Search results for: savings algorithm

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

Procedia PDF Downloads 406

Authors: Fabricio Costa Silva, Samuel Xavier de Souza

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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

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Authors: Prem, Norhaya, Vwrene C., Mohammad Harris A., Amarjit, Fazir M.

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Upper limb surgery is a common orthopedic procedure. After surgery, it is necessary to raise the patient's arm to reduce limb swelling and promote recovery. After an injury or surgery, swelling (edema) in the limbs is common. This swelling can be painful, cause stiffness, and affect movement and ability to do daily activities. One of the easiest ways to manage swelling is to elevate the swollen limb. The goal is to elevate the swollen limb slightly above the level of the heart. This helps the extra fluid move back towards the heart for circulation to the rest of the body. Conventional arm sling or pillows are usually placed under the arm to raise it, but in this way the arm cannot be fixed well and easily slide down, without ideal raising effect. Conventional arm sling need experience to tie the sling and this delay in the application process. To reduce the waiting time and cost, modified Norhaya upper limb elevation sling was designed and made readily available. The sling is made from calico fabric, readily available in the ward. Measurements of patients’ arm lengths are obtained, and fabric sizes are cut into the average arm lengths, as well as 1 size above and below. The cut calico fabric is then sewn together with thick sewing threads. Its application is easy and junior most staff or doctor will be able to apply it on patient. The time taken to set up the sling is also reduced. Feedback gathered from ground staff regarding ease of setting up the sling was tremendous and patient also feel comfort in the modified Norhaya sling. The device can freely adjust the raising height of the affected limb and effectively fix the affected limb to reduce its swelling, thus promoting recovery. This device is worthy to be clinically popularized and applied. The Modified Norhaya upper limb elevation sling is the quickest to set up and the delay in elevating the patient’s hand is significantly reduced. Moreover, it is reproducible and there is also significant cost savings.

Keywords: elevate, effective, sling, timely

Procedia PDF Downloads 206

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: 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

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Authors: Haoxiang Desmond Lim, Jie Wu, Tze How Daniel New, Shengxian Shi

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This paper outlines the development of a novel experimental technique in quantifying supersonic jet flows, in an attempt to avoid seeding particle problems frequently associated with particle-image velocimetry (PIV) techniques at high Mach numbers. Based on optical flow algorithms, the idea behind the technique involves using high speed cameras to capture Schlieren images of the supersonic jet shear layers, before they are subjected to an adapted optical flow algorithm based on the Horn-Schnuck method to determine the associated flow fields. The proposed method is capable of offering full-field unsteady flow information with potentially higher accuracy and resolution than existing point-measurements or PIV techniques. Preliminary study via numerical simulations of a circular de Laval jet nozzle successfully reveals flow and shock structures typically associated with supersonic jet flows, which serve as useful data for subsequent validation of the optical flow based experimental results. For experimental technique, a Z-type Schlieren setup is proposed with supersonic jet operated in cold mode, stagnation pressure of 8.2 bar and exit velocity of Mach 1.5. High-speed single-frame or double-frame cameras are used to capture successive Schlieren images. As implementation of optical flow technique to supersonic flows remains rare, the current focus revolves around methodology validation through synthetic images. The results of validation test offers valuable insight into how the optical flow algorithm can be further improved to improve robustness and accuracy. Details of the methodology employed and challenges faced will be further elaborated in the final conference paper should the abstract be accepted. Despite these challenges however, this novel supersonic flow measurement technique may potentially offer a simpler way to identify and quantify the fine spatial structures within the shock shear layer.

Keywords: Schlieren, optical flow, supersonic jets, shock shear layer

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Authors: Mohammed Al-Yamani, Yaser Miaji

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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: Kiana Zeighami, Morteza Ozlati Moghadam

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Designing a high accuracy and high precision motion controller is one of the important issues in today’s industry. There are effective solutions available in the industry but the real-time performance, smoothness and accuracy of the movement can be further improved. This paper discusses a complete solution to carry out the movement of three stepper motors in three dimensions. The objective is to provide a method to design a fully integrated System-on-Chip (SOC)-based motion controller to reduce the cost and complexity of production by incorporating Field Programmable Gate Array (FPGA) into the design. In the proposed method the FPGA receives its commands from a host computer via wireless internet communication and calculates the motion trajectory for three axes. A profile generator module is designed to realize the interpolation algorithm by translating the position data to the real-time pulses. This paper discusses an approach to implement the linear interpolation algorithm, since it is one of the fundamentals of robots’ movements and it is highly applicable in motion control industries. Along with full profile trajectory, the triangular drive is implemented to eliminate the existence of error at small distances. To integrate the parallelism and real-time performance of FPGA with the power of Central Processing Unit (CPU) in executing complex and sequential algorithms, the NIOS II soft-core processor was added into the design. This paper presents different operating modes such as absolute, relative positioning, reset and velocity modes to fulfill the user requirements. The proposed approach was evaluated by designing a custom-made FPGA board along with a mechanical structure. As a result, a precise and smooth movement of stepper motors was observed which proved the effectiveness of this approach.

Keywords: 3-axis linear interpolation, FPGA, motion controller, micro-stepping

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

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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

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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: Vitor M. Vilas Boas, Cleison D. Silva, Gustavo S. Mafra, Alexandre Trofino Neto

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Motor imagery (MI) based brain-computer interfaces (BCI) uses event-related (de)synchronization (ERS/ ERD), typically recorded using electroencephalography (EEG), to translate brain electrical activity into control commands. To mitigate undesirable artifacts and noise measurements on EEG signals, methods based on band-pass filters defined by a specific frequency band (i.e., 8 – 30Hz), such as the Infinity Impulse Response (IIR) filters, are typically used. Spatial techniques, such as Common Spatial Patterns (CSP), are also used to estimate the variations of the filtered signal and extract features that define the imagined motion. The CSP effectiveness depends on the subject's discriminative frequency, and approaches based on the decomposition of the band of interest into sub-bands with smaller frequency ranges (SBCSP) have been suggested to EEG signals classification. However, despite providing good results, the SBCSP approach generally increases the computational cost of the filtering step in IM-based BCI systems. This paper proposes the use of the Fast Fourier Transform (FFT) algorithm in the IM-based BCI filtering stage that implements SBCSP. The goal is to apply the FFT algorithm to reduce the computational cost of the processing step of these systems and to make them more efficient without compromising classification accuracy. The proposal is based on the representation of EEG signals in a matrix of coefficients resulting from the frequency decomposition performed by the FFT, which is then submitted to the SBCSP process. The structure of the SBCSP contemplates dividing the band of interest, initially defined between 0 and 40Hz, into a set of 33 sub-bands spanning specific frequency bands which are processed in parallel each by a CSP filter and an LDA classifier. A Bayesian meta-classifier is then used to represent the LDA outputs of each sub-band as scores and organize them into a single vector, and then used as a training vector of an SVM global classifier. Initially, the public EEG data set IIa of the BCI Competition IV is used to validate the approach. The first contribution of the proposed method is that, in addition to being more compact, because it has a 68% smaller dimension than the original signal, the resulting FFT matrix maintains the signal information relevant to class discrimination. In addition, the results showed an average reduction of 31.6% in the computational cost in relation to the application of filtering methods based on IIR filters, suggesting FFT efficiency when applied in the filtering step. Finally, the frequency decomposition approach improves the overall system classification rate significantly compared to the commonly used filtering, going from 73.7% using IIR to 84.2% using FFT. The accuracy improvement above 10% and the computational cost reduction denote the potential of FFT in EEG signal filtering applied to the context of IM-based BCI implementing SBCSP. Tests with other data sets are currently being performed to reinforce such conclusions.

Keywords: brain-computer interfaces, fast Fourier transform algorithm, motor imagery, sub-band common spatial patterns

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Authors: Seyed Mehdi Ghezi, Hesam Hasanpoor

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This scientific article explores enhancement methods for ensemble learning with the aim of improving the performance of classifiers in the analysis and classification of brain signals. The research approach in this field consists of two main parts, each with its own strengths and weaknesses. The choice of approach depends on the specific research question and available resources. By combining these approaches and leveraging their respective strengths, researchers can enhance the accuracy and reliability of classification results, consequently advancing our understanding of the brain and its functions. The first approach focuses on utilizing machine learning methods to identify the best features among the vast array of features present in brain signals. The selection of features varies depending on the research objective, and different techniques have been employed for this purpose. For instance, the genetic algorithm has been used in some studies to identify the best features, while optimization methods have been utilized in others to identify the most influential features. Additionally, machine learning techniques have been applied to determine the influential electrodes in classification. Ensemble learning plays a crucial role in identifying the best features that contribute to learning, thereby improving the overall results. The second approach concentrates on designing and implementing methods for selecting the best classifier or utilizing meta-classifiers to enhance the final results in ensemble learning. In a different section of the research, a single classifier is used instead of multiple classifiers, employing different sets of features to improve the results. The article provides an in-depth examination of each technique, highlighting their advantages and limitations. By integrating these techniques, researchers can enhance the performance of classifiers in the analysis and classification of brain signals. This advancement in ensemble learning methodologies contributes to a better understanding of the brain and its functions, ultimately leading to improved accuracy and reliability in brain signal analysis and classification.

Keywords: ensemble learning, brain signals, classification, feature selection, machine learning, genetic algorithm, optimization methods, influential features, influential electrodes, meta-classifiers

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Authors: Philip T. Broughton, Mark P. Bettney, Isla L. Smail

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Effective treatment of ground instability is essential when managing the impacts associated with historic mining. A field trial was undertaken by the Coal Authority to investigate the geotechnical performance and potential use of composite materials comprising resin and fill or stone to safely treat surface collapses, such as crown-holes, associated with shallow mining. Test pits were loosely filled with various granular fill materials. The fill material was injected with commercially available silicate and polyurethane resin foam products. In situ and laboratory testing was undertaken to assess the geotechnical properties of the resultant composite materials. The test pits were subsequently excavated to assess resin permeation. Drilling and resin injection was easiest through clean limestone fill materials. Recycled building waste fill material proved difficult to inject with resin; this material is thus considered unsuitable for use in resin composites. Incomplete resin permeation in several of the test pits created irregular ‘blocks’ of composite. Injected resin foams significantly improve the stiffness and resistance (strength) of the un-compacted fill material. The stiffness of the treated fill material appears to be a function of the stone particle size, its associated compaction characteristics (under loose tipping) and the proportion of resin foam matrix. The type of fill material is more critical than the type of resin to the geotechnical properties of the composite materials. Resin composites can effectively support typical design imposed loads. Compared to other traditional treatment options, such as cement grouting, the use of resin composites is potentially less disruptive, particularly for sites with limited access, and thus likely to achieve significant reinstatement cost savings. The use of resin composites is considered a suitable option for the future treatment of shallow mining collapses.

Keywords: composite material, ground improvement, mining legacy, resin

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Authors: Badrane Nohayla, Bamousse Zineb

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Financial crises, exchange rate volatility, fluctuations in commodity prices, increased competitive pressures, and environmental issues are all threats that businesses face. In light of these diverse challenges, proactive, agile, and innovative cash management becomes an indispensable financial shield, allowing companies to thrive despite the adverse conditions of the global environment. In the same spirit, uncertainty, turbulence, volatility, and competitiveness continue to disrupt economic environments, compelling companies to swiftly master innovative breakthroughs that provide added value. In such a context, innovation emerges as a catalytic vector for performance, aiming to reduce costs, strengthen growth, and ultimately ensure the sustainability of Moroccan companies in the national arena. Moreover, innovation in treasury management promises to be one of the key pillars of financial stability, enabling companies to navigate the tumultuous waters of a globalized environment. Therefore, the objective of this study is to better understand the impact of innovative treasury management on cost optimization and, by extension, performance improvement. To elucidate this relationship, we conducted an exploratory qualitative study with 20 large Moroccan companies operating in the Casablanca-Settat region. The results highlight that innovation at the heart of treasury management is a guarantee of sustainability against the risks of failure and stands as a true pivot of the performance of Moroccan companies, an important parameter of their financial balance and a catalytic vector of their growth in the national economic landscape. In this regard, the present study aims to explore the extent to which innovation at the core of the treasury function serves as an indispensable tool for boosting performance while optimising costs in large Moroccan companies.

Keywords: innovative cash management, artificial intelligence, financial performance, risk management, cost savings

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Authors: Seyed-Yaser Nabavi-Chashmi, Davood Asadi, Karim Ahmadi, Eren Demir

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The landing phase of a UAV is very critical as there are many uncertainties in this phase, which can easily entail a hard landing or even a crash. In this paper, the estimation of relative distance and velocity to the ground, as one of the most important processes during the landing phase, is studied. Using accurate measurement sensors as an alternative approach can be very expensive for sensors like LIDAR, or with a limited operational range, for sensors like ultrasonic sensors. Additionally, absolute positioning systems like GPS or IMU cannot provide distance to the ground independently. The focus of this paper is to determine whether we can measure the relative distance and velocity of UAV and ground in the landing phase using just low-resolution images taken by a monocular camera. The Lucas-Konda feature detection technique is employed to extract the most suitable feature in a series of images taken during the UAV landing. Two different approaches based on Extended Kalman Filters (EKF) have been proposed, and their performance in estimation of the relative distance and velocity are compared. The first approach uses the kinematics of the UAV as the process and the calculated optical flow as the measurement; On the other hand, the second approach uses the feature’s projection on the camera plane (pixel position) as the measurement while employing both the kinematics of the UAV and the dynamics of variation of projected point as the process to estimate both relative distance and relative velocity. To verify the results, a sequence of low-quality images taken by a camera that is moving on a specifically developed testbed has been used to compare the performance of the proposed algorithm. The case studies show that the quality of images results in considerable noise, which reduces the performance of the first approach. On the other hand, using the projected feature position is much less sensitive to the noise and estimates the distance and velocity with relatively high accuracy. This approach also can be used to predict the future projected feature position, which can drastically decrease the computational workload, as an important criterion for real-time applications.

Keywords: altitude estimation, drone, image processing, trajectory planning

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Authors: Elise Machline

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Policy instruments have been developed worldwide to reduce the energy demand of buildings. Two types of such instruments have been green building rating systems and energy efficiency standards for buildings -such as Green Star (Australia), LEED (United States, Leadership in Energy and Environmental Design), Energy Star (United States), and BREEAM (United Kingdom, Building Research Establishment Environmental Assessment Method). The popularity of the idea of sustainable development has allowed the actors to consider the potential value generated by the environmental performance of buildings, labeled “green value” in the literature. Sustainable performances of buildings are expected to improve their attractiveness, increasing their value. A growing number of empirical studies demonstrate that green buildings yield rental/sale premia, as well as higher occupancy rates and thus higher asset values. The results suggest that green buildings are not affordable to all and that their construction tends to have a gentrifying effect. An increasing number of countries are institutionalizing green strategies for affordable housing. In that sense, making green buildings affordable to all will depend on government policies. That research aims to investigate whether green building fosters inequality in Israel, under the banner of sustainability. The method is comparison (of the market value). This method involves comparing the green buildings sale prices with non-certified buildings of the same type that have undergone recent transactions. The “market value” is deduced from those sources by analogy. The results show that, in Israel, green building projects are usually addressed to the middle to upper classes. The green apartment’s sale premium is about 19% (comparing to non-certified dwelling). There is a link between energy and/or environmental performance and the financial value of the dwellings. Moreover, price differential is much higher than the value of energy savings. This perpetuates socio-spatial and socio-economic inequality as well as ecological vulnerability for the poor and other socially marginal groups. Moreover, there are no green affordable housings and the authorities do not subsidy green building or retrofitting.

Keywords: green building, gentrification, social housing, green value, green building certification

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Authors: Azad Hussain, Andrew Cobley

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The demand for lightweight parts with high mechanical strength(s) and integrity, in sectors such as the aerospace and automotive is ever increasing, motivated by the need for weight reduction in order to increase fuel efficiency with components usually manufactured using a high grade primary metal or alloy. For components manufactured using the squeeze casting process, this alloy is usually A356 aluminium (Al), it is one of the most versatile Al alloys; and is used extensively in castings for demanding environments. The A356 castings provide good strength to weight ratio making it an attractive option for components where strength has to be maintained, with the added advantage of weight reduction. In addition, the versatility in castabilitiy, weldability and corrosion resistance are other attributes that provide for the A356 cast alloy to be used in a large array of industrial applications. Conversely, it is rare to use remelted Al in these cases, due the nature of the applications of components in demanding environments, were material properties must be defined to meet certain specifications for example a known strength or ductility. However the use of remelted Al, especially primary grade Al such as A356, would offer significant cost and energy savings for manufacturers using primary alloys, provided that remelted aluminium can offer similar benefits in terms of material microstructure and mechanical properties. This study presents the results of the material microstructure and properties of 100% primary A356 Al and 100% remelt Al cast, manufactured via the direct squeeze cast method. The microstructures of the castings made from remelted A356 Al were then compared with the microstructures of primary A356 Al. The outcome of using remelting Al on the microstructure was examined via different analytical techniques, optical microscopy of polished and etched surfaces, and scanning electron microscopy. Microstructural analysis of the 100% remelted Al when compared with primary Al show similar α-Al phase, primary Al dendrites, particles and eutectic constituents. Mechanical testing of cast samples will elucidate further information as to the suitability of utilising 100% remelt for casting.

Keywords: A356, microstructure, remelt, squeeze casting

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Authors: N. N. Mosola, S. J. Molete, L. S. Masoebe, M. Letsae

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Hand gesture recognition is a technique used to locate, detect, and recognize a hand gesture. Detection and recognition are concepts of Artificial Intelligence (AI). AI concepts are applicable in Human Computer Interaction (HCI), Expert systems (ES), etc. Hand gesture recognition can be used in sign language interpretation. Sign language is a visual communication tool. This tool is used mostly by deaf societies and those with speech disorder. Communication barriers exist when societies with speech disorder interact with others. This research aims to build a hand recognition system for Lesotho’s Sesotho and English language interpretation. The system will help to bridge the communication problems encountered by the mentioned societies. The system has various processing modules. The modules consist of a hand detection engine, image processing engine, feature extraction, and sign recognition. Detection is a process of identifying an object. The proposed system uses Canny pruning Haar and Haarcascade detection algorithms. Canny pruning implements the Canny edge detection. This is an optimal image processing algorithm. It is used to detect edges of an object. The system employs a skin detection algorithm. The skin detection performs background subtraction, computes the convex hull, and the centroid to assist in the detection process. Recognition is a process of gesture classification. Template matching classifies each hand gesture in real-time. The system was tested using various experiments. The results obtained show that time, distance, and light are factors that affect the rate of detection and ultimately recognition. Detection rate is directly proportional to the distance of the hand from the camera. Different lighting conditions were considered. The more the light intensity, the faster the detection rate. Based on the results obtained from this research, the applied methodologies are efficient and provide a plausible solution towards a light-weight, inexpensive system which can be used for sign language interpretation.

Keywords: canny pruning, hand recognition, machine learning, skin tracking

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Authors: Ni Wenli, He Jing

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Repeat–Accumulate (RA) codes are subclass of LDPC codes with fast encoder structures. In this paper, we consider a nonbinary extension of binary LDPC codes over GF(q) and construct a non-binary RA code and a non-binary QC-LDPC code over GF(2^4), we construct non-binary RA codes with linear encoding method and non-binary QC-LDPC codes with algebraic constructions method. And the BER performance of RA and QC-LDPC codes over GF(q) are compared with BP decoding and by simulation over the Additive White Gaussian Noise (AWGN) channels.

Keywords: non-binary RA codes, QC-LDPC codes, performance comparison, BP algorithm

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Authors: M. K. Benhaoua, A. E. H. Benyamina, T. Djeradi, P. Boulet

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In this paper, we propose adaptive routing that considers the routing of communications in order to optimize the overall performance. The routing technique uses a newly proposed Algorithm to route communications between the tasks. The routing we propose of the communications leads to a better optimization of several performance metrics (time and energy consumption). Experimental results show that the proposed routing approach provides significant performance improvements when compared to those using static routing.

Keywords: multi-processor systems-on-chip (mpsocs), network-on-chip (noc), heterogeneous architectures, adaptive routin

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Authors: N. Reichbach, A. Kuperman

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The paper describes a design of a monitoring system for super capacitor packs in propulsion systems, allowing determining the instantaneous energy capacity under power loading. The system contains real-time recursive-least-squares identification mechanism, estimating the values of pack capacitance and equivalent series resistance. These values are required for accurate calculation of the state-of-energy.

Keywords: real-time monitoring, RLS identification algorithm, state-of-energy, super capacitor

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Authors: Abdus-Samiᶜi Imam Arikewuyo

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Islam enjoins the spirit of cooperation among fellow mankind. This is particularly entrenched in the brotherhood phenomenon advocated by Islam. With cooperation, a group of people with diverse qualities and opportunities can facilitate a breakthrough in what is seemingly difficult, if not impossible. This understanding underscores the initiative of establishing multi-purpose cooperative societies for thrifts and savings among bonafide members, especially in developing nations. The spirit and objectives of the Multi-Purpose Cooperatives Societies gave birth to the founding of several of these organizations as a plausible economic outfit in Nigeria to provide succor to the socio-economic predicaments of members. Pertinently, many Islamic cooperative societies sprang up, carving a niche for themselves as Shariᶜah-based economic outfits to accommodate the yearnings of the Muslim populace. KWASU Al-Halal Cooperative Society, Malete, Nigeria, is one such organization that is not only Shariᶜah inclined but also institutional-based. This paper, therefore, aims to examine the operations and activities of this society with a view to determining its strength as a Shariᶜah-based economic outfit for the survival of its clients in a competitive multi-religious atmosphere. The study is both historical and descriptive; thus, it employed interview, observation, focus group discussion and hermeneutical methods as pertinent research tools. The research findings indicated that adopting the Shariᶜah prescriptions, KWASU Al-Halal Cooperative Society, as a representation of the Islamic cooperatives society, fulfilled the essence and goal of its establishment, serving as an outlet for meeting the socio-economic demands of its members regardless of religious inclinations. The paper recommended that stronger efforts should be made on proper record keeping, the conscientiousness of beneficiaries on loan refunds or payments on purchases and education of the loanees and guarantors.

Keywords: efficacy, Islamic cooperatives society, Kwasu al-halal cooperative society, shariᶜah

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Authors: Jonas A. R. H. Lima, Maria Antonia Carravilla

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As productivity concepts are widely related to industrial savings, it is becoming particularly important in a more and more competitive world, to really understand how productivity can be well used in industrial management techniques. Nowadays, consumers are no more willing to pay for mistakes and inefficiencies. Therefore, one way for companies to stay competitive is to control and increase their productivity. This study aims to define clearly the productivity concept, understand how a company can affect productivity, and, if possible, identify the relation between each identified productivity factor. This will help managers, by clarifying the main issues behind productivity concepts and proposing a methodology to measure, control and increase productivity. The main questions to be answered are: what is the importance of productivity for the Portuguese Wood Furniture Industry? Is it possible to control productivity internally, or is it a phenomenon external to companies, hard or even impossible to control? How to understand, control and adjust productivity performance? How to make productivity to become one main asset for maximizing the use of the available resources? This essay will follow a constructive approach mostly based in the research hypothesis mentioned above. For that, a literature review is being done to find the main conceptual frameworks and empirical studies that already exist, and by doing so, highlight eventual knowledge or conflicting research to be addressed in this work. We expect to build theoretical explanations and test theoretical predictions from participants understandings and own experiences, by elaborating field surveys and interviews, to select adjusted productivity indicators and analyze the productivity evolution according the adjustments on other variables. Its intended the conduction of an exploratory work that can simultaneous clarify productivity concepts, objectives, and define frameworks. This investigation intends to migrate from merely academic concepts to a daily basis operational reality of the companies from the Portuguese Wood Furniture Industry highlighting productivity increased importance within modern engineering and industrial management. The ambition is to clarify, systemize and develop a management tool that may not only control but positively influence the way resources are used.

Keywords: industrial management, motivation, productivity, performance indicators, reward management, wood furniture industry

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Authors: Elmabruk Laias, Abdalla M. Hanashi, Mohammed Alnas

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Worldwide Interoperability for Microwave Access (WiMAX) became one of the most challenging issues, since it was responsible for distributing available resources of the network among all users this leaded to the demand of constructing and designing high efficient scheduling algorithms in order to improve the network utilization, to increase the network throughput, and to minimize the end-to-end delay. In this study, the proposed algorithm focuses on an efficient mechanism to serve non-real time traffic in congested networks by considering channel status.

Keywords: WiMAX, Quality of Services (QoS), OPNE, Diff-Serv (DS).

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Authors: Pranay Mathur, Carlo Michelassi, Simi Karatha, Gilda Pedoto

Abstract:

Industrial plant uptime is top most importance for reliable, profitable & sustainable operation. Trip and failed start has major impact on plant reliability and all plant operators focussed on efforts required to minimise the trips & failed starts. The performance of these CTQs are measured with 2 metrics, MTBT(Mean time between trips) and SR (Starting reliability). These metrics helps to identify top failure modes and identify units need more effort to improve plant reliability. Baker Hughes Trip reduction program structured to reduce these unwanted trip 1. Real time machine operational parameters remotely available and capturing the signature of malfunction including related boundary condition. 2. Real time alerting system based on analytics available remotely. 3. Remote access to trip logs and alarms from control system to identify the cause of events. 4. Continuous support to field engineers by remotely connecting with subject matter expert. 5. Live tracking of key CTQs 6. Benchmark against fleet 7. Break down to the cause of failure to component level 8. Investigate top contributor, identify design and operational root cause 9. Implement corrective and preventive action 10. Assessing effectiveness of implemented solution using reliability growth models. 11. Develop analytics for predictive maintenance With this approach , Baker Hughes team is able to support customer in achieving their Reliability Key performance Indicators for monitored units, huge cost savings for plant operators. This Presentation explains these approach while providing successful case studies, in particular where 12nos. of LNG and Pipeline operators with about 140 gas compressing line-ups has adopted these techniques and significantly reduce the number of trips and improved MTBT

Keywords: reliability, availability, sustainability, digital infrastructure, weibull, effectiveness, automation, trips, fail start

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Authors: Ju-Hong Lee, Ching-Wei Liao

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Owing to close antenna spacing between antenna sensors within a compact space, a part of data in one antenna sensor would outflow to other antenna sensors when the antenna sensors in an antenna array operate simultaneously. This phenomenon is called mutual coupling effect (MCE). It has been shown that the performance of antenna array systems can be degraded when the antenna sensors are in close proximity. Especially, in a systems equipped with massive antenna sensors, the degradation of beamforming performance due to the MCE is significantly inevitable. Moreover, it has been shown that even a small angle error between the true direction angle of the desired signal and the steering angle deteriorates the effectiveness of an array beamforming system. However, the true direction vector of the desired signal may not be exactly known in some applications, e.g., the application in land mobile-cellular wireless systems. Therefore, it is worth developing robust techniques to deal with the problem due to the MCE and steering angle error for array beamforming systems. In this paper, we present an efficient technique for performing adaptive beamforming with robust capabilities against the MCE and the steering angle error. Only the data vector received by an antenna array is required by the proposed technique. By using the received array data vector, a correlation matrix is constructed to replace the original correlation matrix associated with the received array data vector. Then, the mutual coupling matrix due to the MCE on the antenna array is estimated through a recursive algorithm. An appropriate estimate of the direction angle of the desired signal can also be obtained during the recursive process. Based on the estimated mutual coupling matrix, the estimated direction angle, and the reconstructed correlation matrix, the proposed technique can effectively cure the performance degradation due to steering angle error and MCE. The novelty of the proposed technique is that the implementation procedure is very simple and the resulting adaptive beamforming performance is satisfactory. Simulation results show that the proposed technique provides much better beamforming performance without requiring complicated complexity as compared with the existing robust techniques.

Keywords: adaptive beamforming, mutual coupling effect, recursive algorithm, steering angle error

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Authors: Mahtab Makaremi Masouleh, Günter Wozniak

Abstract:

This work is part of a major research project concerning the design of a light temporary installable textile flood control structure. The motivation for this work is the great need of applying light structures for the protection of coastal areas from detrimental effects of rapid water runoff. The prime objective of the study is the numerical analysis of the interaction among free surface water flow and slender shaped pliable structures, playing a key role in safety performance of the intended system. First, the behavior of down scale membrane is examined under hydrostatic pressure by the Abaqus explicit solver, which is part of the finite element based commercially available SIMULIA software. Then the procedure to achieve a stable and convergent solution for strongly coupled media including fluids and structures is explained. A partitioned strategy is imposed to make both structures and fluids be discretized and solved with appropriate formulations and solvers. In this regard, finite element method is again selected to analyze the structural domain. Moreover, computational fluid dynamics algorithms are introduced for solutions in flow domains by means of a commercial package of Star CCM+. Likewise, SIMULIA co-simulation engine and an implicit coupling algorithm, which are available communication tools in commercial package of the Star CCM+, enable powerful transmission of data between two applied codes. This approach is discussed for two different cases and compared with available experimental records. In one case, the down scale membrane interacts with open channel flow, where the flow velocity increases with time. The second case illustrates, how the full scale flexible flood barrier behaves when a massive flotsam is accelerated towards it.

Keywords: finite element formulation, finite volume algorithm, fluid-structure interaction, light pliable structure, VOF multiphase model

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Authors: Theodore Panton

Abstract:

As cities in the United States experience critical housing shortages, mass timber presents the opportunity to address this crisis in housing supply while taking advantage of the carbon-positive benefits of sustainably forested wood fiber. Mass timber, however, currently has a low level of adoption in residential multifamily structures due to the risk-averse nature of change within the construction financing, Architecture / Engineering / Contracting (AEC) communities, as well as various agency approval challenges. This study demonstrates how mass timber can be used within the cost and feasibility parameters of a typical multistory residential structure and ultimately address the need for dense urban housing. This study will utilize The Garden District, a mixed-use market-rate housing project in Woodinville, Washington, as a case study to illuminate the potential of mass timber in this application. The Garden District is currently in final stages of permit approval and will commence construction in 2023. It will be the tallest dowel-laminated timber (DLT) residential structure in the United States when completed. This case study includes economic, technical, and design reference points to demonstrate the relevance of the use of this system and its ability to deliver “triple bottom line” results. In terms of results, the study establishes scalable and repeatable approaches to project design and delivery of mass timber in multifamily residential uses and includes economic data, technical solutions, and a summary of end-user advantages. This study discusses the third party tested systems for satisfying acoustical requirements within dwelling units, a key to resolving the use of mass timber within multistory residential use. Lastly, the study will also compare the mass timber solution with a comparable cold formed steel (CFS) system with a similar program, which indicates a net carbon savings of over three million tons over the life cycle of the building.

Keywords: DLT, dowell laminated timber, mass timber, market rate multifamily

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Authors: Deniz Guven, Wil Ward, Jinming Duan, Li Bai

Abstract:

Over the past two decades or so, Optical Coherence Tomography (OCT) has been used to diagnose retina and optic nerve diseases. The retinal nerve fibre layer, for example, is a powerful diagnostic marker for detecting and staging glaucoma. With the advances in optical imaging hardware, the adoption of OCT is now commonplace in clinics. More and more OCT images are being generated, and for these OCT images to have clinical applicability, accurate automated OCT image segmentation software is needed. Oct image segmentation is still an active research area, as OCT images are inherently noisy, with the multiplicative speckling noise. Simple edge detection algorithms are unsuitable for detecting retinal layer boundaries in OCT images. Intensity fluctuation, motion artefact, and the presence of blood vessels also decrease further OCT image quality. In this paper, we introduce a new method for segmenting three-dimensional (3D) OCT images. This involves the use of a Kalman filter, which is commonly used in computer vision for object tracking. The Kalman filter is applied to the 3D OCT image volume to track the retinal layer boundaries through the slices within the volume and thus segmenting the 3D image. Specifically, after some pre-processing of the OCT images, points on the retinal layer boundaries in the first image are identified, and curve fitting is applied to them such that the layer boundaries can be represented by the coefficients of the curve equations. These coefficients then form the state space for the Kalman Filter. The filter then produces an optimal estimate of the current state of the system by updating its previous state using the measurements available in the form of a feedback control loop. The results show that the algorithm can be used to segment the retinal layers in OCT images. One of the limitations of the current algorithm is that the curve representation of the retinal layer boundary does not work well when the layer boundary is split into two, e.g., at the optic nerve, the layer boundary split into two. This maybe resolved by using a different approach to representing the boundaries, such as b-splines or level sets. The use of a Kalman filter shows promise to developing accurate and effective 3D OCT segmentation methods.

Keywords: optical coherence tomography, image segmentation, Kalman filter, object tracking

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Authors: David X. Dong, Qingming Zhang, Meng Lu

Abstract:

Nitrites enter waterways as runoff from croplands and are discharged from many industrial sites. Excessive nitrite inputs to water bodies lead to eutrophication. On-site rapid detection of nitrite is of increasing interest for managing fertilizer application and monitoring water source quality. Existing methods for detecting nitrites use spectrophotometry, ion chromatography, electrochemical sensors, ion-selective electrodes, chemiluminescence, and colorimetric methods. However, these methods either suffer from high cost or provide low measurement accuracy due to their poor selectivity to nitrites. Therefore, it is desired to develop an accurate and economical method to monitor nitrites in environments. We report a low-cost optical sensor, in conjunction with a machine learning (ML) approach to enable high-accuracy detection of nitrites in water sources. The sensor works under the principle of measuring molecular absorptions of nitrites at three narrowband wavelengths (295 nm, 310 nm, and 357 nm) in the ultraviolet (UV) region. These wavelengths are chosen because they have relatively high sensitivity to nitrites; low-cost light-emitting devices (LEDs) and photodetectors are also available at these wavelengths. A regression model is built, trained, and utilized to minimize cross-sensitivities of these wavelengths to the same analyte, thus achieving precise and reliable measurements with various interference ions. The measured absorbance data is input to the trained model that can provide nitrite concentration prediction for the sample. The sensor is built with i) a miniature quartz cuvette as the test cell that contains a liquid sample under test, ii) three low-cost UV LEDs placed on one side of the cell as light sources, with each LED providing a narrowband light, and iii) a photodetector with a built-in amplifier and an analog-to-digital converter placed on the other side of the test cell to measure the power of transmitted light. This simple optical design allows measuring the absorbance data of the sample at the three wavelengths. To train the regression model, absorbances of nitrite ions and their combination with various interference ions are first obtained at the three UV wavelengths using a conventional spectrophotometer. Then, the spectrophotometric data are inputs to different regression algorithm models for training and evaluating high-accuracy nitrite concentration prediction. Our experimental results show that the proposed approach enables instantaneous nitrite detection within several seconds. The sensor hardware costs about one hundred dollars, which is much cheaper than a commercial spectrophotometer. The ML algorithm helps to reduce the average relative errors to below 3.5% over a concentration range from 0.1 ppm to 100 ppm of nitrites. The sensor has been validated to measure nitrites at three sites in Ames, Iowa, USA. This work demonstrates an economical and effective approach to the rapid, reagent-free determination of nitrites with high accuracy. The integration of the low-cost optical sensor and ML data processing can find a wide range of applications in environmental monitoring and management.

Keywords: optical sensor, regression model, nitrites, water quality

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