Search results for: cut transition detection

Authors: Farhad Asadi, Hossein Sadati

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In fields such as neuroscience and especially in cognition modeling of mental processes, uncertainty processing in temporal zone of signal is vital. In this paper, Bayesian online inferences in estimation of change-points location in signal are constructed. This method separated the observed signal into independent series and studies the change and variation of the regime of data locally with related statistical characteristics. We give conditions on simulations of the method when the data characteristics of signals vary, and provide empirical evidence to show the performance of method. It is verified that correlation between series around the change point location and its characteristics such as Signal to Noise Ratios and mean value of signal has important factor on fluctuating in finding proper location of change point. And one of the main contributions of this study is related to representing of these influences of signal statistical characteristics for finding abrupt variation in signal. There are two different structures for simulations which in first case one abrupt change in temporal section of signal is considered with variable position and secondly multiple variations are considered. Finally, influence of statistical characteristic for changing the location of change point is explained in details in simulation results with different artificial signals.

Keywords: Time series, fluctuation in statistical characteristics, optimal learning.

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Authors: Yanqun Li, Hong Geng

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The occurrence of Wenchuan earthquake in 2008 has led to severe damage to the rural areas of Chengdu city, such as the rupture of the social network, the stagnation of economic production and the rupture of living space. The post-disaster reconstruction has become a sustainable issue. As an important link to maintain the order of rural social development, social network should be an important content of post-disaster reconstruction. Therefore, this paper takes rural reconstruction communities in earthquake-stricken areas of Chengdu as the research object and adopts sociological research methods such as field survey, observation and interview to try to understand the transformation of rural social relations network under the influence of earthquake and its impact on rural space. It has found that rural societies under the earthquake generally experienced three phases: the break of stable social relations, the transition of temporary non-normal state, and the reorganization of social networks. The connotation of phased rural social relations also changed accordingly: turn to a new division of labor on the social orientation, turn to a capital flow and redistribution in new production mode on the capital orientation, and turn to relative decentralization after concentration on the spatial dimension. Along with such changes, rural areas have emerged some social issues such as the alienation of competition in the new industry division, the low social connection, the significant redistribution of capital, and the lack of public space. Based on a comprehensive review of these issues, this paper proposes the corresponding response mechanism. First of all, a reasonable division of labor should be established within the villages to realize diversified commodity supply. Secondly, the villages should adjust the industrial type to promote the equitable participation of capital allocation groups. Finally, external public spaces should be added to strengthen the field of social interaction within the communities.

Keywords: Social relations, social support networks, industrial division, capital allocation, public space.

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Authors: A. Baith Mohamed, Ayman Abdel-Hamid, Kareem Youssri Mohamed

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Current proposals for E-passport or ID-Card is similar to a regular passport with the addition of tiny contactless integrated circuit (computer chip) inserted in the back cover, which will act as a secure storage device of the same data visually displayed on the photo page of the passport. In addition, it will include a digital photograph that will enable biometric comparison, through the use of facial recognition technology at international borders. Moreover, the e-passport will have a new interface, incorporating additional antifraud and security features. However, its problems are reliability, security and privacy. Privacy is a serious issue since there is no encryption between the readers and the E-passport. However, security issues such as authentication, data protection and control techniques cannot be embedded in one process. In this paper, design and prototype implementation of an improved E-passport reader is presented. The passport holder is authenticated online by using GSM network. The GSM network is the main interface between identification center and the e-passport reader. The communication data is protected between server and e-passport reader by using AES to encrypt data for protection will transferring through GSM network. Performance measurements indicate a 19% improvement in encryption cycles versus previously reported results.

Keywords: RFID "Radio Frequency Identification", EPC"Electronic Product Code", ICAO "International Civil Aviation Organization", IFF "Identify Friend or Foe"

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Authors: Ashraf M. A. Ahmad

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Delivering streaming video over wireless is an important component of many interactive multimedia applications running on personal wireless handset devices. Such personal devices have to be inexpensive, compact, and lightweight. But wireless channels have a high channel bit error rate and limited bandwidth. Delay variation of packets due to network congestion and the high bit error rate greatly degrades the quality of video at the handheld device. Therefore, mobile access to multimedia contents requires video transcoding functionality at the edge of the mobile network for interworking with heterogeneous networks and services. Therefore, to guarantee quality of service (QoS) delivered to the mobile user, a robust and efficient transcoding scheme should be deployed in mobile multimedia transporting network. Hence, this paper examines the challenges and limitations that the video transcoding schemes in mobile multimedia transporting network face. Then handheld resources, network conditions and content based mobile and wireless video transcoding is proposed to provide high QoS applications. Exceptional performance is demonstrated in the experiment results. These experiments were designed to verify and prove the robustness of the proposed approach. Extensive experiments have been conducted, and the results of various video clips with different bit rate and frame rate have been provided.

Keywords: Content, Object detection, Transcoding, Texture, Temporal, Video.

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Authors: A. Q. Sobia, M. S. Hamidah, I. Azmi, S. F. A. Rafeeqi

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Fibre-reinforced polymer (FRP) strengthened reinforced concrete (RC) structures are susceptible to intense deterioration when exposed to elevated temperatures, particularly in the incident of fire. FRP has the tendency to lose bond with the substrate due to the low glass transition temperature of epoxy; the key component of FRP matrix.  In the past few decades, various types of high performance cementitious composites (HPCC) were explored for the protection of RC structural members against elevated temperature. However, there is an inadequate information on the influence of elevated temperature on the ultra high performance fibre-reinforced cementitious composites (UHPFRCC) containing ground granulated blast furnace slag (GGBS) as a replacement of high alumina cement (HAC) in conjunction with hybrid fibres (basalt and polypropylene fibres), which could be a prospective fire resisting material for the structural components. The influence of elevated temperatures on the compressive as well as flexural strength of UHPFRCC, made of HAC-GGBS and hybrid fibres, were examined in this study. Besides control sample (without fibres), three other samples, containing 0.5%, 1% and 1.5% of basalt fibres by total weight of mix and 1 kg/m³ of polypropylene fibres, were prepared and tested. Another mix was also prepared with only 1 kg/m³ of polypropylene fibres. Each of the samples were retained at ambient temperature as well as exposed to 400, 700 and 1000 °C followed by testing after 28 and 56 days of conventional curing. Investigation of results disclosed that the use of hybrid fibres significantly helped to improve the ambient temperature compressive and flexural strength of UHPFRCC, which was found to be 80 and 14.3 MPa respectively. However, the optimum residual compressive strength was marked by UHPFRCC-CP (with polypropylene fibres only), equally after both curing days (28 and 56 days), i.e. 41%. In addition, the utmost residual flexural strength, after 28 and 56 days of curing, was marked by UHPFRCC– CP and UHPFRCC– CB2 (1 kg/m³ of PP fibres + 1% of basalt fibres) i.e. 39% and 48.5% respectively.

Keywords: Fibre reinforced polymer materials, ground granulated blast furnace slag, high-alumina cement, hybrid fibres.

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Authors: Mansur Hamma-Adama, Tahar Kouider

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Construction Industry has been evolving since the development of Building Information Modelling (BIM). This technological process is unstoppable; it is out to the market with remarkable case studies of solving the long industry’s history of fragmentation. This industry has been changing over time; United States has recorded the most significant development in construction digitalization, Australia, United Kingdom and some other developed nations are also amongst promoters of BIM process and its development. Recently, a developing country like China and Malaysia are keying into the industry’s digital shift, while very little move is seen in South Africa whose development is considered higher and perhaps leader in the digital transition amongst the African countries. To authors’ best knowledge, Nigerian construction industry has never engaged in BIM discussions hence has no attention at national level. Consequently, Nigeria has no “Noteworthy BIM publications.” Decision makers and key stakeholders need to be informed on the current trend of the industry’s development (BIM in specific) and the opportunities of adopting this digitalization trend in relation to the identified challenges. BIM concept can be traced mostly in Architectural practices than engineering practices in Nigeria. A superficial BIM practice is found to be at organisational level only and operating a model based - “BIM stage 1.” Research to adopting this innovation has received very little attention. This piece of work is literature review based, aimed at exploring BIM in Nigeria and its prospects. The exploration reveals limitations in the literature availability as to extensive research in the development of BIM in the country. Numerous challenges were noticed including building collapse, inefficiencies, cost overrun and late project delivery. BIM has potentials to overcome the above challenges and even beyond. Low level of BIM adoption with reasonable level of awareness is noticed. However, lack of policy and guideline as well as serious lack of experts in the field are amongst the major barriers to BIM adoption. The industry needs to embrace BIM to possibly compete with its global counterpart.

Keywords: Adoption, BIM, CAD, construction industry, Nigeria, opportunities.

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Authors: Sunil Patel, Pallab Maji

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To study the effect of adversarial attack environment must be controlled. Autonomous driving includes mainly 5 phases sense, perceive, map, plan, and drive. Autonomous vehicles sense their surrounding with the help of different sensors like cameras, radars, and lidars. Deep learning techniques are considered Blackbox and found to be vulnerable to adversarial attacks. In this research, we study the effect of the various known adversarial attacks with the help of the Unreal Engine-based, high-fidelity, real-time raytraced simulated environment. The goal of this experiment is to find out if adversarial attacks work in moving vehicles and if an unknown network may be targeted. We discovered that the existing Blackbox and Whitebox attacks have varying effects on different traffic signs. We observed that attacks that impair detection in static scenarios do not have the same effect on moving vehicles. It was found that some adversarial attacks with hardly noticeable perturbations entirely blocked the recognition of certain traffic signs. We observed that the daylight condition has a substantial impact on the model's performance by simulating the interplay of light on traffic signs. Our findings have been found to closely resemble outcomes encountered in the real world.

Keywords: Adversarial attack simulation, computer simulation, ray-traced environment, realistic simulation, unreal engine.

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Authors: Mohammed Sharif, Donald Burn

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Large scale climate signals and their teleconnections can influence hydro-meteorological variables on a local scale. Several extreme flow and timing measures, including high flow and low flow measures, from 62 hydrometric stations in Canada are investigated to detect possible linkages with several large scale climate indices. The streamflow data used in this study are derived from the Canadian Reference Hydrometric Basin Network and are characterized by relatively pristine and stable land-use conditions with a minimum of 40 years of record. A composite analysis approach was used to identify linkages between extreme flow and timing measures and climate indices. The approach involves determining the 10 highest and 10 lowest values of various climate indices from the data record. Extreme flow and timing measures for each station were examined for the years associated with the 10 largest values and the years associated with the 10 smallest values. In each case, a re-sampling approach was applied to determine if the 10 values of extreme flow measures differed significantly from the series mean. Results indicate that several stations are impacted by the large scale climate indices considered in this study. The results allow the determination of any relationship between stations that exhibit a statistically significant trend and stations for which the extreme measures exhibit a linkage with the climate indices.

Keywords: flood analysis, low-flow events, climate change, trend analysis, Canada

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Authors: Yao-Hong Tsai

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Due to the sensor technology, video surveillance has become the main way for security control in every big city in the world. Surveillance is usually used by governments for intelligence gathering, the prevention of crime, the protection of a process, person, group or object, or the investigation of crime. Many surveillance systems based on computer vision technology have been developed in recent years. Moving target tracking is the most common task for Unmanned Aerial Vehicle (UAV) to find and track objects of interest in mobile aerial surveillance for civilian applications. The paper is focused on vision-based collision avoidance for UAVs by recurrent neural networks. First, images from cameras on UAV were fused based on deep convolutional neural network. Then, a recurrent neural network was constructed to obtain high-level image features for object tracking and extracting low-level image features for noise reducing. The system distributed the calculation of the whole system to local and cloud platform to efficiently perform object detection, tracking and collision avoidance based on multiple UAVs. The experiments on several challenging datasets showed that the proposed algorithm outperforms the state-of-the-art methods.

Keywords: Unmanned aerial vehicle, object tracking, deep learning, collision avoidance.

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Authors: Hariton N. Costin, Iulian Ciocoiu, Tudor Barbu, Cristian Rotariu

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In this paper three different approaches for person verification and identification, i.e. by means of fingerprints, face and voice recognition, are studied. Face recognition uses parts-based representation methods and a manifold learning approach. The assessment criterion is recognition accuracy. The techniques under investigation are: a) Local Non-negative Matrix Factorization (LNMF); b) Independent Components Analysis (ICA); c) NMF with sparse constraints (NMFsc); d) Locality Preserving Projections (Laplacianfaces). Fingerprint detection was approached by classical minutiae (small graphical patterns) matching through image segmentation by using a structural approach and a neural network as decision block. As to voice / speaker recognition, melodic cepstral and delta delta mel cepstral analysis were used as main methods, in order to construct a supervised speaker-dependent voice recognition system. The final decision (e.g. “accept-reject" for a verification task) is taken by using a majority voting technique applied to the three biometrics. The preliminary results, obtained for medium databases of fingerprints, faces and voice recordings, indicate the feasibility of our study and an overall recognition precision (about 92%) permitting the utilization of our system for a future complex biometric card.

Keywords: Biometry, image processing, pattern recognition, speech analysis.

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Authors: P. Joyce Beryl Princess, Y. Harold Robinson

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Image Processing is a structure of Signal Processing for which the input is the image and the output is also an image or parameter of the image. Image Resolution has been frequently referred as an important aspect of an image. In Image Resolution Enhancement, images are being processed in order to obtain more enhanced resolution. To generate highly resoluted image for a low resoluted input image with high PSNR value. Stationary Wavelet Transform is used for Edge Detection and minimize the loss occurs during Downsampling. Inverse Discrete Wavelet Transform is to get highly resoluted image. Highly resoluted output is generated from the Low resolution input with high quality. Noisy input will generate output with low PSNR value. So Noisy resolution enhancement technique has been used for adaptive sub-band thresholding is used. Downsampling in each of the DWT subbands causes information loss in the respective subbands. SWT is employed to minimize this loss. Inverse Discrete wavelet transform (IDWT) is to convert the object which is downsampled using DWT into a highly resoluted object. Used Image denoising and resolution enhancement techniques will generate image with high PSNR value. Our Proposed method will improve Image Resolution and reached the optimized threshold.

Keywords: Image Processing, Inverse Discrete wavelet transform, PSNR.

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Authors: Z. Ezzouine, A. Nakheli

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This paper presents a high position electromagnetic sensor system (HPESS) that is applicable for moving object detection. The authors have developed a high-performance position sensor prototype dedicated to students’ laboratory. The challenge was to obtain a highly accurate and real-time sensor that is able to calculate position, length or displacement. An electromagnetic solution based on a two coil induction principal was adopted. The HPESS converts mechanical motion to electric energy with direct contact. The output signal can then be fed to an electronic circuit. The voltage output change from the sensor is captured by data acquisition system using LabVIEW software. The displacement of the moving object is determined. The measured data are transmitted to a PC in real-time via a DAQ (NI USB -6281). This paper also describes the data acquisition analysis and the conditioning card developed specially for sensor signal monitoring. The data is then recorded and viewed using a user interface written using National Instrument LabVIEW software. On-line displays of time and voltage of the sensor signal provide a user-friendly data acquisition interface. The sensor provides an uncomplicated, accurate, reliable, inexpensive transducer for highly sophisticated control systems.

Keywords: Electromagnetic sensor, data acquisition, accurately, position measurement.

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Authors: M. Cuevas-González, O. Monserrat, A. Barra, C. Reyes-Carmona, R. M. Mateos, J. P. Galve, R. Sarro, M. Cantalejo, E. Peña, M. Martínez-Corbella, J. A. Luque, J. M. Azañón, A. Millares, M. Béjar, J. A. Navarro, L. Solari

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Geohazard prone areas require continuous monitoring to detect risks, understand the phenomena occurring in those regions and prevent disasters. Satellite interferometry (InSAR) has come to be a trustworthy technique for ground movement detection and monitoring in the last few years. InSAR based techniques allow to process large areas providing high number of displacement measurements at low cost. However, the results provided by such techniques are usually not easy to interpret by non-experienced users hampering its use for decision makers. This work presents a set of tools developed in the framework of different projects (Momit, Safety, U-Geohaz, Riskcoast) and an example of their use in the Granada Coastal area (Spain) is shown. The ADA (Active Displacement Areas) tool has been developed with the aim of easing the management, use and interpretation of InSAR based results. It provides a semi-automatic extraction of the most significant ADAs through the application ADAFinder tool. This tool aims to support the exploitation of the European Ground Motion Service (EU-GMS), which will offer reliable and systematic information on natural and anthropogenic ground motion phenomena across Europe.

Keywords: Ground displacements, InSAR, natural hazards, satellite imagery.

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Authors: Atena Sajedin, Shokoufeh Zakernejad, Soheil Faridi, Mehrdad Javadi, Reza Ebrahimpour

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This paper illustrates the use of a combined neural network model for classification of electrocardiogram (ECG) beats. We present a trainable neural network ensemble approach to develop customized electrocardiogram beat classifier in an effort to further improve the performance of ECG processing and to offer individualized health care. We process a three stage technique for detection of premature ventricular contraction (PVC) from normal beats and other heart diseases. This method includes a denoising, a feature extraction and a classification. At first we investigate the application of stationary wavelet transform (SWT) for noise reduction of the electrocardiogram (ECG) signals. Then feature extraction module extracts 10 ECG morphological features and one timing interval feature. Then a number of multilayer perceptrons (MLPs) neural networks with different topologies are designed. The performance of the different combination methods as well as the efficiency of the whole system is presented. Among them, Stacked Generalization as a proposed trainable combined neural network model possesses the highest recognition rate of around 95%. Therefore, this network proves to be a suitable candidate in ECG signal diagnosis systems. ECG samples attributing to the different ECG beat types were extracted from the MIT-BIH arrhythmia database for the study.

Keywords: ECG beat Classification; Combining Classifiers;Premature Ventricular Contraction (PVC); Multi Layer Perceptrons;Wavelet Transform

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Authors: Radul Shishkov, Petar Penchev

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The digital era has revolutionized architectural practices, with Building Information Modeling (BIM) emerging as a pivotal tool for architects, engineers, and construction professionals. However, the transition from traditional methods to BIM-centric approaches poses significant challenges, particularly in the context of existing structures. This research presents a technical approach to bridge this gap through the development of algorithms that facilitate the automated transformation of 3D point cloud data into detailed BIM models. The core of this research lies in the application of algorithmic modeling and computational design methods to interpret and reconstruct point cloud data — a collection of data points in space, typically produced by 3D scanners — into comprehensive BIM models. This process involves complex stages of data cleaning, feature extraction, and geometric reconstruction, which are traditionally time-consuming and prone to human error. By automating these stages, our approach significantly enhances the efficiency and accuracy of creating BIM models for existing buildings. The proposed algorithms are designed to identify key architectural elements within point clouds, such as walls, windows, doors, and other structural components, and to translate these elements into their corresponding BIM representations. This includes the integration of parametric modeling techniques to ensure that the generated BIM models are not only geometrically accurate but also embedded with essential architectural and structural information. This research contributes significantly to the field of architectural technology by providing a scalable and efficient solution for the integration of existing structures into the BIM framework. It paves the way for more seamless and integrated workflows in renovation and heritage conservation projects, where the accuracy of existing conditions plays a critical role. The implications of this study extend beyond architectural practices, offering potential benefits in urban planning, facility management, and historical preservation.

Keywords: Algorithmic modeling, Building Information Modeling, point cloud, reconstruction.

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Authors: R.S.Sabeenian, V.Palanisamy

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Texture classification is a trendy and a catchy technology in the field of texture analysis. Textures, the repeated patterns, have different frequency components along different orientations. Our work is based on Texture Classification and its applications. It finds its applications in various fields like Medical Image Classification, Computer Vision, Remote Sensing, Agricultural Field, and Textile Industry. Weed control has a major effect on agriculture. A large amount of herbicide has been used for controlling weeds in agriculture fields, lawns, golf courses, sport fields, etc. Random spraying of herbicides does not meet the exact requirement of the field. Certain areas in field have more weed patches than estimated. So, we need a visual system that can discriminate weeds from the field image which will reduce or even eliminate the amount of herbicide used. This would allow farmers to not use any herbicides or only apply them where they are needed. A machine vision precision automated weed control system could reduce the usage of chemicals in crop fields. In this paper, an intelligent system for automatic weeding strategy Multi Resolution Combined Statistical & spatial Frequency is used to discriminate the weeds from the crops and to classify them as narrow, little and broad weeds.

Keywords: crop weed discrimination, MRCSF, MRFM, Weeddetection, Spatial Frequency.

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Authors: Paria Soleimani, Arezoo Neshati

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Myocardial infarction is one of the leading causes of death in the world. Some of these deaths occur even before the patient reaches the hospital. Myocardial infarction occurs as a result of impaired blood supply. Because the most of these deaths are due to coronary artery disease, hence the awareness of the warning signs of a heart attack is essential. Some heart attacks are sudden and intense, but most of them start slowly, with mild pain or discomfort, then early detection and successful treatment of these symptoms is vital to save them. Therefore, importance and usefulness of a system designing to assist physicians in early diagnosis of the acute heart attacks is obvious. The main purpose of this study would be to enable patients to become better informed about their condition and to encourage them to seek professional care at an earlier stage in the appropriate situations. For this purpose, the data were collected on 711 heart patients in Iran hospitals. 28 attributes of clinical factors can be reported by patients; were studied. Three logistic regression models were made on the basis of the 28 features to predict the risk of heart attacks. The best logistic regression model in terms of performance had a C-index of 0.955 and with an accuracy of 94.9%. The variables, severe chest pain, back pain, cold sweats, shortness of breath, nausea and vomiting, were selected as the main features.

Keywords: Coronary heart disease, acute heart attacks, prediction, logistic regression.

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Authors: Aspalella A. Rahman

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Money laundering has been described by many as the lifeblood of crime and is a major threat to the economic and social well-being of societies. It has been recognized that the banking system has long been the central element of money laundering. This is in part due to the complexity and confidentiality of the banking system itself. It is generally accepted that effective anti-money laundering (AML) measures adopted by banks will make it tougher for criminals to get their "dirty money" into the financial system. In fact, for law enforcement agencies, banks are considered to be an important source of valuable information for the detection of money laundering. However, from the banks- perspective, the main reason for their existence is to make as much profits as possible. Hence their cultural and commercial interests are totally distinct from that of the law enforcement authorities. Undoubtedly, AML laws create a major dilemma for banks as they produce a significant shift in the way banks interact with their customers. Furthermore, the implementation of the laws not only creates significant compliance problems for banks, but also has the potential to adversely affect the operations of banks. As such, it is legitimate to ask whether these laws are effective in preventing money launderers from using banks, or whether they simply put an unreasonable burden on banks and their customers. This paper attempts to address these issues and analyze them against the background of the Malaysian AML laws. It must be said that effective coordination between AML regulator and the banking industry is vital to minimize problems faced by the banks and thereby to ensure effective implementation of the laws in combating money laundering.

Keywords: Banking Industry, Bank Negara Money, Laundering, Malaysia.

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Authors: Madhuri Bhavsar, Anupam K Singh, Shrikant Pradhan

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Long term rainfall analysis and prediction is a challenging task especially in the modern world where the impact of global warming is creating complications in environmental issues. These factors which are data intensive require high performance computational modeling for accurate prediction. This research paper describes a prototype which is designed and developed on grid environment using a number of coupled software infrastructural building blocks. This grid enabled system provides the demanding computational power, efficiency, resources, user-friendly interface, secured job submission and high throughput. The results obtained using sequential execution and grid enabled execution shows that computational performance has enhanced among 36% to 75%, for decade of climate parameters. Large variation in performance can be attributed to varying degree of computational resources available for job execution. Grid Computing enables the dynamic runtime selection, sharing and aggregation of distributed and autonomous resources which plays an important role not only in business, but also in scientific implications and social surroundings. This research paper attempts to explore the grid enabled computing capabilities on weather indices from HOAPS data for climate impact modeling and change detection.

Keywords: Climate model, Computational Grid, GridApplication, Heterogeneous Grid

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Authors: Rahil Garnavi, Mohammad Aldeen, M. Emre Celebi, Alauddin Bhuiyan, Constantinos Dolianitis, George Varigos

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Automatic segmentation of skin lesions is the first step towards development of a computer-aided diagnosis of melanoma. Although numerous segmentation methods have been developed, few studies have focused on determining the most discriminative and effective color space for melanoma application. This paper proposes a novel automatic segmentation algorithm using color space analysis and clustering-based histogram thresholding, which is able to determine the optimal color channel for segmentation of skin lesions. To demonstrate the validity of the algorithm, it is tested on a set of 30 high resolution dermoscopy images and a comprehensive evaluation of the results is provided, where borders manually drawn by four dermatologists, are compared to automated borders detected by the proposed algorithm. The evaluation is carried out by applying three previously used metrics of accuracy, sensitivity, and specificity and a new metric of similarity. Through ROC analysis and ranking the metrics, it is shown that the best results are obtained with the X and XoYoR color channels which results in an accuracy of approximately 97%. The proposed method is also compared with two state-ofthe- art skin lesion segmentation methods, which demonstrates the effectiveness and superiority of the proposed segmentation method.

Keywords: Border detection, Color space analysis, Dermoscopy, Histogram thresholding, Melanoma, Segmentation.

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Authors: Guo Xiuhua, Sun Tao, Wu Haifeng, He Wen, Liang Zhigang, Zhang Mengxia, Guo Aimin, Wang Wei

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Purpose: To explore the use of Curvelet transform to extract texture features of pulmonary nodules in CT image and support vector machine to establish prediction model of small solitary pulmonary nodules in order to promote the ratio of detection and diagnosis of early-stage lung cancer. Methods: 2461 benign or malignant small solitary pulmonary nodules in CT image from 129 patients were collected. Fourteen Curvelet transform textural features were as parameters to establish support vector machine prediction model. Results: Compared with other methods, using 252 texture features as parameters to establish prediction model is more proper. And the classification consistency, sensitivity and specificity for the model are 81.5%, 93.8% and 38.0% respectively. Conclusion: Based on texture features extracted from Curvelet transform, support vector machine prediction model is sensitive to lung cancer, which can promote the rate of diagnosis for early-stage lung cancer to some extent.

Keywords: CT image, Curvelet transform, Small pulmonary nodules, Support vector machines, Texture extraction.

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Authors: Yong Zhao, Jian He, Cheng Zhang

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Cardiovascular disease resulting from hypertension poses a significant threat to human health, and early detection of hypertension can potentially save numerous lives. Traditional methods for detecting hypertension require specialized equipment and are often incapable of capturing continuous blood pressure fluctuations. To address this issue, this study starts by analyzing the principle of heart rate variability (HRV) and introduces the utilization of sliding window and power spectral density (PSD) techniques to analyze both temporal and frequency domain features of HRV. Subsequently, a hypertension prediction network that relies on HRV is proposed, combining Resnet, attention mechanisms, and a multi-layer perceptron. The network leverages a modified ResNet18 to extract frequency domain features, while employing an attention mechanism to integrate temporal domain features, thus enabling auxiliary hypertension prediction through the multi-layer perceptron. The proposed network is trained and tested using the publicly available SHAREE dataset from PhysioNet. The results demonstrate that the network achieves a high prediction accuracy of 92.06% for hypertension, surpassing traditional models such as K Near Neighbor (KNN), Bayes, Logistic regression, and traditional Convolutional Neural Network (CNN).

Keywords: Feature extraction, heart rate variability, hypertension, residual networks.

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Authors: N. Siva Shanmugam, G. Buvanashekaran, K. Sankaranarayanasamy

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In this research work, investigations are carried out on Continuous Wave (CW) Nd:YAG laser welding system after preliminary experimentation to understand the influencing parameters associated with laser welding of AISI 304. The experimental procedure involves a series of laser welding trials on AISI 304 stainless steel sheets with various combinations of process parameters like beam power, beam incident angle and beam incident angle. An industrial 2 kW CW Nd:YAG laser system, available at Welding Research Institute (WRI), BHEL Tiruchirappalli, is used for conducting the welding trials for this research. After proper tuning of laser beam, laser welding experiments are conducted on AISI 304 grade sheets to evaluate the influence of various input parameters on weld bead geometry i.e. bead width (BW) and depth of penetration (DOP). From the laser welding results, it is noticed that the beam power and welding speed are the two influencing parameters on depth and width of the bead. Three dimensional finite element simulation of high density heat source have been performed for laser welding technique using finite element code ANSYS for predicting the temperature profile of laser beam heat source on AISI 304 stainless steel sheets. The temperature dependent material properties for AISI 304 stainless steel are taken into account in the simulation, which has a great influence in computing the temperature profiles. The latent heat of fusion is considered by the thermal enthalpy of material for calculation of phase transition problem. A Gaussian distribution of heat flux using a moving heat source with a conical shape is used for analyzing the temperature profiles. Experimental and simulated values for weld bead profiles are analyzed for stainless steel material for different beam power, welding speed and beam incident angle. The results obtained from the simulation are compared with those from the experimental data and it is observed that the results of numerical analysis (FEM) are in good agreement with experimental results, with an overall percentage of error estimated to be within ±6%.

Keywords: Laser welding, Butt weld, 304 SS, FEM.

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Authors: Amri Yusoff, Shahrizuan Shafiril, Ashardi Abas, Norma Che Yusoff

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Nowadays, illegal logging has been causing many effects including flash flood, avalanche, global warming, and etc. The purpose of this study was to maintain the earth ecosystem by keeping and regulate Malaysia’s treasurable rainforest by utilizing a new technology that will assist in real-time alert and give faster response to the authority to act on these illegal activities. The methodology of this research consisted of design stages that have been conducted as well as the system model and system architecture of the prototype in addition to the proposed hardware and software that have been mainly used such as microcontroller, sensor with the implementation of GSM, and GPS integrated system. This prototype was deployed at Royal Belum forest in December 2014 for phase 1 and April 2015 for phase 2 at 21 pinpoint locations. The findings of this research were the capture of data in real-time such as temperature, humidity, gaseous, fire, and rain detection which indicate the current natural state and habitat in the forest. Besides, this device location can be detected via GPS of its current location and then transmitted by SMS via GSM system. All of its readings were sent in real-time for further analysis. The data that were compared to meteorological department showed that the precision of this device was about 95% and these findings proved that the system is acceptable and suitable to be used in the field.

Keywords: Remote monitoring system, forest data, GSM, GPS, wireless sensor.

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Authors: M. A. Ahmadi, O. Alidoust, I. Sadrinejad, M. Nayeri

Abstract:

Self-compacting concrete (SCC), a new kind of high performance concrete (HPC) have been first developed in Japan in 1986. The development of SCC has made casting of dense reinforcement and mass concrete convenient, has minimized noise. Fresh self-compacting concrete (SCC) flows into formwork and around obstructions under its own weight to fill it completely and self-compact (without any need for vibration), without any segregation and blocking. The elimination of the need for compaction leads to better quality concrete and substantial improvement of working conditions. SCC mixes generally have a much higher content of fine fillers, including cement, and produce excessively high compressive strength concrete, which restricts its field of application to special concrete only. To use SCC mixes in general concrete construction practice, requires low cost materials to make inexpensive concrete. Rice husk ash (RHA) has been used as a highly reactive pozzolanic material to improve the microstructure of the interfacial transition zone (ITZ) between the cement paste and the aggregate in self compacting concrete. Mechanical experiments of RHA blended Portland cement concretes revealed that in addition to the pozzolanic reactivity of RHA (chemical aspect), the particle grading (physical aspect) of cement and RHA mixtures also exerted significant influences on the blending efficiency. The scope of this research was to determine the usefulness of Rice husk ash (RHA) in the development of economical self compacting concrete (SCC). The cost of materials will be decreased by reducing the cement content by using waste material like rice husk ash instead of. This paper presents a study on the development of Mechanical properties up to 180 days of self compacting and ordinary concretes with rice-husk ash (RHA), from a rice paddy milling industry in Rasht (Iran). Two different replacement percentages of cement by RHA, 10%, and 20%, and two different water/cementicious material ratios (0.40 and 0.35), were used for both of self compacting and normal concrete specimens. The results are compared with those of the self compacting concrete without RHA, with compressive, flexural strength and modulus of elasticity. It is concluded that RHA provides a positive effect on the Mechanical properties at age after 60 days. Base of the result self compacting concrete specimens have higher value than normal concrete specimens in all test except modulus of elasticity. Also specimens with 20% replacement of cement by RHA have the best performance.

Keywords: Self compacting concrete (SCC), Rice husk ash(RHA), Mechanical properties.

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Authors: F. Kashanian, M. M. Masoudi, A. Akbari, A. Shamloo, M. R. Zand, S. S. Salehi

Abstract:

Nano-sized materials present new opportunities in biology and medicine and they are used as biomedical tools for investigation, separation of molecules and cells. To achieve more effective cancer therapy, it is essential to select cancer cells exactly. This research suggests that using the antibody-functionalized nontoxic Arginine-doped magnetic nanoparticles (A-MNPs), has been prosperous in detection, capture, and magnetic separation of circulating tumor cells (CTCs) in tumor tissue. In this study, A-MNPs were synthesized via a simple precipitation reaction and directly immobilized Ep-CAM EBA-1 antibodies over superparamagnetic A-MNPs for Mucin BCA-225 in breast cancer cell. The samples were characterized by vibrating sample magnetometer (VSM), FT-IR spectroscopy, Tunneling Electron Microscopy (TEM) and Scanning Electron Microscopy (SEM). These antibody-functionalized nontoxic A-MNPs were used to capture breast cancer cell. Through employing a strong permanent magnet, the magnetic separation was achieved within a few seconds. Antibody-Conjugated nontoxic Arginine-doped Fe₃O₄ nanoparticles have the potential for the future study to capture CTCs which are released from tumor tissue and for drug delivery, and these results demonstrate that the antibody-conjugated A-MNPs can be used in magnetic hyperthermia techniques for cancer treatment.

Keywords: Tumor tissue, antibody, magnetic nanoparticle, CTCs capturing.

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Authors: Ayman A. Aly, Abdallah A. Alshnnaway

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The general idea behind the filter is to average a pixel using other pixel values from its neighborhood, but simultaneously to take care of important image structures such as edges. The main concern of the proposed filter is to distinguish between any variations of the captured digital image due to noise and due to image structure. The edges give the image the appearance depth and sharpness. A loss of edges makes the image appear blurred or unfocused. However, noise smoothing and edge enhancement are traditionally conflicting tasks. Since most noise filtering behaves like a low pass filter, the blurring of edges and loss of detail seems a natural consequence. Techniques to remedy this inherent conflict often encompass generation of new noise due to enhancement. In this work a new fuzzy filter is presented for the noise reduction of images corrupted with additive noise. The filter consists of three stages. (1) Define fuzzy sets in the input space to computes a fuzzy derivative for eight different directions (2) construct a set of IFTHEN rules by to perform fuzzy smoothing according to contributions of neighboring pixel values and (3) define fuzzy sets in the output space to get the filtered and edged image. Experimental results are obtained to show the feasibility of the proposed approach with two dimensional objects.

Keywords: Additive noise, edge preserving filtering, fuzzy image filtering, noise reduction, two dimensional mechanical images.

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Authors: D. Kreutzer, S. Müller-Abdelrazeq, I. Isenhardt

Abstract:

Resource scarcity, energy transition and the planned climate neutrality pose enormous challenges for manufacturing companies. In order to achieve these goals and a holistic sustainable development, the European Union has listed the circular economy as part of the Circular Economy Action Plan. In addition to a reduction in resource consumption, reduced emissions of greenhouse gases and a reduced volume of waste, the principles of the circular economy also offer enormous economic potential for companies, such as the generation of new circular business models. However, many manufacturing companies, especially small and medium-sized enterprises, do not have the necessary capacity to plan their transformation. They need support and strategies on the path to circular transformation because this change affects not only production but also the entire company. Maturity models offer an approach to determine the current status of companies’ transformation processes. In addition, companies can use the models to identify transformation strategies and thus promote the transformation process. While maturity models are established in other areas, e.g., IT or project management, only a few circular economy maturity models can be found in the scientific literature. The aim of this paper is to analyze the identified maturity models of the circular economy through a systematic literature review (SLR) and, besides other aspects, to check their completeness as well as their quality. For this purpose, circular economy maturity models at the company's (micro) level were identified from the literature, compared, and analyzed with regard to their theoretical and methodological structure. A specific focus was placed, on the one hand, on the analysis of the business units considered in the respective models and, on the other hand, on the underlying metrics and indicators in order to determine the individual maturity level of the entire company. The results of the literature review show, for instance, a significant difference in the number and types of indicators as well as their metrics. For example, most models use subjective indicators and very few objective indicators in their surveys. It was also found that there are rarely well-founded thresholds between the levels. Based on the generated results, concrete ideas and proposals for a research agenda in the field of circular economy maturity models are made.

Keywords: Circular economy, maturity model, maturity assessment, systematic literature review.

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Authors: Hazem M. El-Bakry

Abstract:

In this paper a new fast simplification method is presented. Such method realizes Karnough map with large number of variables. In order to accelerate the operation of the proposed method, a new approach for fast detection of group of ones is presented. Such approach implemented in the frequency domain. The search operation relies on performing cross correlation in the frequency domain rather than time one. It is proved mathematically and practically that the number of computation steps required for the presented method is less than that needed by conventional cross correlation. Simulation results using MATLAB confirm the theoretical computations. Furthermore, a powerful solution for realization of complex functions is given. The simplified functions are implemented by using a new desigen for neural networks. Neural networks are used because they are fault tolerance and as a result they can recognize signals even with noise or distortion. This is very useful for logic functions used in data and computer communications. Moreover, the implemented functions are realized with minimum amount of components. This is done by using modular neural nets (MNNs) that divide the input space into several homogenous regions. Such approach is applied to implement XOR function, 16 logic functions on one bit level, and 2-bit digital multiplier. Compared to previous non- modular designs, a clear reduction in the order of computations and hardware requirements is achieved.

Keywords: Boolean Functions, Simplification, KarnoughMap, Implementation of Logic Functions, Modular NeuralNetworks.

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Authors: Hazem M. El-Bakry

Abstract:

In this paper a new fast simplification method is presented. Such method realizes Karnough map with large number of variables. In order to accelerate the operation of the proposed method, a new approach for fast detection of group of ones is presented. Such approach implemented in the frequency domain. The search operation relies on performing cross correlation in the frequency domain rather than time one. It is proved mathematically and practically that the number of computation steps required for the presented method is less than that needed by conventional cross correlation. Simulation results using MATLAB confirm the theoretical computations. Furthermore, a powerful solution for realization of complex functions is given. The simplified functions are implemented by using a new desigen for neural networks. Neural networks are used because they are fault tolerance and as a result they can recognize signals even with noise or distortion. This is very useful for logic functions used in data and computer communications. Moreover, the implemented functions are realized with minimum amount of components. This is done by using modular neural nets (MNNs) that divide the input space into several homogenous regions. Such approach is applied to implement XOR function, 16 logic functions on one bit level, and 2-bit digital multiplier. Compared to previous non- modular designs, a clear reduction in the order of computations and hardware requirements is achieved.

Keywords: Boolean functions, simplification, Karnough map, implementation of logic functions, modular neural networks.

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