Search results for: image registration techniques

Authors: Shagufta Tabassum

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The study of dielectric properties in a binary mixture of liquids is very useful to understand the liquid structure, molecular interaction, dynamics, and kinematics of the mixture. Time-domain reflectometry (TDR) is a powerful tool for studying the cooperation and molecular dynamics of the H-bonded system. In this paper, we discuss the basic calibration and normalization procedure for time-domain reflectometry measurements. Our approach is to explain the different types of error occur during TDR measurements and how these errors can be eliminated or minimized.

Keywords: time domain reflectometry measurement techinque, cable and connector loss, oscilloscope loss, and normalization technique

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Authors: Manik Sharma, Ganapathy Krishnamurthi

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The cost of some drugs and medical treatments has risen in recent years, that many patients are having to go without. A classification project could make researchers more efficient. One of the more surprising reasons behind the cost is how long it takes to bring new treatments to market. Despite improvements in technology and science, research and development continues to lag. In fact, finding new treatment takes, on average, more than 10 years and costs hundreds of millions of dollars. If successful, we could dramatically improve the industry's ability to model cellular images according to their relevant biology. In turn, greatly decreasing the cost of treatments and ensure these treatments get to patients faster. This work aims at solving a part of this problem by creating a cellular image classification model which can decipher the genetic perturbations in cell (occurring naturally or artificially). Another interesting question addressed is what makes the deep-learning model decide in a particular fashion, which can further help in demystifying the mechanism of action of certain perturbations and paves a way towards the explainability of the deep-learning model.

Keywords: cellular images, genetic perturbations, deep-learning, explainability

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Authors: Nadjette Belhamra, Abdelouahed Chala, Ibrahim Guasmi

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Titanium nitride coatings have been extensively used in industry, such as in cutting tools. TiN coating were deposited by chemical vapour deposition (CVD) on carbide insert at a temperature between 850°C and 1100°C, which often exceeds the hardening treatment temperature of the metals. The objective of this work is to realize, to characterize of TiN coating and to apply it in the turning of steel 42CrMo4 under lubrification. Various experimental techniques were employed for the microstructural characterization of the coatings, e. g., X-ray diffraction (XRD), scanning electron microscope (SEM) model JOEL JSM-5900 LV, equipped with energy dispersive X-ray (EDX). The results show that TiN-coated demonstrate a good wear resistance.

Keywords: hard coating TiN, carbide inserts, machining, turning, wear

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Authors: Vineetha Bettaiah, Heggere S. Ranganath

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This paper presents a Hierarchical Piecewise Linear Approximation (HPLA) for the representation of time series data in which the time series is treated as a curve in the time-amplitude image space. The curve is partitioned into segments by choosing perceptually important points as break points. Each segment between adjacent break points is recursively partitioned into two segments at the best point or midpoint until the error between the approximating line and the original curve becomes less than a pre-specified threshold. The HPLA representation achieves dimensionality reduction while preserving prominent local features and general shape of time series. The representation permits course-fine processing at different levels of details, allows flexible definition of similarity based on mathematical measures or general time series shape, and supports time series data mining operations including query by content, clustering and classification based on whole or subsequence similarity.

Keywords: data mining, dimensionality reduction, piecewise linear representation, time series representation

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Authors: Blessing Olamide Taiwo, Andrew Palangio, Chirag Savaliya, Jenil Patel

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Oversized material resulting from blasting presents a notable drawback in the transportation of run-off-mine material due to increased expenses associated with handling, decreased efficiency in loading, and greater wear on digging equipment. Its irregular size and weight demand additional resources and time for secondary breakage, impacting overall productivity and profitability. This paper addresses the limitations of interpreting image analysis software results and applying them to the assessment of blast-generated oversized materials. This comprehensive guide utilizes both ortho mosaic and digital photos to provide critical approaches for optimizing fragmentation analysis and improving decision-making in mining operations. It briefly covers post-blast assessment, blast block heat map interpretation, and material loading decision-making recommendations.

Keywords: blast result assessment, WipFrag, oversize identification, orthomosaic images, production optimization

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Authors: R. Horchani

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Optical pumping technique with laser fields combined with photo-association of ultra-cold atoms leads to control on demand the vibrational and/or the rotational population of molecules. Here, we review the basic concepts and main steps should be followed, including the excitation schemes and detection techniques we use to achieve the ro-vibrational cooling of Cs2 molecules. We also discuss the extension of this technique to other molecules. In addition, we present a theoretical model used to support the experiment. These simulations can be widely used for the preparation of various experiments since they allow the optimization of several important experimental parameters.

Keywords: cold molecule, photo-association, optical pumping, vibrational and rotational cooling

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Authors: Soheila Sadeghi

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Accurate cost prediction is essential for effective project management and decision-making in the construction industry. This study aims to develop a cost prediction model for construction projects using Machine Learning techniques and Particle Swarm Optimization (PSO). The research utilizes a comprehensive dataset containing project cost estimates, actual costs, resource details, and project performance metrics from a road reconstruction project. The methodology involves data preprocessing, feature selection, and the development of an Artificial Neural Network (ANN) model optimized using PSO. The study investigates the impact of various input features, including cost estimates, resource allocation, and project progress, on the accuracy of cost predictions. The performance of the optimized ANN model is evaluated using metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-squared. The results demonstrate the effectiveness of the proposed approach in predicting project costs, outperforming traditional benchmark models. The feature selection process identifies the most influential variables contributing to cost variations, providing valuable insights for project managers. However, this study has several limitations. Firstly, the model's performance may be influenced by the quality and quantity of the dataset used. A larger and more diverse dataset covering different types of construction projects would enhance the model's generalizability. Secondly, the study focuses on a specific optimization technique (PSO) and a single Machine Learning algorithm (ANN). Exploring other optimization methods and comparing the performance of various ML algorithms could provide a more comprehensive understanding of the cost prediction problem. Future research should focus on several key areas. Firstly, expanding the dataset to include a wider range of construction projects, such as residential buildings, commercial complexes, and infrastructure projects, would improve the model's applicability. Secondly, investigating the integration of additional data sources, such as economic indicators, weather data, and supplier information, could enhance the predictive power of the model. Thirdly, exploring the potential of ensemble learning techniques, which combine multiple ML algorithms, may further improve cost prediction accuracy. Additionally, developing user-friendly interfaces and tools to facilitate the adoption of the proposed cost prediction model in real-world construction projects would be a valuable contribution to the industry. The findings of this study have significant implications for construction project management, enabling proactive cost estimation, resource allocation, budget planning, and risk assessment, ultimately leading to improved project performance and cost control. This research contributes to the advancement of cost prediction techniques in the construction industry and highlights the potential of Machine Learning and PSO in addressing this critical challenge. However, further research is needed to address the limitations and explore the identified future research directions to fully realize the potential of ML-based cost prediction models in the construction domain.

Keywords: cost prediction, construction projects, machine learning, artificial neural networks, particle swarm optimization, project management, feature selection, road reconstruction

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Authors: Mohd Zamri Osman, Mohd Aizaini Maarof, Mohd Foad Rohani

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Human skin detection recognized as the primary step in most of the applications such as face detection, illicit image filtering, hand recognition and video surveillance. The performance of any skin detection applications greatly relies on the two components: feature extraction and classification method. Skin color is the most vital information used for skin detection purpose. However, color feature alone sometimes could not handle images with having same color distribution with skin color. A color feature of pixel-based does not eliminate the skin-like color due to the intensity of skin and skin-like color fall under the same distribution. Hence, the statistical color analysis will be exploited such mean and standard deviation as an additional feature to increase the reliability of skin detector. In this paper, we studied the effectiveness of statistical color feature for human skin detection. Furthermore, the paper analyzed the integrated color and texture using eight classifiers with three color spaces of RGB, YCbCr, and HSV. The experimental results show that the integrating statistical feature using Random Forest classifier achieved a significant performance with an F1-score 0.969.

Keywords: color space, neural network, random forest, skin detection, statistical feature

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Authors: S. M. Rifat, André L. Marchildon, Mark F. Tachie

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The effect of upstream surface roughness over a smooth forward facing step in an open channel was investigated using a particle image velocimetry technique. Three different upstream surface topographies consisting of hydraulically smooth wall, sandpaper 36 grit and sand grains were examined. Besides the wall roughness conditions, all other upstream flow characteristics were kept constant. It was also observed that upstream roughness decreased the approach velocity by 2% and 10% but increased the turbulence intensity by 14% and 35% at the wall-normal distance corresponding to the top plane of the step compared to smooth upstream. The results showed that roughness decreased the reattachment lengths by 14% and 30% compared to smooth upstream. Although the magnitudes of maximum positive and negative Reynolds shear stress in separated and reattached region were 0.02Ue for all the cases, the physical size of both the maximum and minimum contour levels were decreased by increasing upstream roughness.

Keywords: forward facing step, open channel, separated and reattached turbulent flows, wall roughness

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Authors: Jeena R. S., Sukesh Kumar A.

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Stroke is one of the major reasons of adult disability and morbidity in many of the developing countries like India. Early diagnosis of stroke is essential for timely prevention and cure. Various conventional statistical methods and computational intelligent models have been developed for predicting the risk and outcome of stroke. This research work focuses on a multilevel approach for predicting the occurrence of stroke based on various risk factors and invasive techniques like retinal imaging. This risk prediction model can aid in clinical decision making and help patients to have an improved and reliable risk prediction.

Keywords: prediction, retinal imaging, risk factors, stroke

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Authors: Sezin Ekşioğlu

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Multilabel classification has a huge importance for several applications, it is also a challenging research topic. It is a kind of supervised learning that contains binary targets. The distance between multilabel and binary classification is having more than one class in multilabel classification problems. Features can belong to one class or many classes. There exists a wide range of applications for multi label prediction such as image labeling, text categorization, gene functionality. Even though features are classified in many classes, they may not always be properly classified. There are many ensemble methods for the classification. However, most of the researchers have been concerned about better multilabel methods. Especially little ones focus on both efficiency of classifiers and pairwise relationships at the same time in order to implement better multilabel classification. In this paper, we worked on modified ensemble methods by getting benefit from k-Nearest Neighbors and neural network structure to address issues within a beneficial way and to get better impacts from the multilabel classification. Publicly available datasets (yeast, emotion, scene and birds) are performed to demonstrate the developed algorithm efficiency and the technique is measured by accuracy, F1 score and hamming loss metrics. Our algorithm boosts benchmarks for each datasets with different metrics.

Keywords: multilabel, classification, neural network, KNN

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Authors: Nizar Souiden, Riadh Ladhari

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Like tobacco and alcohol, recreational marijuana falls under the umbrella of ‘sin’ industries’. Notwithstanding this general negative image surrounding marijuana use, some scholars argue that most of the widely believed claims made about recreational marijuana users are irrelevant and that marijuana use can even improve individuals’ decision-making. This study intends to shed light on this particular product category (i.e., marijuana) often overlooked or portrayed as taboo from a business view. More specifically, it investigates whether legalizing the consumption of recreational marijuana would be perceived as ethical and whether companies/organizations involved in the commercialization of this particular product would be held socially responsible. Based on primary data collected in Canada, this study aims to answer the following questions: 1) What moral thoughts do individuals hold with regard to the consumption of recreational marijuana? 2) How do these moral thoughts determine consumers’ attitude toward the consumption of recreational marijuana? Regardless of the legalization of recreational marijuana in some countries such as Canada, probing people’s opinions, and investigating their attitudes toward the consumption of recreational marijuana is of important interest to different stakeholders such as consumers, public organizations, private businesses, and trade associations.

Keywords: recreational marijuana, moral thoughts, ethics, attitude

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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: S. M. Kulkarni, D. S. Bormane, S. L. Nalbalwar

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The rapid development in the technology have dramatic impact on the medical health care field. Medical data base obtained with latest machines like CT Machine, MRI scanner requires large amount of memory storage and also it requires large bandwidth for transmission of data in telemedicine applications. Thus, there is need for video compression. As the database of medical images contain number of frames (slices), hence while coding of these images there is need of motion estimation. Motion estimation finds out movement of objects in an image sequence and gets motion vectors which represents estimated motion of object in the frame. In order to reduce temporal redundancy between successive frames of video sequence, motion compensation is preformed. In this paper three step search (TSS) block matching algorithm is implemented on different types of video sequences. It is shown that three step search algorithm produces better quality performance and less computational time compared with exhaustive full search algorithm.

Keywords: block matching, exhaustive search motion estimation, three step search, video compression

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Authors: Beloulou Mohamed Lamine, Fedili Benamar, Meliani Walid, Chaid Dalila, Lamara Abdelhak

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Introduction: Post-surgical chronic pain (PSCP) is a pathological condition with a rather complex etiopathogenesis that extensively involves sensitization processes and neuronal damage. The neuropathic component of these pains is almost always present, with variable expression depending on the type of surgery. Objective: To assess the presumed beneficial effect of Regional Anesthesia-Analgesia Techniques (RAAT) on the development of post-surgical chronic neuropathic pain (PSCNP) in various surgical procedures. Patients and Methods: A comparative study involving 510 patients distributed across five surgical models (mastectomy, thoracotomy, hernioplasty, cholecystectomy, and major abdominal-pelvic surgery) and randomized into two groups: Group A (240) receiving conventional postoperative analgesia and Group B (270) receiving balanced analgesia, including the implementation of a Regional Anesthesia-Analgesia Technique (RAAT). These patients were longitudinally followed over a 6-month period, with postsurgical chronic neuropathic pain (PSCNP) defined by a Neuropathic Pain Score DN2≥ 3. Comparative measurements through univariate and multivariable analyses were performed to identify associations between the development of PSCNP and certain predictive factors, including the presumed preventive impact (protective effect) of RAAT. Results: At the 6th month post-surgery, 419 patients were analyzed (Group A= 196 and Group B= 223). The incidence of PSCNP was 32.2% (n=135). Among these patients with chronic pain, the prevalence of neuropathic pain was 37.8% (95% CI: [29.6; 46.5]), with n=51/135. It was significantly lower in Group B compared to Group A, with respective percentages of 31.4% vs. 48.8% (p-value = 0.035). The most significant differences were observed in breast and thoracopulmonary surgeries. In a multiple regression analysis, two predictors of PSCNP were identified: the presence of preoperative pain at the surgical site as a risk factor (OR: 3.198; 95% CI [1.326; 7.714]) and RAAT as a protective factor (OR: 0.408; 95% CI [0.173; 0.961]). Conclusion: The neuropathic component of PSCNP can be observed in different types of surgeries. Regional analgesia included in a multimodal approach to postoperative pain management has proven to be effective for acute pain and seems to have a preventive impact on the development of PSCNP and its neuropathic nature, particularly in surgeries that are more prone to chronicization.

Keywords: post-surgical chronic pain, post-surgical chronic neuropathic pain, regional anesthesia-analgesia techniques, neuropathic pain score DN2, preventive impact

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Authors: Wanida Wonsawat

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Bioreduction of silver nanoparticles (AgNPs) from silver ions (Ag+) using water extract of Thai basil leaf was successfully carried out. The basil leaf extract provided a reducing agent and stabilizing agent for a synthesis of metal nanoparticles. Silver nanoparticles received from cut and uncut basil leaf was compared. The resulting silver nanoparticles are characterized by UV-Vis spectroscopy. The maximum intensities of silver nanoparticle from cut and uncut basil leaf were 410 and 420, respectively. The techniques involved are simple, eco-friendly and rapid.

Keywords: basil leaves, silver nanoparticles, green synthesis, plant extract

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Authors: Mehran Barani Shikhrobat, Roger Flanagan

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The construction planning and scheduling based on using the current tools and techniques is resulted deterministic in nature (Gantt chart, CPM) or applying a very little probability of completion (PERT) for each task. However, every project embodies assumptions and influences and should start with a complete set of clearly defined goals and constraints that remain constant throughout the duration of the project. Construction planners continue to apply the traditional methods and tools of “hard” project management that were developed for “ideal projects,” neglecting the potential influence of complexity on the design and construction process. The aim of this research is to investigate the emergence and growth of complexity in project planning and to provide a model to consider the influence of complexity on the total project duration at the post-contract award pre-construction stage of a project. The literature review showed that complexity originates from different sources of environment, technical, and workflow interactions. They can be divided into two categories of complexity factors, first, project tasks, and second, project organisation management. Project tasks may originate from performance, lack of resources, or environmental changes for a specific task. Complexity factors that relate to organisation and management refer to workflow and interdependence of different parts. The literature review highlighted the ineffectiveness of traditional tools and techniques in planning for complexity. However, this research focus on understanding the fundamental causes of the complexity of construction projects were investigated through a questionnaire with industry experts. The results were used to develop a model that considers the core complexity factors and their interactions. System dynamics were used to investigate the model to consider the influence of complexity on project planning. Feedback from experts revealed 20 major complexity factors that impact project planning. The factors are divided into five categories known as core complexity factors. To understand the weight of each factor in comparison, the Analytical Hierarchy Process (AHP) analysis method is used. The comparison showed that externalities are ranked as the biggest influence across the complexity factors. The research underlines that there are many internal and external factors that impact project activities and the project overall. This research shows the importance of considering the influence of complexity on the project master plan undertaken at the post-contract award pre-construction phase of a project.

Keywords: project planning, project complexity measurement, planning uncertainty management, project risk management, strategic project scheduling

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Authors: Jaber Nikpouri, Arsalan Amralizadeh

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In robotics manipulators, the trajectory should be optimum, thus the torque of the robot can be minimized in order to save power. This paper includes an optimal path planning scheme for a robotic manipulator. Recently, techniques based on metaheuristics of natural computing, mainly evolutionary algorithms (EA), have been successfully applied to a large number of robotic applications. In this paper, the improved BBO algorithm is used to minimize the objective function in the presence of different obstacles. The simulation represents that the proposed optimal path planning method has satisfactory performance.

Keywords: biogeography-based optimization, path planning, obstacle detection, robotic manipulator

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Authors: Desplanches Maxime

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Accurate estimation of the remaining useful life of lithium-ion batteries for electronic devices is crucial. Data-driven methodologies encounter challenges related to data volume and acquisition protocols, particularly in capturing a comprehensive range of aging indicators. To address these limitations, we propose a hybrid approach that integrates an electrochemical model with state-of-the-art data analysis techniques, yielding a comprehensive database. Our methodology involves infusing an aging phenomenon into a Newman model, leading to the creation of an extensive database capturing various aging states based on non-destructive parameters. This database serves as a robust foundation for subsequent analysis. Leveraging advanced data analysis techniques, notably principal component analysis and t-Distributed Stochastic Neighbor Embedding, we extract pivotal information from the data. This information is harnessed to construct a regression function using either random forest or support vector machine algorithms. The resulting predictor demonstrates a 5% error margin in estimating remaining battery life, providing actionable insights for optimizing usage. Furthermore, the database was built from the Newman model calibrated for aging and performance using data from a European project called Teesmat. The model was then initialized numerous times with different aging values, for instance, with varying thicknesses of SEI (Solid Electrolyte Interphase). This comprehensive approach ensures a thorough exploration of battery aging dynamics, enhancing the accuracy and reliability of our predictive model. Of particular importance is our reliance on the database generated through the integration of the electrochemical model. This database serves as a crucial asset in advancing our understanding of aging states. Beyond its capability for precise remaining life predictions, this database-driven approach offers valuable insights for optimizing battery usage and adapting the predictor to various scenarios. This underscores the practical significance of our method in facilitating better decision-making regarding lithium-ion battery management.

Keywords: Li-ion battery, aging, diagnostics, data analysis, prediction, machine learning, electrochemical model, regression

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Authors: Puneet Chawla, Balwinder Singh

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Recently, increased attention has been devoted to the voltage instability phenomenon in power systems. Many techniques have been proposed in the literature for evaluating and predicting voltage stability using steady state analysis methods. In this paper, P-V and Q-V curves have been generated for a 57 bus Patiala Rajpura circle of India. The power-flow program is developed in MATLAB using Newton-Raphson method. Using Q-V curves, the weakest bus of the power system and the maximum reactive power change permissible on that bus is calculated. STATCOMs are placed on the weakest bus to improve the voltage and hence voltage stability and also the power transmission capability of the line.

Keywords: voltage stability, reactive power, power flow, weakest bus, STATCOM

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Authors: Tesfaye Alamirew Dessie

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Our study focuses on developing a Model Predictive Controller (MPC) and evaluating it against a traditional PID for a pasteurization process. Utilizing system identification from the experimental data, the dynamics of the pasteurization process were calculated. Using best fit with data validation, residual, and stability analysis, the quality of several model architectures was evaluated. The validation data fit the auto-regressive with exogenous input (ARX322) model of the pasteurization process by roughly 80.37 percent. The ARX322 model structure was used to create MPC and PID control techniques. After comparing controller performance based on settling time, overshoot percentage, and stability analysis, it was found that MPC controllers outperform PID for those parameters.

Keywords: MPC, PID, ARX, pasteurization

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Authors: Henok Hailemariam, Frank Wuttke

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Collapsible soils are weak soils that appear to be stable in their natural state, normally dry condition, but rapidly deform under saturation (wetting), thus generating large and unexpected settlements which often yield disastrous consequences for structures unwittingly built on such deposits. In this study, a prediction model for the relative subsidence of stressed collapsible soils based on dielectric permittivity measurement is presented. Unlike most existing methods for soil subsidence prediction, this model does not require moisture content as an input parameter, thus providing the opportunity to obtain accurate estimation of the relative subsidence of collapsible soils using dielectric measurement only. The prediction model is developed based on an existing relative subsidence prediction model (which is dependent on soil moisture condition) and an advanced theoretical frequency and temperature-dependent electromagnetic mixing equation (which effectively removes the moisture content dependence of the original relative subsidence prediction model). For large scale sub-surface soil exploration purposes, the spatial sub-surface soil dielectric data over wide areas and high depths of weak (collapsible) soil deposits can be obtained using non-destructive high frequency electromagnetic (HF-EM) measurement techniques such as ground penetrating radar (GPR). For laboratory or small scale in-situ measurements, techniques such as an open-ended coaxial line with widely applicable time domain reflectometry (TDR) or vector network analysers (VNAs) are usually employed to obtain the soil dielectric data. By using soil dielectric data obtained from small or large scale non-destructive HF-EM investigations, the new model can effectively predict the relative subsidence of weak soils without the need to extract samples for moisture content measurement. Some of the resulting benefits are the preservation of the undisturbed nature of the soil as well as a reduction in the investigation costs and analysis time in the identification of weak (problematic) soils. The accuracy of prediction of the presented model is assessed by conducting relative subsidence tests on a collapsible soil at various initial soil conditions and a good match between the model prediction and experimental results is obtained.

Keywords: collapsible soil, dielectric permittivity, moisture content, relative subsidence

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Authors: M. Beyzaiea, S. Mohammadia

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High aspect ratio metallic (silver, cobalt) nanowire arrays were fabricated using ac electrodeposition techniques into the nanoporous alumina template. The template with long pore dept fabricated by hard anodization (HA) and thinned for ac electrodeposition. Template preparation was done in short time by using HA technique and high speed thing process. The TEM and XRD investigation confirm the three dimensional nucleation growth mechanism of metallic nanowire inside the nanoporous alumina that fabricated by HA process.

Keywords: metallic, nanowire, nanoporous alumina, ac electrodeposition

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Authors: Paulo Cesar Pereira Junior, Alexandre Monteiro, Rafael da Luz Ribeiro, Antonio Carlos Sobieranski, Aldo von Wangenheim

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In this paper, we present a comparison between convolutional neural networks and classical computer vision approaches, for the specific precision agriculture problem of weed mapping on sugarcane fields aerial images. A systematic literature review was conducted to find which computer vision methods are being used on this specific problem. The most cited methods were implemented, as well as four models of convolutional neural networks. All implemented approaches were tested using the same dataset, and their results were quantitatively and qualitatively analyzed. The obtained results were compared to a human expert made ground truth for validation. The results indicate that the convolutional neural networks present better precision and generalize better than the classical models.

Keywords: convolutional neural networks, deep learning, digital image processing, precision agriculture, semantic segmentation, unmanned aerial vehicles

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Authors: Thanakrit Taweesoontorn, Sarucha Yanyong, Poom Konghuayrob

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Robotic arms have gained popularity in various industries due to their accuracy and efficiency. This research proposes a method for bin-picking tasks using the Cobot, combining the YOLOv5 CNNs model for object detection and pose estimation with traditional feature detection (FAST), feature description (BRISK), and matching algorithms. By integrating these algorithms and utilizing a small-scale depth sensor camera for capturing depth and color images, the system achieves real-time object detection and accurate pose estimation, enabling the robotic arm to pick objects correctly in both position and orientation. Furthermore, the proposed method is implemented within the ROS framework to provide a seamless platform for robotic control and integration. This integration of robotics, cameras, and AI technology contributes to the development of industrial robotics, opening up new possibilities for automating challenging tasks and improving overall operational efficiency.

Keywords: robotic vision, image processing, applications of robotics, artificial intelligent

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Authors: Metuku Shyamsunder, Kakarla Subbarao, P. Prasanna

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In radars, an electromagnetic waveform is sent, and an echo of the same signal is received by the receiver. From this received signal, by extracting various parameters such as round trip delay, Doppler frequency it is possible to find distance, speed, altitude, etc. However, nowadays as the technology increases, intruders are intercepting transmitted signal as it reaches them, and they will be extracting the characteristics and trying to modify them. So there is a need to develop a system whose signal cannot be identified by no cooperative intercept receivers. That is why LPI radars came into existence. In this paper, a brief discussion on LPI radar and its modulation (polytime code (PT1)), detection (cyclostationary (DFSM & FAM) techniques such as DFSM, FAM are presented and compared with respect to computational complexity.

Keywords: LPI radar, polytime codes, cyclostationary DFSM, FAM

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Authors: Cynthia Adlerstein

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Physical learning environment (PLE) has been considered, after family and educators, as the third teacher. There have been conflicting and converging viewpoints on the role of the physical dimensions of places to learn, in facilitating educational innovation and quality. Despite the different approaches, PLE has been widely recognized as a key factor in the quality of the learning experience , and in the levels of learning achievement in ECE . The conceptual frameworks of the field assume that PLE consists of a complex web of factors that shape the overall conditions for learning, and that much more interdisciplinary and complementary methodologies of research and development are required. Although the relevance of PLE attracts a broad international consensus, in Chile it remains under-researched and weakly regulated by public policy. Gaining deeper contextual understanding and more thoughtfully-designed recommendations require the use of innovative assessment tools that cross cultural and disciplinary boundaries to produce new hybrid approaches and improvements. When considering a PLE-based change process for ECE improvement, a central question is what dimensions, variables and indicators could allow a comprehensive assessment of PLE in Chilean kindergartens? Based on a grounded theory social justice inquiry, we adopted a mixed method design, that enabled a multivocal and interdisciplinary construction of data. By using in-depth interviews, discussion groups, questionnaires, and documental analysis, we elicited the PLE discourses of politicians, early childhood practitioners, experts in architectural design and ergonomics, ECE stakeholders, and 3 to 5 year olds. A constant comparison method enabled the construction of the dimensions, variables and indicators through which PLE assessment is possible. Subsequently, the instrument was applied in a sample of 125 early childhood classrooms, to test reliability (internal consistency) and validity (content and construct). As a result, an interdisciplinary and multivocal tool for assessing physical learning environments was constructed and validated, for Chilean kindergartens. The tool is structured upon 7 dimensions (wellbeing, flexible, empowerment, inclusiveness, symbolically meaningful, pedagogically intentioned, institutional management) 19 variables and 105 indicators that are assessed through observation and registration on a mobile app. The overall reliability of the instrument is .938 while the consistency of each dimension varies between .773 (inclusive) and .946 (symbolically meaningful). The validation process through expert opinion and factorial analysis (chi-square test) has shown that the dimensions of the assessment tool reflect the factors of physical learning environments. The constructed assessment tool for kindergartens highlights the significance of the physical environment in early childhood educational settings. The relevance of the instrument relies in its interdisciplinary approach to PLE and in its capability to guide innovative learning environments, based on educational habitability. Though further analysis are required for concurrent validation and standardization, the tool has been considered by practitioners and ECE stakeholders as an intuitive, accessible and remarkable instrument to arise awareness on PLE and on equitable distribution of learning opportunities.

Keywords: Chilean kindergartens, early childhood education, physical learning environment, third teacher

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Authors: M. Haruna, O. O. Onafowokan, G. Holt, K. Anderson, R. G. Middleton

Abstract:

Background: Requirements for satisfactory outcomes following total hip arthroplasty (THA) include restoration of femoral offset, version, and leg length. Various techniques have been described for restoring these biomechanical parameters, with leg length restoration being the most predominantly described. We describe a “quartered head technique” (QHT) which uses a stepwise series of femoral head osteotomies to identify and preserve the centre of rotation of the femoral head during THA in order to ensure reconstruction of leg length, offset and stem version, such that hip biomechanics are restored as near to normal as possible. This study aims to identify whether using the QHT during hip arthroplasty effectively restores leg length and femoral offset to within acceptable parameters. Methods: A retrospective review of 206 hips was carried out, leaving 124 hips in the final analysis. Power analysis indicated a minimum of 37 patients required. All operations were performed using an anterolateral approach by a single surgeon. All femoral implants were cemented, collarless, polished double taper CPT® stems (Zimmer, Swindon, UK). Both cemented, and uncemented acetabular components were used (Zimmer, Swindon, UK). Leg length, version, and offset were assessed intra-operatively and reproduced using the QHT. Post-operative leg length and femoral offset were determined and compared with the contralateral native hip, and the difference was then calculated. For the determination of leg length discrepancy (LLD), we used the method described by Williamson & Reckling, which has been shown to be reproducible with a measurement error of ±1mm. As a reference, the inferior margin of the acetabular teardrop and the most prominent point of the lesser trochanter were used. A discrepancy of less than 6mm LLD was chosen as acceptable. All peri-operative radiographs were assessed by two independent observers. Results: The mean absolute post-operative difference in leg length from the contralateral leg was +3.58mm. 84% of patients (104/124) had LLD within ±6mm of the contralateral limb. The mean absolute post-operative difference in offset from contralateral leg was +3.88mm (range -15 to +9mm, median 3mm). 90% of patients (112/124) were within ±6mm offset of the contralateral limb. There was no statistical difference noted between observer measurements. Conclusion: The QHT provides a simple, inexpensive yet effective method of maintaining femoral leg length and offset during total hip arthroplasty. Combining this technique with pre-operative templating or other techniques described may enable surgeons to reduce even further the discrepancies between pre-operative state and post-operative outcome.

Keywords: leg length discrepancy, technical tip, total hip arthroplasty, operative technique

Procedia PDF Downloads 69

Authors: Kate Duggan

Abstract:

Introduction: Functional Communication Training (FCT) is an approach which aims to reduce behaviours of concern by teaching more effective ways to communicate. It requires identification of the function of the behaviour of concern, through gathering information from key stakeholders and completing observations of the individual’s behaviour including antecedents to, and consequences of the behaviour. Appropriate communicative alternatives are then identified and taught to the individual using systematic instruction techniques. Behaviours of concern demonstrated by individuals with autism spectrum conditions (ASC) frequently have a communication function. When contributing to positive behavior support plans, speech and language therapists and other professionals working with individuals with ASC need to identify alternative communicative behaviours which are equally reinforcing as the existing behaviours of concern. Successful implementation of FCT is dependent on an effective ‘response match’. The new way of communicating must be equally as effective as the behaviour previously used and require the same amount or less effort from the individual. It must also be understood by the communication partners the individual encounters and be appropriate to their communicative contexts. Method: Four case studies within a non-maintained special school environment were described and analysed. A response match framework was used to identify the effectiveness of functional communication training delivered by the student’s speech and language therapist, teacher and learning support assistants. The success of systematic instruction techniques used to develop new communicative behaviours was evaluated using the CODES framework. Findings: Functional communication training can be used as part of a positive behaviour support approach for students within this setting. All case studies reviewed demonstrated ‘response success’, in that the desired response was gained from the new communicative behaviour. Barriers to the successful embedding of new communicative behaviours were encountered. In some instances, the new communicative behaviour could not be consistently understood across all communication partners which reduced ‘response recognisability’. There was also evidence of increased physical or cognitive difficulty in employing the new communicative behaviour which reduced the ‘response effectivity’. Successful use of ‘thinning schedules of reinforcement’, taught students to tolerate a delay to reinforcement once the new communication behaviour was learned.

Keywords: augmentative and alternative communication, autism spectrum conditions, behaviours of concern, functional communication training

Procedia PDF Downloads 106

Authors: Urip Wahyuningsih, Indarti, Yuhri Inang Prihatina

Abstract:

The current development of batik art has given rise to various batik industries. The emergence of the batik industry is in order to meet the needs of the increasing share of the batik fashion market. This gives rise to competitiveness between the batik industry to compete for a share of the existing batik clothing market. Conditions like this also occur in Klampar Pamekasan Maduira Village, as one of the Batik Tourism Villages in Indonesia, it must continue to improve by trying to maintain the characteristics of Klampar Pamekasan Madura batik fashion and must also always innovate so that it remains highly competitive so that it remains one of the places popular batik tourist destination. Ready-to-wear or ready-to-wear clothing is clothing that is mass produced and produced in various sizes and colors, which can be purchased directly and worn easily. Patterned batik cloth is basically batik cloth that has the pattern lines of the clothing parts arranged efficiently, so there is no need to bother designing the pattern layout of the clothing parts on the batik cloth to be cut. Quilting can be defined as the art of combining fabric materials of certain sizes and cuts to form unique motifs. Based on several things above, breakthrough production innovation is needed without abandoning the characteristic of Klampar Pamekasan Madura Batik as one of the Batik Tourism Villages in Indonesia. One innovation that can be done is creating ready-to-wear patterned batik clothing products using a quilting technique. The method used in this research is the Double Diamond Design Process method. This method is divided into 4 phases namely, discover (namely the stage of designing the theme of the ready-to-wear patterned batik fashion innovation concept using quilting techniques in the Batik Village, Klampar Village, Pamekasasan, Madura), define (determine the design summary and present challenges to the design), develop ( presents prototypes developed, tested, reviewed and refined) and deliver (selected designs are produced, pass final tests and are ready to be commercialized). The research produces patterned batik products that are ready to wear with quilting techniques that are validated by experts and accepted by the public.

Keywords: competitiveness, ready to wear, innovation, quilting, klampar batik vllage

Procedia PDF Downloads 37