Search results for: hand geometry features

Authors: S. Souli, Z. Lachiri

Abstract:

In this paper we propose a robust environmental sound classification approach, based on spectrograms features driven from log-Gabor filters. This approach includes two methods. In the first methods, the spectrograms are passed through an appropriate log-Gabor filter banks and the outputs are averaged and underwent an optimal feature selection procedure based on a mutual information criteria. The second method uses the same steps but applied only to three patches extracted from each spectrogram.

To investigate the accuracy of the proposed methods, we conduct experiments using a large database containing 10 environmental sound classes. The classification results based on Multiclass Support Vector Machines show that the second method is the most efficient with an average classification accuracy of 89.62 %.

Keywords: Environmental sounds, Log-Gabor filters, Spectrogram, SVM Multiclass, Visual features.

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Authors: Mohammad R. Kavian Nezhad, Carlos F. Lange, Brian A. Fleck

Abstract:

This research presents the validation study of a computational fluid dynamics (CFD) model developed to simulate the scalar dispersion emitted from rooftop sources around the buildings at the University of Alberta North Campus. The ANSYS CFX code was used to perform the numerical simulation of the wind regime and pollutant dispersion by solving the 3D steady Reynolds-averaged Navier-Stokes (RANS) equations on a building-scale high-resolution grid. The validation study was performed in two steps. First, the CFD model performance in 24 cases (eight wind directions and three wind speeds) was evaluated by comparing the predicted flow fields with the available data from the previous measurement campaign designed at the North Campus, using the standard deviation method (SDM), while the estimated results of the numerical model showed maximum average percent errors of approximately 53% and 37% for wind incidents from the North and Northwest, respectively. Good agreement with the measurements was observed for the other six directions, with an average error of less than 30%. In the second step, the reliability of the implemented turbulence model, numerical algorithm, modeling techniques, and the grid generation scheme was further evaluated using the Mock Urban Setting Test (MUST) dispersion dataset. Different statistical measures, including the fractional bias (FB), the mean geometric bias (MG), and the normalized mean square error (NMSE), were used to assess the accuracy of the predicted dispersion field. Our CFD results are in very good agreement with the field measurements.

Keywords: CFD, plume dispersion, complex urban geometry, validation study, wind flow.

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Authors: Xiao Chen, Xiaoying Kong, Min Xu

Abstract:

This paper presents a road vehicle detection approach for the intelligent transportation system. This approach mainly uses low-cost magnetic sensor and associated data collection system to collect magnetic signals. This system can measure the magnetic field changing, and it also can detect and count vehicles. We extend Mel Frequency Cepstral Coefficients to analyze vehicle magnetic signals. Vehicle type features are extracted using representation of cepstrum, frame energy, and gap cepstrum of magnetic signals. We design a 2-dimensional map algorithm using Vector Quantization to classify vehicle magnetic features to four typical types of vehicles in Australian suburbs: sedan, VAN, truck, and bus. Experiments results show that our approach achieves a high level of accuracy for vehicle detection and classification.

Keywords: Vehicle classification, signal processing, road traffic model, magnetic sensing.

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Authors: Kahraman Ayyildiz, Stefan Conrad

Abstract:

In this paper we present a system for classifying videos by frequency spectra. Many videos contain activities with repeating movements. Sports videos, home improvement videos, or videos showing mechanical motion are some example areas. Motion of these areas usually repeats with a certain main frequency and several side frequencies. Transforming repeating motion to its frequency domain via FFT reveals these frequencies. Average amplitudes of frequency intervals can be seen as features of cyclic motion. Hence determining these features can help to classify videos with repeating movements. In this paper we explain how to compute frequency spectra for video clips and how to use them for classifying. Our approach utilizes series of image moments as a function. This function again is transformed into its frequency domain.

Keywords: action recognition, frequency feature, motion recognition, repeating movement, video classification

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Authors: Kalyanasundaram.D., Kavitha. S

Abstract:

Field experiments were conducted at Annamalai University Experimental Farm, Department of Agronomy; to device suitable weed control measures for direct seeded puddled rice and to study the effect of the weed control measures on the soil microbial population. The treatments comprised of incorporation of pressmud @ 6.25 t ha-1 and application of herbicide butachlor @1.5 kg a. i. ha- 1 with and without safener 4 days after sowing (DAS), 8 DAS alone and also in conjunction with hand weeding at 30 DAS. Hand weeding twice and a weedy check were also maintained. At maximum tillering stage, the population of bacteria was significantly reduced by butachlor application. The injury to microbes caused by herbicide disappeared with the advancement of crop's age and at flowering stage of crop, there was no significant difference among the treatments. The fungal and actinomycetes population remained unaltered by weed control treatments at both the stages of observation.

Keywords: Butachlor, Herbicide, Direct sown rice, Microbial population

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Authors: Bruno Trstenjak, Dzenana Donko

Abstract:

Data mining and classification of objects is the process of data analysis, using various machine learning techniques, which is used today in various fields of research. This paper presents a concept of hybrid classification model improved with the expert knowledge. The hybrid model in its algorithm has integrated several machine learning techniques (Information Gain, K-means, and Case-Based Reasoning) and the expert’s knowledge into one. The knowledge of experts is used to determine the importance of features. The paper presents the model algorithm and the results of the case study in which the emphasis was put on achieving the maximum classification accuracy without reducing the number of features.

Keywords: Case based reasoning, classification, expert's knowledge, hybrid model.

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Authors: Amirhossein Lotfi, Huaizhong Li, Dzung Viet Dao

Abstract:

Interest in natural fiber-reinforced composites (NFRC) is progressively growing both in terms of academia research and industrial applications thanks to their abundant advantages such as low cost, biodegradability, eco-friendly nature and relatively good mechanical properties. However, their widespread use is still presumed as challenging because of the specificity of their non-homogeneous structure, limited knowledge on their machinability characteristics and parameter settings, to avoid defects associated with the machining process. The present work is aimed to investigate the effect of the cutting tool geometry and material on the drilling-induced delamination, thrust force and hole quality produced when drilling a fully biodegradable flax/poly (lactic acid) composite laminate. Three drills with different geometries and material were used at different drilling conditions to evaluate the machinability of the fabricated composites. The experimental results indicated that the choice of cutting tool, in terms of material and geometry, has a noticeable influence on the cutting thrust force and subsequently drilling-induced damages. The lower value of thrust force and better hole quality was observed using high-speed steel (HSS) drill, whereas Carbide drill (with point angle of 130^o) resulted in the highest value of thrust force. Carbide drill presented higher wear resistance and stability in variation of thrust force with a number of holes drilled, while HSS drill showed the lower value of thrust force during the drilling process. Finally, within the selected cutting range, the delamination damage increased noticeably with feed rate and moderately with spindle speed.

Keywords: Natural fiber-reinforced composites, machinability, thrust force, delamination.

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Authors: Sheam-Chyun Lin, Wei-Kai Chen, Hung-Cheng Yen, Yung-Jen Cheng, Yu-Cheng Chen

Abstract:

Result from the constant dwindle in natural resources, the alternative way to reduce the costs in our daily life would be urgent to be found in the near future. As the ancient technique based on the theory of solar chimney since roman times, the double-skin façade are simply composed of two large glass panels in purpose of daylighting and also natural ventilation in the daytime. Double-skin façade is generally installed on the exterior side of buildings as function as the window, so there is always a huge amount of passive solar energy the façade would receive to induce the airflow every sunny day. Therefore, this article imposes a domestic double-skin window for residential usage and attempts to improve the volume flow rate inside the cavity between the panels by the frame geometry design, the installation of outlet guide plate and the solar energy collection system. Note that the numerical analyses are applied to investigate the characteristics of flow field, and the boundary conditions in the simulation are totally based on the practical experiment of the original prototype. Then we redesign the prototype from the knowledge of the numerical results and fluid dynamic theory, and later the experiments of modified prototype will be conducted to verify the simulation results. The velocities at the inlet of each case are increase by 5%, 45% and 15% from the experimental data, and also the numerical simulation results reported 20% improvement in volume flow rate both for the frame geometry design and installation of outlet guide plate.

Keywords: Solar energy, Double-skin façades, Thermal buoyancy, Fluid machinery.

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Authors: Süha Yılmaz, Melih Turgut

Abstract:

These In this work, a regular unit speed curve in six dimensional Euclidean space, whose Frenet curvatures are constant, is considered. Thereafter, a method to calculate Frenet apparatus of this curve is presented.

Keywords: Classical Differential Geometry, Euclidean 6-space, Frenet Apparatus of the curves.

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Authors: Wen-Haw Chen

Abstract:

This paper proposes a method to improve the shortest path problem on a NURBS (Non-uniform rational basis spline) surfaces. It comes from an application of the theory in classic differential geometry on surfaces and can improve the distance problem not only on surfaces but in the Euclidean 3-space R3 .

Keywords: shortest paths, geodesic-like method, NURBS surfaces.

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Authors: Bita Tadayon, Nasser Salmasi

Abstract:

This research deals with a flexible flowshop scheduling problem with arrival and delivery of jobs in groups and processing them individually. Due to the special characteristics of each job, only a subset of machines in each stage is eligible to process that job. The objective function deals with minimization of sum of the completion time of groups on one hand and minimization of sum of the differences between completion time of jobs and delivery time of the group containing that job (waiting period) on the other hand. The problem can be stated as FFc / rj , Mj / irreg which has many applications in production and service industries. A mathematical model is proposed, the problem is proved to be NPcomplete, and an effective heuristic method is presented to schedule the jobs efficiently. This algorithm can then be used within the body of any metaheuristic algorithm for solving the problem.

Keywords: flexible flowshop scheduling, group processing, machine eligibility constraint, mathematical modeling.

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Authors: Sheroz Khan, Mustafa Zeki, Shihab Abdel Hameed, AHM Zahirul Alam, Aisha Hassan Abdalla, A. F. Salami, W. A. Lawal

Abstract:

A novel circuit for generating a signal embedded with features about data from three sensors is presented. This suggested circuit is making use of a resistance-to-time converter employing a bridge amplifier, an integrator and a comparator. The second resistive sensor (Rz) is transformed into duty cycle. Another bridge with varying resistor, (Ry) in the feedback of an OP AMP is added in series to change the amplitude of the resulting signal in a proportional relationship while keeping the same frequency and duty cycle representing proportional changes in resistors Rx and Rz already mentioned. The resultant output signal carries three types of information embedded as variations of its frequency, duty cycle and amplitude.

Keywords: Integrator, Comparator, Bridge Circuit, Resistanceto-Time Converter, Conditioning Circuit.

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Authors: Sule Yucelbas, Gulay Tezel, Cuneyt Yucelbas, Seral Ozsen

Abstract:

In this study, it was tried to identify some heart rhythm disorders by electrocardiography (ECG) data that is taken from MIT-BIH arrhythmia database by subtracting the required features, presenting to artificial neural networks (ANN), artificial immune systems (AIS), artificial neural network based on artificial immune system (AIS-ANN) and particle swarm optimization based artificial neural network (PSO-NN) classifier systems. The main purpose of this study is to evaluate the performance of hybrid AIS-ANN and PSO-ANN classifiers with regard to the ANN and AIS. For this purpose, the normal sinus rhythm (NSR), atrial premature contraction (APC), sinus arrhythmia (SA), ventricular trigeminy (VTI), ventricular tachycardia (VTK) and atrial fibrillation (AF) data for each of the RR intervals were found. Then these data in the form of pairs (NSR-APC, NSR-SA, NSR-VTI, NSR-VTK and NSR-AF) is created by combining discrete wavelet transform which is applied to each of these two groups of data and two different data sets with 9 and 27 features were obtained from each of them after data reduction. Afterwards, the data randomly was firstly mixed within themselves, and then 4-fold cross validation method was applied to create the training and testing data. The training and testing accuracy rates and training time are compared with each other.

As a result, performances of the hybrid classification systems, AIS-ANN and PSO-ANN were seen to be close to the performance of the ANN system. Also, the results of the hybrid systems were much better than AIS, too. However, ANN had much shorter period of training time than other systems. In terms of training times, ANN was followed by PSO-ANN, AIS-ANN and AIS systems respectively. Also, the features that extracted from the data affected the classification results significantly.

Keywords: AIS, ANN, ECG, hybrid classifiers, PSO.

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Authors: Mohamed Ammar Abbassi, Kamel Guedri, Mohamed Naceur Borjini, Kamel Halouani, Belkacem Zeghmati

Abstract:

The radiative heat transfer problem is investigated numerically for 2D complex geometry biomass pyrolysis reactor composed of two pyrolysis chambers and a heat recuperator. The fumes are a mixture of carbon dioxide and water vapor charged with absorbing and scattering particles and soot. In order to increase gases residence time and heat transfer, the heat recuperator is provided with many inclined, vertical, horizontal, diffuse and grey baffles of finite thickness and has a complex geometry. The Finite Volume Method (FVM) is applied to study radiative heat transfer. The blocked-off region procedure is used to treat the geometrical irregularities. Eight cases are considered in order to demonstrate the effect of adding baffles on the walls of the heat recuperator and on the walls of the pyrolysis rooms then choose the best case giving the maximum heat flux transferred to the biomass in the pyrolysis chambers. Ray effect due to the presence of baffles is studied and demonstrated to have a crucial effect on radiative heat flux on the walls of the pyrolysis rooms. Shadow effect caused by the presence of the baffles is also studied. The non grey radiative heat transfer is studied for the real existent configuration. The Weighted Sum of The Grey Gases (WSGG) Model of Kim and Song is used as non grey model. The effect of soot volumetric fraction on the non grey radiative heat flux is investigated and discussed.

Keywords: Baffles, Blocked-off region procedure, FVM, Heat recuperation, Radiative heat transfer, Shadow effect.

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Authors: Hadi Seyedarabi, Ali Aghagolzadeh, Sohrab Khanmohammadi

Abstract:

Face and facial expressions play essential roles in interpersonal communication. Most of the current works on the facial expression recognition attempt to recognize a small set of the prototypic expressions such as happy, surprise, anger, sad, disgust and fear. However the most of the human emotions are communicated by changes in one or two of discrete features. In this paper, we develop a facial expressions synthesis system, based on the facial characteristic points (FCP's) tracking in the frontal image sequences. Selected FCP's are automatically tracked using a crosscorrelation based optical flow. The proposed synthesis system uses a simple deformable facial features model with a few set of control points that can be tracked in original facial image sequences.

Keywords: Deformable face model, facial animation, facialcharacteristic points, optical flow.

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Authors: Roberto Sabatini, Terry Moore, Chris Hill

Abstract:

This paper presents a novel Global Navigation Satellite System (GNSS) Avionics Based Integrity Augmentation (ABIA) system architecture suitable for civil and military air platforms, including Unmanned Aircraft Systems (UAS). Taking the move from previous research on high-accuracy Differential GNSS (DGNSS) systems design, integration and experimental flight test activities conducted at the Italian Air Force Flight Test Centre (CSV-RSV), our research focused on the development of a novel approach to the problem of GNSS ABIA for mission- and safety-critical air vehicle applications and for multi-sensor avionics architectures based on GNSS. Detailed mathematical models were developed to describe the main causes of GNSS signal outages and degradation in flight, namely: antenna obscuration, multipath, fading due to adverse geometry and Doppler shift. Adopting these models in association with suitable integrity thresholds and guidance algorithms, the ABIA system is able to generate integrity cautions (predictive flags) and warnings (reactive flags), as well as providing steering information to the pilot and electronic commands to the aircraft/UAS flight control systems. These features allow real-time avoidance of safety-critical flight conditions and fast recovery of the required navigation performance in case of GNSS data losses. In other words, this novel ABIA system addresses all three cornerstones of GNSS integrity augmentation in mission- and safety-critical applications: prediction (caution flags), reaction (warning flags) and correction (alternate flight path computation).

Keywords: Global Navigation Satellite Systems (GNSS), Integrity Augmentation, Unmanned Aircraft Systems, Aircraft Based Augmentation, Avionics Based Integrity Augmentation, Safety-Critical Applications.

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Authors: Latha Parameswaran, K. Anbumani

Abstract:

Multimedia security is an incredibly significant area of concern. A number of papers on robust digital watermarking have been presented, but there are no standards that have been defined so far. Thus multimedia security is still a posing problem. The aim of this paper is to design a robust image-watermarking scheme, which can withstand a different set of attacks. The proposed scheme provides a robust solution integrating image moment normalization, content dependent watermark and discrete wavelet transformation. Moment normalization is useful to recover the watermark even in case of geometrical attacks. Content dependent watermarks are a powerful means of authentication as the data is watermarked with its own features. Discrete wavelet transforms have been used as they describe image features in a better manner. The proposed scheme finds its place in validating identification cards and financial instruments.

Keywords: Watermarking, moments, wavelets, content-based, benchmarking.

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Authors: Sanghoon Kim, Sun-Tae Chung, Souhwan Jung, Seoungseon Jeon, Jaemin Kim, Seongwon Cho

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In this paper, we propose a face recognition algorithm using AAM and Gabor features. Gabor feature vectors which are well known to be robust with respect to small variations of shape, scaling, rotation, distortion, illumination and poses in images are popularly employed for feature vectors for many object detection and recognition algorithms. EBGM, which is prominent among face recognition algorithms employing Gabor feature vectors, requires localization of facial feature points where Gabor feature vectors are extracted. However, localization method employed in EBGM is based on Gabor jet similarity and is sensitive to initial values. Wrong localization of facial feature points affects face recognition rate. AAM is known to be successfully applied to localization of facial feature points. In this paper, we devise a facial feature point localization method which first roughly estimate facial feature points using AAM and refine facial feature points using Gabor jet similarity-based facial feature localization method with initial points set by the rough facial feature points obtained from AAM, and propose a face recognition algorithm using the devised localization method for facial feature localization and Gabor feature vectors. It is observed through experiments that such a cascaded localization method based on both AAM and Gabor jet similarity is more robust than the localization method based on only Gabor jet similarity. Also, it is shown that the proposed face recognition algorithm using this devised localization method and Gabor feature vectors performs better than the conventional face recognition algorithm using Gabor jet similarity-based localization method and Gabor feature vectors like EBGM.

Keywords: Face Recognition, AAM, Gabor features, EBGM.

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Authors: Tharini N. de Silva, Xiao Zhibo, Zhao Rui, Mao Kezhi

Abstract:

Causal relation identification is a crucial task in information extraction and knowledge discovery. In this work, we present two approaches to causal relation identification. The first is a classification model trained on a set of knowledge-based features. The second is a deep learning based approach training a model using convolutional neural networks to classify causal relations. We experiment with several different convolutional neural networks (CNN) models based on previous work on relation extraction as well as our own research. Our models are able to identify both explicit and implicit causal relations as well as the direction of the causal relation. The results of our experiments show a higher accuracy than previously achieved for causal relation identification tasks.

Keywords: Causal relation identification, convolutional neural networks, natural Language Processing, Machine Learning.

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Authors: K. Massila, R. D. Stein, S. M. Suhaizan, A. A. Azlianor

Abstract:

A number of mass spectrometry applications are already available as web-based and windows-based systems to calculate isotope pattern and to display the mass spectrum based on the specific molecular formula besides providing necessary information. These applications were evaluated and compared with our new alternative application called Theoretical Isotope Generator (TIG) in terms of its functionality and features provided to prove this new application is working better and performing well. TIG provides extra features than others, complete with several functionality such as drawing, normalizing and zooming the generated graph that convey with the molecular information in a number of formats by providing the details of the calculation and molecules. Thus, any chemist, students, lecturers and researchers from anywhere could use TIG to gain related information on molecules and their relative intensity.

Keywords: Isotope pattern calculator, mass number, massspectrum, relative intensity.

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Authors: Nebi Gedik

Abstract:

One of the significant and continual public health problems in the world is breast cancer. Early detection is very important to fight the disease, and mammography has been one of the most common and reliable methods to detect the disease in the early stages. However, it is a difficult task, and computer-aided diagnosis (CAD) systems are needed to assist radiologists in providing both accurate and uniform evaluation for mass in mammograms. In this study, a multiresolution statistical method to classify mammograms as normal and abnormal in digitized mammograms is used to construct a CAD system. The mammogram images are represented by wave atom transform, and this representation is made by certain groups of coefficients, independently. The CAD system is designed by calculating some statistical features using each group of coefficients. The classification is performed by using support vector machine (SVM).

Keywords: Wave atom transform, statistical features, multi-resolution representation, mammogram.

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Authors: Maria Istvanova

Abstract:

The purpose of the comparative study of the affricate consonants in Chinese and Slovak is to increase the awareness of the main distinguishing features between these two languages taking into consideration this particular group of consonants. We determine the main difficulties of the Slovak learners in the process of acquiring correct pronunciation of affricate initial consonants in Chinese based on the understanding of the distinguishing features of Chinese and Slovak affricates in combination with the experimental measuring of voice onset time (VOT) values. The software tool Praat is used for the analysis of the recorded language samples. The language samples contain recordings of a Chinese native speaker and Slovak students of Chinese with different language proficiency levels. Based on the results of the analysis in Praat, we identify erroneous pronunciation and provide clarification of its cause.

Keywords: Chinese, comparative study, initial consonants, pronunciation, Slovak

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Authors: Sello Mokwena, Monyepao Thabang

Abstract:

In this study, we propose a comprehensive approach to address the challenges in person re-identification models. By combining a centroid tracking algorithm with a Siamese convolutional neural network model, our method excels in detecting, tracking, and capturing robust person features across non-overlapping camera views. The algorithm efficiently identifies individuals in the camera network, while the neural network extracts fine-grained global features for precise cross-image comparisons. The approach's effectiveness is further accentuated by leveraging the camera network topology for guidance. Our empirical analysis of benchmark datasets highlights its competitive performance, particularly evident when background subtraction techniques are selectively applied, underscoring its potential in advancing person re-identification techniques.

Keywords: Camera network, convolutional neural network topology, person tracking, person re-identification, Siamese.

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Authors: M. Kashfi, K. Jahani

Abstract:

Sound attenuation in absorptive silencers has been analyzed in this paper. The structure of such devices is as follows. When the rigid duct of an expansion chamber has been lined by a packed absorptive material under a perforated membrane, incident sound waves will be dissipated by the absorptive liners. This kind of silencer, usually are applicable for medium to high frequency ranges. Several conditions for different absorptive materials, variety in their thicknesses, and different shapes of the expansion chambers have been studied in this paper. Also, graphs of sound attenuation have been compared between empty expansion chamber and duct of silencer with applying liner. Plane waves have been assumed in inlet and outlet regions of the silencer. Presented results that have been achieved by applying finite element method (FEM), have shown the dependence of the sound attenuation spectrum to flow resistivity and the thicknesses of the absorptive materials, and geometries of the cross section (configuration of the silencer). As flow resistivity and thickness of absorptive materials increase, sound attenuation improves. In this paper, diagrams of the transmission loss (TL) for absorptive silencers in five different cross sections (rectangle, circle, ellipse, square, and rounded rectangle as the main geometry) have been presented. Also, TL graphs for silencers using different absorptive material (glass wool, wood fiber, and kind of spongy materials) as liner with three different thicknesses of 5 mm, 15 mm, and 30 mm for glass wool liner have been exhibited. At first, the effect of substances of the absorptive materials with the specific flow resistivity and densities on the TL spectrum, then the effect of the thicknesses of the glass wool, and at last the efficacy of the shape of the cross section of the silencer have been investigated.

Keywords: Transmission loss, absorptive material, flow resistivity, thickness, frequency.

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Authors: Davide Fuscà, Kanan Rahimli, Roberto Leuzzi

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Prevent abuse and illegal activities in a given area of the sea is a very difficult and expensive task. Artificial intelligence offers the possibility to implement new methods to identify the vessel class type from the kinematic features of the vessel itself. The task strictly depends on the quality of the data. This paper explores the application of a deep Long Short-Term Memory model by using AIS flow only with a relatively low quality. The proposed model reaches high accuracy on detecting nine vessel classes representing the most common vessel types in the Ionian-Adriatic Sea. The model has been applied during the Adriatic-Ionian trial period of the international EU ANDROMEDA H2020 project to identify vessels performing behaviours far from the expected one, depending on the declared type.

Keywords: maritime surveillance, artificial intelligence, behaviour analysis, LSTM

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Authors: Bhaskar Thakker, Anoop Lal Vyas

Abstract:

Wrist pulse analysis for identification of health status is found in Ancient Indian as well as Chinese literature. The preprocessing of wrist pulse is necessary to remove outlier pulses and fluctuations prior to the analysis of pulse pressure signal. This paper discusses the identification of irregular pulses present in the pulse series and intricacies associated with the extraction of time domain pulse features. An approach of Dynamic Time Warping (DTW) has been utilized for the identification of outlier pulses in the wrist pulse series. The ambiguity present in the identification of pulse features is resolved with the help of first derivative of Ensemble Average of wrist pulse series. An algorithm for detecting tidal and dicrotic notch in individual wrist pulse segment is proposed.

Keywords: Wrist Pulse Segment, Ensemble Average, Dynamic Time Warping (DTW), Pulse Similarity Vector.

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Authors: Anukriti Kumar, Tanmay Singh, Dinesh Kumar Vishwakarma

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Traffic control has been one of the most common and irritating problems since the time automobiles have hit the roads. Problems like traffic congestion have led to a significant time burden around the world and one significant solution to these problems can be the proper implementation of the Intelligent Transport System (ITS). It involves the integration of various tools like smart sensors, artificial intelligence, position technologies and mobile data services to manage traffic flow, reduce congestion and enhance driver's ability to avoid accidents during adverse weather. Road and traffic signs’ recognition is an emerging field of research in ITS. Classification problem of traffic signs needs to be solved as it is a major step in our journey towards building semi-autonomous/autonomous driving systems. The purpose of this work focuses on implementing an approach to solve the problem of traffic sign classification by developing a Convolutional Neural Network (CNN) classifier using the GTSRB (German Traffic Sign Recognition Benchmark) dataset. Rather than using hand-crafted features, our model addresses the concern of exploding huge parameters and data method augmentations. Our model achieved an accuracy of around 97.6% which is comparable to various state-of-the-art architectures.

Keywords: Multiclass classification, convolution neural network, OpenCV, Data Augmentation.

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Authors: Efthymios Gravas, Anastasios Alexandridis, Antonios Stavropoulos

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The growing interest in the issue of intangible assets not only in the scientific community but also in some professional bodies internationally can be explained by several points of view. From the business perspective, enterprises are increasingly motivated by external and internal forces to measure and proactively manage their intangibles. With respect to the issue of intangibles, goodwill has been debated in many countries throughout the world. Despite the numerous efforts and the existence of international accounting standards there is not yet a common accepted accounting treatment for goodwill. This study attempts on the one hand to impress the accounting treatment of goodwill internationally, on the other hand analyses the major subjects in relation to the accounting treatment of goodwill in Greece, since 2005, year where the international accounting standards have been in use for the Greek listed companies. The results indicate that the accounting treatment for the goodwill in Greece, despite the effort for accounting harmonization in Europe from 2005, sustains many differences especially for the no listed companies.

Keywords: Intangible Assets, Goodwill, International Accounting Standards, Greek Accounting System and Law

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Authors: Rafenomananjara Tsinjo Nirina, Tomoo Sekito, Andrianaivoravelona Jaconnet Oliva

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Manufacturing a composite material from post-consumer bottles is an interesting outlet since Madagascar is still facing the challenges of managing plastic waste on the one hand and appropriate waste treatment facilities are not yet developed on the other hand. New waste management options are needed to divert End-Of-Life (EOL) soft plastic wastes from landfills and incineration. Waste polyethylene terephthalate (PET) bottles might be considered as a valuable resource and recovered into polymer concrete. The methodology is easy to implement and appropriate to the local context in Madagascar. This approach will contribute to the production of ecological building materials that might be profitable for the environment and the construction sector. This work aims to study the feasibility of using the post-consumer PET bottles as an alternative binding agent instead of the conventional Portland cement and water. Then, the mechanical and physical properties of the materials were evaluated.

Keywords: PET recycling, polymer concrete, ecological building materials, pollution mitigation.

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Authors: Manpreet Singh, Parvinder Singh, Suman

Abstract:

The data is available in abundance in any business organization. It includes the records for finance, maintenance, inventory, progress reports etc. As the time progresses, the data keep on accumulating and the challenge is to extract the information from this data bank. Knowledge discovery from these large and complex databases is the key problem of this era. Data mining and machine learning techniques are needed which can scale to the size of the problems and can be customized to the application of business. For the development of accurate and required information for particular problem, business analyst needs to develop multidimensional models which give the reliable information so that they can take right decision for particular problem. If the multidimensional model does not possess the advance features, the accuracy cannot be expected. The present work involves the development of a Multidimensional data model incorporating advance features. The criterion of computation is based on the data precision and to include slowly change time dimension. The final results are displayed in graphical form.

Keywords: Multidimensional, data precision.

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