Search results for: improved Canny algorithm

Authors: Yi Jen Wang, Yu Ju Chen

Abstract:

Background: Heart rate variability (HRV) biofeedback can promote autonomic nervous function, sleep quality and reduce psychological stress. In HRV biofeedback training, it is hoped that through the guidance of machine video or audio, the patient can breathe slowly according to his own heart rate changes so that the heart and lungs can achieve resonance, thereby promoting the related effects of autonomic nerve function; while, it is also pointed out that if slow breathing of 6 times per minute can also guide the case to achieve the effect of cardiopulmonary resonance. However, there is no relevant research to explore the comparison of the effectiveness of cardiopulmonary resonance by using video or audio HRV biofeedback training and metronome-guided slow breathing. Purpose: To compare the promotion of autonomic nervous function performance between using HRV biofeedback and slow breathing guided by a metronome. Method: This research is a kind of experimental design with convenient sampling; the cases are randomly divided into the heart rate variability biofeedback training group and the slow breathing training group. The HRV biofeedback training group will conduct HRV biofeedback training in a four-week laboratory and use the home training device for autonomous training; while the slow breathing training group will conduct slow breathing training in the four-week laboratory using the mobile phone APP breathing metronome to guide the slow breathing training, and use the mobile phone APP for autonomous training at home. After two groups were enrolled and four weeks after the intervention, the autonomic nervous function-related performance was repeatedly measured. Using the chi-square test, student’s t-test and other statistical methods to analyze the results, and use p <0.05 as the basis for statistical significance. Results: A total of 27 subjects were included in the analysis. After four weeks of training, the HRV biofeedback training group showed significant improvement in the HRV indexes (SDNN, RMSSD, HF, TP) and sleep quality. Although the stress index also decreased, it did not reach statistical significance; the slow breathing training group was not statistically significant after four weeks of training, only sleep quality improved significantly, while the HRV indexes (SDNN, RMSSD, TP) all increased. Although HF and stress indexes decreased, they were not statistically significant. Comparing the difference between the two groups after training, it was found that the HF index improved significantly and reached statistical significance in the HRV biofeedback training group. Although the sleep quality of the two groups improved, it did not reach that level in a statistically significant difference. Conclusion: HRV biofeedback training is more effective in promoting autonomic nervous function than slow breathing training, but the effects of reducing stress and promoting sleep quality need to be explored after increasing the number of samples. The results of this study can provide a reference for clinical or community health promotion. In the future, it can also be further designed to integrate heart rate variability biological feedback training into the development of AI artificial intelligence wearable devices, which can make it more convenient for people to train independently and get effective feedback in time.

Keywords: autonomic nervous function, HRV biofeedback, heart rate variability, slow breathing

Procedia PDF Downloads 171

Authors: A. Guemache, M. Omari

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Oxides with formula Ca1-xSrx MnO3 (0≤x≤0.2) were synthesized using co-precipitation method. The identification of the obtained phase was carried out using infrared spectroscopy and X-ray diffraction. Thermogravimetric and differential analysis was permitted to characterize different transformations of precursors which take place during one heating cycle. The study of electrochemical behavior was carried out by cyclic voltammetry and impedance spectroscopy. The obtained results show that apparent catalytic activity improved when increasing the concentration of strontium. Anodic current densities varies from 1.3 to 5.9 mA/cm2 at the rate scan of 20 mV.s-1 and a potential 0.8 V for oxides with composition x=0 to 0.2.

Keywords: oxide, co-precipitation, electrochemical properties, cathode-type

Procedia PDF Downloads 283

Authors: Deboraj Muchahary, Amlan Deep Borah Abir J. Mondal, Alak Majumder

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A discrete cosine transform (DCT) is described and a technique to compute it using fast Fourier transform (FFT) is developed. In this work, DCT of a finite length sequence is obtained by incorporating CORDIC methodology in radix-2 FFT algorithm. The proposed methodology is simple to comprehend and maintains a regular structure, thereby reducing computational complexity. DCTs are used extensively in the area of digital processing for the purpose of pattern recognition. So the efficient computation of DCT maintaining a transparent design flow is highly solicited.

Keywords: DCT, DFT, CORDIC, FFT

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Authors: Fabricio Costa Silva, Samuel Xavier de Souza

Abstract:

A Global Positioning System (GPS) receiver is responsible to determine position, velocity and timing information by using satellite information. To get this information are necessary to combine an incoming and a locally generated signal. The procedure called acquisition need to found two information, the frequency and phase of the incoming signal. This is very time consuming, so there are several techniques to reduces the computational complexity, but each of then put projects issues in conflict. I this papers we present a method that can reduce the computational complexity by reducing the search space and paralleling the search.

Keywords: GPS, acquisition, complexity, parallelism

Procedia PDF Downloads 530

Authors: Akalework Mengesha

Abstract:

The adoption of improved agricultural technologies has been connected with higher earnings and lower poverty, enhanced nutritional status, lower staple food prices, and increased employment opportunities for landless laborers. The adoption and extension of the technologies are vastly crucial in that it enables the countries to achieve the millennium development goals (MDG) of reducing extreme poverty and hunger. There are efforts which directed to the enlargement and provision of modern crop varieties in sub-Saharan Africa in the past 30 years. Nevertheless, by and large, the adoption and expansion of rates for improved technologies have insulated behind other regions. This research aims to assess social and economic barriers in the adoption and expansion of agricultural technologies by local communities living around a private agricultural farm in Woliatta Zone, Southern Ethiopia. The study has been carried out among rural households which are located in the three localities selected for the study in the Woliatta zone. Across sectional mixed method, the design was used to address the study objective. The qualitative method was employed (in-depth interview, key informant, and focus group discussion) involving a total of 42 in-depth informants, 17 key-informant interviews, 2 focus group discussions comprising of 10 individuals in each group through purposive sampling techniques. The survey method was mainly used in the study to examine the impact of attitudinal, demographic, and socioeconomic variables on farmers’ adoption of agricultural technologies for quantitative data. The finding of the study revealed that Amibara commercial farm has not made a resolute and well-organized effort to extend agricultural technology to the surrounding local community. A comprehensive agricultural technology transfer scheme hasn’t been put in place by the commercial farm ever since it commenced operating in the study area. Besides, there is an ongoing conflict of interest between the farm and the community, which has kept on widening through time, bounds to be irreversible.

Keywords: adoption, technology transfer, agriculture, barriers

Procedia PDF Downloads 142

Authors: Dylan Santos De Pinho, Nabil Ouerhani

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Energy consumption of machine-tools is becoming critical for machine-tool builders and end-users because of economic, ecological and legislation-related reasons. Many machine-tool builders are seeking for solutions that allow the reduction of energy consumption of machine-tools while preserving the same productivity rate and the same quality of machined parts. In this paper, we present the first results of a project conducted jointly by academic and industrial partners to reduce the energy consumption of a Swiss-Type lathe. We employ genetic algorithms to find optimal machining parameters – the set of parameters that lead to the best trade-off between energy consumption, part quality and tool lifetime. Three main machining process parameters are considered in our optimization technique, namely depth of cut, spindle rotation speed and material feed rate. These machining process parameters have been identified as the most influential ones in the configuration of the Swiss-type machining process. A state-of-the-art multi-objective genetic algorithm has been used. The algorithm combines three fitness functions, which are objective functions that permit to evaluate a set of parameters against the three objectives: energy consumption, quality of the machined parts, and tool lifetime. In this paper, we focus on the investigation of the fitness function related to energy consumption. Four different energy consumption related fitness functions have been investigated and compared. The first fitness function refers to the Kienzle cutting force model. The second fitness function uses the Material Removal Rate (RMM) as an indicator of energy consumption. The two other fitness functions are non-deterministic, learning-based functions. One fitness function uses a simple Neural Network to learn the relation between the process parameters and the energy consumption from experimental data. Another fitness function uses Lasso regression to determine the same relation. The goal is, then, to find out which fitness functions predict best the energy consumption of a Swiss-Type machining process for the given set of machining process parameters. Once determined, these functions may be used for optimization purposes – determine the optimal machining process parameters leading to minimum energy consumption. The performance of the four fitness functions has been evaluated. The Tornos DT13 Swiss-Type Lathe has been used to carry out the experiments. A mechanical part including various Swiss-Type machining operations has been selected for the experiments. The evaluation process starts with generating a set of CNC (Computer Numerical Control) programs for machining the part at hand. Each CNC program considers a different set of machining process parameters. During the machining process, the power consumption of the spindle is measured. All collected data are assigned to the appropriate CNC program and thus to the set of machining process parameters. The evaluation approach consists in calculating the correlation between the normalized measured power consumption and the normalized power consumption prediction for each of the four fitness functions. The evaluation shows that the Lasso and Neural Network fitness functions have the highest correlation coefficient with 97%. The fitness function “Material Removal Rate” (MRR) has a correlation coefficient of 90%, whereas the Kienzle-based fitness function has a correlation coefficient of 80%.

Keywords: adaptive machining, genetic algorithms, smart manufacturing, parameters optimization

Procedia PDF Downloads 142

Authors: Kaleb Shiferaw, Berhanu Geberemedhin

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Ethiopian farmers have a long tradition of beekeeping and the country has huge potential for honey production. However traditional mode of production still dominates the sub sector which negatively affect the total production and productivity. A number of studies have been conducted to better understand the working honey production, however, none of them systematically investigate the extent of technical efficiency of the sub-sector. This paper uses Stochastic Frontier production model to quantifying the extent of technical efficiency and identify exogenous determinant of inefficiency. The result showed that consistent with other studies traditional practice dominate small scale honey production in Ethiopia. The finding also revealed that use of purchased inputs such as bee forage and other supplement is very limited among honey producers indicating that natural bee forage is the primary source of bee forage. The immediate consequence of all these is low production and productivity. The number of hives the household owns, whether the household used improved apiculture technologies, availability of natural forest which is the primary sources of nectar for bees and amount of land owned by the households were found to have a significant influence on the amount of honey produced by beekeeper. Our result further showed that the mean technical efficiency of honey producers is 0.79 implying that, on average honey producer produce 80 percent of the maximum output. The implication is that 20 percent of the potential output is lost due to technical inefficiency. Number of hives owned by a honey produces, distance to district town-a proxy to market access, household wealth, and whether the household head has a leadership role in the PA affect the technical efficiency of honey producers. The finding suggest that policies that aim to expand the use of improved hives is expected to increase the honey production at household level. The result also suggest that investment on rural infrastructure would be instrumental in improving technical efficiency of honey producer.

Keywords: small-scale honey producer, Ethiopia, technical efficiency in apiculture, stochastic frontier analysis

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Authors: Tanvi P. Medshinge, Prasad Vaidya, Monisha E. Royan

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In India, cooling loads in residential sector is a major contributor to its total energy consumption. Due to the increasing cooling need, the market penetration of air-conditioners is further expected to rise. Natural Ventilation (NV), however, possesses great potential to save significant energy consumption especially for residential buildings in moderate climates. As multifamily residential apartment buildings are designed by repetitive use of prototype designs, deriving individual NV based design prototype solutions for a combination of different wind incidence angles and orientations would provide significant opportunity to address the rise in cooling loads by residential sector. This paper presents the results of NV performance of a selected prototype apartment design with a cluster of four units in Pune, India, and an attempt to improve the NV performance through design modifications. The water table apparatus, a physical modelling tool, is used to study the flow patterns and simulate wind-induced NV performance. Quantification of NV performance is done by post processing images captured from video recordings in terms of percentage of area with good and poor access to ventilation. NV performance of the existing design for eight wind incidence angles showed that of the cluster of four units, the windward units showed good access to ventilation for all rooms, and the leeward units had lower access to ventilation with the bedrooms in the leeward units having the least access. The results showed improved performance in all the units for all wind incidence angles to more than 80% good access to ventilation. Some units showed an additional improvement to more than 90% good access to ventilation. This process of design and performance evaluation improved some individual units from 0% to 100% for good access to ventilation. The results demonstrate the ease of use and the power of the water table apparatus for performance-based design to simulate wind induced NV.  

Keywords: fluid dynamics, prototype design, natural ventilation, simulations, water table apparatus, wind incidence angles

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Authors: Litim Tarek, Taamallah Ouahiba

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This work relates to the physico-geometrical aspect of the surface layers of 16NC6 steel having undergone the burnishing treatment by hard steel ball. The results show that the optimal effects of burnishing are closely linked to the shape and the material of the active part of the device as well as to the surface plastic deformation ability of the material to be treated. Thus the roughness is improved by more than 70%, and the consolidation rate is increased by 30%. In addition, modeling of the rational traction curves provides a work hardening coefficient of up to 0.3 in the presence of burnishing.

Keywords: 16NC6 steel, burnishing, hardening, roughness

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Authors: Seyed Mohsen Mousavi, Ali Reza Mahjoub, Bahjat Afshari Razani

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In this work, Fe/ZnO hollow spherical structures with high surface area using the template glucose was prepared by the hydrothermal method using an ultrasonic bath at room temperature was produced and were identified by FT-IR, XRD, FE-SEM and BET. The photocatalytic activity of synthesized spherical Fe/ZnO hollow sphere were studied in the destruction of Congo Red and Methylene Blue as Azo dyes. The results showed that the photocatalytic activity of Fe/ZnO hollow spherical structures is improved compared with ZnO hollow sphere and other morphologys.

Keywords: azo dyes, Fe/ZnO hollow sphere, hollow sphere nanostructures, photocatalyst

Procedia PDF Downloads 356

Authors: Divyesh Patel, Tanuja Srivastava

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This paper considers the NP-hard problem of reconstructing binary matrices satisfying exactly-1-4-adjacency constraint from its row and column projections. This problem is formulated into a maximization problem. The objective function gives a measure of adjacency constraint for the binary matrices. The maximization problem is solved by the simulated annealing algorithm and experimental results are presented.

Keywords: discrete tomography, exactly-1-4-adjacency, simulated annealing, binary matrices

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Authors: Ching-Yuan Chen, Lung-Kwang Pan

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Objective: Back pain is a major health problem costing billions of health budgets annually in Taiwan. Thousands of back pain surgeries are performed annually with up to 40% of patients complaining of back pain at time of post-surgery causing failed back surgery syndrome (FBSS), although diagnosis in these patients may be complex. The aim of study is to assess the feasibility of using bone SPECT-CT imaging to localize the active lesions causing persistent, recurrent or new backache after spine surgery. Materials and Methods: Bone SPECT-CT imaging was performed after the intravenous injection of 20 mCi of 99mTc-MDP for all the patients with diagnosis of FBSS. Patients were evaluated using status of subjectively pain relief, functional improvement and degree of satisfaction by reviewing the medical records and questionnaires in a 2 more years’ follow-up. Results: We enrolled a total of 16 patients were surveyed in our hospital from Jan. 2015 to Dec. 2016. Four people on SPEC/CT imaging ensured significant lesions were undergone a revised surgery (surgical treatment group). The mean visual analogue scale (VAS) decreased 5.3 points and mean Oswestry disability index (ODI) improved 38 points in the surgical group. The remaining 12 on SPECT/CT imaging were diagnosed as no significant lesions then received drug treatment (medical treatment group). The mean VAS only decreased 2 .1 point and mean ODI improved 12.6 points in the medical treatment group. In the posttherapeutic evaluation, the pain of the surgical treatment group showed a satisfactory improvement. In the medical treatment group, 10 of the 12 were also satisfied with the symptom relief while the other 2 did not improve significantly. Conclusions: Findings on SPECT-CT imaging appears to be easily explained the patients' pain. We recommended that SPECT/CT imaging was a feasible and useful clinical tool to improve diagnostic confidence or specificity when evaluating patients with FBSS.

Keywords: failed back surgery syndrome, oswestry disability index, SPECT-CT imaging, 99mTc-MDP, visual analogue scale

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Authors: Kamaljit Pati, Manas Kumar Mohanty, Sanjib Sadhu

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A heuristic has been designed to generate a random simple monotone polygon from a given set of ‘n’ points lying on a 2-Dimensional plane. Our heuristic generates a random monotone polygon in O(n) time after O(nℓogn) preprocessing time which is improved over the previous work where a random monotone polygon is produced in the same O(n) time but the preprocessing time is O(k) for n < k < n2. However, our heuristic does not generate all possible random polygons with uniform probability. The space complexity of our proposed heuristic is O(n).

Keywords: sorting, monotone polygon, visibility, chain

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Authors: Md. Wasiul Islam, Md. Jamius Shams Sowrov

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The Sundarbans is the largest single tract of mangrove forest in the world. This is located in the southwest corner of Bangladesh. This is a unique ecosystem which is a great breeding and nursing ground for a great biodiversity. It supports the livelihood of about 3.5 million coastal dwellers and also protects the coastal belt and inland areas from various natural calamities. Historically, the management of the Sundarbans was controlled by the Bangladesh Forest Department following top-down approach without the involvement of local communities. Such fence and fining-based blue-print approach was not effective to protect the forest which caused Sundarbans to degrade severely in the recent past. Fifty percent of the total tree cover has been lost in the last 30 years. Therefore, local multi-stakeholder based bottom-up co-management approach was introduced at some of the parts of the Sundarbans in 2006 to improve the biodiversity status by enhancing the protection level of the forest. Various co-management organizations were introduced under co-management approach where the local community people could actively involve in various activities related to the management and welfare of the Sundarbans including the decision-making process to achieve the goal. From this backdrop, the objective of the study was to assess the performance of co-management organizations to facilitate sustainable management of the Sundarbans mangrove forests. The qualitative study followed face-to-face interview to collect data using two sets of semi-structured questionnaires. A total of 40 respondents participated in the research that was from eight villagers under two forest ranges. 32 representatives from the local communities as well as 8 official representatives involved in co-management approach were interviewed using snowball sampling technique. The study shows that the co-management approach improved governance system of the Sundarbans through active participation of the local community people and their interactions with the officials via the platform of co-management organizations. It facilitated accountability and transparency system to some extent through following some formal and informal rules and regulations. It also improved the power structure of the management process by fostering local empowerment process particularly the women. Moreover, people were able to learn from their interactions with and within the co-management organizations as well as interventions improved environmental awareness and promoted social learning. The respondents considered good governance as the most important factor for achieving the goal of sustainable management and biodiversity conservation of the Sundarbans. The success of co-management planning process also depends on the active and functional participation of different stakeholders including the local communities where co-management organizations were considered as the most functional platform. However, the governance system was also facing various challenges which resulted in barriers to the sustainable management of the Sundarbans mangrove forest. But still there were some members involved in illegal forest operations and created obstacles against sustainable management of the Sundarbans. Respondents recommended greater patronization from the government, financial and logistic incentives for alternative income generation opportunities with effective participatory monitoring and evaluation system to improve sustainable management of the Sundarbans.

Keywords: Bangladesh, co-management approach, co-management organizations, governance, Sundarbans, sustainable management

Procedia PDF Downloads 168

Authors: M. Saard, A. Kolk, K. Sepp, L. Pertens, L. Reinart, C. Kööp

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Introduction: Social competence is often impaired in children with acquired brain injury (ABI), but evidence-based rehabilitation for social skills has remained undeveloped. Modern technology-based methods create effective and safe learning environments for pediatric social skills remediation. The aim of the study was to implement our structured model of neuro rehab for socio-cognitive deficit using multitouch-multiuser tabletop (MMT) computer-based platforms and virtual reality (VR) technology. Methods: 40 children aged 8-13 years (yrs) have participated in the pilot study: 30 with ABI -epilepsy, traumatic brain injury and/or tic disorder- and 10 healthy age-matched controls. From the patients, 12 have completed the training (M = 11.10 yrs, SD = 1.543) and 20 are still in training or in the waiting-list group (M = 10.69 yrs, SD = 1.704). All children performed the first individual and paired assessments. For patients, second evaluations were performed after the intervention period. Two interactive applications were implemented into rehabilitation design: Snowflake software on MMT tabletop and NoProblem on DiamondTouch Table (DTT), which allowed paired training (2 children at once). Also, in individual training sessions, HTC Vive VR device was used with VR metaphors of difficult social situations to treat social anxiety and train social skills. Results: At baseline (B) evaluations, patients had higher deficits in executive functions on the BRIEF parents’ questionnaire (M = 117, SD = 23.594) compared to healthy controls (M = 22, SD = 18.385). The most impaired components of social competence were emotion recognition, Theory of Mind skills (ToM), cooperation, verbal/non-verbal communication, and pragmatics (Friendship Observation Scale scores only 25-50% out of 100% for patients). In Sentence Completion Task and Spence Anxiety Scale, the patients reported a lack of friends, behavioral problems, bullying in school, and social anxiety. Outcome evaluations: Snowflake on MMT improved executive and cooperation skills and DTT developed communication skills, metacognitive skills, and coping. VR, video modelling and role-plays improved social attention, emotional attitude, gestural behaviors, and decreased social anxiety. NEPSY-II showed improvement in Affect Recognition [B = 7, SD = 5.01 vs outcome (O) = 10, SD = 5.85], Verbal ToM (B = 8, SD = 3.06 vs O = 10, SD = 4.08), Contextual ToM (B = 8, SD = 3.15 vs O = 11, SD = 2.87). ToM Stories test showed an improved understanding of Intentional Lying (B = 7, SD = 2.20 vs O = 10, SD = 0.50), and Sarcasm (B=6, SD = 2.20 vs O = 7, SD = 2.50). Conclusion: Neurorehabilitation based on the Structured Model of Neurorehab for Socio-Cognitive Deficit in children with ABI were effective in social skills remediation. The model helps to understand theoretical connections between components of social competence and modern interactive computerized platforms. We encourage therapists to implement these next-generation devices into the rehabilitation process as MMT and VR interfaces are motivating for children, thus ensuring good compliance. Improving children’s social skills is important for their and their families’ quality of life and social capital.

Keywords: acquired brain injury, children, social skills deficit, technology-based neurorehabilitation

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Authors: Nurudeen Ayeni, Kasali Bello, Ovi Abayeh

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Study of the effect of dye–resin complexation on the degree of dye absorption were carried out using Procion Blue MX-R to dye cotton fabric in the presence hexamethylol melamine (MR 6) and its phosphate derivative (MPR 4) for resination. The highest degree of dye exhaustion was obtained at 400 C for 1 hour with the resinated fabric showing more affinity for the dye than the ordinary fiber. Improved fastness properties was recorded which show a relatively higher stability of dye–resin–cellulose network formed.

Keywords: cotton fabric, reactive dye, dyeing, resination

Procedia PDF Downloads 400

Authors: Fabricio Costa Silva, Samuel Xavier de Souza

Abstract:

A Global Positioning System (GPS) receiver is responsible to determine position, velocity and timing information by using satellite information. To get this information's are necessary to combine an incoming and a locally generated signal. The procedure called acquisition need to found two information, the frequency and phase of the incoming signal. This is very time consuming, so there are several techniques to reduces the computational complexity, but each of then put projects issues in conflict. I this papers we present a method that can reduce the computational complexity by reducing the search space and paralleling the search.

Keywords: GPS, acquisition, low complexity, parallelism

Procedia PDF Downloads 493

Authors: Brittany Richardson, Ying Wang

Abstract:

For physical therapy, exercise prescription, athlete training, and regular fitness training, it is crucial to perform health assessments or fitness assessments periodically. An accurate assessment is propitious for tracking recovery progress, preventing potential injury and making long-range training plans. Assessments include necessary measurements, height, weight, blood pressure, heart rate, body fat, etc. and advanced evaluation, muscle group strength, stability-mobility, and movement evaluation, etc. In the current standard assessment procedures, the accuracy of assessments, especially advanced evaluations, largely depends on the experience of physicians, coaches, and personal trainers. And it is challenging to track clients’ progress in the current assessment. Unlike the tradition assessment, in this paper, we present a deep learning based face recognition algorithm for accurate, comprehensive and trackable assessment. Based on the result from our assessment, physicians, coaches, and personal trainers are able to adjust the training targets and methods. The system categorizes the difficulty levels of the current activity for the client or user, furthermore make more comprehensive assessments based on tracking muscle group over time using a designed landmark detection method. The system also includes the function of grading and correcting the form of the clients during exercise. Experienced coaches and personal trainer can tell the clients' limit based on their facial expression and muscle group movements, even during the first several sessions. Similar to this, using a convolution neural network, the system is trained with people’s facial expression to differentiate challenge levels for clients. It uses landmark detection for subtle changes in muscle groups movements. It measures the proximal mobility of the hips and thoracic spine, the proximal stability of the scapulothoracic region and distal mobility of the glenohumeral joint, as well as distal mobility, and its effect on the kinetic chain. This system integrates data from other fitness assistant devices, including but not limited to Apple Watch, Fitbit, etc. for a improved training and testing performance. The system itself doesn’t require history data for an individual client, but the history data of a client can be used to create a more effective exercise plan. In order to validate the performance of the proposed work, an experimental design is presented. The results show that the proposed work contributes towards improving the quality of exercise plan, execution, progress tracking, and performance.

Keywords: exercise prescription, facial recognition, landmark detection, fitness assessments

Procedia PDF Downloads 127

Authors: Monireh Ahmadi Bani, Adel Khorramrouz, Lalenoor Morvarid, Bagheri Mahtab

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Background:- Foot disorders are common in musculoskeletal problems. Plantar pressure distribution measurement is one the most important part of foot disorders diagnosis for quantitative analysis. However, the association of plantar pressure and foot disorders is not clear. With the growth of dataset and machine learning methods, the relationship between foot disorders and plantar pressures can be detected. Significance of the study:- The purpose of this study was to predict the probability of common foot disorders based on peak plantar pressure distribution and center of pressure during walking. Methodologies:- 2323 participants were assessed in a foot therapy clinic between 2015 and 2021. Foot disorders were diagnosed by an experienced physician and then they were asked to walk on a force plate scanner. After the data preprocessing, due to the difference in walking time and foot size, we normalized the samples based on time and foot size. Some of force plate variables were selected as input to a deep neural network (DNN), and the probability of any each foot disorder was measured. In next step, we used support vector machine (SVM) and run dataset for each foot disorder (classification of yes or no). We compared DNN and SVM for foot disorders prediction based on plantar pressure distributions and center of pressure. Findings:- The results demonstrated that the accuracy of deep learning architecture is sufficient for most clinical and research applications in the study population. In addition, the SVM approach has more accuracy for predictions, enabling applications for foot disorders diagnosis. The detection accuracy was 71% by the deep learning algorithm and 78% by the SVM algorithm. Moreover, when we worked with peak plantar pressure distribution, it was more accurate than center of pressure dataset. Conclusion:- Both algorithms- deep learning and SVM will help therapist and patients to improve the data pool and enhance foot disorders prediction with less expense and error after removing some restrictions properly.

Keywords: deep neural network, foot disorder, plantar pressure, support vector machine

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Authors: Tyler T. Procko, Steve Collins

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New features in .NET (6 and above) permit streamlined access to information residing in JSON-capable relational databases, such as SQL Server (2016 and above). Traditional methods of data access now comparatively involve unnecessary steps which compromise system performance. This work posits that the established ORM (Object Relational Mapping) based methods of data access in applications and APIs result in common issues, e.g., object-relational impedance mismatch. Recent developments in C# and .NET Core combined with a framework of modern SQL Server coding conventions have allowed better technical solutions to the problem. As an amelioration, this work details the language features and coding conventions which enable this streamlined approach, resulting in an open-source .NET library implementation called Codeless Data Access (CODA). Canonical approaches rely on ad-hoc mapping code to perform type conversions between the client and back-end database; with CODA, no mapping code is needed, as JSON is freely mapped to SQL and vice versa. CODA streamlines API data access by improving on three aspects of immediate concern to web developers, database engineers and cybersecurity professionals: Simplicity, Speed and Security. Simplicity is engendered by cutting out the “middleman” steps, effectively making API data access a whitebox, whereas traditional methods are blackbox. Speed is improved because of the fewer translational steps taken, and security is improved as attack surfaces are minimized. An empirical evaluation of the speed of the CODA approach in comparison to ORM approaches ] is provided and demonstrates that the CODA approach is significantly faster. CODA presents substantial benefits for API developer workflows by simplifying data access, resulting in better speed and security and allowing developers to focus on productive development rather than being mired in data access code. Future considerations include a generalization of the CODA method and extension outside of the .NET ecosystem to other programming languages.

Keywords: API data access, database, JSON, .NET core, SQL server

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Authors: Mohamed Amine Haraoubia, Abdelaziz Hamzaoui, Najib Essounbouli

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The pursuit of the MPPT has led to the development of many kinds of controllers, one of which is the Fuzzy Logic Controller, which has proven its worth. To further tune this controller this paper will discuss and analyze the use of Genetic Algorithms to tune the Fuzzy Logic Controller. It will provide an introduction to both systems, and test their compatibility and performance.

Keywords: fuzzy logic controller, fuzzy logic, genetic algorithm, maximum power point, maximum power point tracking

Procedia PDF Downloads 369

Authors: Yong-Jun Kim, Jae-Hyun Ro, Chang-Bin Ha, Hyoung-Kyu Song

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This paper proposes the cooperative transmission scheme with pre-coding because the cellular communication requires high reliability. The cooperative transmission scheme uses pre-coding method with limited feedback information among small cells. Particularly, the proposed scheme has adaptive mode according to the position of mobile station. Thus, demand of recent wireless communication is resolved by this scheme. From the simulation results, the proposed scheme has better performance compared to the conventional scheme in the cellular network.

Keywords: CDD, cellular network, pre-coding, SPC

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Authors: Alexander Musyoki, Collins Odour

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Over the past few years, the bakery industry in Kenya has experienced significant growth largely in part to the increased adoption of technology and automation in their processes; more specifically due to the adoption of bakery management systems to help in running bakeries. While they have been largely responsible for the improved productivity and efficiency in bakeries, most of them are now outdated and pose more challenges than benefits. The proposed online bakery management system mentioned in this paper aims to address this by allowing bakery owners to track inventory, budget, job progress, and data analytics on each job and in doing so, promote the Sustainable Development Goals 3 and 12, which aim to ensure healthy lives and promote sustainable economic growth as the proposed benefits of these features include scalability, easy accessibility, reduced acquisition costs, better reliability, and improved functionality that will allow bakeries to become more competitive, reduce waste and track inventory more efficiently. To better understand the challenges, a comprehensive study has been performed to assess these traditional systems and try to understand if an online bakery management system can prove to be advantageous to bakery owners. The study conducted gathered feedback from bakery owners and employees in Nairobi County, Kenya using an online survey with a response rate of about 86% from the target population. The responses cited complex and hard to use bakery management systems (59.7%), lack of portability from one device to the other (58.1%) and high acquisition costs (51.6%) as the top challenges of traditional bakery management systems. On the other hand, some of the top benefits that most of the respondents would realize from the online bakery management system was better reliability (58.1%) and reduced acquisition costs (58.1%). Overall, the findings suggest that an online bakery management system has a lot of advantages over traditional systems and is likely to be well-received in the market. In conclusion, the proposed online bakery management system has the potential to improve the efficiency and competitiveness of small-sized bakeries in Nairobi County. Further research is recommended to expand the sample size and diversity of respondents and to conduct more in-depth analyses of the data collected.

Keywords: ICT, technology and automation, bakery management systems, food innovation

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Authors: Egemen Sert, Leila Hedayatifar, Rachel A. Rigg, Amir Akhavan, Olha Buchel, Dominic Elias Saadi, Aabir Abubaker Kar, Alfredo J. Morales, Yaneer Bar-Yam

Abstract:

The complexity of providing timely and cost-effective distribution of finished goods from industrial facilities to customers makes effective operational coordination difficult, yet effectiveness is crucial for maintaining customer service levels and sustaining a business. Logistics planning becomes increasingly complex with growing numbers of customers, varied geographical locations, the uncertainty of future orders, and sometimes extreme competitive pressure to reduce inventory costs. Linear optimization methods become cumbersome or intractable due to a large number of variables and nonlinear dependencies involved. Here we develop a complex systems approach to optimizing logistics networks based upon dimensional reduction methods and apply our approach to a case study of a manufacturing company. In order to characterize the complexity in customer behavior, we define a “customer space” in which individual customer behavior is described by only the two most relevant dimensions: the distance to production facilities over current transportation routes and the customer's demand frequency. These dimensions provide essential insight into the domain of effective strategies for customers; direct and indirect strategies. In the direct strategy, goods are sent to the customer directly from a production facility using box or bulk trucks. In the indirect strategy, in advance of an order by the customer, goods are shipped to an external warehouse near a customer using trains and then "last-mile" shipped by trucks when orders are placed. Each strategy applies to an area of the customer space with an indeterminate boundary between them. Specific company policies determine the location of the boundary generally. We then identify the optimal delivery strategy for each customer by constructing a detailed model of costs of transportation and temporary storage in a set of specified external warehouses. Customer spaces help give an aggregate view of customer behaviors and characteristics. They allow policymakers to compare customers and develop strategies based on the aggregate behavior of the system as a whole. In addition to optimization over existing facilities, using customer logistics and the k-means algorithm, we propose additional warehouse locations. We apply these methods to a medium-sized American manufacturing company with a particular logistics network, consisting of multiple production facilities, external warehouses, and customers along with three types of shipment methods (box truck, bulk truck and train). For the case study, our method forecasts 10.5% savings on yearly transportation costs and an additional 4.6% savings with three new warehouses.

Keywords: logistics network optimization, direct and indirect strategies, K-means algorithm, dimensional reduction

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Authors: Heeba A. Gurku

Abstract:

Introduction: Cone Beam CT(CBCT) images play an integral part in proper patient positioning in cancer patients undergoing radiation therapy treatment. But these images are low in quality. The purpose of this study is to generate high-quality synthetic CT images from CBCT using generative models. Material and Methods: This study utilized two datasets from The Cancer Imaging Archive (TCIA) 1) Lung cancer dataset of 20 patients (with full view CBCT images) and 2) Pancreatic cancer dataset of 40 patients (only 27 patients having limited view images were included in the study). Cycle Generative Adversarial Networks (GAN) and its variant Attention Guided Generative Adversarial Networks (AGGAN) models were used to generate the synthetic CTs. Models were evaluated by visual evaluation and on four metrics, Structural Similarity Index Measure (SSIM), Peak Signal Noise Ratio (PSNR) Mean Absolute Error (MAE) and Root Mean Square Error (RMSE), to compare the synthetic CT and original CT images. Results: For pancreatic dataset with limited view CBCT images, our study showed that in Cycle GAN model, MAE, RMSE, PSNR improved from 12.57to 8.49, 20.94 to 15.29 and 21.85 to 24.63, respectively but structural similarity only marginally increased from 0.78 to 0.79. Similar, results were achieved with AGGAN with no improvement over Cycle GAN. However, for lung dataset with full view CBCT images Cycle GAN was able to reduce MAE significantly from 89.44 to 15.11 and AGGAN was able to reduce it to 19.77. Similarly, RMSE was also decreased from 92.68 to 23.50 in Cycle GAN and to 29.02 in AGGAN. SSIM and PSNR also improved significantly from 0.17 to 0.59 and from 8.81 to 21.06 in Cycle GAN respectively while in AGGAN SSIM increased to 0.52 and PSNR increased to 19.31. In both datasets, GAN models were able to reduce artifacts, reduce noise, have better resolution, and better contrast enhancement. Conclusion and Recommendation: Both Cycle GAN and AGGAN were significantly able to reduce MAE, RMSE and PSNR in both datasets. However, full view lung dataset showed more improvement in SSIM and image quality than limited view pancreatic dataset.

Keywords: CT images, CBCT images, cycle GAN, AGGAN

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Authors: Mohammed Al-Yamani, Yaser Miaji

Abstract:

DHL Customer Solutions and Innovation Department (CSI) have been experiencing difficulties while comparing quotes for different customers in different years. Currently, the employees are processing data by opening several loaded Excel files where the quotes are and manually copying values to another Excel Workbook where the comparison is made. This project consists of developing a new and effective database for DHL CSI department so that information is stored altogether on the same catalog. That being said, we have been assigned to find an efficient algorithm that can deal with the different formats of the Excel Workbooks to copy and store the express customer rates for core products (DOX, WPX, IMP) for comparisons purposes.

Keywords: DHL, solution design, ORACLE, EXCEL

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Authors: A. Guemache, M. Omari

Abstract:

Oxides with formula Ca1-xSrx MnO3(0≤x≤0.2) were synthesized using co precipitation method. The identification of the obtained phase was carried out using infrared spectroscopy and x-ray diffraction. Thermogravimetric and differential analysis was permitted to characterize different transformations of precursors which take place during one heating cycle. The study of electrochemical behavior was carried out by cyclic voltammetry and impedance spectroscopy. The obtained results show that apparent catalytic activity improved when increasing the concentration of strontium. Anodic current densities varies from 1.3 to 5.9 mA/cm2 at the rate scan of 20 mV.s-1 and a potential 0.8 V for oxides with composition x=0 to 0.2.

Keywords: oxide, co-precipitation, thermal analysis, electrochemical properties

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Authors: Dacheng Li, Bo Huang, Qinjin Han, Ming Li

Abstract:

Remotely sensed imagery with the high spatial and temporal characteristics, which it is hard to acquire under the current land observation satellites, has been considered as a key factor for monitoring environmental changes over both global and local scales. On a basis of the limited high spatial-resolution observations, challenged studies called spatiotemporal fusion have been developed for generating high spatiotemporal images through employing other auxiliary low spatial-resolution data while with high-frequency observations. However, a majority of spatiotemporal fusion approaches yield to satisfactory assumption, empirical but unstable parameters, low accuracy or inefficient performance. Although the spatiotemporal fusion methodology via sparse representation theory has advantage in capturing reflectance changes, stability and execution efficiency (even more efficient when overcomplete dictionaries have been pre-trained), the retrieval of high-accuracy dictionary and its response to fusion results are still pending issues. In this paper, we employ additional image pairs (here each image-pair includes a Landsat Operational Land Imager and a Moderate Resolution Imaging Spectroradiometer acquisitions covering the partial area of Baotou, China) only into the coupled dictionary training process based on K-SVD (K-means Singular Value Decomposition) algorithm, and attempt to improve the fusion results of two existing sparse representation based fusion models (respectively utilizing one and two available image-pair). The results show that more eligible image pairs are probably related to a more accurate overcomplete dictionary, which generally indicates a better image representation, and is then contribute to an effective fusion performance in case that the added image-pair has similar seasonal aspects and image spatial structure features to the original image-pair. It is, therefore, reasonable to construct multi-dictionary training pattern for generating a series of high spatial resolution images based on limited acquisitions.

Keywords: spatiotemporal fusion, sparse representation, K-SVD algorithm, dictionary learning

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Authors: Reza Safdari, Goli Arji, Robab Abdolkhani Maryam zahmatkeshan

Abstract:

Background and purpose: Cardiovascular diseases are among the most common diseases in all societies. The most important step in minimizing myocardial infarction and its complications is to minimize its risk factors. The amount of medical data is increasingly growing. Medical data mining has a great potential for transforming these data into information. Using data mining techniques to generate predictive models for identifying those at risk for reducing the effects of the disease is very helpful. The present study aimed to collect data related to risk factors of heart infarction from patients’ medical record and developed predicting models using data mining algorithm. Methods: The present work was an analytical study conducted on a database containing 350 records. Data were related to patients admitted to Shahid Rajaei specialized cardiovascular hospital, Iran, in 2011. Data were collected using a four-sectioned data collection form. Data analysis was performed using SPSS and Clementine version 12. Seven predictive algorithms and one algorithm-based model for predicting association rules were applied to the data. Accuracy, precision, sensitivity, specificity, as well as positive and negative predictive values were determined and the final model was obtained. Results: five parameters, including hypertension, DLP, tobacco smoking, diabetes, and A+ blood group, were the most critical risk factors of myocardial infarction. Among the models, the neural network model was found to have the highest sensitivity, indicating its ability to successfully diagnose the disease. Conclusion: Risk prediction models have great potentials in facilitating the management of a patient with a specific disease. Therefore, health interventions or change in their life style can be conducted based on these models for improving the health conditions of the individuals at risk.

Keywords: decision trees, neural network, myocardial infarction, Data Mining

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Authors: Makamas Kumprou, Pipatana Amatachaya, Sugalya Amatachaya, Thiwabhorn Thaweewannakij, Preeda Arayawichanon

Abstract:

Many individuals with spinal cord injury (SCI) need an ambulatory assistive device (AAD) to promote their independence and experience of task-specific walking practice. Without a periodic follow-up for their walking progression, however, many individuals may use the same AAD even though up to 66% of them had the potential to progress walking ability. This may distort their optimal ability and increase the possibility of having negative impacts due to the long-lasting used of an AAD. However, these findings were cross-sectionally collected without data confirmation for the benefit or negative impacts of those who changed the types of AAD used. Therefore, this study prospectively assessed the proportion of ambulatory individuals with SCI who were able to progress their walking ability as determined using a type of AAD, and the changes of their functional ability as well as the incidence of falls over 6 months. Twenty-four subjects with SCI who daily walked with an AAD were involved in the study for 2 visits over 6 months. At the first visit (baseline assessments), the subjects were assessed for their spatiotemporal variables (i.e., cadence, step length, stride length, and step symmetry) and walking ability using the 10-meter walk test (10MWT). Then, they were assessed for the possibility of their walking progression as determined using the ability of walking with the least support AAD with no more than contact guarding assist. Those who were capable of changing an AAD were trained for the ability to walk with a new AAD. Thereafter, all subjects were monthly monitored for incidence of fall over 6 months. At the second visit (after 6 months followed-up), subjects were reassessed for their spatiotemporal variables and 10MWT. The findings indicated that, of all 24 subjects, 8 subjects (33.3%) were able to walk with less support AAD than their usual one. The walking cadence, step length symmetry, and walking ability of these subjects improved significantly greater than those who walked with the same AAD (p < 0.05). Among these subjects, one subject (12.5%) reported fell (3 times) during the follow-up period, whereas 5 subjects (31.3%) who walked with the same AAD experienced at least one fall (range 1 – 16 times). The findings indicated that a large proportion of ambulatory individuals with SCI who daily walked with an AAD could progress their walking ability, whereby their walking ability and safety also significantly improved after they walked with an optimal AAD. The findings suggest the need for a periodic follow-up for an appropriate AAD used for these individuals.

Keywords: walking device, walker, crutches, cane, rehabilitation

Procedia PDF Downloads 123