Search results for: network technology

Authors: Bircan Demiral

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Cognitive radio (CR) is the promising technology that addresses the spectrum scarcity problem for future wireless communications. Orthogonal Frequency Division Multiplexing (OFDM) technology provides more power band ratios for cognitive radio networks (CRNs). While CR is a solution to the spectrum scarcity, it also brings up the capacity problem. In this paper, a novel power allocation algorithm that aims at maximizing the sum capacity in the OFDM based cognitive radio networks is proposed. Proposed allocation algorithm is based on the previously developed water-filling algorithm. To reduce the computational complexity calculating in water filling algorithm, proposed algorithm allocates the total power according to each subcarrier. The power allocated to the subcarriers increases sum capacity. To see this increase, Matlab program was used, and the proposed power allocation was compared with average power allocation, water filling and general power allocation algorithms. The water filling algorithm performed worse than the proposed algorithm while it performed better than the other two algorithms. The proposed algorithm is better than other algorithms in terms of capacity increase. In addition the effect of the change in the number of subcarriers on capacity was discussed. Simulation results show that the increase in the number of subcarrier increases the capacity.

Keywords: cognitive radio network, OFDM, power allocation, water filling

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Authors: Wen Li, Zhengyu Bai, Qi Zhang

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The demand for smart scheduling platform in agriculture, particularly in the scheduling process of machinery equipment, is high. With the continuous development of agricultural machinery equipment technology, a large number of agricultural machinery equipment and agricultural machinery cooperative service organizations continue to appear in China. The large area of cultivated land and a large number of agricultural activities in the central and western regions of China have made the demand for smart and efficient agricultural machinery equipment scheduling platforms more intense. In this study, we design and implement a platform for agricultural machinery equipment scheduling to allocate agricultural machinery equipment resources reasonably. With agricultural machinery equipment scheduling platform taken as the research object, we discuss its research significance and value, use the service blueprint technology to analyze and characterize the agricultural machinery equipment schedule workflow, the network analytic method to obtain the demand platform function requirements, and divide the platform functions through the platform function division diagram. Simultaneously, based on the case-based reasoning (CBR) algorithm, the equipment scheduling module of the agricultural machinery equipment scheduling platform is realized; finally, a design scheme of the agricultural machinery equipment scheduling platform architecture is provided, and the visualization interface of the platform is established via VB programming language. It provides design ideas and theoretical support for the construction of a modern agricultural equipment information scheduling platform.

Keywords: case-based reasoning, service blueprint, system design, ANP, VB programming language

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Authors: Ganesh Nandakumaran, Mehmet Karaata

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A wave is a distributed execution, often made up of a broadcast phase followed by a feedback phase, requiring the participation of all the system processes before a particular event called decision is taken. Wave algorithms with one initiator such as the 1-wave algorithm have been shown to be very efficient for broadcasting messages in tree networks. Extensions of this algorithm broadcasting a sequence of waves using a single initiator have been implemented in algorithms such as the m-wave algorithm. However as the network size increases, having a single initiator adversely affects the message delivery times to nodes further away from the initiator. As a remedy, broadcast waves can be allowed to be initiated by multiple initiator nodes distributed across the network to reduce the completion time of broadcasts. These waves initiated by one or more initiator processes form a collection of waves covering the entire network. Solutions to global-snapshots, distributed broadcast and various synchronization problems can be solved efficiently using waves with multiple concurrent initiators. In this paper, we propose the first stabilizing multi-wave sequence algorithm implementing waves started by multiple initiator processes such that every process in the network receives at least one sequence of broadcasts. Due to being stabilizing, the proposed algorithm can withstand transient faults and do not require initialization. We view a fault as a transient fault if it perturbs the configuration of the system but not its program.

Keywords: distributed computing, multi-node broadcast, propagation of information with feedback and cleaning (PFC), stabilization, wave algorithms

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Authors: Mohanaad Talal Shakir

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This paper tries to propose educational design by using multimedia technology for Engineering of computer Technology, Alma'ref University College in Iraq. This paper evaluates the acceptance, cognition, and interactiveness of the proposed model by students by using the statistical relationship to determine the stage of the model. Objectives of proposed education design are to develop a user-friendly software for education purposes using multimedia technology and to develop animation for 3D model to simulate assembling and disassembling process of high-speed flow.

Keywords: CAL, multimedia, shock tunnel, interactivity, engineering education

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Authors: Taimur Khan, Zakirullah, Muhammad Shahab

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The SARS-CoV-2 variant BA.2.86 (Omicron) has emerged with unique mutations that may increase its transmission and infectivity. This study investigates how these mutations alter the Omicron receptor-binding domain's interaction network and dynamic properties (RBD) compared to the wild-type virus, focusing on its binding affinity to the human ACE2 (hACE2) receptor. Protein-protein docking and all-atom molecular dynamics simulations were used to analyze structural and dynamic differences. Despite the structural similarity to the wild-type virus, the Omicron variant exhibits a distinct interaction network involving new residues that enhance its binding capacity. The dynamic analysis reveals increased flexibility in the RBD, particularly in loop regions crucial for hACE2 interaction. Mutations significantly alter the secondary structure, leading to greater flexibility and conformational adaptability compared to the wild type. Binding free energy calculations confirm that the Omicron RBD has a higher binding affinity (-70.47 kcal/mol) to hACE2 than the wild-type RBD (-61.38 kcal/mol). These results suggest that the altered interaction network and enhanced dynamics of the Omicron variant contribute to its increased infectivity, providing insights for the development of targeted therapeutics and vaccines.

Keywords: SARS-CoV-2, molecular dynamic simulation, receptor binding domain, vaccine

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Authors: Narjis Zehra

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After automobile revolution 1.0, we have automobile revolution 2.0 standing at the horizon, Autonomous Vehicles (AVs). While the technology is developing into more adaptable form, the conversations around its impact on our cities have already started on multiple scales, from academic institutions and community town halls, to the offices of mayors. In order to explore more the AVs impact on Urban transformation, we first inquire if cities can be redesigned or rebuilt. Secondly, we discuss expectation management for the public and policy in terms of what people think/believe AV technology will deliver, and what the current technological evidence suggests the technology and its adoption will look like. Thirdly, based on these discussions, we take Pittsburgh, PA, as a case study to extrapolate what other cities might need to do in order to prepare themselves for the upcoming technological revolution, that may impact more than just the research institutes. Finally, we conclude by suggesting a political way forward to embed urban design with AV technology for equitable cities of tomorrow.

Keywords: urban design, autonomous vehicles, transformation, policy

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Authors: Jaroslav Masek, Juraj Camaj, Eva Nedeliakova

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The paper is focused on the operational model for transport the single wagon consignments on railway network by using two different models of train formation. The paper gives an overview of possibilities of improving the quality of transport services. Paper deals with two models used in problematic of train formatting - time continuously and time discrete. By applying these models in practice, the transport company can guarantee a higher quality of service and expect increasing of transport performance. The models are also applicable into others transport networks. The models supplement a theoretical problem of train formation by new ways of looking to affecting the organization of wagon flows.

Keywords: train formation, wagon flows, marshalling yard, railway technology

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Authors: Joseph Reylan Viray, Kriztine R. Viray

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Call center conversations, more than the business dimensions that they normally manifest, are interactions between human beings. These are communication exchanges that are packed with psychological, cultural and social dimensions that affect the specific experience of the parties. The increasing development of information and communication technology over the past decades brought about important advantages and corresponding disadvantages in the process of communicational transactions in call center industry. It has been established that the technology is so powerful that it strongly affects, among others, call center business. In the present study, the author explores the interplay between the technology being utilized by the industry and the cultural orientations of both the call center agents and their customers in the process of communication exchanges. Specifically, the paper seeks to (1) describe the interplay between culture and technology in inbound call center industry as it affects the communication exchange of the agents and customers; (2) understand the nature and the dynamics of the call center industry as regards the cultural dimensions of Hofstede; and (3) come up with a simple study where the cross-cultural aspect of the call center industry could be highlighted and could provide necessary knowledge to the stakeholders. Cognizant of the complexity of the topic, the researchers employed Hofstede's cultural dimensions. Likewise, another theory that was used in this study is the Computer Mediated Communication Theory.

Keywords: call center industry, culture, Hofstede, CMT, technology

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Authors: Kun Xu, Yuan Xu, Jia Qiao

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The document detection plays an important role in optical character recognition and text analysis. Because the traditional detection methods have weak generalization ability, and deep neural network has complex structure and large number of parameters, which cannot be well applied in mobile devices, this paper proposes a lightweight Document Corner Detection Network (DCDNet). DCDNet is a two-stage architecture. The first stage with Encoder-Decoder structure adopts depthwise separable convolution to greatly reduce the network parameters. After introducing the Feature Attention Union (FAU) module, the second stage enhances the feature information of spatial and channel dim and adaptively adjusts the size of receptive field to enhance the feature expression ability of the model. Aiming at solving the problem of the large difference in the number of pixel distribution between corner and non-corner, Weighted Binary Cross Entropy Loss (WBCE Loss) is proposed to define corner detection problem as a classification problem to make the training process more efficient. In order to make up for the lack of Dataset of document corner detection, a Dataset containing 6620 images named Document Corner Detection Dataset (DCDD) is made. Experimental results show that the proposed method can obtain fast, stable and accurate detection results on DCDD.

Keywords: document detection, corner detection, attention mechanism, lightweight

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Authors: Mehrshad Khosraviani, Faramarz Abbaspour Leyl Abadi

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The fundamental component of the computer network of modern information society will be considered. These networks are connected to the network of the internet generally. Due to the fact that the primary purpose of the Internet is not designed for, in recent decades, none of these networks in many of the attacks has been very important. Today, for the provision of security, different security tools and systems, including intrusion detection systems are used in the network. A common diagnosis system based on artificial immunity, the designer, the Adhasaz Foundation has been evaluated. The idea of using artificial safety methods in the diagnosis of abnormalities in computer networks it has been stimulated in the direction of their specificity, there are safety systems are similar to the common needs of m, that is non-diagnostic. For example, such methods can be used to detect any abnormalities, a variety of attacks, being memory, learning ability, and Khodtnzimi method of artificial immune algorithm pointed out. Diagnosis of the common system of education offered in this paper using only the normal samples is required for network and any additional data about the type of attacks is not. In the proposed system of positive selection and negative selection processes, selection of samples to create a distinction between the colony of normal attack is used. Copa real data collection on the evaluation of ij indicates the proposed system in the false alarm rate is often low compared to other ir methods and the detection rate is in the variations.

Keywords: artificial immune system, abnormality detection, intrusion detection, computer networks

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Authors: C. Angel, S. Asha

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Despite the advances made in various new technologies, application of these technologies for agriculture still remains a formidable task, as it involves integration of diverse domains for monitoring the different process involved in agricultural management. Advances in ambient intelligence technology represents one of the most powerful technology for increasing the yield of agricultural crops and to mitigate the impact of water scarcity, climatic change and methods for managing pests, weeds, and diseases. This paper proposes a GPS-assisted, machine to machine solutions that combine information collected by multiple sensors for the automated management of paddy crops. To maintain the economic viability of paddy cultivation, the various techniques used in agriculture are discussed and a novel system which uses ambient intelligence technique is proposed in this paper. The ambient intelligence based agricultural system gives a great scope.

Keywords: ambient intelligence, agricultural technology, smart agriculture, precise farming

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Authors: Lamaa Sellami, Bechir Alaya

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Nowadays, video-on-demand (VoD) applications are becoming one of the tendencies driving vehicular network users. In this paper, considering the unpredictable vehicle density, the unexpected acceleration or deceleration of the different cars included in the vehicular traffic load, and the limited radio range of the employed communication scheme, we introduce the “Dynamic Vehicular Clustering” (DVC) algorithm as a new scheme for video streaming systems over VANET. The proposed algorithm takes advantage of the concept of small cells and the introduction of wireless backhauls, inspired by the different features and the performance of the Long Term Evolution (LTE)- Advanced network. The proposed clustering algorithm considers multiple characteristics such as the vehicle’s position and acceleration to reduce latency and packet loss. Therefore, each cluster is counted as a small cell containing vehicular nodes and an access point that is elected regarding some particular specifications.

Keywords: video-on-demand, vehicular ad-hoc network, mobility, vehicular traffic load, small cell, wireless backhaul, LTE-advanced, latency, packet loss

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Authors: Faiza Manzoor

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The main purpose of this study is to examine the socioeconomic and institutional determinants that affect farmers' probability of agricultural technology adoption. Primary survey data was gathered from 350 small household farmers of developing economies, and the empirical analysis was completed by exploiting a logit and probit model. The outcomes of this research emphasize that socioeconomic factors such as farmers' age, education level, and farm size impact farmers' behavior in adopting digital farming technology. The results show that cooperative membership and institutional characteristics such as access to credit and extension services have also affected small farmers' use and adoption of agricultural technologies. Digitalization also helps farmers by increasing their understanding and improving their decision-making abilities. Some policy implications and future directions are discussed.

Keywords: agricultural technology adoption, socioeconomic, institutional, factors

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Authors: Tobias Milz, L. Macpherson, Varvara Vetrova

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The law is a fundamental pillar of human societies as it shapes, controls and governs how humans conduct business, behave and interact with each other. Recent advances in computer-assisted technologies such as NLP, data science and AI are creating opportunities to support the practice, research and study of this pervasive domain. It is therefore not surprising that there has been an increase in investments into supporting technologies for the legal industry (also known as “legal tech” or “law tech”) over the last decade. A sub-discipline of particular appeal is concerned with assisted legal research. Supporting law researchers and practitioners to retrieve information from the vast amount of ever-growing legal documentation is of natural interest to the legal research community. One tool that has been in use for this purpose since the early nineteenth century is legal citation indexing. Among other use cases, they provided an effective means to discover new precedent cases. Nowadays, computer-assisted network analysis tools can allow for new and more efficient ways to reveal the “hidden” information that is conveyed through citation behavior. Unfortunately, access to openly available legal data is still lacking in New Zealand and access to such networks is only commercially available via providers such as LexisNexis. Consequently, there is a need to create, analyze and provide a legal citation network with sufficient data to support legal research tasks. This paper describes the development and analysis of a legal citation Network for New Zealand containing over 300.000 decisions from 125 different courts of all areas of law and jurisdiction. Using python, the authors assembled web crawlers, scrapers and an OCR pipeline to collect and convert court decisions from openly available sources such as NZLII into uniform and machine-readable text. This facilitated the use of regular expressions to identify references to other court decisions from within the decision text. The data was then imported into a graph-based database (Neo4j) with the courts and their respective cases represented as nodes and the extracted citations as links. Furthermore, additional links between courts of connected cases were added to indicate an indirect citation between the courts. Neo4j, as a graph-based database, allows efficient querying and use of network algorithms such as PageRank to reveal the most influential/most cited courts and court decisions over time. This paper shows that the in-degree distribution of the New Zealand legal citation network resembles a power-law distribution, which indicates a possible scale-free behavior of the network. This is in line with findings of the respective citation networks of the U.S. Supreme Court, Austria and Germany. The authors of this paper provide the database as an openly available data source to support further legal research. The decision texts can be exported from the database to be used for NLP-related legal research, while the network can be used for in-depth analysis. For example, users of the database can specify the network algorithms and metrics to only include specific courts to filter the results to the area of law of interest.

Keywords: case citation network, citation analysis, network analysis, Neo4j

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Authors: José Luis Carrillo-Medina, Roberto Latorre

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Experimental evidence has revealed that different living neural systems can sign their output signals with some specific neural signature. Although experimental and modeling results suggest that neural signatures can have an important role in the activity of neural networks in order to identify the source of the information or to contextualize a message, the functional meaning of these neural fingerprints is still unclear. The existence of cellular mechanisms to identify the origin of individual neural signals can be a powerful information processing strategy for the nervous system. We have recently built different models to study the ability of a neural network to process information based on the emission and recognition of specific neural fingerprints. In this paper we further analyze the features that can influence on the information processing ability of this kind of networks. In particular, we focus on the role that the duration of a refractory period in each neuron after emitting a signed message can play in the network collective dynamics.

Keywords: neural signature, neural fingerprint, processing based on signal identification, self-organizing neural network

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Authors: Jeana Cadby, Kae Miyazawa

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With rapidly expanding worldwide adoption of mobile technologies, Information and Communication Technology (ITC) is a major energy user and a contributor to global carbon emissions, due to infrastructure and operational energy consumption. The agricultural sector is also significantly responsible for contributing to global carbon emissions. However, ICT cloud computing using mobile technology can directly reduce environmental impacts in the agricultural sector through applications and mobile connectivity, such as precision fertilizer and pesticide applications, or access to weather data, for example. While direct impacts are easily calculated, indirect environmental impacts from ICT cloud computing usage have not been thoroughly investigated. For example, while women may be more poorly equipped for adaptation to environmentally sustainable agricultural practices due to resource constraints, this research concludes that indirect environmental benefits can be achieved by improving rural access to mobile technology for women. Women in advanced roles and secure land tenure are more likely to invest in long-term agricultural conservation strategies, which protect against environmental degradation. This study examines how ICT using mobile technology advances the role of women in rural agricultural systems and indirectly reduces environmental impacts from agricultural production, through literature examination from secondary sources. Increasing access for women to ICT mobile technology provides indirect environmental and social benefits in the rural agricultural sector.

Keywords: cloud computing, environmental benefits, mobile technology, women

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Authors: K. Govender, M. Sinclair

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This paper demonstrates a proof of concept whereby shorter trips and land use densification can be promoted through an alternative approach to planning and implementation of road infrastructure in the South African context. It briefly discusses how the development of the Compact City concept relies on a combination of promoting shorter trips and densification through a change in focus in road infrastructure provision. The methodology developed in this paper uses a traffic model to test the impact of synthesized deterrence functions on congestion locations in the road network through the assignment of traffic on the study network. The results from this study demonstrate that intelligent planning of road infrastructure can indeed promote reduced urban sprawl, increased residential density and mixed-use areas which are supported by an efficient public transport system; and reduced dependence on the freeway network with a fixed road infrastructure budget. The study has resonance for all cities where urban sprawl is seemingly unstoppable.

Keywords: compact cities, densification, road infrastructure planning, transportation modelling

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Authors: Deba Kumar Sarma, Sanjib Kr. Rajbongshi

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Surface finish is an important product quality in machining. At first, experiments were carried out to investigate the effect of the cutting tool nose radius (considering 1mm and 0.65mm) in prediction of surface finish with process parameters of cutting speed, feed and depth of cut. For all possible cutting conditions, full factorial design was considered as two levels four parameters. Commercial Mild Steel bar and High Speed Steel (HSS) material were considered as work-piece and cutting tool material respectively. In order to obtain functional relationship between process parameters and surface roughness, neural network was used which was found to be capable for the prediction of surface roughness within a reasonable degree of accuracy. It was observed that tool nose radius of 1mm provides better surface finish in comparison to 0.65 mm. Also, it was observed that feed rate has a significant influence on surface finish.

Keywords: full factorial design, neural network, nose radius, surface finish

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Authors: Hasan Coruk, Recep Cakir

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This study aimed at investigating the effect of multimedia containing ‘the structure and properties of matter’ unit on students’ academic achievement level and self-efficacy relating to science and technology course. The study used an experimental design with pre-test and post-test groups. The data collection tools were ‘Science and Technology Course Achievement Test’ and ‘Science and Technology Self-Efficacy Scale’. The sample of the study consisted of 8th grade students at a primary school in Tokat Province. The study was carried out with 42 students from two classes, 21 (8 males, 13 females) from experimental group and 21 (13 males and 8 females) from control group. The data were analyzed in SPSS.18 software. The findings of the study indicated that the use of multimedia increased the students’ academic achievement in science and technology course in comparison with traditional teaching methods. It was also determined that there was not a significant difference in students’ course-oriented self-efficacy levels regarding the two methods. Necessary and feasible suggestions were put forward for whom it concerns.

Keywords: multimedia learning, science and technology, the structure-properties of matter, self-efficacy, academic achievement

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Authors: Zi Y. Kang, Tracy D. Cassidy, Tom Cassidy

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3D apparel CAD is one of the remarkable products in advanced technology which enables intuitive design, visualisation and evaluation of garments through stereoscopic drape simulation. The progressive improvements of 3D apparel CAD have led to the creation of more realistic clothing simulation which is used not only in design development but also in presentation, promotion and communication for fashion as well as other industries such as film, game and social network services. As a result, 3D clothing technology is becoming more ubiquitous in human culture and lives today. This study considers that such phenomenon implies that the technology has reached maturity and it is time to inspect the status of current technology and to explore its potential uses in ways to create cultural values to further move forward. For this reason, this study aims to generate virtual costumes as culturally significant objects using 3D apparel CAD and to assess its capability, applicability and attitudes of the audience towards clothing simulation through comparison with physical counterparts. Since the access to costume collection is often limited due to the conservative issues, the technology may make valuable contribution by democratization of culture and knowledge for museums and its audience. This study is expected to provide foundation knowledge for development of clothing technology and for expanding its boundary of practical uses. To prevent any potential damage, two replicas of the costumes in the 1860s and 1920s at the Museum of London were chosen as samples. Their structural, visual and physical characteristics were measured and collected using patterns, scanned images of fabrics and objective fabric measurements with scale, KES-F (Kawabata Evaluation System of Fabrics) and Titan. Commercial software, DC Suite 5.0 was utilised to create virtual costumes applying collected data and the following outcomes were produced for the evaluation: Images of virtual costumes and video clips showing static and dynamic simulation. Focus groups were arranged with fashion design students and the public for evaluation which exposed the outcomes together with physical samples, fabrics swatches and photographs. The similarities, application and acceptance of virtual costumes were estimated through discussion and a questionnaire. The findings show that the technology has the capability to produce realistic or plausible simulation but expression of some factors such as details and capability of light material requires improvements. While the use of virtual costumes was viewed as more interesting and futuristic replacements to physical objects by the public group, the fashion student group noted more differences in detail and preferred physical garments highlighting the absence of tangibility. However, the advantages and potential of virtual costumes as effective and useful visual references for educational and exhibitory purposes were underlined by both groups. Although 3D apparel CAD has sufficient capacity to assist garment design process, it has limits in identical replication and more study on accurate reproduction of details and drape is needed for its technical improvements. Nevertheless, the virtual costumes in this study demonstrated the possibility of the technology to contribute to cultural and knowledgeable value creation through its applicability and as an interesting way to offer 3D visual information.

Keywords: digital clothing technology, garment simulation, 3D Apparel CAD, virtual costume

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Authors: M. Khajezadeh, M. Saied Fallah Niasar, S. Ali Asli, D. Davani Davari, M. Godarzi, Y. Asgari

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This study aims to explore the joint application of both Boston and Ansoff matrices in the operational development of the product. We conduct deep analysis, by utilizing the Artificial Neural Network, to predict the position of the product in the market while the company is interested in increasing its share. The data are gathered from two industries, called hygiene and detergent. In doing so, the effort is being made by investigating the behavior of top player companies and, recommend strategic orientations. In conclusion, this combination analysis is appropriate for operational development; as well, it plays an important role in providing the position of the product in the market for both hygiene and detergent industries. More importantly, it will elaborate on the company’s strategies to increase its market share related to a combination of the Boston Consulting Group (BCG) Matrix and Ansoff Matrix.

Keywords: artificial neural network, portfolio analysis, BCG matrix, Ansoff matrix

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Authors: Jingting Luo, Yong Wang, Ying Wang

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Pedestrian detection in image or video data is a very important and challenging task in security surveillance. The difficulty of this task is to locate and detect pedestrians of different scales in complex scenes accurately. To solve these problems, a deep neural network (RT-YOLOv3) is proposed to realize real-time pedestrian detection at different scales in security monitoring. RT-YOLOv3 improves the traditional YOLOv3 algorithm. Firstly, the deep residual network is added to extract vehicle features. Then six convolutional neural networks with different scales are designed and fused with the corresponding scale feature maps in the residual network to form the final feature pyramid to perform pedestrian detection tasks. This method can better characterize pedestrians. In order to further improve the accuracy and generalization ability of the model, a hybrid pedestrian data set training method is used to extract pedestrian data from the VOC data set and train with the INRIA pedestrian data set. Experiments show that the proposed RT-YOLOv3 method achieves 93.57% accuracy of mAP (mean average precision) and 46.52f/s (number of frames per second). In terms of accuracy, RT-YOLOv3 performs better than Fast R-CNN, Faster R-CNN, YOLO, SSD, YOLOv2, and YOLOv3. This method reduces the missed detection rate and false detection rate, improves the positioning accuracy, and meets the requirements of real-time detection of pedestrian objects.

Keywords: pedestrian detection, feature detection, convolutional neural network, real-time detection, YOLOv3

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Authors: Po-Kun Tsai

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The issue of climate change is essential in the 21st century. State governments have launched types of strategic industrial policies to encourage more widespread R&D in green technology. Research also indicates that technology is an essential tool to mitigate some of extreme situations. However, one could learn from several prominent cases in international trade area that they have been easily argued and disputed by the foreign counterparts. Thus, how to justify the public sector’s R&D measures under the current world trading system and how to promote the transfer of environmentally sound technologies (EST) to developing states are crucial. The study is to undertake a preliminary examination of the current R&D research area in green technology in Taiwan. Through selective interviews and comparative approach, it tries to identify the loopholes under the current legal framework in Taiwan. It would be, as a basis, for further legal and policy recommendations for the benefits of mankind.

Keywords: government, R&D, innovation, environmentally sound technology (EST)

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Authors: Milagros Jaén-Vargas, Javier García Martínez, Karla Miriam Reyes Leiva, María Fernanda Trujillo-Guerrero, Francisco Fernandes, Sérgio Barroso Gonçalves, Miguel Tavares Silva, Daniel Simões Lopes, José Javier Serrano Olmedo

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Human Activity Recognition (HAR) is having a growing impact in creating new applications and is responsible for emerging new technologies. Also, the use of wearable sensors is an important key to exploring the human body's behavior when performing activities. Hence, the use of these dispositive is less invasive and the person is more comfortable. In this study, a database that includes three activities is used. The activities were acquired from inertial measurement unit sensors (IMU) and motion capture systems (MOCAP). The main objective is differentiating the performance from four Deep Learning (DL) models: Deep Neural Network (DNN), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) and hybrid model Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM), when considering acceleration, velocity and position and evaluate if integrating the IMU acceleration to obtain velocity and position represent an increment in performance when it works as input to the DL models. Moreover, compared with the same type of data provided by the MOCAP system. Despite the acceleration data is cleaned when integrating, results show a minimal increase in accuracy for the integrated signals.

Keywords: HAR, IMU, MOCAP, acceleration, velocity, position, feature maps

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Authors: Henni Mansour Abdelwaheb

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This study discusses the design of a bounded position/force feedback controller developed to ensure position and force tracking for bilateral teleoperation arms operating with variable delay, and actuator saturation. Also, an adaptive robust Radial Basis Function (RBF) neural network is used to estimate the environment torque. The parameters of the environment torque are then sent from the slave site to the master site as a non-power signal to avoid passivity problems. Moreover, a nonlinear function is applied to each controller term as a smooth saturation function, providing a bounded control signal and preserving the system’s actuators. Lastly, the Lyapunov approach demonstrates the global stability of the controlled system, and numerical experiment results further confirm the validity of the presented strategy.

Keywords: teleoperation manipulators system, time-varying delay, actuator saturation, adaptive robust rbf neural network approximation, uncertainties

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Authors: Onyedikachi Ulelu, Andrew P. Longstaff, Simon Fletcher, Simon Parkinson

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The widespread interest in 'Industry 4.0' or 'digital manufacturing' has led to significant research requiring the acquisition of data from sensors, instruments, and machine signals. In-depth research then identifies methods of analysis of the massive amounts of data generated before and during manufacture to solve a particular problem. The ultimate goal is for industrial Internet of Things (IIoT) data to be processed automatically to assist with either visualisation or autonomous system decision-making. However, the collection and processing of data in an industrial environment come with a cost. Little research has been undertaken on how to specify optimally what data to capture, transmit, process, and store at various levels of an edge-cloud network. The first step in this specification is to categorise IIoT data for efficient and effective use. This paper proposes the required attributes and classification to take manufacturing digital data from various sources to determine the most suitable location for data processing on the edge-cloud network. The proposed classification framework will minimise overhead in terms of network bandwidth/cost and processing time of machine tool data via efficient decision making on which dataset should be processed at the ‘edge’ and what to send to a remote server (cloud). A fast-and-frugal heuristic method is implemented for this decision-making. The framework is tested using case studies from industrial machine tools for machine productivity and maintenance.

Keywords: data classification, decision making, edge computing, industrial IoT, industry 4.0

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Authors: Ahmad Fauzi Makarim, Okkie Puspitorini, Hani'ah Mahmudah, Nur Adi Siswandari, Ari Wijayanti

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As a maritime country, Indonesia needs a solution in maritime connectivity which can assist the maritime communication system which including communication from harbor to the ship or ship to ship. The needs of many application services for maritime communication, whether for safety reasons until voyage service to help the process of voyage activity needs connection with a high bandwith. To support the government efforts in handling that kind of problem, a research is conducted in maritime communication issue by applying the new developed technology in Indonesia, namely IEEE 802.11. In this research, 3 outdoor WiFi devices are used in which have a frequency of 5.8 GHz. Maritime of Tanjung Perak harbor in Surabaya until Karang Jamuang Island are used as the location of the research with defining permission of ship node spreading by Navigation District Class 1. That maritime area formed by state 1 and state 2 areas which are the narrow area with average wave height of 0.7 meter based on the data from BMKG S urabaya. After that, wave height used as one of the parameters which are used in analyzing characteristic of signal propagation at sea surface, so it can be determined on the coverage area of transmitter system. In this research has been used three samples of outdoor wifi, there is the coverage of device A can be determined about 2256 meter, device B 4000 meter, and device C 1174 meter. Then to analyze of network connectivity for the ship to ship is used AODV routing algorithm system based on the value of the power transmit was smallest of all nodes within the transmitter coverage.

Keywords: maritime of Indonesia, maritime communications, outdoor wifi, coverage, AODV

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Authors: Jia Xin Low, Keng Wah Choo

Abstract:

This paper presents an automatic normal and abnormal heart sound classification model developed based on deep learning algorithm. MITHSDB heart sounds datasets obtained from the 2016 PhysioNet/Computing in Cardiology Challenge database were used in this research with the assumption that the electrocardiograms (ECG) were recorded simultaneously with the heart sounds (phonocardiogram, PCG). The PCG time series are segmented per heart beat, and each sub-segment is converted to form a square intensity matrix, and classified using convolutional neural network (CNN) models. This approach removes the need to provide classification features for the supervised machine learning algorithm. Instead, the features are determined automatically through training, from the time series provided. The result proves that the prediction model is able to provide reasonable and comparable classification accuracy despite simple implementation. This approach can be used for real-time classification of heart sounds in Internet of Medical Things (IoMT), e.g. remote monitoring applications of PCG signal.

Keywords: convolutional neural network, discrete wavelet transform, deep learning, heart sound classification

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Authors: Sheela Tiwari, R. Naresh, R. Jha

Abstract:

The paper presents an investigation into the effect of neural network predictive control of UPFC on the transient stability performance of a multi-machine power system. The proposed controller consists of a neural network model of the test system. This model is used to predict the future control inputs using the damped Gauss-Newton method which employs ‘backtracking’ as the line search method for step selection. The benchmark 2 area, 4 machine system that mimics the behavior of large power systems is taken as the test system for the study and is subjected to three phase short circuit faults at different locations over a wide range of operating conditions. The simulation results clearly establish the robustness of the proposed controller to the fault location, an increase in the critical clearing time for the circuit breakers and an improved damping of the power oscillations as compared to the conventional PI controller.

Keywords: identification, neural networks, predictive control, transient stability, UPFC

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Authors: Farshid Hajati, Federico Girosi, Shima Ghassempour

Abstract:

We present a Deep Belief Network (DBN) method for clustering health trajectories. Deep Belief Network (DBN) is a deep architecture that consists of a stack of Restricted Boltzmann Machines (RBM). In a deep architecture, each layer learns more complex features than the past layers. The proposed method depends on DBN in clustering without using back propagation learning algorithm. The proposed DBN has a better a performance compared to the deep neural network due the initialization of the connecting weights. We use Contrastive Divergence (CD) method for training the RBMs which increases the performance of the network. The performance of the proposed method is evaluated extensively on the Health and Retirement Study (HRS) database. The University of Michigan Health and Retirement Study (HRS) is a nationally representative longitudinal study that has surveyed more than 27,000 elderly and near-elderly Americans since its inception in 1992. Participants are interviewed every two years and they collect data on physical and mental health, insurance coverage, financial status, family support systems, labor market status, and retirement planning. The dataset is publicly available and we use the RAND HRS version L, which is easy to use and cleaned up version of the data. The size of sample data set is 268 and the length of the trajectories is equal to 10. The trajectories do not stop when the patient dies and represent 10 different interviews of live patients. Compared to the state-of-the-art benchmarks, the experimental results show the effectiveness and superiority of the proposed method in clustering health trajectories.

Keywords: health trajectory, clustering, deep learning, DBN

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