. The data collected from White Saveh Cement Company is used for in this study.

Keywords: Cement Rotary Kiln, Fault Detection, Delay Estimation Method, Locally Linear Neuro Fuzzy Model, LOLIMOT.

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Authors: Yilun Shang

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In this paper, we investigate the appearance of the giant component in random subgraphs G(p) of a given large finite graph family Gn = (Vn, En) in which each edge is present independently with probability p. We show that if the graph Gn satisfies a weak isoperimetric inequality and has bounded degree, then the probability p under which G(p) has a giant component of linear order with some constant probability is bounded away from zero and one. In addition, we prove the probability of abnormally large order of the giant component decays exponentially. When a contact graph is modeled as Gn, our result is of special interest in the study of the spread of infectious diseases or the identification of community in various social networks.

Keywords: subgraph, expander, random graph, giant component, percolation.

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Authors: Robert Cep, Adam Janasek, Lenka Cepova, Josef Prochazka

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The paper is dealing by testing of ceramic cutting tools with an interrupted machining. Tests will be provided on fixture – interrupted cut simulator. This simulator has 4 mouldings on circumference and cutting edge is put a shocks during 1 revolution. Criteria of tool wear are destruction of cutting tool or 6000 shocks. Like testing cutting tool material will be products of Sandvik Coromant 6190, 620, 650 and 670. Machined materials was be steels 15 128 (13MoCrV6). Cutting speed (408 m.min-1 and 580 m.min-1) and cutting feed (0,15 mm; 0,2 mm; 0,25 mm and 0,3 mm) were variable parameters and cutting depth was constant parameter.

Keywords: Ceramic Cutting Tools, Interrupted Cut, Machining, Cutting Tests.

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Authors: Ali K. M. Al-Nasrawi, Uday A. Al-Hamdany, Sarah M. Hamylton, Brian G. Jones, Yasir M. Alyazichi

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Surface elevation dynamics have always responded to disturbance regimes. Creating Digital Elevation Models (DEMs) to detect surface dynamics has led to the development of several methods, devices and data clouds. DEMs can provide accurate and quick results with cost efficiency, in comparison to the inherited geomatics survey techniques. Nowadays, remote sensing datasets have become a primary source to create DEMs, including LiDAR point clouds with GIS analytic tools. However, these data need to be tested for error detection and correction. This paper evaluates various DEMs from different data sources over time for Apple Orchard Island, a coastal site in southeastern Australia, in order to detect surface dynamics. Subsequently, 30 chosen locations were examined in the field to test the error of the DEMs surface detection using high resolution global positioning systems (GPSs). Results show significant surface elevation changes on Apple Orchard Island. Accretion occurred on most of the island while surface elevation loss due to erosion is limited to the northern and southern parts. Concurrently, the projected differential correction and validation method aimed to identify errors in the dataset. The resultant DEMs demonstrated a small error ratio (≤ 3%) from the gathered datasets when compared with the fieldwork survey using RTK-GPS. As modern modelling approaches need to become more effective and accurate, applying several tools to create different DEMs on a multi-temporal scale would allow easy predictions in time-cost-frames with more comprehensive coverage and greater accuracy. With a DEM technique for the eco-geomorphic context, such insights about the ecosystem dynamic detection, at such a coastal intertidal system, would be valuable to assess the accuracy of the predicted eco-geomorphic risk for the conservation management sustainability. Demonstrating this framework to evaluate the historical and current anthropogenic and environmental stressors on coastal surface elevation dynamism could be profitably applied worldwide.

Keywords: DEMs, eco-geomorphic-dynamic processes, geospatial information science. Remote sensing, surface elevation changes.

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Authors: Hazem M. El-Bakry, Ahmed A. Mohammed

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In this paper, an intelligent algorithm for optimal document archiving is presented. It is kown that electronic archives are very important for information system management. Minimizing the size of the stored data in electronic archive is a main issue to reduce the physical storage area. Here, the effect of different types of Arabic fonts on electronic archives size is discussed. Simulation results show that PDF is the best file format for storage of the Arabic documents in electronic archive. Furthermore, fast information detection in a given PDF file is introduced. Such approach uses fast neural networks (FNNs) implemented in the frequency domain. The operation of these networks relies on performing cross correlation in the frequency domain rather than spatial one. It is proved mathematically and practically that the number of computation steps required for the presented FNNs is less than that needed by conventional neural networks (CNNs). Simulation results using MATLAB confirm the theoretical computations.

Keywords: Information Storage and Retrieval, Electronic Archiving, Fast Information Detection, Cross Correlation, Frequency Domain.

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Authors: Soon Jai Yi, Sun-Hong Kim, Hang-Geun Jeong, Seong-Ik Cho

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This paper presents a method of reducing the feedback delay time of DWA(Data Weighted Averaging) used in sigma-delta modulators. The delay time reduction results from the elimination of the latch at the quantizer output and also from the falling edge operation. The designed sigma-delta modulator improves the timing margin about 16%. The sub-circuits of sigma-delta modulator such as SC(Switched Capacitor) integrator, 9-level quantizer, comparator, and DWA are designed with the non-ideal characteristics taken into account. The sigma-delta modulator has a maximum SNR (Signal to Noise Ratio) of 84 dB or 13 bit resolution.

Keywords: Sigma-delta modulator, multibit, DWA

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Authors: Sellappan Narayanagounder, Karuppusami Gurusami

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The traditional Failure Mode and Effects Analysis (FMEA) uses Risk Priority Number (RPN) to evaluate the risk level of a component or process. The RPN index is determined by calculating the product of severity, occurrence and detection indexes. The most critically debated disadvantage of this approach is that various sets of these three indexes may produce an identical value of RPN. This research paper seeks to address the drawbacks in traditional FMEA and to propose a new approach to overcome these shortcomings. The Risk Priority Code (RPC) is used to prioritize failure modes, when two or more failure modes have the same RPN. A new method is proposed to prioritize failure modes, when there is a disagreement in ranking scale for severity, occurrence and detection. An Analysis of Variance (ANOVA) is used to compare means of RPN values. SPSS (Statistical Package for the Social Sciences) statistical analysis package is used to analyze the data. The results presented are based on two case studies. It is found that the proposed new methodology/approach resolves the limitations of traditional FMEA approach.

Keywords: Failure mode and effects analysis, Risk priority code, Critical failure mode, Analysis of variance.

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Authors: Vishal Awasthi, Atul Kumar Agnihotri

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Face is a non-intrusive strong biometrics for identification of original and dummy facial by different artificial means. Face recognition is extremely important in the contexts of computer vision, psychology, surveillance, pattern recognition, neural network, content based video processing. The availability of a widespread face database is crucial to test the performance of these face recognition algorithms. The openly available face databases include face images with a wide range of poses, illumination, gestures and face occlusions but there is no dummy face database accessible in public domain. This paper presents a face detection algorithm based on the image segmentation in terms of distance from a fixed point and template matching methods. This proposed work is having the most appropriate number of nodal points resulting in most appropriate outcomes in terms of face recognition and detection. The time taken to identify and extract distinctive facial features is improved in the range of 90 to 110 sec. with the increment of efficiency by 3%.

Keywords: Face recognition, Principal Component Analysis, PCA, Linear Discriminant Analysis, LDA, Improved Support Vector Machine, iSVM, elastic bunch mapping technique.

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Authors: Md. Zahangir Alom, Mei-Lan Piao, Md. Ashraful Alam, Nam Kim, Jae-Hyeung Park

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A new approach for facial expressions recognition based on face context and adaptively weighted sub-pattern PCA (Aw-SpPCA) has been presented in this paper. The facial region and others part of the body have been segmented from the complex environment based on skin color model. An algorithm has been proposed to accurate detection of face region from the segmented image based on constant ratio of height and width of face (δ= 1.618). The paper also discusses on new concept to detect the eye and mouth position. The desired part of the face has been cropped to analysis the expression of a person. Unlike PCA based on a whole image pattern, Aw-SpPCA operates directly on its sub patterns partitioned from an original whole pattern and separately extracts features from them. Aw-SpPCA can adaptively compute the contributions of each part and a classification task in order to enhance the robustness to both expression and illumination variations. Experiments on single standard face with five types of facial expression database shows that the proposed method is competitive.

Keywords: Aw-SpPC, Expressoin Recognition, Face context, Face Detection, PCA

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Authors: S. Shanthi, V. Muralibhaskaran

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Mammography has been one of the most reliable methods for early detection of breast cancer. There are different lesions which are breast cancer characteristic such as microcalcifications, masses, architectural distortions and bilateral asymmetry. One of the major challenges of analysing digital mammogram is how to extract efficient features from it for accurate cancer classification. In this paper we proposed a hybrid feature extraction method to detect and classify all four signs of breast cancer. The proposed method is based on multiscale surrounding region dependence method, Gabor filters, multi fractal analysis, directional and morphological analysis. The extracted features are input to self adaptive resource allocation network (SRAN) classifier for classification. The validity of our approach is extensively demonstrated using the two benchmark data sets Mammographic Image Analysis Society (MIAS) and Digital Database for Screening Mammograph (DDSM) and the results have been proved to be progressive.

Keywords: Feature extraction, fractal analysis, Gabor filters, multiscale surrounding region dependence method, SRAN.

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Authors: Kripesh Adhikari, Hamid Bouchachia, Hammadi Nait-Charif

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Falls are one of the major causes of injury and death among elderly people aged 65 and above. A support system to identify such kind of abnormal activities have become extremely important with the increase in ageing population. Pose estimation is a challenging task and to add more to this, it is even more challenging when pose estimations are performed on challenging poses that may occur during fall. Location of the body provides a clue where the person is at the time of fall. This paper presents a vision-based tracking strategy where available joints are grouped into three different feature points depending upon the section they are located in the body. The three feature points derived from different joints combinations represents the upper region or head region, mid-region or torso and lower region or leg region. Tracking is always challenging when a motion is involved. Hence the idea is to locate the regions in the body in every frame and consider it as the tracking strategy. Grouping these joints can be beneficial to achieve a stable region for tracking. The location of the body parts provides a crucial information to distinguish normal activities from falls.

Keywords: Fall detection, machine learning, deep learning, pose estimation, tracking.

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Authors: M. Benachour, N. Benachour, M. Benguediab

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In this investigation an empirical study was made on fatigue crack initiation on 7075 T6 and 7075 T71 Al-alloys under constant amplitude loading. In initiation stage, local strain approach at the notch was applied. Single Edge Notch Tensile specimen with semi circular notch is used. Based on experimental results, effect of mean stress, is highlights on fatigue initiation life. Results show that fatigue life initiation is affected by notch geometry and mean stress. 

Keywords: Fatigue crack initiation, Al-Alloy, mean stress, heat treatment state.

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Authors: Michael Moeti, Khuliso Sigama, Thapelo Samuel Matlala

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One of the principal challenges that the world is confronted with is insecurity. The crime rate is increasing exponentially, and protecting our physical assets, especially in the motorist sector, is becoming impossible when applying our own strength. The need to develop technological solutions that detect and report theft without any human interference is inevitable. This is critical, especially for vehicle owners, to ensure theft detection and speedy identification towards recovery efforts in cases where a vehicle is missing or attempted theft is taking place. The vehicle theft detection system uses Convolutional Neural Network (CNN) to recognize the driver's face captured using an installed mobile phone device. The location identification function uses a Global Positioning System (GPS) to determine the real-time location of the vehicle. Upon identification of the location, Global System for Mobile Communications (GSM) technology is used to report or notify the vehicle owner about the whereabouts of the vehicle. The installed mobile app was implemented by making use of Python as it is undoubtedly the best choice in machine learning. It allows easy access to machine learning algorithms through its widely developed library ecosystem. The graphical user interface was developed by making use of JAVA as it is better suited for mobile development. Google's online database (Firebase) was used as a means of storage for the application. The system integration test was performed using a simple percentage analysis. 60 vehicle owners participated in this study as a sample, and questionnaires were used in order to establish the acceptability of the system developed. The result indicates the efficiency of the proposed system, and consequently, the paper proposes that the use of the system can effectively monitor the vehicle at any given place, even if it is driven outside its normal jurisdiction. More so, the system can be used as a database to detect, locate and report missing vehicles to different security agencies.

Keywords: Convolutional Neural Network, CNN, location identification, tracking, GPS, GSM.

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Authors: Daniel Y. Abebe, Jaehyouk Choi

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From the past earthquake events, many people get hurt at the exit while they are trying to go out of the buildings because of the exit doors are unable to be opened. The door is not opened because it deviates from its the original position. The aim of this research is to develop and evaluate a new type safety door that keeps the door frame in its original position or keeps its edge angles perpendicular during and post-earthquake. The proposed door is composed of three components: outer frame joined to the wall, inner frame (door frame) and circular hollow section connected to the inner and outer frame which is used as seismic energy dissipating device.

Keywords: Earthquake disaster, FE analysis, Low yield point steel, Safety-doors.

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Authors: Hadeer R. M. Tawfik, Rania A. K. Birry, Amani A. Saad

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Eyes are considered to be the most sensitive and important organ for human being. Thus, any eye disorder will affect the patient in all aspects of life. Cataract is one of those eye disorders that lead to blindness if not treated correctly and quickly. This paper demonstrates a model for automatic detection, classification, and grading of cataracts based on image processing techniques and artificial intelligence. The proposed system is developed to ease the cataract diagnosis process for both ophthalmologists and patients. The wavelet transform combined with 2D Log Gabor Wavelet transform was used as feature extraction techniques for a dataset of 120 eye images followed by a classification process that classified the image set into three classes; normal, early, and advanced stage. A comparison between the two used classifiers, the support vector machine SVM and the artificial neural network ANN were done for the same dataset of 120 eye images. It was concluded that SVM gave better results than ANN. SVM success rate result was 96.8% accuracy where ANN success rate result was 92.3% accuracy.

Keywords: Cataract, classification, detection, feature extraction, grading, log-gabor, neural networks, support vector machines, wavelet.

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