Search results for: early detection

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: Abdelghani Chahmi

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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: Chinedu Ifedi Okeke

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The behavioural orientations of fathers, which resonate in the way they relate to members of their families and other community members, appear to have a variety of implications for the early development of children. In this paper, a conceptual map of fathers’ lifestyles is adopted to provide an interconnected network of father lifestyles. Empirical evidence from a qualitative case study of 25 Black fathers, who had been purposively selected from a suburb in one rural Eastern Cape municipality in South Africa, is reported in this paper. Semi-structured in-depth interviews were used to obtain data, which was analysed thematically. Findings identify and provide evidence of father lifestyles that are incongruent with the kind of parental behaviour needed to support the healthy early development of children. Findings suggest that these negative lifestyles appear to incapacitate fathers who fail to make a positive contribution to their children’s early development. To ensure that fathers make the expected contributions to their children’s early development, policies aimed at rehabilitating fathers who are involved in the negative lifestyles reported in this paper should be put in place.

Keywords: childhood development, fathering, fathers, intervention strategies, lifestyles, South Africa

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Authors: Hussin K. Ragb, Vijayan K. Asari

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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

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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

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Authors: N. Ghatwary, A. Ahmed, H. Jalab

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In this paper, an approach for the liver tumor detection in computed tomography (CT) images is represented. The detection process is based on classifying the features of target liver cell to either tumor or non-tumor. Fractional differential (FD) is applied for enhancement of Liver CT images, with the aim of enhancing texture and edge features. Later on, a fusion method is applied to merge between the various enhanced images and produce a variety of feature improvement, which will increase the accuracy of classification. Each image is divided into NxN non-overlapping blocks, to extract the desired features. Support vector machines (SVM) classifier is trained later on a supplied dataset different from the tested one. Finally, the block cells are identified whether they are classified as tumor or not. Our approach is validated on a group of patients’ CT liver tumor datasets. The experiment results demonstrated the efficiency of detection in the proposed technique.

Keywords: fractional differential (FD), computed tomography (CT), fusion, aplha, texture features.

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Authors: Simon B. N. Thompson

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Cortisol is essential to the regulation of the immune system and yawning is a pathological symptom of multiple sclerosis (MS). Electromyography activity (EMG) in the jaw muscles typically rises when the muscles are moved and with yawning is highly correlated with cortisol levels in healthy people. Saliva samples from 59 participants were collected at the start and after yawning, or at the end of the presentation of yawning-provoking stimuli, in the absence of a yawn, together with EMG data and questionnaire data: Hospital Anxiety and Depression Scale, Yawning Susceptibility Scale, General Health Questionnaire, demographic, health details. Exclusion criteria: chronic fatigue, diabetes, fibromyalgia, heart condition, high blood pressure, hormone replacement therapy, multiple sclerosis, stroke. Significant differences were found between the saliva cortisol samples for the yawners, t (23) = -4.263, p = 0.000, as compared with the non-yawners between rest and post-stimuli, which was non-significant. Significant evidence was found to support the Thompson Cortisol Hypothesis suggesting that rises in cortisol levels are associated with yawning. Further research is exploring the use of cortisol as an early diagnostic tool for MS. Ethics approval granted and professional code of conduct, confidentiality, and safety issues are approved therein.

Keywords: cortisol, multiple sclerosis, yawning, thompson cortisol hypothesis

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Authors: Ali A. Aldosari

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Change detection of Earth surface features is extremely important for better understanding of our environment in order to promote better decision making. Al-Jawf is remarkable for its abundant agricultural water where there is fertile agricultural land due largely to underground water. As result, this region has large areas of cultivation of dates, olives and fruits trees as well as other agricultural products such as Alfa Alfa and wheat. However this agricultural area was declined due to the reduction of government supports in the last decade. This reduction was not officially recorded or measured in this region at large scale or governorate level. Remote sensing data are primary sources extensively used for change detection in agriculture applications. This study is applied the technology of GIS and used the Normalized Difference Vegetation Index (NDVI) which can be used to measure and analyze the spatial and temporal changes in the agriculture areas in the Aljouf region.

Keywords: spatial analysis, geographical information system, change detection

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Authors: Lin Yanyan

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This research set out to understand early childhood practitioners’ perceptions of continuous professional development (CPD) opportunities and its relationship to career progression and leadership roles in Singapore. The small-scale qualitative inductive study was conducted in two phases. Phase one used close-ended questionnaires with a total of 24 early years practitioner participants, while phase two included a total of 5 participants who were invited to participate in the second part of the data collection. Semi-structured interviews were used at phase two to elicit deeper responses from parents and teachers. Findings from the study were then thematically coded and analysed. The findings from both questionnaires and interviews showed that early years practitioners perceived CPD to be important to their professional growth, but there was no conclusive link that CPD necessarily led to the progression of leadership roles in the early years. Participants experience of CPD was strongly determined by their employer- the preschool operator, being government-funded or a private entity, which resulted in key differences emerging between their responses. Participants also experienced road blocks in their pursuit of CPD, in the form of staff shortage, budget constraints and lack of autonomy as their employers imposed specific CPD courses on them to suit the organisational needs, rather than their personal or professional needs.

Keywords: continuous professional development (CPD), early years practitioners (EYP), career progression, leadership

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Authors: M. Naresh, Pratik Chaturvedi, Srishti Yadav, Varun Dutt, K. V. Uday

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Changes in the Earth’s climate are likely to increase natural hazards such as drought, floods, earthquakes, landslides, etc. The present study focusing on to early warning systems (EWS) of landslides, major issues in Himalayan region without prominence to deforestation, encroachments and un-engineered cutting of slopes and reforming for infrastructural purposes. EWS can be depicted by conducting a series of flume tests using micro-electro mechanical systems sensors data after reaching threshold values under controlled laboratory conditions. Based on the threshold value database, an alert will be sent via SMS.

Keywords: slope-instability, flume test, sensors, early warning system

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Authors: Duc V. Nguyen

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Induction motors are widely used in different industry areas and can experience various kinds of faults in stators and rotors. In general, fault detection and diagnosis techniques for induction motors can be supervised by measuring quantities such as noise, vibration, and temperature. The installation of mechanical sensors in order to assess the health conditions of a machine is typically only done for expensive or load-critical machines, where the high cost of a continuous monitoring system can be Justified. Nevertheless, induced current monitoring can be implemented inexpensively on machines with arbitrary sizes by using current transformers. In this regard, effective and low-cost fault detection techniques can be implemented, hence reducing the maintenance and downtime costs of motors. This work proposes a method for fault detection and diagnosis of induction motors, which combines classical fast Fourier transform and modern/advanced machine learning techniques. The proposed method is validated on real-world data and achieves a precision of 99.7% for fault detection and 100% for fault classification with minimal expert knowledge requirement. In addition, this approach allows users to be able to optimize/balance risks and maintenance costs to achieve the highest benet based on their requirements. These are the key requirements of a robust prognostics and health management system.

Keywords: fault detection, FFT, induction motor, predictive maintenance

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Authors: Temesgen Geremew

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The excessive emission of heavy metal ions from industrial processes poses a serious threat to both the environment and human health. This study presents a distinct approach utilizing (PTh-MnS/CoS NPs) for the highly selective and sensitive detection of Hg²⁺ ions in water. Such detection is crucial for safeguarding human health, protecting the environment, and accurately assessing toxicity. The fabrication method employs a simple and efficient chemical precipitation technique, harmoniously combining polythiophene, MnS, and CoS NPs to create highly active substrates for SERS. The MnS@Hg²⁺ exhibits a distinct Raman shift at 1666 cm⁻¹, enabling specific identification and demonstrating the highest responsiveness among the studied semiconductor substrates with a detection limit of only 1 nM. This investigation demonstrates reliable and practical SERS detection for Hg²⁺ ions. Relative standard deviation (RSD) ranged from 0.49% to 9.8%, and recovery rates varied from 96% to 102%, indicating selective adsorption of Hg²⁺ ions on the synthesized substrate. Furthermore, this research led to the development of a remarkable set of substrates, including (MnS, CoS, MnS/CoS, and PTh-MnS/CoS) nanoparticles were created right there on SiO₂/Si substrate, all exhibiting sensitive, robust, and selective SERS for Hg²⁺ ion detection. These platforms effectively monitor Hg²⁺ concentrations in real environmental samples.

Keywords: surface-enhanced raman spectroscopy (SERS), sensor, mercury ions, nanoparticles, and polythiophene.

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Authors: Sheldon McCall, Miao Yu, Liyun Gong, Shigang Yue, Stefanos Kollias

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Falls are a significant health concern for older adults globally, and prompt identification is critical to providing necessary healthcare support. Our study proposes a new fall detection method using computer vision based on modern deep learning techniques. Our approach involves training a trans- former model on a large 2D pose dataset for general action recognition, followed by transfer learning. Specifically, we freeze the first few layers of the trained transformer model and train only the last two layers for fall detection. Our experimental results demonstrate that our proposed method outperforms both classical machine learning and deep learning approaches in fall/non-fall classification. Overall, our study suggests that our proposed methodology could be a valuable tool for identifying falls.

Keywords: healthcare, fall detection, transformer, transfer learning

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Authors: Mohammed A. Alfayyadh, Halla A. AlAbdulhadi, Mahdi H. Almubarak

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Purpose: Eales’ disease is an idiopathic vasculitis that affects the peripheral retina. It is characterized by recurrent vitreous hemorrhage as a complication of retinal neovascularization. It is more prevalent in India and affects young males. Here we present a patient with neovascular glaucoma as a rare first presentation of Eales’ disease. Observations: This is a 24-year-old Indian gentleman, who complained of a sudden decrease in vision in the left eye over less than 24 hours, along with frontal headache and eye pain for the last three weeks. Ocular examination revealed peripheral retinal ischemia in the right eye, very high intraocular pressure, rubeosis iridis, vitreous hemorrhage and extensive retinal ischemia in the left eye, vascular sheathing and neovascularization in both eyes. Purified protein derivative skin test was positive. The patient was managed with anti-glaucoma, intravitreal anti-vascular endothelial growth factor and laser photocoagulation. Systemic steroids and anti-tuberculous therapy were also initiated. Conclusions: Neovascular glaucoma is an infrequent complication of Eales’ disease. However, the lack of early detection of the disease in the early stages might lead to such serious complication.

Keywords: case report, Eales’ disease, mycobacterium tuberculosis, neovascular glaucoma

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Authors: Bin Liu

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Protein remote homology detection and fold recognition are two most important tasks in protein sequence analysis, which is critical for protein structure and function studies. In this study, we combined the profile-based features with various string kernels, and constructed several computational predictors for protein remote homology detection and fold recognition. Experimental results on two widely used benchmark datasets showed that these methods outperformed the competing methods, indicating that these predictors are useful computational tools for protein sequence analysis. By analyzing the discriminative features of the training models, some interesting patterns were discovered, reflecting the characteristics of protein superfamilies and folds, which are important for the researchers who are interested in finding the patterns of protein folds.

Keywords: protein remote homology detection, protein fold recognition, profile-based features, Support Vector Machines (SVMs)

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Authors: Izuru Nomura, Tatsuya Takino, Yuji Kageyama, Shin Nagata, Hiroyuki Kamata

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We propose a method of crater detection from the image of the lunar surface captured by the small space probe. We use the principal component analysis (PCA) to detect craters. Nevertheless, considering　severe environment of the space, it is impossible to use generic computer in practice. Accordingly, we have to implement the method in FPGA. This paper compares FPGA and generic computer by the processing time of a method of crater detection using principal component analysis.

Keywords: crater, PCA, eigenvector, strength value, FPGA, processing time

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Authors: Aulanni’am Aulanni’am

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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

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Authors: Salaam Mujeeb, Adeola Segun, Abdullahi Olasunkanmi

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Dementia is becoming a major concern as the world population is increasing and elderly populations are being neglected. Liver and kidney Diseases have been implicated as risk factors in the etiology of Dementia. This study, therefore, evaluates the plasma Gamma Glutamyl Transferase (GGT) activity and plasma Electrolytes in other to find an association between the biomarkers and Dementia. The subjects (38) were age and sex-matched with their corresponding controls and structured questionnaires were used to obtain medical information. Using spectrophotometric and ion selective Electrode techniques respectively, we found and elevated GGT activity in the Dementia Subjects. Remarkably, no association was found between the plasma Electrolytes level and Dementia subjects. It was also observed that severity of Dementia worsens with age. Moreover, the condition of the dementia subjects worsens with reducing weight. Furthermore, the presence of Comorbidity e.g. Hypertension, Obesity, Diabetes and Habits like Smoking, Drugs and Alcohol consumption interferes with Electrolyte balance. Weight loss monitoring and IBM check are advised in Elderly individuals particularly females as they may be inductive of early or future cognitive impairments. Therefore, it might be useful as an early detection tool. Government and society should invest more on the Geriatric population by establishing Old people's home and providing social care services.

Keywords: clinical characteristics, dementia, electrolytes, gamma glutamyl transpeptidase, GGT

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Authors: Nuur Ezaini Akmar Ismail, Norbazilah Rahim, Norul Huda Md Rasdi, Maslina Daud

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A web application is the most targeted by the attacker because the web application is accessible by the end users. It has become more advantageous to the attacker since not all the end users aware of what kind of sensitive data already leaked by them through the Internet especially via social network in shake on ‘sharing’. The attacker can use this information such as personal details, a favourite of artists, a favourite of actors or actress, music, politics, and medical records to customize phishing attack thus trick the user to click on malware-laced attachments. The Phishing attack is one of the most popular attacks for social engineering technique against web applications. There are several methods to detect phishing websites such as Blacklist/Whitelist based detection, heuristic-based, and visual similarity-based detection. This paper illustrated a comparison between the heuristic-based technique using features of a uniform resource locator (URL) and visual similarity-based detection techniques that compares the content of a suspected phishing page with the legitimate one in order to detect new phishing sites based on the paper reviewed from the past few years. The comparison focuses on three indicators which are false positive and negative, accuracy of the method, and time consumed to detect phishing website.

Keywords: heuristic-based technique, phishing detection, social engineering and visual similarity-based technique

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Authors: Meruert Serik, Nassipzhan Duisegaliyeva, Danara Tleumagambetova

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The article highlights and describes the characteristic features of real-time face detection in images and videos using machine learning algorithms. Students of educational programs reviewed the research work "6B01511-Computer Science", "7M01511-Computer Science", "7M01525- STEM Education," and "8D01511-Computer Science" of Eurasian National University named after L.N. Gumilyov. As a result, the advantages and disadvantages of Haar Cascade (Haar Cascade OpenCV), HoG SVM (Histogram of Oriented Gradients, Support Vector Machine), and MMOD CNN Dlib (Max-Margin Object Detection, convolutional neural network) detectors used for face detection were determined. Dlib is a general-purpose cross-platform software library written in the programming language C++. It includes detectors used for determining face detection. The Cascade OpenCV algorithm is efficient for fast face detection. The considered work forms the basis for the development of machine learning methods by future computer science teachers.

Keywords: algorithm, artificial intelligence, education, machine learning

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Authors: Rana F. Obeidat, Suzanne S. Dickerson, Gregory G. Homish, Nesreen M. Alqaissi, Robin M. Lally

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Background: Despite the fact that breast cancer is the most prevalent cancer among Jordanian women, practically nothing is known about their perceptions of early stage breast cancer and surgical treatment. Objective: To gain understanding of the diagnosis and surgical treatment experience of Jordanian women diagnosed with early stage breast cancer. Methods: An interpretive phenomenological approach was used for this study. A purposive sample of 28 Jordanian women who were surgically treated for early stage breast cancer within 6 months of the interview was recruited. Data were collected using individual interviews and analyzed using Heideggerian hermeneutical methodology. Results: Fear had a profound effect on Jordanian women’s stories of diagnosis and surgical treatment of early stage breast cancer. Women’s experience with breast cancer and its treatment was shaped by their pre-existing fear of breast cancer, the disparity in the quality of care at various health care institutions, and sociodemographic factors (e.g., education, age). Conclusions: Early after the diagnosis, fear was very strong and women lost perspective of the fact that this disease was treatable and potentially curable. To control their fears, women unconditionally trusted God, the health care system, surgeons, family, friends, and/or neighbors, and often accepted treatment offered by their surgeons without questioning. Implications for practice: Jordanian healthcare providers have a responsibility to listen to their patients, explore meanings they ascribe to their illness, and provide women with proper education and support necessary to help them cope with their illness.

Keywords: breast cancer, early stage, Jordanian, experience, phenomenology

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Authors: Pajaree Donkhampa, Fuangfa Unob

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Ceftazidime is an antibiotic drug commonly used to treat several human and veterinary infections. However, the presence of ceftazidime residues in the environment may induce microbial resistance and cause side effects to humans. Therefore, monitoring the level of ceftazidime in environmental resources is important. In this work, a melamine foam platform was proposed for simultaneous extraction and colorimetric detection of ceftazidime based on the azo dye formation on the surface. The melamine foam was chemically modified with polyethyleneimine (PEI) and characterized by scanning electron microscopy (SEM) and Fourier transform infrared spectroscopy (FTIR). Ceftazidime is a sample that was extracted on the PEI-modified melamine foam and further reacted with nitrite in an acidic medium to form an intermediate diazonium ion. The diazotized molecule underwent an azo coupling reaction with chromotropic acid to generate a red-colored compound. The material color changed from pale yellow to pink depending on the ceftazidime concentration. The photo of the obtained material was taken by a smartphone camera and the color intensity was determined by Image J software. The material fabrication and ceftazidime extraction and detection procedures were optimized. The detection of a sub-ppm level of ceftazidime was achieved without using a complex analytical instrument.

Keywords: colorimetric detection, ceftazidime, melamine foam, extraction, azo dye

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Authors: Saeedeh Hajihosseini, Zahra Aghili, Navid Nasirizadeh

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An innovative method of a DNA electrochemical biosensor based on Oracet Blue (OB) as an electroactive label and gold electrode (AuE) for detection of Helicobacter pylori, was offered. A single–stranded DNA probe with a thiol modification was covalently immobilized on the surface of the AuE by forming an Au–S bond. Differential pulse voltammetry (DPV) was used to monitor DNA hybridization by measuring the electrochemical signals of reduction of the OB binding to double– stranded DNA (ds–DNA). Our results showed that OB–based DNA biosensor has a decent potential for detection of single–base mismatch in target DNA. Selectivity of the proposed DNA biosensor was further confirmed in the presence of non–complementary and complementary DNA strands. Under optimum conditions, the electrochemical signal had a linear relationship with the concentration of the target DNA ranging from 0.3 nmol L-1 to 240.0 nmol L-1, and the detection limit was 0.17 nmol L-1, whit a promising reproducibility and repeatability.

Keywords: DNA biosensor, oracet blue, Helicobacter pylori, electrode (AuE)

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Authors: Olesya Bolkhovskaya, Alexey Davydov, Alexander Maltsev

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In this paper two approaches to joint signal detection, time of arrival (ToA) and angle of arrival (AoA) estimation in multi-element antenna array are investigated. Two scenarios were considered: first one, when the waveform of the useful signal is known a priori and, second one, when the waveform of the desired signal is unknown. For first scenario, the antenna array signal processing based on multi-element matched filtering (MF) with the following non-coherent detection scheme and maximum likelihood (ML) parameter estimation blocks is exploited. For second scenario, the signal processing based on the antenna array elements covariance matrix estimation with the following eigenvector analysis and ML parameter estimation blocks is applied. The performance characteristics of both signal processing schemes are thoroughly investigated and compared for different useful signals and noise parameters.

Keywords: antenna array, signal detection, ToA, AoA estimation

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Authors: Sunanta Klibthong

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In view of the Thailand government policy creating increasing awareness of opportunity for children with special needs, the number of children with disabilities enrolled in kindergartens in Thailand has increased. This study explores early childhood teachers’ experiences and concerns of teaching children with disabilities in inclusive classrooms. The population of the study was private early childhood teachers who teach in inclusive classrooms in Thailand. Quantitative data obtained through a questionnaire were supplemented by early childhood teachers’ interviews to identify key experiences and concerns of the teachers when teaching children with and without disabilities in the same classrooms. The results of this study indicated that many teachers face challenges including lack of professional development opportunities, difficulty identifying the needs of all children and how to use effective strategies to support inclusive practices in their classrooms. Teachers also expressed concern about parents’ lack of willingness to accept children without disabilities studying together with those with disabilities in the same classrooms. Findings from this study can inform program support for parents and professional support needs of teachers in the provision of high-quality inclusive programs for all students.

Keywords: the concern, early childhood, experience, inclusive education, Thailand

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Authors: Ali Berkan Ural

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This paper presents the analysis, evaluation, and pre-diagnosis of early stage breast based diagnostic problems (breast cancer, nodulesorlumps) by Computer Aided Diagnosing (CAD) system from mammogram radiological images. According to the statistics, the time factor is crucial to discover the disease in the patient (especially in women) as possible as early and fast. In the study, a new algorithm is developed using advanced image processing and deep learning method to detect and classify the problem at earlystagewithmoreaccuracy. This system first works with image processing methods (Image acquisition, Noiseremoval, Region Growing Segmentation, Morphological Operations, Breast BorderExtraction, Advanced Segmentation, ObtainingRegion Of Interests (ROIs), etc.) and segments the area of interest of the breast and then analyzes these partly obtained area for cancer detection/lumps in order to diagnosis the disease. After segmentation, with using the Spectrogramimages, 5 different deep learning based methods (specified Convolutional Neural Network (CNN) basedAlexNet, ResNet50, VGG16, DenseNet, Xception) are applied to classify the breast based problems.

Keywords: computer aided diagnosis, breast cancer, region growing, segmentation, deep learning

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Authors: Luiz Daniel Garay Trindade, Antonio De Freitas Valle Neto, Fabio Paulo Basso, Elder De Macedo Rodrigues, Maicon Bernardino, Daniel Welfer, Daniel Muller

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Agriculture plays an important role in society since it is one of the main sources of food in the world. To help the production and yield of crops, precision agriculture makes use of technologies aiming at improving productivity and quality of agricultural commodities. One of the problems hampering quality of agricultural production is the disease affecting crops. Failure in detecting diseases in a short period of time can result in small or big damages to production, causing financial losses to farmers. In order to provide a map of the contributions destined to the early detection of plant diseases and a comparison of the accuracy of the selected studies, a systematic literature review of the literature was performed, showing techniques for digital image processing and neural networks. We found 35 interesting tool support alternatives to detect disease in 19 plants. Our comparison of these studies resulted in an overall average accuracy of 87.45%, with two studies very closer to obtain 100%.

Keywords: pattern recognition, image processing, deep learning, precision agriculture, smart farming, agricultural automation

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Authors: Insaf Bellamine, Hamid Tairi

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Detecting moving objects in sequences is an essential step for video analysis. This paper mainly contributes to the Color Space-Time Interest Points (CSTIP) extraction and detection. We propose a new method for detection of moving objects. Two main steps compose the proposed method. First, we suggest to apply the algorithm of the detection of Color Space-Time Interest Points (CSTIP) on both components of the Color Structure-Texture Image Decomposition which is based on a Partial Differential Equation (PDE): a color geometric structure component and a color texture component. A descriptor is associated to each of these points. In a second stage, we address the problem of grouping the points (CSTIP) into clusters. Experiments and comparison to other motion detection methods on challenging sequences show the performance of the proposed method and its utility for video analysis. Experimental results are obtained from very different types of videos, namely sport videos and animation movies.

Keywords: Color Space-Time Interest Points (CSTIP), Color Structure-Texture Image Decomposition, Motion Detection, clustering

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Authors: Hyun-Woo Cho

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The detection and identification of multivariate manufacturing processes are quite important in order to maintain good product quality. Unusual behaviors or events encountered during its operation can have a serious impact on the process and product quality. Thus they should be detected and identified as soon as possible. This paper focused on the efficient representation of process measurement data in detecting and identifying abnormalities. This qualitative method is effective in representing fault patterns of process data. In addition, it is quite sensitive to measurement noise so that reliable outcomes can be obtained. To evaluate its performance a simulation process was utilized, and the effect of adopting linear and nonlinear methods in the detection and identification was tested with different simulation data. It has shown that the use of a nonlinear technique produced more satisfactory and more robust results for the simulation data sets. This monitoring framework can help operating personnel to detect the occurrence of process abnormalities and identify their assignable causes in an on-line or real-time basis.

Keywords: detection, monitoring, identification, measurement data, multivariate techniques

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Authors: A. Sopharak, B. Uyyanonvara, S. Barman

Abstract:

Diabetic retinopathy is characterized by the development of retinal microaneurysms. The damage can be prevented if disease is treated in its early stages. In this paper, we are comparing Support Vector Machine (SVM) and Naïve Bayes (NB) classifiers for automatic microaneurysm detection in images acquired through non-dilated pupils. The Nearest Neighbor classifier is used as a baseline for comparison. Detected microaneurysms are validated with expert ophthalmologists’ hand-drawn ground-truths. The sensitivity, specificity, precision and accuracy of each method are also compared.

Keywords: diabetic retinopathy, microaneurysm, naive Bayes classifier, SVM classifier

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