Search results for: wireless sensors networks

Authors: Angeliki Peponi, Paulo Morgado, Jorge Trindade

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Artificial Neural Networks (ANNs) and Geographic Information Systems (GIS) are applied as a robust tool for modeling and forecasting the erosion changes in Costa Caparica, Lisbon, Portugal, for 2021. ANNs present noteworthy advantages compared with other methods used for prediction and decision making in urban coastal areas. Multilayer perceptron type of ANNs was used. Sensitivity analysis was conducted on natural and social forces and dynamic relations in the dune-beach system of the study area. Variations in network’s parameters were performed in order to select the optimum topology of the network. The developed methodology appears fitted to reality; however further steps would make it better suited.

Keywords: artificial neural networks, backpropagation, coastal urban zones, erosion prediction

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Authors: Caner Koc, Dilara Gerdan Koc, Mustafa Vatandas

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The fundamental components of autonomous agricultural robot design, such as having a working understanding of coordinates, correctly constructing the desired route, and sensing environmental elements, are the most important. A variety of sensors, hardware, and software are employed by agricultural robots to find these systems.These enable the fully automated driving system of an autonomous vehicle to simulate how a human-driven vehicle would respond to changing environmental conditions. To calculate the vehicle's motion trajectory using data from the sensors, this automation system typically consists of a sophisticated software architecture based on object detection and driving decisions. In this study, the software architecture of an autonomous agricultural vehicle is compared to the trajectory planning techniques.

Keywords: agriculture 5.0, computational intelligence, motion planning, trajectory planning

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Authors: Papakonstantinou Apostolos, Argyrios Moustakas, Panagiotis Zervos, Dimitrios Stefanakis, Manolis Tsapakis, Nektarios Spyridakis, Mary Paspaliari, Christos Kontos, Antonis Legakis, Sarantis Houzouris, Konstantinos Topouzelis

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For years unmanned and remotely operated robots have been used as tools in industry research and education. The rapid development and miniaturization of sensors that can be attached to remotely operated vehicles in recent years allowed industry leaders and researchers to utilize them as an affordable means for data acquisition in air, land, and sea. Despite the recent developments in the ground and unmanned airborne vehicles, a small number of Unmanned Surface Vehicle (USV) platforms are targeted for mapping and monitoring environmental parameters for research and industry purposes. The ARGO project is developed an open-design USV equipped with multi-level control hardware architecture and state-of-the-art sensors and payloads for the autonomous monitoring of environmental parameters in large sea areas. The proposed USV is a catamaran-type USV controlled over a wireless radio link (5G) for long-range mapping capabilities and control for a ground-based control station. The ARGO USV has a propulsion control using 2x fully redundant electric trolling motors with active vector thrust for omnidirectional movement, navigation with opensource autopilot system with high accuracy GNSS device, and communication with the 2.4Ghz digital link able to provide 20km of Line of Sight (Los) range distance. The 3-meter dual hull design and composite structure offer well above 80kg of usable payload capacity. Furthermore, sun and friction energy harvesting methods provide clean energy to the propulsion system. The design is highly modular, where each component or payload can be replaced or modified according to the desired task (industrial or research). The system can be equipped with Multiparameter Sonde, measuring up to 20 water parameters simultaneously, such as conductivity, salinity, turbidity, dissolved oxygen, etc. Furthermore, a high-end multibeam echo sounder can be installed in a specific boat datum for shallow water high-resolution seabed mapping. The system is designed to operate in the Aegean Sea. The developed USV is planned to be utilized as a system for autonomous data acquisition, mapping, and monitoring bathymetry and various environmental parameters. ARGO USV can operate in small or large ports with high maneuverability and endurance to map large geographical extends at sea. The system presents state of the art solutions in the following areas i) the on-board/real-time data processing/analysis capabilities, ii) the energy-independent and environmentally friendly platform entirely made using the latest aeronautical and marine materials, iii) the integration of advanced technology sensors, all in one system (photogrammetric and radiometric footprint, as well as its connection with various environmental and inertial sensors) and iv) the information management application. The ARGO web-based application enables the system to depict the results of the data acquisition process in near real-time. All the recorded environmental variables and indices are presented, allowing users to remotely access all the raw and processed information using the implemented web-based GIS application.

Keywords: monitor marine environment, unmanned surface vehicle, mapping bythometry, sea environmental monitoring

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Authors: Oluwatosin Olofintoye, Josiah Adeyemo, Gbemileke Shomade

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The relationship between rainfall and runoff is an important issue in surface water hydrology therefore the understanding and development of accurate rainfall-runoff models and their applications in water resources planning, management and operation are of paramount importance in hydrological studies. This paper reviews some of the previous works on the rainfall-runoff process modeling. The hydrologic applications of conceptual models and artificial neural networks (ANNs) for the precipitation-runoff process modeling were studied. Gradient training methods such as error back-propagation (BP) and evolutionary algorithms (EAs) are discussed in relation to the training of artificial neural networks and it is shown that application of EAs to artificial neural networks training could be an alternative to other training methods. Therefore, further research interest to exploit the abundant expert knowledge in the area of artificial intelligence for the solution of hydrologic and water resources planning and management problems is needed.

Keywords: artificial intelligence, artificial neural networks, evolutionary algorithms, gradient training method, rainfall-runoff model

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Authors: Arjmand Khaliq, Pemra Sohaib

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There is increasing trend of use of smart devices in the present age. The growth of computing techniques and advancement in hardware has also brought the use of sensors and smart devices to a high degree during the course of time. So use of smart devices for control, management communication and optimization has become very popular. This paper gives proposed methodology which involves sensing and switching unite for load, two way communications between utility company and smart phones of consumers using cellular techniques and price signaling resulting active participation of user in energy management .The goal of this proposed control methodology is active participation of user in energy management with accommodation of renewable energy resource. This will provide load adjustment according to consumer’s choice, increased security and reliability for consumer, switching of load according to consumer need and monitoring and management of energy.

Keywords: cellular networks, energy management, renewable energy source, smart grid technology

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Authors: Asma Ben Yaghlane, Mohamed Naceur Azaiez, Mehdi Mrad

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We investigate the large scale of networks in the context of network survivability under attack. We use appropriate techniques to evaluate and the attacker-based- and the defender-based-network survivability. The attacker is unaware of the operated links by the defender. Each attacked link has some pre-specified probability to be disconnected. The defender choice is so that to maximize the chance of successfully sending the flow to the destination node. The attacker however will select the cut-set with the highest chance to be disabled in order to partition the network. Moreover, we extend the problem to the case of selecting the best p paths to operate by the defender and the best k cut-sets to target by the attacker, for arbitrary integers p,k > 1. We investigate some variations of the problem and suggest polynomial-time solutions.

Keywords: defense/attack strategies, large scale, networks, partitioning a network

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Authors: David Tena-Gago, Jose M. Alcaraz Calero, Qi Wang

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The evolution and integration of automated vehicles have become more and more tangible in recent years. State-of-the-art technological advances in the field of camera-based Artificial Intelligence (AI) and computer vision greatly favor the performance and reliability of the Advanced Driver Assistance System (ADAS), leading to a greater knowledge of vehicular operation and resembling human behavior. However, the exclusive use of this technology still seems insufficient to control vehicular operation at 100%. To reveal the degree of accuracy of the current camera-based automated driving AI modules, this paper studies the structure and behavior of one of the main solutions in a controlled testing environment. The results obtained clearly outline the lack of reliability when using exclusively the AI model in the perception stage, thereby entailing using additional complementary sensors to improve its safety and performance.

Keywords: accuracy assessment, AI-driven mobility, artificial intelligence, automated vehicles

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Authors: Babak Mohajeri

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In recent years, the conventional business model of ownership has been changed towards accessibility in a variety of markets. Two trends can be observed in the evolution of this rental-like business model. Firstly, the technological development that enables the emergence of new business models. These new business models increasingly become agile and flexible. For example Spotify, an online music stream company provides consumers access to over millions of music tracks, conveniently through the smartphone, tablet or computer. Similarly, Car2Go, the car sharing company accesses its members with flexible and nearby sharing cars. The second trend is the increasing communication and connections via social networks. This trend enables a shift to peer-to-peer accessibility based business models. Conventionally, companies provide access for their customers to own companies products or services. In peer-to-peer model, nonetheless, companies facilitate access and connection across their customers to use other customers owned property or skills, competencies or services .The is so-called the sharing economy business model. The aim of this study is to investigate into a new and emerging type of the sharing economy model in which role of customers and service providers may dramatically change. This new model is called Collaborative Service Networks. We propose a mechanism for Collaborative Service Networks business model. Uber and Airbnb, two successful growing companies, have been selected for our case studies and their business models are analyzed. Finally, we study the emergence of the collaborative service networks in the manufacturing domain. Our finding results to a new manufacturing paradigm called social manufacturing.

Keywords: sharing economy, collaborative service networks, social manufacturing, manufacturing development

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Authors: Mustafa Kamoona, Mohamed El-Sharkawy

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Although the latest Wireless Local Area Network technology Wi-Fi 802.11i standard addresses many of the security weaknesses of the antecedent Wired Equivalent Privacy (WEP) protocol, there are still scenarios where the network security are still vulnerable. The first security model that 802.11i offers is the Personal model which is very cheap and simple to install and maintain, yet it uses a Pre Shared Key (PSK) and thus has a low to medium security level. The second model that 802.11i provide is the Enterprise model which is highly secured but much more expensive and difficult to install/maintain and requires the installation and maintenance of an authentication server that will handle the authentication and key management for the wireless network. A central issue with the personal model is that the PSK needs to be shared with all the devices that are connected to the specific Wi-Fi network. This pre-shared key, unless changed regularly, can be cracked using offline dictionary attacks within a matter of hours. The key is burdensome to change in all the connected devices manually unless there is some kind of algorithm that coordinate this PSK update. The key idea of this paper is to propose a new algorithm that proactively and effectively coordinates the pre-shared key generation, management, and distribution in the cheap WPA/WPA2 personal security model using only a DD-WRT router.

Keywords: Wi-Fi, WPS, TLS, DD-WRT

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Authors: Wheyming Tina Song, Chi-Hao Hong, Peisyuan Lin

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An important performance measure for stochastic manufacturing networks is the system reliability, defined as the probability that the production output meets or exceeds a specified demand. The system parameters include the capacity of each workstation and numbers of the conforming parts produced in each workstation. We establish that eighteen archival publications, containing twenty-one examples, provide incorrect values of the system reliability. The author recently published the Song Rule, which provides the correct analytical system-reliability value; it is, however, computationally inefficient for large networks. In this paper, we use Monte Carlo simulation (implemented in C and Flexsim) to provide estimates for the above-mentioned twenty-one examples. The simulation estimates are consistent with the analytical solution for small networks but is computationally efficient for large networks. We argue here for three advantages of Monte Carlo simulation: (1) understanding stochastic systems, (2) validating analytical results, and (3) providing estimates even when analytical and numerical approaches are overly expensive in computation. Monte Carlo simulation could have detected the published analysis errors.

Keywords: Monte Carlo simulation, analytical results, leading digit rule, standard error

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Authors: Robert Poenaru, Mihail Pleşa

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In this work, a solution that addresses the protection of pre-trained neural networks is developed: Singularization. This method involves applying permutations to the weight matrices of a pre-trained model, introducing a form of structured noise that obscures the original model’s architecture. These permutations make it difficult for an attacker to reconstruct the original model, even if the permuted weights are obtained. Experimental benchmarks indicate that the application of singularization has a profound impact on model performance, often degrading it to the point where retraining from scratch becomes necessary to recover functionality, which is particularly effective for securing intellectual property in neural networks. Moreover, unlike other approaches, singularization is lightweight and computationally efficient, which makes it well suited for resource-constrained environments. Our experiments also demonstrate that this technique performs efficiently in various image classification tasks, highlighting its broad applicability and practicality in real-world scenarios.

Keywords: machine learning, ANE, CNN, security

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Authors: N. Nalini, Lokesh B. Bhajantri

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In Distributed Sensor Networks, the sensor nodes are prone to failure due to energy depletion and some other reasons. In this regard, fault tolerance of network is essential in distributed sensor environment. Energy efficiency, network or topology control and fault-tolerance are the most important issues in the development of next-generation Distributed Sensor Networks (DSNs). This paper proposes a node fault detection and recovery using Genetic Algorithm (GA) in DSN when some of the sensor nodes are faulty. The main objective of this work is to provide fault tolerance mechanism which is energy efficient and responsive to network using GA, which is used to detect the faulty nodes in the network based on the energy depletion of node and link failure between nodes. The proposed fault detection model is used to detect faults at node level and network level faults (link failure and packet error). Finally, the performance parameters for the proposed scheme are evaluated.

Keywords: distributed sensor networks, genetic algorithm, fault detection and recovery, information technology

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Authors: Ch. Sridevi, A. Chalapathi Rao, P. Srinivasulu

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The location accuracy of data products is a critical parameter in assessing the geometric performance of satellite sensors. This study focuses on reliability analysis of onboard sensors to evaluate their performance in terms of location accuracy performance over time. The analysis utilizes field failure data and employs the weibull distribution to determine the reliability and in turn to understand the improvements or degradations over a period of time. The analysis begins by scrutinizing the location accuracy error which is the root mean square (RMS) error of differences between ground control point coordinates observed on the product and the map and identifying the failure data with reference to time. A significant challenge in this study is to thoroughly analyze the possibility of an infant mortality phase in the data. To address this, the Weibull distribution is utilized to determine if the data exhibits an infant stage or if it has transitioned into the operational phase. The shape parameter beta plays a crucial role in identifying this stage. Additionally, determining the exact start of the operational phase and the end of the infant stage poses another challenge as it is crucial to eliminate residual infant mortality or wear-out from the model, as it can significantly increase the total failure rate. To address this, an approach utilizing the well-established statistical Laplace test is applied to infer the behavior of sensors and to accurately ascertain the duration of different phases in the lifetime and the time required for stabilization. This approach also helps in understanding if the bathtub curve model, which accounts for the different phases in the lifetime of a product, is appropriate for the data and whether the thresholds for the infant period and wear-out phase are accurately estimated by validating the data in individual phases with Weibull distribution curve fitting analysis. Once the operational phase is determined, reliability is assessed using Weibull analysis. This analysis not only provides insights into the reliability of individual sensors with regards to location accuracy over the required period of time, but also establishes a model that can be applied to automate similar analyses for various sensors and parameters using field failure data. Furthermore, the identification of the best-performing sensor through this analysis serves as a benchmark for future missions and designs, ensuring continuous improvement in sensor performance and reliability. Overall, this study provides a methodology to accurately determine the duration of different phases in the life data of individual sensors. It enables an assessment of the time required for stabilization and provides insights into the reliability during the operational phase and the commencement of the wear-out phase. By employing this methodology, designers can make informed decisions regarding sensor performance with regards to location accuracy, contributing to enhanced accuracy in satellite-based applications.

Keywords: bathtub curve, geometric performance, Laplace test, location accuracy, reliability analysis, Weibull analysis

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Authors: Ramazan I. Kadiev, Arcady Ponosov

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Mathematical models of gene regulatory networks can in many cases be described by ordinary differential equations with switching nonlinearities, where the initial value problem is ill-posed. Several regularization methods are known in the case of deterministic networks, but the presence of stochastic noise leads to several technical difficulties. In the presentation, it is proposed to apply the methods of the stochastic singular perturbation theory going back to Yu. Kabanov and Yu. Pergamentshchikov. This approach is used to regularize the above ill-posed problem, which, e.g., makes it possible to design stable numerical schemes. Several examples are provided in the presentation, which support the efficiency of the suggested analysis. The method can also be of interest in other fields of biomathematics, where differential equations contain switchings, e.g., in neural field models.

Keywords: ill-posed problems, singular perturbation analysis, stochastic differential equations, switching nonlinearities

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Authors: L. Parisi

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Kinematic data wisely correlate vector quantities in space to scalar parameters in time to assess the degree of symmetry between the intact limb and the amputated limb with respect to a normal model derived from the gait of control group participants. Furthermore, these particular data allow a doctor to preliminarily evaluate the usefulness of a certain rehabilitation therapy. Kinetic curves allow the analysis of ground reaction forces (GRFs) to assess the appropriateness of human motion. Electromyography (EMG) allows the analysis of the fundamental lower limb force contributions to quantify the level of gait asymmetry. However, the use of this technological tool is expensive and requires patient’s hospitalization. This research work suggests overcoming the above limitations by applying artificial neural networks.

Keywords: kinetics, kinematics, cyclograms, neural networks, transtibial amputation

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Authors: Sayed Isahaq Hossain, K. T. Chang, Moustapha Ndour

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Inference of the phreatic line through earth dams is of paramount importance because it could be directly associated with piping phenomena which may lead to the dam failure. Normally in the field, the instrumentations such as ‘diver’ and ‘standpipe’ are to be used to identify the seepage conditions which only provide point data with a fair amount of interpolation or assumption. Here in this paper, we employed high-resolution fiber optic cable sensors (FOCS) based on Raman Scattering in order to obtain a very accurate phreatic line and seepage profile. Unlike the above-mention devices which pinpoint the water level location, this kind of Distributed Fiber Optics Sensing gives us more reliable information due to its inherent characteristics of continuous measurement.

Keywords: standpipe, diver, FOCS, monitoring, Raman scattering

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Authors: Asma Ben Yaghlane, Mohamed Naceur Azaiez

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We consider the problem of attacks on networks. We define the concept of system survivability in networks in the presence of intelligent threats. Our setting of the problem assumes a flow to be sent from one source node to a destination node. The attacker attempts to disable the network by preventing the flow to reach its destination while the defender attempts to identify the best path-set to use to maximize the chance of arrival of the flow to the destination node. Our concept is shown to be different from the classical concept of network reliability. We distinguish two types of network survivability related to the defender and to the attacker of the network, respectively. We prove that the defender-based-network survivability plays the role of a lower bound while the attacker-based-network survivability plays the role of an upper bound of network reliability. We also prove that both concepts almost never agree nor coincide with network reliability. Moreover, we use the shortest-path problem to determine the defender-based-network survivability and the min-cut problem to determine the attacker-based-network survivability. We extend the problem to a variety of models including the minimum-spanning-tree problem and the multiple source-/destination-network problems.

Keywords: defense/attack strategies, information, networks, reliability, survivability

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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: Chun-Ming Huang, Yi-Jun Liu, Yi-Jie Hsieh, Jin-Ju Chue, Wei-Lin Lai, Chun-Yu Chen, Chih-Chyau Yang, Chien-Ming Wu

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The market of wearable devices has been growing rapidly in two years. The integration of sensors and wearable devices has become the trend of the next technology products. Thus, the academics and industries are eager to cultivate talented persons in sensing technology. Currently, academic and industries have more and more demands on the integrations of versatile sensors and applications, especially for the teams who focus on the development of sensor circuit architectures. These teams tape-out many MEMs sensors chips through the chip fabrication service from National Chip Implementation Center (CIC). However, most of these teams are only able to focus on the circuit design of MEMs sensors; they lack the key support of further system demonstration. This paper follows the CIC’s main mission of promoting the chip/system advanced design technology and aims to establish the environments of the modularized sensing system platform and the system design flow with the measurement and calibration technology. These developed environments are used to support these research teams and help academically advanced sensor designs to perform the system demonstration. Thus, the research groups can promote and transfer their advanced sensor designs to industrial and further derive the industrial economic values. In this paper, the modularized sensing platform is proposed to enable the system demonstration for advanced sensor chip design. The environment of sensor measurement and calibration is established for academic to achieve an accurate sensor result. Two reference sensor designs cooperated with the modularized sensing platform are given to show the sensing system integration and demonstration. These developed environments and platforms are currently provided to academics in Taiwan, and so that the academics can obtain a better environment to perform the system demonstration and improve the research and teaching quality.

Keywords: modularized sensing platform, sensor design and calibration, sensor system, sensor system design flow

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Authors: S. Cobos-Guzman

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This paper presents the results of a mechatronic design based on a 4-wheel omnidirectional mobile robot that can be used in indoor logistic applications. The low-level control has been selected using two open-source hardware (Raspberry Pi 3 Model B+ and Arduino Mega 2560) that control four industrial motors, four ultrasound sensors, four optical encoders, a vision system of two cameras, and a Hokuyo URG-04LX-UG01 laser scanner. Moreover, the system is powered with a lithium battery that can supply 24 V DC and a maximum current-hour of 20Ah.The Robot Operating System (ROS) has been implemented in the Raspberry Pi and the performance is evaluated with the selection of the sensors and hardware selected. The mechatronic system is evaluated and proposed safe modes of power distribution for controlling all the electronic devices based on different tests. Therefore, based on different performance results, some recommendations are indicated for using the Raspberry Pi and Arduino in terms of power, communication, and distribution of control for different devices. According to these recommendations, the selection of sensors is distributed in both real-time controllers (Arduino and Raspberry Pi). On the other hand, the drivers of the cameras have been implemented in Linux and a python program has been implemented to access the cameras. These cameras will be used for implementing a deep learning algorithm to recognize people and objects. In this way, the level of intelligence can be increased in combination with the maps that can be obtained from the laser scanner.

Keywords: autonomous, indoor robot, mechatronic, omnidirectional robot

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Authors: R. Schweighoffer, N. Nagy, E. Reeves, B. Liebig

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Due to aging populations, the need for seamless palliative care provision is of central interest for western societies. An essential aspect of palliative care delivery is the quality of collaboration amongst palliative care providers. Therefore, the current research is based on Bainbridge’s conceptual framework, which provides an outline for the evaluation of palliative care provision. This study is the first one to investigate the predictive validity of spatial distribution on the quantity of interaction amongst various palliative care providers. Furthermore, based on the familiarity principle, we examine whether the extent of collaboration influences the perceived quality of collaboration among palliative care providers in urban versus rural areas of Switzerland. Based on a population-representative survey of Swiss palliative care providers, the results of the current study show that professionals in densely populated areas report higher absolute numbers of interactions and are more satisfied with their collaborative practice. This indicates that palliative care providers who work in urban areas are better embedded into networks than their counterparts in more rural areas. The findings are especially important, considering that efficient collaboration is a prerequisite to achieve satisfactory patient outcomes. Conclusively, measures should be taken to foster collaboration in weakly interconnected palliative care networks.

Keywords: collaboration, healthcare networks, palliative care, Switzerland

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

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

Keywords: causal realtion extraction, relation extracton, convolutional neural network, text representation

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Authors: Claudia Camargo Wilson, Enrique Javier de la Vega Bustillos, Jesús Everardo Olguín Tiznado, Juan Andrés López Barreras, Sandra K. Enriquez

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This study focuses on the application of sensory thermography, as a non-invasive method to evaluate the musculoskeletal injuries that industry workers performing Highly Repetitive Movements (HRM) may acquire. It was made at a wireless company having the target of analyze temperatures in worker’s wrists, elbows and shoulders in workstations during their activities, this thru sensorial thermography with the goal of detecting maximum temperatures (Tmax) that could indicate possible injuries. The tests were applied during 3 hours for only 2 workers that work in workstations where there’s been the highest index of injuries and accidents. We were made comparisons for each part of the body that were study for both because of the similitude between the activities of the workstations; they were requiring both an immediate evaluation. The Tmax was recorder during the test of the worker 2, in the left wrist, reaching a temperature of 35.088ºC and with a maximum increase of 1.856°C.

Keywords: thermography, maximum temperaturas (Tmax), highly repetitive movements (HRM), operator

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Authors: Sanaz Seraj, Shohre Rouhani

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Recently, graphene has gained much attention because of its unique optical, mechanical, electrical, and thermal properties. Graphene has been used as a key material in the technological applications in various areas such as sensors, drug delivery, super capacitors, transparent conductor, and solar cell. It has a superior quenching efficiency for various fluorophores. Based on these unique properties, the optical sensors with graphene materials as the energy acceptors have demonstrated great success in recent years. During quenching, the emission of a fluorophore is perturbed by a quencher which can be a substrate or biomolecule, and due to this phenomenon, fluorophore-quencher has been used for selective detection of target molecules. Among fluorescence dyes, 1,8-naphthalimide is well known for its typical intramolecular charge transfer (ICT) and photo-induced charge transfer (PET) fluorophore, strong absorption and emission in the visible region, high photo stability, and large Stokes shift. Derivatives of 1,8-naphthalimides have found applications in some areas, especially fluorescence sensors. Herein, the fluorescence quenching of graphene oxide has been carried out on a naphthalimide dye as a fluorescent probe model. The quenching ability of graphene oxide on naphthalimide dye was studied by UV-VIS and fluorescence spectroscopy. This study showed that graphene is an efficient quencher for fluorescent dyes. Therefore, it can be used as a suitable candidate sensing platform. To the best of our knowledge, studies on the quenching and absorption of naphthalimide dyes by graphene oxide are rare.

Keywords: fluorescence, graphene oxide, naphthalimide dye, quenching

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Authors: Thiago Spilborghs Bueno Meyer, Plinio Thomaz Aquino Junior

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

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

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Authors: Ahmed Al-Adaileh, Souheil Khaddaj

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Outcomes of exhaustion of natural resources started influencing each spirit on this planet. Energy is an essential factor in this aspect. To restore the circumstance to the appropriate track, all attempts must focus on two fundamental branches: producing electricity from clean and renewable reserves and decreasing the overall unnecessary consumption of energy. The focal point of this paper will be on lessening the power consumption in the household's segment. This paper is an attempt to give a clear understanding of a framework called Reduction of Energy Consumption in Household Sector (RECHS) and how it should help householders to reduce their power consumption by substituting their household appliances, turning-off the appliances when stand-by modus is detected, and scheduling their appliances operation periods. Technically, the framework depends on utilizing Z-Wave compatible plug-ins which will be connected to the usual house devices to gauge and control them remotely and semi-automatically. The suggested framework underpins numerous quality characteristics, for example, integrability, scalability, security and adaptability.

Keywords: smart energy management systems, internet of things, wireless mesh networks, microservices, cloud computing, big data

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Authors: Aymen Sassi

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The equipment of vehicles with wireless communication capabilities is expected to be the key to the evolution to next generation intelligent transportation systems (ITS). The IEEE community has been continuously working on the development of an efficient vehicular communication protocol for the enhancement of Wireless Access in Vehicular Environment (WAVE). Vehicular communication systems, called V2X, support vehicle to vehicle (V2V) and vehicle to infrastructure (V2I) communications. The efficiency of such communication systems depends on several factors, among which the surrounding environment and mobility are prominent. Accordingly, this study focuses on the evaluation of the real performance of vehicular communication with special focus on the effects of the real environment and mobility on V2X communication. It starts by identifying the real maximum range that such communication can support and then evaluates V2I and V2V performances. The Arada LocoMate OBU transmission system was used to test and evaluate the impact of the transmission range in V2X communication. The evaluation of V2I and V2V communication takes the real effects of low and high mobility on transmission into account.

Keywords: IEEE 802.11p, V2I, V2X, mobility, PLR, Arada LocoMate OBU, maximum range

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Authors: Ghobad Gorji, Hasan Golabi

Abstract:

The goal of this paper is to describe the design of an ultra-wideband (UWB) system that is optimized for the low-rate wireless personal area network application. To this aim, we propose a system based on direct chaotic communication (DCC) technology. Based on this system, a 2-GHz wide chaotic signal is directly generated into the lower band of the UWB spectrum, i.e., 3.1–5.1 GHz. For this system, two simple modulation schemes, namely chaotic on-off keying (COOK) and differential chaos shift keying (DCSK), were studied before, and their performance was evaluated. We propose a modulation scheme, namely Double DCSK, to improve the performance of UWB DCC. Different characteristics of these systems, with Monte Carlo simulations based on the Additive White Gaussian Noise (AWGN) and the IEEE 802.15.4a standard channel models, are compared.

Keywords: UWB, DCC, IEEE 802.15.4a, COOK, DCSK

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Authors: Shahed Vahedi, Mohd Salmi Md Noorani

Abstract:

This paper investigates cluster synchronization phenomena between community networks. We focus on the situation where a variety of dynamics occur in the clusters. In particular, we show that different synchronization states simultaneously occur between the networks. The controller is designed having an adaptive control gain, and theoretical results are derived via Lyapunov stability. Simulations on well-known dynamical systems are provided to elucidate our results.

Keywords: cluster synchronization, adaptive control, community network, simulation

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Authors: Nada Souissi, Mourad Mroua

Abstract:

The main purpose of this study is to examine the interaction between financial asset volatility, economic factors and investor's behavioral indicators related to both the company's and the markets stocks for the period from January 2000 to January2020. Using multiple linear regression and Bayesian Networks modeling, results show a positive and negative relationship between investor's psychology index, economic factors and predicted stock market return. We reveal that the application of the Bayesian Discrete Network contributes to identify the different cause and effect relationships between all economic, financial variables and psychology index.

Keywords: Financial asset return predictability, Economic factors, Investor's psychology index, Bayesian approach, Probabilistic networks, Parametric learning

Procedia PDF Downloads 149