Search results for: recognition algorithm

Authors: Rujia Chen, Akbar Ghobakhlou, Ajit Narayanan

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This study investigates the task of identifying musical instruments in polyphonic compositions using Convolutional Neural Networks (CNNs) from spectrogram inputs, focusing on binary classification. The model showed promising results, with an accuracy of 97% on solo instrument recognition. When applied to polyphonic combinations of 1 to 10 instruments, the overall accuracy was 64%, reflecting the increasing challenge with larger ensembles. These findings contribute to the field of Music Information Retrieval (MIR) by highlighting the potential and limitations of current approaches in handling complex musical arrangements. Future work aims to include a broader range of musical sounds, including electronic and synthetic sounds, to improve the model's robustness and applicability in real-time MIR systems.

Keywords: binary classifier, CNN, spectrogram, instrument

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Authors: S. Neelima, P. S. Subramanyam

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A distribution system is an interface between the bulk power system and the consumers. Among these systems, radial distributions system is popular because of low cost and simple design. In distribution systems, the voltages at buses reduces when moved away from the substation, also the losses are high. The reason for a decrease in voltage and high losses is the insufficient amount of reactive power, which can be provided by the shunt capacitors. But the placement of the capacitor with an appropriate size is always a challenge. Thus, the optimal capacitor placement problem is to determine the location and size of capacitors to be placed in distribution networks in an efficient way to reduce the power losses and improve the voltage profile of the system. For this purpose, in this paper, two stage methodologies are used. In the first stage, the load flow of pre-compensated distribution system is carried out using ‘dimension reducing distribution load flow algorithm (DRDLFA)’. On the basis of this load flow the potential locations of compensation are computed. In the second stage, Genetic Algorithm (GA) technique is used to determine the optimal location and size of the capacitors such that the cost of the energy loss and capacitor cost to be a minimum. The above method is tested on IEEE 9 and 34 bus system and compared with other methods in the literature.

Keywords: dimension reducing distribution load flow algorithm, DRDLFA, genetic algorithm, electrical distribution network, optimal capacitors placement, voltage profile improvement, loss reduction

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Authors: Bliss Singhal

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Machine learning (ML) is a branch of Artificial Intelligence (AI) where computers analyze data and find patterns in the data. The study focuses on the detection of metastatic cancer using ML. Metastatic cancer is the stage where cancer has spread to other parts of the body and is the cause of approximately 90% of cancer-related deaths. Normally, pathologists spend hours each day to manually classifying whether tumors are benign or malignant. This tedious task contributes to mislabeling metastasis being over 60% of the time and emphasizes the importance of being aware of human error and other inefficiencies. ML is a good candidate to improve the correct identification of metastatic cancer, saving thousands of lives and can also improve the speed and efficiency of the process, thereby taking fewer resources and time. So far, the deep learning methodology of AI has been used in research to detect cancer. This study is a novel approach to determining the potential of using preprocessing algorithms combined with classification algorithms in detecting metastatic cancer. The study used two preprocessing algorithms: principal component analysis (PCA) and the genetic algorithm, to reduce the dimensionality of the dataset and then used three classification algorithms: logistic regression, decision tree classifier, and k-nearest neighbors to detect metastatic cancer in the pathology scans. The highest accuracy of 71.14% was produced by the ML pipeline comprising of PCA, the genetic algorithm, and the k-nearest neighbor algorithm, suggesting that preprocessing and classification algorithms have great potential for detecting metastatic cancer.

Keywords: breast cancer, principal component analysis, genetic algorithm, k-nearest neighbors, decision tree classifier, logistic regression

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Authors: Ng Liang Shen, Hau Yuan Wen

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Arrhythmia analysis of ECG signal plays a major role in diagnosing most of the cardiac diseases. Therefore, a single arrhythmia detection of an electrocardiographic (ECG) record can determine multiple pattern of various algorithms and match accordingly each ECG beats based on Machine Learning supervised learning. These researchers used different features and classification methods to classify different arrhythmia types. A major problem in these studies is the fact that the symptoms of the disease do not show all the time in the ECG record. Hence, a successful diagnosis might require the manual investigation of several hours of ECG records. The point of this paper presents investigations cardiovascular ailment in Electrocardiogram (ECG) Signals for Cardiac Arrhythmia utilizing examination of ECG irregular wave frames via heart beat as correspond arrhythmia which with Machine Learning Pattern Recognition.

Keywords: electrocardiogram, ECG, classification, machine learning, pattern recognition, detection, QRS

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Authors: U. Sabura Banu, S. K. Lakshmanaprabu

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The improvement of meta-heuristic algorithm encourages control engineer to design an optimal controller for industrial process. Most real-world industrial processes are non-linear multivariable process with high interaction. Even in sub-process unit, thousands of loops are available mostly interacting in nature. Optimal controller design for such process are still challenging task. Closed loop controller design by multiloop PID involves a tedious procedure by performing interaction study and then PID auto-tuning the loop with higher interaction. Finally, detuning the controller to accommodate the effects of the other process variables. Fractional order PID controllers are replacing integer order PID controllers recently. Design of Multiloop Fractional Order (MFO) PID controller is still more complicated. Cuckoo algorithm, a swarm intelligence technique is used to optimally tune the MFO PID controller with easiness minimizing Integral Time Absolute Error. The closed loop performance is tested under servo, regulatory and servo-regulatory conditions.

Keywords: Cuckoo algorithm, mutliloop fractional order PID controller, two Interacting conical tank process

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Authors: Hyung Joo Lee, Jin Young Choi, Gun Soo Park, Kyo Sun Oh, Dong Jun Won

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The fluctuation of the output of the renewable generator caused by weather conditions must be mitigated because it imposes strain on the system and adversely affects power quality. In this paper, we focus on mitigating the output fluctuation of the photovoltaic (PV) using battery energy storage system (BESS). To satisfy tight conditions of system, proposed algorithm is developed. This algorithm focuses on adjusting the integrated output curve considering state of capacity (SOC) of the battery. In this paper, the simulation model is PSCAD / EMTDC software. SOC of the battery and the overall output curve are shown using the simulation results. We also considered losses and battery efficiency.

Keywords: photovoltaic generation, battery energy storage system, renewable integration, power smoothing

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Authors: Saeed Kamarian, Mahmoud Shakeri

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Sandwich structures are used in a variety of engineering applications including aircraft, construction and transportation where strong, stiff and light structures are required. In this paper, frequency maximization of Functionally Graded Sandwich (FGS) beams resting on Pasternak foundations is investigated. A generalized power-law distribution with four parameters is considered for material distribution through the thicknesses of face layers. Since the search space is large, the optimization processes becomes so complicated and too much time consuming. Thus a novel meta–heuristic called Imperialist Competitive Algorithm (ICA) which is a socio-politically motivated global search strategy is implemented to improve the speed of optimization process. Results show the success of applying ICA for engineering problems especially for design optimization of FGM sandwich beams.

Keywords: sandwich beam, functionally graded materials, optimization, imperialist competitive algorithm

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Authors: Yossiri Ariyakul, Piyakiat Insom, Poonyawat Sangiamkulthavorn, Takamichi Nakamoto

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In this paper, the method of optimization algorithm for sensor network comprised of movable sensor nodes which can be used for odor source localization was proposed. A sensor node is composed of an odor sensor, an anemometer, and a wireless communication module. The odor intensity measured from the sensor nodes are sent to the processor to perform the localization based on optimization algorithm by which the odor source localization map is obtained as a result. The map can represent the exact position of the odor source or show the direction toward it remotely. The proposed method was experimentally validated by creating the odor source localization map using three, four, and five sensor nodes in which the accuracy to predict the position of the odor source can be observed.

Keywords: odor sensor, odor source localization, optimization, sensor network

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Authors: Ayush Shrivastava, Arpit Chaudhary, Devang Kulshreshtha, Vibhav Prakash Singh, Rajeev Srivastava

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Early diagnosis of breast cancer can improve the survival rate by detecting cancer at an early stage. Breast region segmentation is an essential step in the analysis of digital mammograms. Accurate image segmentation leads to better detection of cancer. It aims at separating out Region of Interest (ROI) from rest of the image. The procedure begins with removal of labels, annotations and tags from the mammographic image using morphological opening method. Pectoral Muscle Sliding Window Algorithm (PMSWA) is used for removal of pectoral muscle from mammograms which is necessary as the intensity values of pectoral muscles are similar to that of ROI which makes it difficult to separate out. After removing the pectoral muscle, Dispersed Region Growing Algorithm (DRGA) is used for segmentation of mammogram which disperses seeds in different regions instead of a single bright region. To demonstrate the validity of our segmentation method, 322 mammographic images from Mammographic Image Analysis Society (MIAS) database are used. The dataset contains medio-lateral oblique (MLO) view of mammograms. Experimental results on MIAS dataset show the effectiveness of our proposed method.

Keywords: CAD, dispersed region growing algorithm (DRGA), image segmentation, mammography, pectoral muscle sliding window algorithm (PMSWA)

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Authors: Jianqin Qu

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This paper proposes a rigid point set matching algorithm in arbitrary dimensions based on the idea of symmetric covariant function. A group of functions of the points in the set are formulated using rigid invariants. Each of these functions computes a pair of correspondence from the given point set. Then the computed correspondences are used to recover the unknown rigid transform parameters. Each computed point can be geometrically interpreted as the weighted mean center of the point set. The algorithm is compact, fast, and dimension free without any optimization process. It either computes the desired transform for noiseless data in linear time, or fails quickly in exceptional cases. Experimental results for synthetic data and 2D/3D real data are provided, which demonstrate potential applications of the algorithm to a wide range of problems.

Keywords: covariant point, point matching, dimension free, rigid registration

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Authors: Chuanbo Xu, Xinying Li, Gejirifu De, Yunna Wu

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Energy Internet (EI) is a new form that deeply integrates the Internet and the entire energy process from production to consumption. The assessment of energy efficient performance is of vital importance for the long-term sustainable development of EI project. Although the newly proposed fuzzy integrated cloud evaluation algorithm considers the randomness of uncertainty, it relies too much on the experience and knowledge of experts. Fortunately, the enrichment of EI data has enabled the utilization of data-driven methods. Therefore, the main purpose of this work is to assess the energy efficient of park-level EI by using a combination of a data-driven method with the fuzzy integrated cloud evaluation algorithm. Firstly, the indicators for the energy efficient are identified through literature review. Secondly, the artificial neural network (ANN)-based data-driven method is employed to cluster the values of indicators. Thirdly, the energy efficient of EI project is calculated through the fuzzy integrated cloud evaluation algorithm. Finally, the applicability of the proposed method is demonstrated by a case study.

Keywords: energy efficient, energy internet, data-driven, fuzzy integrated evaluation, cloud model

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Authors: Sai Sankalp Vemavarapu

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This paper discusses the application of machine learning in the field of transportation engineering for predicting engineering properties of pavement more accurately and efficiently. Predicting the elastic properties aid us in assessing the current road conditions and taking appropriate measures to avoid any inconvenience to commuters. This improves the longevity and sustainability of the pavement layer while reducing its overall life-cycle cost. As an example, we have implemented differential evolution (DE) in the back-calculation of the elastic modulus of multi-layered pavement. The proposed DE global optimization back-calculation approach is integrated with a forward response model. This approach treats back-calculation as a global optimization problem where the cost function to be minimized is defined as the root mean square error in measured and computed deflections. The optimal solution which is elastic modulus, in this case, is searched for in the solution space by the DE algorithm. The best DE parameter combinations and the most optimum value is predicted so that the results are reproducible whenever the need arises. The algorithm’s performance in varied scenarios was analyzed by changing the input parameters. The prediction was well within the permissible error, establishing the supremacy of DE.

Keywords: cost function, differential evolution, falling weight deflectometer, genetic algorithm, global optimization, metaheuristic algorithm, multilayered pavement, pavement condition assessment, pavement layer moduli back calculation

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Authors: Brittany Richardson, Ying Wang

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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

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Authors: Anteneh Getachew Gebrie, Rabian Wangkeeree

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The purpose of this paper is to introduce iterative algorithm solving split system of minimization problem given as a task of finding a common minimizer point of finite family of proper, lower semicontinuous convex functions and whose image under a bounded linear operator is also common minimizer point of another finite family of proper, lower semicontinuous convex functions. We obtain strong convergence of the sequence generated by our algorithm under some suitable conditions on the parameters. The iterative schemes are developed with a way of selecting the step sizes such that the information of operator norm is not necessary. Some applications and numerical experiment is given to analyse the efficiency of our algorithm.

Keywords: Hilbert Space, minimization problems, Moreau-Yosida approximate, split feasibility problem

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Authors: Fatima Muhammad Shitu

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This study seeks to investigate the effect of the keyword strategy on lexico–semantic acquisition, recognition, retention and comprehension in an ESL context. The aim of the study is to determine whether the keyword strategy can be used to enhance acquisition. As a quasi- experimental research, the objectives of the study include: To determine the extent to which the scores obtained by the subjects, who were trained on the use of the keyword strategy for acquisition, differ at the pre-tests and the post–tests and also to find out the relationship in the scores obtained at these tests levels. The sample for the study consists of 300 hundred undergraduate ESL Students in the Federal College of Education, Kano. The seventy-five lexical items for acquisition belong to the lexical field category known as register, and they include Medical, Agriculture and Photography registers (MAP). These were divided in the ratio twenty-five (25) lexical items in each lexical field. The testing technique was used to collect the data while the descriptive and inferential statistics were employed for data analysis. For the purpose of testing, the two kinds of tests administered at each test level include the WARRT (Word Acquisition, Recognition, and Retention Test) and the CCPT (Cloze Comprehension Passage Test). The results of the study revealed that there are significant differences in the scores obtained between the pre-tests, and the post–tests and there are no correlations in the scores obtained as well. This implies that the keyword strategy has effectively enhanced the acquisition of the lexical items studied.

Keywords: keyword, lexical, semantics, strategy

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Authors: Thiago Spilborghs Bueno Meyer, Plinio Thomaz Aquino Junior

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Through speech, which privileges the functional and interactive nature of the text, it is possible to ascertain the spatiotemporal circumstances, the conditions of production and reception of the discourse, the explicit purposes such as informing, explaining, convincing, etc. These conditions allow bringing the interaction between humans closer to the human-robot interaction, making it natural and sensitive to information. However, it is not enough to understand what is said; it is necessary to recognize emotions for the desired interaction. The validity of the use of neural networks for feature selection and emotion recognition was verified. For this purpose, it is proposed the use of neural networks and comparison of models, such as recurrent neural networks and deep neural networks, in order to carry out the classification of emotions through speech signals to verify the quality of recognition. It is expected to enable the implementation of robots in a domestic environment, such as the HERA robot from the RoboFEI@Home team, which focuses on autonomous service robots for the domestic environment. Tests were performed using only the Mel-Frequency Cepstral Coefficients, as well as tests with several characteristics of Delta-MFCC, spectral contrast, and the Mel spectrogram. To carry out the training, validation and testing of the neural networks, the eNTERFACE’05 database was used, which has 42 speakers from 14 different nationalities speaking the English language. The data from the chosen database are videos that, for use in neural networks, were converted into audios. It was found as a result, a classification of 51,969% of correct answers when using the deep neural network, when the use of the recurrent neural network was verified, with the classification with accuracy equal to 44.09%. The results are more accurate when only the Mel-Frequency Cepstral Coefficients are used for the classification, using the classifier with the deep neural network, and in only one case, it is possible to observe a greater accuracy by the recurrent neural network, which occurs in the use of various features and setting 73 for batch size and 100 training epochs.

Keywords: emotion recognition, speech, deep learning, human-robot interaction, neural networks

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Authors: K. Y. Tseng, C. Y. Wu

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This paper employs a heuristic algorithm to solve engineering problems including truss structure optimization and optimal chiller loading (OCL) problems. Two different type algorithms, real-valued differential evolution (DE) and modified binary differential evolution (MBDE), are successfully integrated and then can obtain better performance in solving engineering problems. In order to demonstrate the performance of the proposed algorithm, this study adopts each one testing case of truss structure optimization and OCL problems to compare the results of other heuristic optimization methods. The result indicates that the proposed algorithm can obtain similar or better solution in comparing with previous studies.

Keywords: differential evolution, Truss structure optimization, optimal chiller loading, modified binary differential evolution

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Authors: Xu LiYun, Briand Florent, Fan GuoLiang

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The loop-layout design problem (LLDP) aims at optimizing the sequence of positioning of the machines around the cyclic production line. Traffic congestion is the usual criteria to minimize in this type of problem, i.e. the number of additional cycles spent by each part in the network until the completion of its required routing sequence of machines. This paper aims at applying several improvements mechanisms such as a positioned-based crossover operator for the Genetic Algorithm (GA) called a Two Points Crossover (TPC) and an offspring selection process. The performance of the improved GA is measured using well-known examples from literature and compared to other evolutionary algorithms. Good results show that GA can still be competitive for this type of problem against more recent evolutionary algorithms.

Keywords: crossover, genetic algorithm, layout design problem, loop-layout, manufacturing optimization

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Authors: Renkai Wang, Tingcun Wei

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In this paper, a digital control algorithm based on delta-operator is presented for high-frequency digitally-controlled DC-DC switching converters. The stability and the controlling accuracy of the DC-DC switching converters are improved by using the digital control algorithm based on delta-operator without increasing the hardware circuit scale. The design method of voltage compensator in delta-domain using PID (Proportion-Integration- Differentiation) control is given in this paper, and the simulation results based on Simulink platform are provided, which have verified the theoretical analysis results very well. It can be concluded that, the presented control algorithm based on delta-operator has better stability and controlling accuracy, and easier hardware implementation than the existed control algorithms based on z-operator, therefore it can be used for the voltage compensator design in high-frequency digitally- controlled DC-DC switching converters.

Keywords: digitally-controlled DC-DC switching converter, digital voltage compensator, delta-operator, finite word length, stability

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Authors: Sandra Baßler, Peter Dünow, Mathias Marquardt

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A Fourier series based learning control (FSBLC) algorithm for tracking trajectories of mechanical systems with unknown nonlinearities is presented. Two processes are introduced to which the FSBLC with PD controller is applied. One is a simplified service robot capable of climbing stairs due to special wheels and the other is a propeller driven pendulum with nearly the same requirements on control. Additionally to the investigation of learning the feed forward for the desired trajectories some considerations on the implementation of such an algorithm on low cost microcontroller hardware are made. Simulations of the service robot as well as practical experiments on the pendulum show the capability of the used FSBLC algorithm to perform the task of improving control behavior for repetitive task of such mechanical systems.

Keywords: climbing stairs, FSBLC, ILC, service robot

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Authors: Mahshid Arabi

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In recent years, remarkable advancements in the fields of artificial intelligence and machine learning have led to the development of facial recognition technologies. These technologies are now employed in a wide range of applications, including security, surveillance, healthcare, and education. In the field of education, the identification of university entrance exam candidates has been one of the fundamental challenges. Traditional methods such as using ID cards and handwritten signatures are not only inefficient and prone to fraud but also susceptible to errors. In this context, utilizing advanced technologies like facial recognition can be an effective and efficient solution to increase the accuracy and reliability of identity verification in entrance exams. This article examines the use of FaceMatch software for recognizing the faces of university entrance exam candidates in Iran. The main objective of this research is to evaluate the efficiency and accuracy of FaceMatch software in identifying university entrance exam candidates to prevent fraud and ensure the authenticity of individuals' identities. Additionally, this research investigates the advantages and challenges of using this technology in Iran's educational systems. This research was conducted using an experimental method and random sampling. In this study, 1000 university entrance exam candidates in Iran were selected as samples. The facial images of these candidates were processed and analyzed using FaceMatch software. The software's accuracy and efficiency were evaluated using various metrics, including accuracy rate, error rate, and processing time. The research results indicated that FaceMatch software could accurately identify candidates with a precision of 98.5%. The software's error rate was less than 1.5%, demonstrating its high efficiency in facial recognition. Additionally, the average processing time for each candidate's image was less than 2 seconds, indicating the software's high efficiency. Statistical evaluation of the results using precise statistical tests, including analysis of variance (ANOVA) and t-test, showed that the observed differences were significant, and the software's accuracy in identity verification is high. The findings of this research suggest that FaceMatch software can be effectively used as a tool for identifying university entrance exam candidates in Iran. This technology not only enhances security and prevents fraud but also simplifies and streamlines the exam administration process. However, challenges such as preserving candidates' privacy and the costs of implementation must also be considered. The use of facial recognition technology with FaceMatch software in Iran's educational systems can be an effective solution for preventing fraud and ensuring the authenticity of university entrance exam candidates' identities. Given the promising results of this research, it is recommended that this technology be more widely implemented and utilized in the country's educational systems.

Keywords: facial recognition, FaceMatch software, Iran, university entrance exam

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Authors: Raziyeh Shamsi

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In this paper, we present a multiple objective linear programming (MOLP) problem in full fuzzy case and find Data Envelopment Analysis(DEA) targets. In the presented model, we are seeking the least inputs and the most outputs in the production possibility set (PPS) with the variable return to scale (VRS) assumption, so that the efficiency projection is obtained for all decision making units (DMUs). Then, we provide an algorithm for finding DEA targets interactively in the full fuzzy case, which solves the full fuzzy problem without defuzzification. Owing to the use of interactive methods, the targets obtained by our algorithm are more applicable, more realistic, and they are according to the wish of the decision maker. Finally, an application of the algorithm in 21 educational institutions is provided.

Keywords: DEA, MOLP, full fuzzy, target

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Authors: I. Nižetić Kosović, T. Jagušt

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Lately, with the increasing number of location-based applications, demand for highly accurate and reliable indoor localization became urgent. This is a challenging problem, due to the measurement variance which is the consequence of various factors like obstacles, equipment properties and environmental changes in complex nature of indoor environments. In this paper we propose low-cost custom-setup infrastructure solution and localization algorithm based on the Weighted Centroid Localization (WCL) method. Localization accuracy is increased by several enhancements: calibration of RSSI values gained from wireless nodes, repetitive measurements of RSSI to exclude deviating values from the position estimation, and by considering orientation of the device according to the wireless nodes. We conducted several experiments to evaluate the proposed algorithm. High accuracy of ~1m was achieved.

Keywords: indoor environment, received signal strength indicator, weighted centroid localization, wireless localization

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Authors: Ebrahim Behrouzian Nejad, Rezvan Alipoor Sabzevari

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Cloud computing systems are large-scale distributed systems, so that they focus more on large scale resource sharing, cooperation of several organizations and their use in new applications. One of the main challenges in this realm is resource allocation. There are many different ways to resource allocation in cloud computing. One of the common methods to resource allocation are economic methods. Among these methods, the auction-based method has greater prominence compared with Fixed-Price method. The double combinatorial auction is one of the proper ways of resource allocation in cloud computing. This method includes two phases: winner determination and resource allocation. In this paper a new method has been presented to determine winner in double combinatorial auction-based resource allocation using Imperialist Competitive Algorithm (ICA). The experimental results show that in our new proposed the number of winner users is higher than genetic algorithm. On other hand, in proposed algorithm, the number of winner providers is higher in genetic algorithm.

Keywords: cloud computing, resource allocation, double auction, winner determination

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Authors: Tamir Michal

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The subject of Bedouin dispersion has accompanied the State of Israel from the day of its establishment. From a legal point of view, this subject has offered a launchpad for creative judicial decisions. Thus, for example, the first court decision in Israel to recognize affirmative action (Avitan), dealt with a petition submitted by a Jew appealing the refusal of the State to recognize the Petitioner’s entitlement to the long-term lease of a plot designated for Bedouins. The Supreme Court dismissed the petition, holding that there existed a public interest in assisting Bedouin to establish permanent urban settlements, an interest which justifies giving them preference by selling them plots at subsidized prices. In another case (The Forum for Coexistence in the Negev) the Supreme Court extended equitable relief for the purpose of constructing a bridge, even though the construction infringed the Law, in order to allow the children of dispersed Bedouin to reach school. Against this background, the recent verdict, delivered during the Protective Edge military campaign, which dismissed a petition aimed at forcing the State to spread out Protective Structures in Bedouin villages in the Negev against the risk of being hit from missiles launched from Gaza (Abu Afash) is disappointing. Even if, in arguendo, no selective discrimination was involved in the State’s decision not to provide such protection, the decision, and its affirmation by the Court, is problematic when examined through the prism of the Theory of Recognition. The article analyses the issue by tools of theory of Recognition, according to which people develop their identities through mutual relations of recognition in different fields. In the social context, the path to recognition is cognitive respect, which is provided by means of legal rights. By seeing other participants in Society as bearers of rights and obligations, the individual develops an understanding of his legal condition as reflected in the attitude to others. Consequently, even if the Court’s decision may be justified on strict legal grounds, the fact that Jewish settlements were protected during the military operation, whereas Bedouin villages were not, is a setback in the struggle to make the Bedouin citizens with equal rights in Israeli society. As the Court held, ‘Beyond their protective function, the Migunit [Protective Structures] may make a moral and psychological contribution that should not be undervalued’. This contribution is one that the Bedouin did not receive in the Abu Afash verdict. The basic thesis is that the Court’s verdict analyzed above clearly demonstrates that the reliance on classical liberal instruments (e.g., equality) cannot secure full appreciation of all aspects of Bedouin life, and hence it can in fact prejudice them. Therefore, elements of the recognition theory should be added, in order to find the channel for cognitive dignity, thereby advancing the Bedouins’ ability to perceive themselves as equal human beings in the Israeli society.

Keywords: bedouin dispersion, cognitive respect, recognition theory, sustainable development

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Authors: Jianwei Ma, Diriba Gemechu

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In seismic data processing, attenuation of random noise is the basic step to improve quality of data for further application of seismic data in exploration and development in different gas and oil industries. The signal-to-noise ratio of the data also highly determines quality of seismic data. This factor affects the reliability as well as the accuracy of seismic signal during interpretation for different purposes in different companies. To use seismic data for further application and interpretation, we need to improve the signal-to-noise ration while attenuating random noise effectively. To improve the signal-to-noise ration and attenuating seismic random noise by preserving important features and information about seismic signals, we introduce the concept of anisotropic total fractional order denoising algorithm. The anisotropic total fractional order variation model defined in fractional order bounded variation is proposed as a regularization in seismic denoising. The split Bregman algorithm is employed to solve the minimization problem of the anisotropic total fractional order variation model and the corresponding denoising algorithm for the proposed method is derived. We test the effectiveness of theproposed method for synthetic and real seismic data sets and the denoised result is compared with F-X deconvolution and non-local means denoising algorithm.

Keywords: anisotropic total fractional order variation, fractional order bounded variation, seismic random noise attenuation, split Bregman algorithm

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Authors: Rishabh Nagpal, Riya Uchagaonkar, Venkata Naga Narasimha Ashish Mernedi, Ahmed Hambaba

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The rapid evolution of technology in the communication sector continually seeks to bridge the gap between different communities, notably between the deaf community and the hearing world. This project develops a comprehensive American Sign Language (ASL) recognition system, leveraging the advanced capabilities of convolutional neural networks (CNNs) and vision transformers (ViTs) to interpret and translate ASL in real-time. The primary objective of this system is to provide an effective communication tool that enables seamless interaction through accurate sign language interpretation. The architecture of the proposed system integrates dual networks -VGG16 for precise spatial feature extraction and vision transformers for contextual understanding of the sign language gestures. The system processes live input, extracting critical features through these sophisticated neural network models, and combines them to enhance gesture recognition accuracy. This integration facilitates a robust understanding of ASL by capturing detailed nuances and broader gesture dynamics. The system is evaluated through a series of tests that measure its efficiency and accuracy in real-world scenarios. Results indicate a high level of precision in recognizing diverse ASL signs, substantiating the potential of this technology in practical applications. Challenges such as enhancing the system’s ability to operate in varied environmental conditions and further expanding the dataset for training were identified and discussed. Future work will refine the model’s adaptability and incorporate haptic feedback to enhance the interactivity and richness of the user experience. This project demonstrates the feasibility of an advanced ASL recognition system and lays the groundwork for future innovations in assistive communication technologies.

Keywords: sign language, computer vision, vision transformer, VGG16, CNN

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Authors: T. Thein, S. Kalyar Myo

Abstract:

Human uses visual information for understanding the speech contents in noisy conditions or in situations where the audio signal is not available. The primary advantage of visual information is that it is not affected by the acoustic noise and cross talk among speakers. Using visual information from the lip movements can improve the accuracy and robustness of automatic speech recognition. However, a major challenge with most automatic lip reading system is to find a robust and efficient method for extracting the linguistically relevant speech information from a lip image sequence. This is a difficult task due to variation caused by different speakers, illumination, camera setting and the inherent low luminance and chrominance contrast between lip and non-lip region. Several researchers have been developing methods to overcome these problems; the one is lip reading. Moreover, it is well known that visual information about speech through lip reading is very useful for human speech recognition system. Lip reading is the technique of a comprehensive understanding of underlying speech by processing on the movement of lips. Therefore, lip reading system is one of the different supportive technologies for hearing impaired or elderly people, and it is an active research area. The need for lip reading system is ever increasing for every language. This research aims to develop a visual teaching method system for the hearing impaired persons in Myanmar, how to pronounce words precisely by identifying the features of lip movement. The proposed research will work a lip reading system for Myanmar Consonants, one syllable consonants (င (Nga)၊ ည (Nya)၊ မ (Ma)၊ လ (La)၊ ၀ (Wa)၊ သ (Tha)၊ ဟ (Ha)၊ အ (Ah) ) and two syllable consonants ( က(Ka Gyi)၊ ခ (Kha Gway)၊ ဂ (Ga Nge)၊ ဃ (Ga Gyi)၊ စ (Sa Lone)၊ ဆ (Sa Lain)၊ ဇ (Za Gwe) ၊ ဒ (Da Dway)၊ ဏ (Na Gyi)၊ န (Na Nge)၊ ပ (Pa Saug)၊ ဘ (Ba Gone)၊ ရ (Ya Gaug)၊ ဠ (La Gyi) ). In the proposed system, there are three subsystems, the first one is the lip localization system, which localizes the lips in the digital inputs. The next one is the feature extraction system, which extracts features of lip movement suitable for visual speech recognition. And the final one is the classification system. In the proposed research, Two Dimensional Discrete Cosine Transform (2D-DCT) and Linear Discriminant Analysis (LDA) with Active Contour Model (ACM) will be used for lip movement features extraction. Support Vector Machine (SVM) classifier is used for finding class parameter and class number in training set and testing set. Then, experiments will be carried out for the recognition accuracy of Myanmar consonants using the only visual information on lip movements which are useful for visual speech of Myanmar languages. The result will show the effectiveness of the lip movement recognition for Myanmar Consonants. This system will help the hearing impaired persons to use as the language learning application. This system can also be useful for normal hearing persons in noisy environments or conditions where they can find out what was said by other people without hearing voice.

Keywords: feature extraction, lip reading, lip localization, Active Contour Model (ACM), Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), Two Dimensional Discrete Cosine Transform (2D-DCT)

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Authors: Moheb R. Girgis, Tarek M. Mahmoud, Bahgat A. Abdullatif, Ahmed M. Rabie

Abstract:

Mesh clients, mesh routers and gateways are components of Wireless Mesh Network (WMN). In WMN, gateways connect to Internet using wireline links and supply Internet access services for users. We usually need multiple gateways, which takes time and costs a lot of money set up, due to the limited wireless channel bit rate. WMN is a highly developed technology that offers to end users a wireless broadband access. It offers a high degree of flexibility contrasted to conventional networks; however, this attribute comes at the expense of a more complex construction. Therefore, a challenge is the planning and optimization of WMNs. In this paper, we concentrate on this challenge using a genetic algorithm and simulated annealing. The genetic algorithm and simulated annealing enable searching for a low-cost WMN configuration with constraints and determine the number of used gateways. Experimental results proved that the performance of the genetic algorithm and simulated annealing in minimizing WMN network costs while satisfying quality of service. The proposed models are presented to significantly outperform the existing solutions.

Keywords: wireless mesh networks, genetic algorithms, simulated annealing, topology design

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Authors: Barrere Sarah

Abstract:

The area of liberty, security, and justice within the European Union is still a work in progress. No one can deny that the EU struggles between a monistic and a dualist approach. The aim of our essay is to first review how the European law is perceived by the rest of the international scene. It will then discuss two main mechanisms at play: the interpretation of larger international treaties and the penal mechanisms of European law. Finally, it will help us understand the role of a penal Europe on the international scene with concrete examples. Special attention will be paid to cases that deal with fundamental rights as they represent an interesting case study in Europe and in the rest of the World. It could illustrate the aforementioned duality currently present in the Union’s interpretation of international public law. On the other hand, it will explore some specific European penal mechanism through mutual recognition and the European arrest warrant in the transnational criminality frame. Concerning the interpretation of the treaties, it will first, underline the ambiguity and the general nature of some treaties that leave the EU exposed to tension and misunderstanding then it will review the validity of an EU act (whether or not it is compatible with the rules of International law). Finally, it will focus on the most complete manifestation of liberty, security and justice through the principle of mutual recognition. Used initially in commercial matters, it has become “the cornerstone” of European construction. It will see how it is applied in judicial decisions (its main event and achieving success is via the European arrest warrant) and how European member states have managed to develop this cooperation.

Keywords: European penal law, international scene, liberty security and justice area, mutual recognition

Procedia PDF Downloads 392