Search results for: quickest change detection

Authors: Karen Bell

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It is vital to integrate a working-class perspective into the just transition to an inclusive and sustainable society because of the particular expertise and interests that working-class people bring to the debates and actions. In class societies, those who are not well represented in the current structures of power can find it easier to see when the system is not working. They are also more likely to be impacted by the environmental crises because wealthier people can change their dwelling places, jobs and other aspects of their lives in the face of risks. Therefore, challenging the ‘post-material values thesis’, this paper argues that, if enabled to do so, working-class people are more likely to identify what needs to be addressed and changed in transition and can be more motivated to make the changes necessary than other social groups. However, they are often excluded from environmental decision-making and environmental social movements. The paper is based on a mixed methodology; drawing on secondary data, interview material, participant observation and documentary analysis. It is based on years of research and activism on environmental issues in working-class communities. The analysis and conclusion discusses the seven kinds of change required to address this problem: 1) organizational change - participatory practice (2) legislative change - make class an equalities and human rights issue (3) policy change - reduce inequality (4) social movement change - radicalize the environmental movement and support the environmental working-class (5) political change - create an eco-social state based on sharing (6) cultural change - integrate social and environmental justice, and (7) revolutionary change - dismantle capitalism.

Keywords: environmentalism, just transition, sustainability, working class

Procedia PDF Downloads 139

Authors: C. P. Providakis, G. M. Angeli, M. J. Favvata, N. A. Papadopoulos, C. E. Chalioris, C. G. Karayannis

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An effort for the detection of damages in the reinforcement bars of reinforced concrete members using PZTs is presented. The damage can be the result of excessive elongation of the steel bar due to steel yielding or due to local steel corrosion. In both cases the damage is simulated by considering reduced diameter of the rebar along the damaged part of its length. An integration approach based on both electro-mechanical admittance methodology and guided wave propagation technique is used to evaluate the artificial damage on the examined longitudinal steel bar. Two actuator PZTs and a sensor PZT are considered to be bonded on the examined steel bar. The admittance of the Sensor PZT is calculated using COMSOL 3.4a. Fast Furrier Transformation for a better evaluation of the results is employed. An effort for the quantification of the damage detection using the root mean square deviation (RMSD) between the healthy condition and damage state of the sensor PZT is attempted. The numerical value of the RSMD yields a level for the difference between the healthy and the damaged admittance computation indicating this way the presence of damage in the structure. Experimental measurements are also presented.

Keywords: concrete reinforcement, damage detection, electromechanical admittance, experimental measurements, finite element method, guided waves, PZT

Procedia PDF Downloads 279

Authors: Chia-Yen Lee, Hao-Jen Wang, Jhih-Hao Lai

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In recent years, infrared (IR) imaging has been considered as a potential tool to assess the efficacy of chemotherapy and early detection of breast cancer. Regions of tumor growth with high metabolic rate and angiogenesis phenomenon lead to the high temperatures. Observation of differences between the heat maps in long term is useful to help assess the growth of breast cancer cells and detect breast cancer earlier, wherein the multi-time infrared image alignment technology is a necessary step. Representative feature points detection and matching are essential steps toward the good performance of image registration and quantitative analysis. However, there is no clear boundary on the infrared images and the subject's posture are different for each shot. It cannot adhesive markers on a body surface for a very long period, and it is hard to find anatomic fiducial markers on a body surface. In other words, it’s difficult to detect and match features in an IR sequence images. In this study, automated feature detection and matching algorithms with two type of automatic feature points (i.e., vascular branch points and modified Harris corner) are developed respectively. The preliminary results show that the proposed method could identify the representative feature points on the IR breast images successfully of 98% accuracy and the matching results of 93% accuracy.

Keywords: Harris corner, infrared image, feature detection, registration, matching

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Authors: Unity Chipfupa, Aluwani Tagwi, Edilegnaw Wale

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Climate change adaptation studies are challenged by a limited understanding of how non-cognitive factors such as psychological capital affect adaptation decisions of smallholder farmers. The concept of psychological capital has not been fully applied in the empirical literature on climate change adaptation strategies. Hence, the study was meant to assess how psychological capital endowment affects climate change adaptation among smallholder farmers. A multivariate probit regression model was estimated using data collected from 328 smallholder farmers in KwaZulu-Natal, South Africa. The findings indicate that, among other factors, self-confidence and hope or aspirations in farming influence climate change adaptation decisions of smallholders. The psychological capital theory proved to be comprehensive in identifying specific psychological dimensions associated with adaptation decisions. However, the non-alignment of approaches for measuring non-cognitive factors made it difficult to compare results among different studies. In conclusion, the study recommends the need for practical ways for enhancing smallholders’ endowment with key non-cognitive abilities. Researchers should develop and agree on a comprehensive framework for assessing non-cognitive factors critical for climate change adaptation. This will improve the use of positive psychology theories to advance the literature on climate change adaptation. Other key recommendations include targeted support for communities facing higher risks of climate change, improving smallholders’ ability to adapt, promotion of social networks and the inclusion of farming objectives as an important indicator in climate change adaptation research.

Keywords: adaptive capacity, climate change adaptation, psychological capital, multivariate probit, non-cognitive factors.

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Authors: A. Flammini, R. Morbidelli, C. Saltalippi

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The increase of air surface temperature at global scale is a fact, with values around 0.85 ºC since the late nineteen century, as well as a significant change in main features of rainfall regime. Nevertheless, the detected climatic changes are not equally distributed all over the world, but exhibit specific characteristics in different regions. Therefore, studying the evolution of climatic indices in different geographical areas with a prefixed standard approach becomes very useful in order to analyze the existence of climatic trend and compare results. In this work, a methodology to investigate the climatic change and its effects on a wide set of climatic indices is proposed and applied at regional scale in the case study of a Mediterranean area, Umbria region in Italy. From data of the available temperature stations, nine temperature indices have been obtained and the existence of trends has been checked by applying the non-parametric Mann-Kendall test, while the non-parametric Pettitt test and the parametric Standard Normal Homogeneity Test (SNHT) have been applied to detect the presence of break points. In addition, aimed to characterize the rainfall regime, data from 11 rainfall stations have been used and a trend analysis has been performed on cumulative annual rainfall depth, daily rainfall, rainy days, and dry periods length. The results show a general increase in any temperature indices, even if with a trend pattern dependent of indices and stations, and a general decrease of cumulative annual rainfall and average daily rainfall, with a time rainfall distribution over the year different from the past.

Keywords: climatic change, temperature, rainfall regime, trend analysis

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Authors: Gui Yewei, Du Yanxia, Xiao Guangming, Liu Lei, Wei Dong, Yang Xiaofeng

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A phase change material (PCM) is a substance which absorbs a large amount of energy when undergoing a change of solid-liquid phase. The good physical and chemical properties of C or SiC foam reveal the possibility of using them as a thermal conductivity enhancer for the PCM. C or SiC foam composite PCM has a high effective conductivity and becomes one of the most interesting thermal storage techniques due to its advantage of simplicity and reliability. The paper developed a numerical method to simulate the heat transfer of SiC and C foam composite PCM, a finite volume technique was used to discretize the heat diffusion equation while the phase change process was modeled using the equivalent specific heat method. The effects of the porosity were investigated based on the numerical method, and the effects of the geometric model of the microstructure on the equivalent thermal conductivity was studies.

Keywords: SiC foam, composite, phase change material, heat transfer

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Authors: Jessie Rapoza, Petr Moroshkin, Jimmy Xu

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Chirality is omnipresent, in nature, in life, and in the field of physics. One intriguing example is the homochirality that has remained a great secret of life. Another is the pairs of mirror-image molecules – enantiomers. They are identical in atomic composition and therefore indistinguishable in the scalar physical properties. Yet, they can be either therapeutic or toxic, depending on their chirality. Recent studies suggest a potential link between abnormal levels of certain D-amino acids and some serious health impairments, including schizophrenia, amyotrophic lateral sclerosis, and potentially cancer. Although indistinguishable in their scalar properties, the chirality of a molecule reveals itself in interaction with the surrounding of a certain chirality, or more generally, a broken mirror-symmetry. In this work, we report on a system for chiral molecule detection, in which the mirror-symmetry is doubly broken, first by asymmetric structuring a nanopatterned plasmonic surface than by the incidence of circularly polarized light (CPL). In this system, the incident circularly-polarized light induces a surface plasmon polariton (SPP) wave, propagating along the asymmetric plasmonic surface. This SPP field itself is chiral, evanescently bound to a near-field zone on the surface (~10nm thick), but with an amplitude greatly intensified (by up to 104) over that of the incident light. It hence probes just the molecules on the surface instead of those in the volume. In coupling to molecules along its path on the surface, the chiral SPP wave favors one chirality over the other, allowing for chirality detection via the change in an optical rectification current measured at the edges of the sample. The asymmetrically structured surface converts the high-frequency electron plasmonic-oscillations in the SPP wave into a net DC drift current that can be measured at the edge of the sample via the mechanism of optical rectification. The measured results validate these design concepts and principles. The observed optical rectification current exhibits a clear differentiation between a pair of enantiomers. Experiments were performed by focusing a 1064nm CW laser light at the sample - a gold grating microchip submerged in an approximately 1.82M solution of either L-arabinose or D-arabinose and water. A measurement of the current output was then recorded under both rights and left circularly polarized lights. Measurements were recorded at various angles of incidence to optimize the coupling between the spin-momentums of the incident light and that of the SPP, that is, spin-momentum locking. In order to suppress the background, the values of the photocurrent for the right CPL are subtracted from those for the left CPL. Comparison between the two arabinose enantiomers reveals a preferential signal response of one enantiomer to left CPL and the other enantiomer to right CPL. In sum, this work reports on the first experimental evidence of the feasibility of chiral molecule detection via optical rectification in a metal meta-grating. This nanoscale interfaced electrical detection technology is advantageous over other detection methods due to its size, cost, ease of use, and integration ability with read-out electronic circuits for data processing and interpretation.

Keywords: Chirality, detection, molecule, spin

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Authors: Mehrshad Khosraviani, Faramarz Abbaspour Leyl Abadi

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The fundamental component of the computer network of modern information society will be considered. These networks are connected to the network of the internet generally. Due to the fact that the primary purpose of the Internet is not designed for, in recent decades, none of these networks in many of the attacks has been very important. Today, for the provision of security, different security tools and systems, including intrusion detection systems are used in the network. A common diagnosis system based on artificial immunity, the designer, the Adhasaz Foundation has been evaluated. The idea of using artificial safety methods in the diagnosis of abnormalities in computer networks it has been stimulated in the direction of their specificity, there are safety systems are similar to the common needs of m, that is non-diagnostic. For example, such methods can be used to detect any abnormalities, a variety of attacks, being memory, learning ability, and Khodtnzimi method of artificial immune algorithm pointed out. Diagnosis of the common system of education offered in this paper using only the normal samples is required for network and any additional data about the type of attacks is not. In the proposed system of positive selection and negative selection processes, selection of samples to create a distinction between the colony of normal attack is used. Copa real data collection on the evaluation of ij indicates the proposed system in the false alarm rate is often low compared to other ir methods and the detection rate is in the variations.

Keywords: artificial immune system, abnormality detection, intrusion detection, computer networks

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Authors: Mary Funke Olabanji, Thando Ndarana, Nerhene Davis, Sylvester Okechukwu Ilo

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Scientific evidence indicates that climate change is already being experienced by farmers, and its impacts are felt on agricultural and food systems. Understanding the perceptions of farmers on climate change and how they respond to this change is essential to the development and implementation of appropriate policies for agriculture and food security. This paper aims to contribute to the understanding of farmers’ perceptions of climate change, adopted coping strategies, long-term implications of their adaptation choices, and barriers to their decisions to adapt. Data were randomly collected from 73 respondents in five districts located in the Olifants catchment of South Africa. A combination of descriptive statistics and Chi-Square statistical tests using the Statistical Package for Social Science (SPSS) was used to analyse the data obtained from the survey. Results show that smallholder farmers have an in-depth perception of climate change. The most significant changes perceived by farmers were increased temperature and low rainfall. The results equally revealed that smallholder farmers in the Olifants catchment had adopted several adaptation strategies in response to the perceived climate change. The significant adaptation strategies from the results include changing cropping patterns and planting date, use of improved seed variety, and chemical fertilizers. The study, therefore, concludes that crop diversification and agroforestry were more effective and sustainable in mitigating the impact of climate change.

Keywords: adaptation, climate change, perception, smallholder farmers

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Authors: Sung Duk Kim, Kwan U. Kim, Jin Sim, Ryong Jin Jang

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The phenomenon of seasonal variations in the Earth’s rotation velocity was discovered in the 1930s when a crystal clock was developed and analyzed in a quantitative way for the first time between 1955 and 1968 when observation data of the seasonal variations was analyzed by an atomic clock. According to the previous investigation, atmospheric circulation is supposed to be a factor affecting the seasonal variations in the Earth’s rotation velocity in many cases, but the problem has not been solved yet. In order to solve the problem, it is necessary to apply dynamics to consider the Earth’s spatial motion, rotation, and change of shape of the Earth (movement of materials in and out of the Earth and change of the Earth’s figure) at the same time and in interrelation to the accuracy of post-Newtonian approximation regarding the Earth body as a system of mass points because the stability of the Earth’s rotation angular velocity is in the range of 10⁻⁸~10⁻⁹. For it, the equation was derived, which can consider the 3 kinds of motion above mentioned at the same time by taking the effect of the resultant external force on the Earth’s rotation into account in a relativistic way to the accuracy of post-Newtonian approximation. Therefore, the equation has been solved to obtain the theoretical values of periodic change in the Earth’s rotation velocity, and they have been compared with the astronomical observation data so to reveal the cause for the periodic change in the Earth’s rotation velocity.

Keywords: Earth rotation, moment function, periodic change, seasonal variation, relativistic change

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Authors: Gabriel Sunday Ayayia

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The paper argues that Ubuntu, as a philosophy that emphasizes the interconnectedness of all living things and importance of community and mutual support, can be used as a social framework to address the problems of climate change and promote environmental sustainability. The research demonstrate that Ubuntu is an ideological concept that encourages collective action on climate change, with the emphasis on individual and collective commitment to taking concrete action to address the problems of climate change. The paper shows that Ubuntu can be employed as a social tool that would enhance the cultivation of shared identity and promote the sense of shared response responsibility to develop the resilience to cope with climate change. Using qualitative and quantitative methodologies, the study establishes the imperativeness of mutual support and cooperation through the lens of Ubuntu as a human-centered scalable response to the debacle of climate change. It recommends that we can build a society that values the environment and promotes sustainable practices by encouraging community involvement in sustainable initiatives by integrating Ubuntu-based principles to our decision-making processes, collaboration, leadership, human agency and governance.

Keywords: ubuntu, climate change, humanity, collective actions, community-based

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Authors: Atefeh Heydari, Mohammadali Tavakoli, Naomie Salim

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In recent years, we have witnessed that online reviews are the most important source of customers’ opinion. They are progressively more used by individuals and organisations to make purchase and business decisions. Unfortunately, for the reason of profit or fame, frauds produce deceptive reviews to hoodwink potential customers. Their activities mislead not only potential customers to make appropriate purchasing decisions and organisations to reshape their business, but also opinion mining techniques by preventing them from reaching accurate results. Spam reviews could be divided into two main groups, i.e. multiple and singleton spam reviews. Detecting a singleton spam review that is the only review written by a user ID is extremely challenging due to lack of clue for detection purposes. Singleton spam reviews are very harmful and various features and proofs used in multiple spam reviews detection are not applicable in this case. Current research aims to propose a novel supervised technique to detect singleton spam reviews. To achieve this, various features are proposed in this study and are to be combined with the most appropriate features extracted from literature and employed in a classifier. In order to compare the performance of different classifiers, SVM and naive Bayes classification algorithms were used for model building. The results revealed that SVM was more accurate than naive Bayes and our proposed technique is capable to detect singleton spam reviews effectively.

Keywords: classification algorithms, Naïve Bayes, opinion review spam detection, singleton review spam detection, support vector machine

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Authors: Hadj Abd El Kader Benghenia, Fethi Bereksi Reguig

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In clinical medicine, blood pressure, raised blood hemodynamic monitoring is rich pathophysiological information of cardiovascular system, of course described through factors such as: blood volume, arterial compliance and peripheral resistance. In this work, we are interested in analyzing these signals to propose a detection algorithm to delineate the different sequences and especially systolic blood pressure (SBP), diastolic blood pressure (DBP), and the wave and dicrotic to do their analysis in order to extract the cardiovascular parameters.

Keywords: blood pressure, SBP, DBP, detection algorithm

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Authors: Mohit Gupta, Chialing Wei, Thomas Czerniawski

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In this paper, we have built a neural network that can detect walls on 2D sheets and subsequently create a 3D model in Revit using Dynamo. The training set includes 3500 labeled images, and the detection algorithm used is YOLO. Typically, engineers/designers make concentrated efforts to convert 2D cad drawings to 3D models. This costs a considerable amount of time and human effort. This paper makes a contribution in automating the task of 3D walls modeling. 1. Detecting Walls in 2D cad and generating 3D pop-ups in Revit. 2. Saving designer his/her modeling time in drafting elements like walls from 2D cad to 3D representation. An object detection algorithm YOLO is used for wall detection and localization. The neural network is trained over 3500 labeled images of size 256x256x3. Then, Dynamo is interfaced with the output of the neural network to pop-up 3D walls in Revit. The research uses modern technological tools like deep learning and artificial intelligence to automate the process of generating 3D walls without needing humans to manually model them. Thus, contributes to saving time, human effort, and money.

Keywords: neural networks, Yolo, 2D to 3D transformation, CAD object detection

Procedia PDF Downloads 133

Authors: Shreyas Gambhirrao, Aditya Vichare, Aniket Tembhurne, Shahuraj Bhosale

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In today's rapidly evolving environment, stress has emerged as a significant health concern across different age groups. Stress that isn't controlled, whether it comes from job responsibilities, health issues, or the never-ending news cycle, can have a negative effect on our well-being. The problem is further aggravated by the ongoing connection to technology. In this high-tech age, identifying and controlling stress is vital. In order to solve this health issue, the study focuses on three key metrics for stress detection: body temperature, heart rate, and galvanic skin response (GSR). These parameters along with the Support Vector Machine classifier assist the system to categorize stress into three groups: 1) Stressed, 2) Not stressed, and 3) Moderate stress. Proposed training model, a NodeMCU combined with particular sensors collects data in real-time and rapidly categorizes individuals based on their stress levels. Real-time stress detection is made possible by this creative combination of hardware and software.

Keywords: real time stress detection, NodeMCU, sensors, heart-rate, body temperature, galvanic skin response (GSR), support vector machine

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Authors: Abdmohammad Mousavi, Mohsen Shams, Mehdi Akbartabar Toori, Ali Mousavizadeh, Mohammad Ali Morowatisharifabad

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Introduction: The effectiveness of Transtheoretical Model constructs on dietary behavior change has been subject to questions by some studies. The objective of this study was to determine the relationship between self–efficacy and decisional balance as mediator variables and transfer obese and overweight women among the stages of behavior change of calorie balanced dietary intake. Method: In this cross-sectional study, 448 obese and overweight 20-44 years old women were selected from three health centers in Yasuj, a city in south west of Iran. Anthropometric data were measured using standard techniques. Demographic, stages of change, self-efficacy and decisional balance data were collected by questionnaires and analyzed using One–Way ANOVA and Generalized Linear Models tests. Results: Demographic and anthropometric variables were not different significantly in different stages of change related to calorie intake except the pre-high school level of education (P=.047, OR=502, 95% CI= .255 ~ .990). Mean scores of Self-efficacy ( F(4.425)= 27.09, P= .000), decisional balance (F(4.394), P= .004), and pros (F(4.430)=5.33, P=000) were different significantly in five stages of change. However, the cons did not show a significant change in this regard (F(4.400)=1.83, P=.123). Discussion: Women movement through the stages of changes for calorie intake behavior can be predicted by self efficacy, decisional balance and pros.

Keywords: transtheoretical model, stages of change, self efficacy, decisional balance, calorie intake, women

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Authors: Abdellatif Chebboub

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Agricultural production is sensitive to weather and thus directly affected by climate change. Plausible estimates of these climate change impacts require combined use of climate, crop, and economic models. Results from previous studies vary substantially due to differences in models, scenarios, and data. This paper is part of a collective effort to systematically integrate these three types of models. We focus on the economic component of the assessment, investigating how nine global economic models of agriculture represent endogenous responses to seven standardized climate change scenarios produced by two climate and five crop models. These responses include adjustments in yields, area, consumption, and international trade. We apply biophysical shocks derived from the Intergovernmental Panel on Climate Change’s representative concentration pathway with end-of-century radiative forcing of 8.5 W/m2. The mean biophysical yield effect with no incremental CO2 fertilization is a 17% reduction globally by 2050 relative to a scenario with unchanging climate. Endogenous economic responses reduce yield loss to 11%, increase area of major crops by 11%, and reduce consumption by 3%. Agricultural production, cropland area, trade, and prices show the greatest degree of variability in response to climate change, and consumption the lowest. The sources of these differences include model structure and specification; in particular, model assumptions about ease of land use conversion, intensification, and trade. This study identifies where models disagree on the relative responses to climate shocks and highlights research activities needed to improve the representation of agricultural adaptation responses to climate change.

Keywords: climate change, agriculture, weather change, danger of climate change

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Authors: Akachi Odoemene

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Climate change and land grabbing are tightly interconnected in ways that are both diverse and complex. They have impacted each other in significant ways too. Both phenomena are not only a political reality, but have diverse dire implications, especially for food and livelihood security of vulnerable populations in developing economies. The critical nexus and interactions of climate change and land grabbing remain one of the challenges of sustainable development in modern times. The nuanced understanding of the nexus, importance and implications of climate change and land grabbing are the primary focus of this chapter. It begins with conceptual clarifications, particularly arguing that the absence of some important principles of engagement underline and define a land grab. It also analyses and notes a good number of contemporary land deals as 'one-sided', in which wealthy entities connive with local elites to exploit and disposes rural poor populations. The paper not only examines both global and local factors that drive land grabbing and, in some cases, their connections with the incidence of climate change, but also explores their crucial links with such sector as agriculture. It is argued and exhibited in the paper why certain societies are susceptible to the incidence of climate change and land grabbing, while the overall consequences of these phenomena on the affected societies are further interrogated. The paper concludes that the lack of political will by global political leaders to effectively combat and resolve critical issues associated with both climate change and land grabbing remains a daunting challenge. It notes that these phenomena – climate change and land grabbing – if not abated, will certainly become another set of global tragic episodes to be regretted in the future.

Keywords: climate change, land grabbing, global governance, developing economies

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Authors: Yuanqing Li, Changyi Lei, Zhaopeng Xue, Zhuo Zheng, Yanbo Long

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In agricultural production and breeding, implementing automatic picking robot in orchard farming to reduce human labour and error is challenging. The core function of it is automatic identification based on machine vision. This paper focuses on apple detection and counting in orchards and implements several deep learning methods. Extensive datasets are used and a semi-automatic annotation method is proposed. The proposed deep learning models are in state-of-the-art YOLO family. In view of the essence of the models with various backbones, a multi-dimensional comparison in details is made in terms of counting accuracy, mAP and model memory, laying the foundation for realising automatic precision agriculture.

Keywords: agricultural object detection, deep learning, machine vision, YOLO family

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Authors: Mohammad Arabi

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The development of advanced technologies in the field of signal processing and vibration analysis has enabled more accurate analysis and fault detection in electrical systems. This research investigates the application of temporal and frequency features in detecting faults in electric motor bearings, aiming to enhance fault detection accuracy and prevent unexpected failures. The use of methods such as deep learning algorithms and neural networks in this process can yield better results. The main objective of this research is to evaluate the efficiency and accuracy of methods based on temporal and frequency features in identifying faults in electric motor bearings to prevent sudden breakdowns and operational issues. Additionally, the feasibility of using techniques such as machine learning and optimization algorithms to improve the fault detection process is also considered. This research employed an experimental method and random sampling. Vibration signals were collected from electric motors under normal and faulty conditions. After standardizing the data, temporal and frequency features were extracted. These features were then analyzed using statistical methods such as analysis of variance (ANOVA) and t-tests, as well as machine learning algorithms like artificial neural networks and support vector machines (SVM). The results showed that using temporal and frequency features significantly improves the accuracy of fault detection in electric motor bearings. ANOVA indicated significant differences between normal and faulty signals. Additionally, t-tests confirmed statistically significant differences between the features extracted from normal and faulty signals. Machine learning algorithms such as neural networks and SVM also significantly increased detection accuracy, demonstrating high effectiveness in timely and accurate fault detection. This study demonstrates that using temporal and frequency features combined with machine learning algorithms can serve as an effective tool for detecting faults in electric motor bearings. This approach not only enhances fault detection accuracy but also simplifies and streamlines the detection process. However, challenges such as data standardization and the cost of implementing advanced monitoring systems must also be considered. Utilizing temporal and frequency features in fault detection of electric motor bearings, along with advanced machine learning methods, offers an effective solution for preventing failures and ensuring the operational health of electric motors. Given the promising results of this research, it is recommended that this technology be more widely adopted in industrial maintenance processes.

Keywords: electric motor, fault detection, frequency features, temporal features

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Authors: Aysun Sezer

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Magnetic Resonance Imaging (MRI) is one of the advanced diagnostic tools for evaluating shoulder pathologies. Proton Density (PD)-weighted MRI sequences prove highly effective in detecting edema. However, they are deficient in the anatomical identification of bones due to a trauma-induced decrease in signal-to-noise ratio and blur in the traumatized cortices. Computer-based diagnostic systems require precise segmentation, identification, and localization of anatomical regions in medical imagery. Deep learning-based object detection algorithms exhibit remarkable proficiency in real-time object identification and localization. In this study, the YOLOv8 model was employed to detect humeral head and scapular regions in 665 axial PD-weighted MR images. The YOLOv8 configuration achieved an overall success rate of 99.60% and 89.90% for detecting the humeral head and scapula, respectively, with an intersection over union (IoU) of 0.5. Our findings indicate a significant promise of employing YOLOv8-based detection for the humerus and scapula regions, particularly in the context of PD-weighted images affected by both noise and intensity inhomogeneity.

Keywords: YOLOv8, object detection, humerus, scapula, IRM

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Authors: Yufeng Li, Yinan Ma, Jing Wu, Chengnian Long

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Infrared object detection aims at separating small and dim target from clutter background and its capabilities extend beyond the limits of visible light, making it invaluable in a wide range of applications such as improving safety, security, efficiency, and functionality. However, existing methods are usually sensitive to the noise of the input infrared image, leading to a decrease in target detection accuracy and an increase in the false alarm rate in high-noise environments. To address this issue, an infrared small target detection algorithm called YOLO-IR is proposed in this paper to improve the robustness to high infrared noise. To address the problem that high noise significantly reduces the clarity and reliability of target features in infrared images, we design a soft-threshold coordinate attention mechanism to improve the model’s ability to extract target features and its robustness to noise. Since the noise may overwhelm the local details of the target, resulting in the loss of small target features during depth down-sampling, we propose a deep and shallow feature fusion neck to improve the detection accuracy. In addition, because the generalized Intersection over Union (IoU)-based loss functions may be sensitive to noise and lead to unstable training in high-noise environments, we introduce a Wasserstein-distance based loss function to improve the training of the model. The experimental results show that YOLO-IR achieves a 5.0% improvement in recall and a 6.6% improvement in F1-score over existing state-of-art model.

Keywords: infrared small target detection, high noise, robustness, soft-threshold coordinate attention, feature fusion

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Authors: Yun-Tao Zhang, Jong-Yeop Bae, Whoi-Yul Kim

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Background modeling and subtraction in video analysis has been widely proved to be an effective method for moving objects detection in many computer vision applications. Over the past years, a large number of approaches have been developed to tackle different types of challenges in this field. However, the dynamic background and illumination variations are two of the most frequently occurring issues in the practical situation. This paper presents a new two-layer model based on codebook algorithm incorporated with local binary pattern (LBP) texture measure, targeted for handling dynamic background and illumination variation problems. More specifically, the first layer is designed by block-based codebook combining with LBP histogram and mean values of RGB color channels. Because of the invariance of the LBP features with respect to monotonic gray-scale changes, this layer can produce block-wise detection results with considerable tolerance of illumination variations. The pixel-based codebook is employed to reinforce the precision from the outputs of the first layer which is to eliminate false positives further. As a result, the proposed approach can greatly promote the accuracy under the circumstances of dynamic background and illumination changes. Experimental results on several popular background subtraction datasets demonstrate a very competitive performance compared to previous models.

Keywords: background subtraction, codebook model, local binary pattern, dynamic background, illumination change

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Authors: Adnan A. Y. Mustafa

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Image matching is a fundamental problem that arises frequently in many aspects of robot and computer vision. It can become a time-consuming process when matching images to a database consisting of hundreds of images, especially if the images are big. One approach to reducing the time complexity of the matching process is to reduce the search space in a pre-matching stage, by simply removing dissimilar images quickly. The Probabilistic Matching Model for Binary Images (PMMBI) showed that dissimilarity detection between binary images can be accomplished quickly by random pixel mapping and is size invariant. The model is based on the gamma binary similarity distance that recognizes an image and its inverse as containing the same scene and hence considers them to be the same image. However, in many applications, an image and its inverse are not treated as being the same but rather dissimilar. In this paper, we present a comparative analysis of dissimilarity detection between PMMBI based on the gamma binary similarity distance and a modified PMMBI model based on a similarity distance that does distinguish between an image and its inverse as being dissimilar.

Keywords: binary image, dissimilarity detection, probabilistic matching model for binary images, image mapping

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Authors: Hemant Kumar

Abstract:

Brahmaputra River is known according to the Hindu mythology the son of the Lord Brahma. According to this name, the river Brahmaputra creates mass destruction during the monsoon season in Assam, India. It is a state situated in North-East part of India. This is one of the essential states out of the seven countries of eastern India, where almost all entire Brahmaputra flow carried out. The other states carry their tributaries. In the present case study, the spatial analysis performed in this specific case the number of MODIS data are acquired. In the method of detecting the change, the spray content was found during heavy rainfall and in the flooded monsoon season. By this method, particularly the analysis over the Brahmaputra outflow determines the flooded season. The charged particle-associated in aerosol content genuinely verifies the heavy water content below the ground surface, which is validated by trend analysis through rainfall spectrum data. This is confirmed by in-situ sampled view data from a different position of Brahmaputra River. Further, a Hyperion Hyperspectral 30 m resolution data were used to scan the sediment deposits, which is also confirmed by in-situ sampled view data from a different position.

Keywords: aerosol, change detection, spatial analysis, trend analysis

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Authors: Cebrail Çiflikli, Emre Öner Tartan

Abstract:

Parallel to the fast worldwide increase of elderly population and spreading unhealthy life habits, there is a significant rise in the number of patients and health problems. The supervision of people who have health problems and oversight in detection of people who have potential risks, bring a considerable cost to health system and increase workload of physician. To provide an efficient solution to this problem, in the recent years mobile applications have shown their potential for wide usage in health monitoring. In this paper we present an Android mobile application that records and evaluates ECG signal using Pan-Tompkins algorithm for QRS detection. The application model includes an alarm mechanism that is proposed to be used for sending message including abnormality information and location information to health supervisor.

Keywords: Android mobile application, ECG monitoring, QRS detection, Pan-Tompkins Algorithm

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Authors: Ilia Zharikov, Philipp Nikitin, Ilia Vasiliev, Vladimir Dokholyan

Abstract:

With the increasing popularity of document analysis and recognition systems, text detection (TD) and optical character recognition (OCR) in document images become challenging tasks. However, according to our best knowledge, no publicly available datasets for these particular problems exist. In this paper, we introduce a Distorted Document Images dataset (DDI-100) and provide a detailed analysis of the DDI-100 in its current state. To create the dataset we collected 7000 unique document pages, and extend it by applying different types of distortions and geometric transformations. In total, DDI-100 contains more than 100,000 document images together with binary text masks, text and character locations in terms of bounding boxes. We also present an analysis of several state-of-the-art TD and OCR approaches on the presented dataset. Lastly, we demonstrate the usefulness of DDI-100 to improve accuracy and stability of the considered TD and OCR models.

Keywords: document analysis, open dataset, optical character recognition, text detection

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Authors: Recep Kesli, Huseyin Bilgin, Yasar Unlu, Gokhan Gungor

Abstract:

Aim: The aim of this study was to compare diagnostic values of two different molecular-based tests (GenoType® HelicoDR ve Seeplex® H. pylori-ClaR- ACE Detection) in detection presence of the H. pylori from gastric biopsy specimens. In addition to this also was aimed to determine resistance ratios of H. pylori strains against to clarytromycine and quinolone isolated from gastric biopsy material cultures by using both the genotypic (GenoType® HelicoDR, Seeplex ® H. pylori -ClaR- ACE Detection) and phenotypic (gradient strip, E-test) methods. Material and methods: A total of 266 patients who admitted to Konya Education and Research Hospital Department of Gastroenterology with dyspeptic complaints, between January 2011-June 2013, were included in the study. Microbiological and histopathological examinations of biopsy specimens taken from antrum and corpus regions were performed. The presence of H. pylori in all the biopsy samples was investigated by five differnt dignostic methods together: culture (C) (Portagerm pylori-PORT PYL, Pylori agar-PYL, GENbox microaer, bioMerieux, France), histology (H) (Giemsa, Hematoxylin and Eosin staining), rapid urease test (RUT) (CLOtest, Cimberly-Clark, USA), and two different molecular tests; GenoType® HelicoDR, Hain, Germany, based on DNA strip assay, and Seeplex ® H. pylori -ClaR- ACE Detection, Seegene, South Korea, based on multiplex PCR. Antimicrobial resistance of H. pylori isolates against clarithromycin and levofloxacin was determined by GenoType® HelicoDR, Seeplex ® H. pylori -ClaR- ACE Detection, and gradient strip (E-test, bioMerieux, France) methods. Culture positivity alone or positivities of both histology and RUT together was accepted as the gold standard for H. pylori positivity. Sensitivity and specificity rates of two molecular methods used in the study were calculated by taking the two gold standards previously mentioned. Results: A total of 266 patients between 16-83 years old who 144 (54.1 %) were female, 122 (45.9 %) were male were included in the study. 144 patients were found as culture positive, and 157 were H and RUT were positive together. 179 patients were found as positive with GenoType® HelicoDR and Seeplex ® H. pylori -ClaR- ACE Detection together. Sensitivity and specificity rates of studied five different methods were found as follows: C were 80.9 % and 84.4 %, H + RUT were 88.2 % and 75.4 %, GenoType® HelicoDR were 100 % and 71.3 %, and Seeplex ® H. pylori -ClaR- ACE Detection were, 100 % and 71.3 %. A strong correlation was found between C and H+RUT, C and GenoType® HelicoDR, and C and Seeplex ® H. pylori -ClaR- ACE Detection (r:0.644 and p:0.000, r:0.757 and p:0.000, r:0.757 and p:0.000, respectively). Of all the isolated 144 H. pylori strains 24 (16.6 %) were detected as resistant to claritromycine, and 18 (12.5 %) were levofloxacin. Genotypic claritromycine resistance was detected only in 15 cases with GenoType® HelicoDR, and 6 cases with Seeplex ® H. pylori -ClaR- ACE Detection. Conclusion: In our study, it was concluded that; GenoType® HelicoDR and Seeplex ® H. pylori -ClaR- ACE Detection was found as the most sensitive diagnostic methods when comparing all the investigated other ones (C, H, and RUT).

Keywords: Helicobacter pylori, GenoType® HelicoDR, Seeplex ® H. pylori -ClaR- ACE Detection, antimicrobial resistance

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Authors: Brendah Nagula, Omolo Fredrick Okalebo, Ronald Ssengendo, Ivan Bamweyana

Abstract:

The Kampala Physical Development Plan (KPDP) was developed in 2012 and projected both long term and short term developments within the City .The purpose of the plan was to not only shape the city into a spatially planned area but also to control the urban sprawl trends that had expanded with pronounced instances of informal settlements. This plan was approved by the National Physical Planning Board and a signature was appended by the Minister in 2013. Much as the KPDP plan has been implemented using different approaches such as detailed planning, development control, subdivision planning, carrying out construction inspections, greening and beautification, there is still limited knowledge on the level of conformance towards this plan. Therefore, it is yet to be determined whether it has been effective in shaping the City into an ideal spatially planned area. Attaining a clear picture of the level of conformance towards the KPDP 2012 through evaluation between the planned and the existing land use in Kampala City was performed. Methods such as Supervised Classification and Post Classification Change Detection were adopted to perform this evaluation. Scrutiny of findings revealed Central Division registered the lowest level of conformance to the planning standards specified in the KPDP 2012 followed by Nakawa, Rubaga, Kawempe, and Makindye. Furthermore, mixed-use development was identified as the land use with the highest level of non-conformity of 25.11% and institutional land use registered the highest level of conformance of 84.45 %. The results show that the aspect of location was not carefully considered while allocating uses in the KPDP whereby areas located near the Central Business District have higher land rents and hence require uses that ensure profit maximization. Also, the prominence of development towards mixed-use denotes an increased demand for land towards compact development that was not catered for in the plan. Therefore in order to transform Kampala city into a spatially planned area, there is need to carefully develop detailed plans especially for all the Central Division planning precincts indicating considerations for land use densification.

Keywords: spatial plan, post classification change detection, Kampala city, landuse

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Authors: Ganesh B. Shinde, Vijaya B. Musande

Abstract:

Cotton crop identification based on the timely information has significant advantage to the different implications of food, economic and environment. Due to the significant advantages, the accurate detection of cotton crop regions using supervised learning procedure is challenging problem in remote sensing. Here, classifiers on the direct image are played a major role but the results are not much satisfactorily. In order to further improve the effectiveness, variety of vegetation indices are proposed in the literature. But, recently, the major challenge is to find the better vegetation indices for the cotton crop identification through the proposed methodology. Accordingly, fuzzy c-means clustering is combined with neural network algorithm, trained by Levenberg-Marquardt for cotton crop classification. To experiment the proposed method, five LISS-III satellite images was taken and the experimentation was done with six vegetation indices such as Simple Ratio, Normalized Difference Vegetation Index, Enhanced Vegetation Index, Green Atmospherically Resistant Vegetation Index, Wide-Dynamic Range Vegetation Index, Green Chlorophyll Index. Along with these indices, Green Leaf Area Index is also considered for investigation. From the research outcome, Green Atmospherically Resistant Vegetation Index outperformed with all other indices by reaching the average accuracy value of 95.21%.

Keywords: Fuzzy C-Means clustering (FCM), neural network, Levenberg-Marquardt (LM) algorithm, vegetation indices

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