Search results for: mesh network

Authors: Shathya Duobiene, Gediminas Račiukaitis

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Solar-powered wireless sensor nodes work best when they operate continuously with minimal energy consumption. Wireless Sensor Networks (WSNs) are a new technology opens up wide studies, and advancements are expanding the prevalence of numerous monitoring applications and real-time aid for environments. The Selective Surface Activation Induced by Laser (SSAIL) technology is an exciting development that gives the design of WSNs more flexibility in terms of their shape, dimensions, and materials. This research work proposes a methodology for using SSAIL technology for forest ecosystem monitoring by wireless sensor networks. WSN monitoring the temperature and humidity were deployed, and their architectures are discussed. The paper presents the experimental outcomes of deploying newly built sensor nodes in forested areas. Finally, a practical method is offered to extend the WSN's lifespan and ensure its continued operation. When operational, the node is independent of the base station's power supply and uses only as much energy as necessary to sense and transmit data.

Keywords: internet of things (IoT), wireless sensor network, sensor nodes, SSAIL technology, forest ecosystem

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Authors: Leah Ning

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This paper discusses the development of a machine learning algorithm that accurately detects metastatic breast cancer (cancer has spread elsewhere from its origin part) in selected images that come from pathology scans of lymph node sections. Being able to develop an accurate artificial intelligence (AI) algorithm would help significantly in breast cancer diagnosis since manual examination of lymph node scans is both tedious and oftentimes highly subjective. The usage of AI in the diagnosis process provides a much more straightforward, reliable, and efficient method for medical professionals and would enable faster diagnosis and, therefore, more immediate treatment. The overall approach used was to train a convolution neural network (CNN) based on a set of pathology scan data and use the trained model to binarily classify if a new scan were benign or malignant, outputting a 0 or a 1, respectively. The final model’s prediction accuracy is very high, with 100% for the train set and over 70% for the test set. Being able to have such high accuracy using an AI model is monumental in regard to medical pathology and cancer detection. Having AI as a new tool capable of quick detection will significantly help medical professionals and patients suffering from cancer.

Keywords: breast cancer detection, AI, machine learning, algorithm

Procedia PDF Downloads 93

Authors: Cinthia Campoverde, Mateo Benavidez, Victor Arias, Milton Torres

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This article focuses on the 3D electromagnetic mapping of the intensity of the signal received by a mobile antenna within the open areas of the Department of Livestock of the Escuela Superior Politecnica de Chimborazo (ESPOCH), located in the city of Riobamba, Ecuador. The transmitting antenna belongs to the mobile telephone company ”TUENTI”, and is analyzed in the 2 GHz bands, operating at a frequency of 1940 MHz, using Long Term Evolution (LTE). Power signal strength data in the area were measured empirically using the ”Network Cell Info” application. A total of 170 samples were collected, distributed in 19 concentric circles around the base station. 3 campaigns were carried out at the same time, with similar traffic, and average values were obtained at each point, which varies between -65.33 dBm to -101.67 dBm. Also, the two virtualization software used are Sketchup and Unreal. Finally, the virtualized environment was visualized through virtual reality using Oculus 3D glasses, where the power levels are displayed according to a range of powers.

Keywords: reception power, LTE technology, virtualization, virtual reality, power levels

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Authors: Shayan Mohajer Hamidi

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Federated Learning (FL) has emerged as a promising paradigm for training machine learning models on distributed data without the need for centralized data aggregation. In the realm of wireless communications, FL has the potential to leverage the vast amounts of data generated by wireless devices to improve model performance and enable intelligent applications. However, the fairness aspect of FL in wireless communications remains largely unexplored. This abstract presents an idea for fair federated learning in wireless communications, addressing the challenges of imbalanced data distribution, privacy preservation, and resource allocation. Firstly, the proposed approach aims to tackle the issue of imbalanced data distribution in wireless networks. In typical FL scenarios, the distribution of data across wireless devices can be highly skewed, resulting in unfair model updates. To address this, we propose a weighted aggregation strategy that assigns higher importance to devices with fewer samples during the aggregation process. By incorporating fairness-aware weighting mechanisms, the proposed approach ensures that each participating device's contribution is proportional to its data distribution, thereby mitigating the impact of data imbalance on model performance. Secondly, privacy preservation is a critical concern in federated learning, especially in wireless communications where sensitive user data is involved. The proposed approach incorporates privacy-enhancing techniques, such as differential privacy, to protect user privacy during the model training process. By adding carefully calibrated noise to the gradient updates, the proposed approach ensures that the privacy of individual devices is preserved without compromising the overall model accuracy. Moreover, the approach considers the heterogeneity of devices in terms of computational capabilities and energy constraints, allowing devices to adaptively adjust the level of privacy preservation to strike a balance between privacy and utility. Thirdly, efficient resource allocation is crucial for federated learning in wireless communications, as devices operate under limited bandwidth, energy, and computational resources. The proposed approach leverages optimization techniques to allocate resources effectively among the participating devices, considering factors such as data quality, network conditions, and device capabilities. By intelligently distributing the computational load, communication bandwidth, and energy consumption, the proposed approach minimizes resource wastage and ensures a fair and efficient FL process in wireless networks. To evaluate the performance of the proposed fair federated learning approach, extensive simulations and experiments will be conducted. The experiments will involve a diverse set of wireless devices, ranging from smartphones to Internet of Things (IoT) devices, operating in various scenarios with different data distributions and network conditions. The evaluation metrics will include model accuracy, fairness measures, privacy preservation, and resource utilization. The expected outcomes of this research include improved model performance, fair allocation of resources, enhanced privacy preservation, and a better understanding of the challenges and solutions for fair federated learning in wireless communications. The proposed approach has the potential to revolutionize wireless communication systems by enabling intelligent applications while addressing fairness concerns and preserving user privacy.

Keywords: federated learning, wireless communications, fairness, imbalanced data, privacy preservation, resource allocation, differential privacy, optimization

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Authors: S. Biondi, V. Catania, R. Di Natale, A. R. Intilisano, D. Panno

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One of the major goals of Spoken Dialog Systems (SDS) is to understand what the user utters. In the SDS domain, the Spoken Language Understanding (SLU) Module classifies user utterances by means of a pre-definite conceptual knowledge. The SLU module is able to recognize only the meaning previously included in its knowledge base. Due the vastity of that knowledge, the information storing is a very expensive process. Updating and managing the knowledge base are time-consuming and error-prone processes because of the rapidly growing number of entities like proper nouns and domain-specific nouns. This paper proposes a solution to the problem of Name Entity Recognition (NER) applied to a SDS domain. The proposed solution attempts to automatically recognize the meaning associated with an utterance by using the PANKOW (Pattern based Annotation through Knowledge On the Web) method at runtime. The method being proposed extracts information from the Web to increase the SLU knowledge module and reduces the development effort. In particular, the Google Search Engine is used to extract information from the Facebook social network.

Keywords: spoken dialog system, spoken language understanding, web semantic, name entity recognition

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Authors: William Michael Short

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Semantic Web’ technologies such as the Unified Medical Language System Metathesaurus, SNOMED-CT, and MeSH have been touted as transformational for the way users access online medical and health information, enabling both the automated analysis of natural-language data and the integration of heterogeneous healthrelated resources distributed across the Internet through the use of standardized terminologies that capture concepts and relationships between concepts that are expressed differently across datasets. However, the approaches that have so far characterized ‘semantic bioinformatics’ have not yet fulfilled the promise of the Semantic Web for medical and health information retrieval applications. This paper argues within the perspective of cognitive linguistics and cognitive anthropology that four features of human meaning-making must be taken into account before the potential of semantic technologies can be realized for this domain. First, many semantic technologies operate exclusively at the level of the word. However, texts convey meanings in ways beyond lexical semantics. For example, transitivity patterns (distributions of active or passive voice) and modality patterns (configurations of modal constituents like may, might, could, would, should) convey experiential and epistemic meanings that are not captured by single words. Language users also naturally associate stretches of text with discrete meanings, so that whole sentences can be ascribed senses similar to the senses of words (so-called ‘discourse topics’). Second, natural language processing systems tend to operate according to the principle of ‘one token, one tag’. For instance, occurrences of the word sound must be disambiguated for part of speech: in context, is sound a noun or a verb or an adjective? In syntactic analysis, deterministic annotation methods may be acceptable. But because natural language utterances are typically characterized by polyvalency and ambiguities of all kinds (including intentional ambiguities), such methods leave the meanings of texts highly impoverished. Third, ontologies tend to be disconnected from everyday language use and so struggle in cases where single concepts are captured through complex lexicalizations that involve profile shifts or other embodied representations. More problematically, concept graphs tend to capture ‘expert’ technical models rather than ‘folk’ models of knowledge and so may not match users’ common-sense intuitions about the organization of concepts in prototypical structures rather than Aristotelian categories. Fourth, and finally, most ontologies do not recognize the pervasively figurative character of human language. However, since the time of Galen the widespread use of metaphor in the linguistic usage of both medical professionals and lay persons has been recognized. In particular, metaphor is a well-documented linguistic tool for communicating experiences of pain. Because semantic medical knowledge-bases are designed to help capture variations within technical vocabularies – rather than the kinds of conventionalized figurative semantics that practitioners as well as patients actually utilize in clinical description and diagnosis – they fail to capture this dimension of linguistic usage. The failure of semantic technologies in these respects degrades the efficiency and efficacy not only of medical research, where information retrieval inefficiencies can lead to direct financial costs to organizations, but also of care provision, especially in contexts of patients’ self-management of complex medical conditions.

Keywords: ambiguity, bioinformatics, language, meaning, metaphor, ontology, semantic web, semantics

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Authors: Mingxin Li, Liya Ni

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Autonomous parking is a valuable feature applicable to many robotics applications such as tour guide robots, UV sanitizing robots, food delivery robots, and warehouse robots. With auto-parking, the robot will be able to park at the charging zone and charge itself without human intervention. As compared to self-driving vehicles, auto-parking is more challenging for a small-scale mobile robot only equipped with a front camera due to the camera view limited by the robot’s height and the narrow Field of View (FOV) of the inexpensive camera. In this research, auto-parking of a small-scale mobile robot with a front camera only was achieved in a four-step process: Firstly, transfer learning was performed on the AlexNet, a popular pre-trained convolutional neural network (CNN). It was trained with 150 pictures of empty parking slots and 150 pictures of occupied parking slots from the view angle of a small-scale robot. The dataset of images was divided into a group of 70% images for training and the remaining 30% images for validation. An average success rate of 95% was achieved. Secondly, the image of detected empty parking space was processed with edge detection followed by the computation of parametric representations of the boundary lines using the Hough Transform algorithm. Thirdly, the positions of the entrance point and center of available parking space were predicted based on the robot kinematic model as the robot was driving closer to the parking space because the boundary lines disappeared partially or completely from its camera view due to the height and FOV limitations. The robot used its wheel speeds to compute the positions of the parking space with respect to its changing local frame as it moved along, based on its kinematic model. Lastly, the predicted entrance point of the parking space was used as the reference for the motion control of the robot until it was replaced by the actual center when it became visible again by the robot. The linear and angular velocities of the robot chassis center were computed based on the error between the current chassis center and the reference point. Then the left and right wheel speeds were obtained using inverse kinematics and sent to the motor driver. The above-mentioned four subtasks were all successfully accomplished, with the transformed learning, image processing, and target prediction performed in MATLAB, while the motion control and image capture conducted on a self-built small scale differential drive mobile robot. The small-scale robot employs a Raspberry Pi board, a Pi camera, an L298N dual H-bridge motor driver, a USB power module, a power bank, four wheels, and a chassis. Future research includes three areas: the integration of all four subsystems into one hardware/software platform with the upgrade to an Nvidia Jetson Nano board that provides superior performance for deep learning and image processing; more testing and validation on the identification of available parking space and its boundary lines; improvement of performance after the hardware/software integration is completed.

Keywords: autonomous parking, convolutional neural network, image processing, kinematics-based prediction, transfer learning

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Authors: Carmen E. Zapata, José F. Jiménez, Mauricio Ramiréz, Natalia A. Cano

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The majority of the urban areas in Latin America face the challenges associated with city planning and development problems, attributed to human, technical, and economical factors; therefore, we cannot ignore the issues related to climate change because the city modifies the natural landscape in a significant way transforming the radiation balance and heat content in the urbanized areas. These modifications provoke changes in the temperature distribution known as “the heat island effect”. According to this phenomenon, we have the need to conceive the urban planning based on climatological patterns that will assure its sustainable functioning, including the particularities of the climate variability. In the present study, it is identified the Local Climate Zones (LCZ) in the Metropolitan Area of the Aburrá Valley (Colombia) with the objective of relocate the air quality monitoring stations as a partial solution to the problem of how to measure representative air quality levels in a city for a local scale, but with instruments that measure in the microscale.

Keywords: air quality, monitoring, local climatic zones, valley, monitoring stations

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Authors: Zahra Khan

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Hydrogels are a macromolecular network of hydrophilic copolymers with physical or chemical cross-linking structures with significant water uptake capabilities. They are a promising substrate for surface-enhanced Raman spectroscopy (SERS) as they are both flexible and biocompatible materials. Conventional SERS-active substrates suffer from limitations such as instability and inflexibility, which restricts their use in broader applications. Gelatin-based hydrogels have been synthesised in a facile and relatively quick method without the use of any toxic cross-linking agents. Composite gel material was formed by combining the gelatin with simple polymers to enhance the functional properties of the gel. Gold nanoparticles prepared by a reproducible seed-mediated growth method were combined into the bulk material during gel synthesis. After gel formation, the gel was submerged in the analyte solution overnight. SERS spectra were then collected from the gel using a standard Raman spectrometer. A wide range of analytes was successfully detected on these hydrogels showing potential for further optimization and use as SERS substrates for biomedical applications.

Keywords: gelatin, hydrogels, flexible materials, SERS

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Authors: Abe D. Desta

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This study suggests a deep learning-based method for identifying and following moving objects in surveillance video. The proposed method uses a fast regional convolution neural network (F-RCNN) trained on a substantial dataset of vehicle images to first detect vehicles. A Kalman filter and a data association technique based on a Hungarian algorithm are then used to monitor the observed vehicles throughout time. However, in general, F-RCNN algorithms have been shown to be effective in achieving high detection accuracy and robustness in this research study. For example, in one study The study has shown that the vehicle detection and tracking, the system was able to achieve an accuracy of 97.4%. In this study, the F-RCNN algorithm was compared to other popular object detection algorithms and was found to outperform them in terms of both detection accuracy and speed. The presented system, which has application potential in actual surveillance systems, shows the usefulness of deep learning approaches in vehicle detection and tracking.

Keywords: artificial intelligence, computer vision, deep learning, fast-regional convolutional neural networks, feature extraction, vehicle tracking

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Authors: Zdravko Eškinja, Lovre Miljanić, Ognjen Kuljača

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This paper is an overview of simulation tools used to model specific thermal dynamics that occurs while controlling double skin façade. Research has been conducted on simplified construction with single zone where one side is glazed. Heat flow and temperature responses are simulated in three different simulation tools: IDA-ICE, EnergyPlus and HAMBASE. The excitation of observed system, used in all simulations, was a temperature step of exterior environment. Air infiltration, insulation and other disturbances are excluded from this research. Although such isolated behaviour is not possible in reality, experiments are carried out to gain novel information about heat flow transients which are not observable under regular conditions. Results revealed new possibilities for adapting the parameters of the neural network regulator. Along numerical simulations, the same set-up has been also tested in a real-time experiment with a 1:18 scaled model and thermal chamber. The comparison analysis brings out interesting conclusion about simulation accuracy in this particular case.

Keywords: double skin façade, experimental tests, heat control, heat flow, simulated tests, simulation tools

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Authors: Pius Agbebi Alaba

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The study emphasized on the effects of contemporary and prospect trends on the development of Hospitality and Tourism in Nigeria. Specifically, the study examined globalization, safety and security, diversity, service, technology, demographic changes and price–value as contemporary trends while prospect trends such as green and Eco-lodgings, Development of mega hotels, Boutique hotels, Intelligent hotels with advanced technology using the guest’s virtual fingerprint in order to perform all the operations, increasing employee salaries in order retain the existing Staff, More emphasis on the internet and technology, Guests’ virtual and physical social network were equally examined. The methodology for the study involved review of existing related study, books, journal and internet. The findings emanated from the exercise showed clearly that the impact of both trends on the development of Hospitality and Tourism in Nigeria would bring about rapid positive transformation of her socio-economic, political and cultural environment. The implication of the study is that it will prepare both private and corporate individuals in hospitality and tourism business for the challenges inherent in both trends.

Keywords: hospitality and tourism, Nigeria's centenary, trends, implications

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Authors: Nadjet Brahmia

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The evolution of urban forms, the new aspects of mobility, the ways of life and economic models make public transport conventional collective low-performing on the majority of largest Algerian cities, particularly in the west of Algeria. On the other side, the information and communication technologies (ICT) open new eventualities to develop a new mode of transport which brings together both the tenders offered by the public service collective and those of the particular vehicle, suitable for urban requirements, social and environmental. Like the concrete examples made in the international countries in terms of on-demand transport systems (ODT) more particularly in the developed countries, this article has for objective the opportunity analysis to establish a service of ODT at the level of a few towns of Oran Wilaya, such a service will be subsequently spread on the totality of the Wilaya if not on the whole of Algeria. In this context, we show the different existing means of transport in the current network whose aim to illustrate the points of insufficiency accented in the present transport system, then we discuss the solutions that may exhibit a service of ODT to the problem studied all around the transport sector, to carry at the end to highlight the capabilities of ODT replying to the transformation of mobilities, this in the light of well-defined cases.

Keywords: mobility, on-demand transport, public transport collective, transport system

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Authors: Manar Amayri, Hussain Kazimi, Quoc-Dung Ngo, Stephane Ploix

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A general approach is proposed to determine occupant behavior (occupancy and activity) in residential buildings and to use these estimates for improved energy management. Occupant behaviour is modelled with a Bayesian Network in an unsupervised manner. This algorithm makes use of domain knowledge gathered via questionnaires and recorded sensor data for motion detection, power, and hot water consumption as well as indoor CO₂ concentration. Two case studies are presented which show the real world applicability of estimating occupant behaviour in this way. Furthermore, experiments integrating occupancy estimation and hot water production control show that energy efficiency can be increased by roughly 5% over known optimal control techniques and more than 25% over rule-based control while maintaining the same occupant comfort standards. The efficiency gains are strongly correlated with occupant behaviour and accuracy of the occupancy estimates.

Keywords: energy, management, control, optimization, Bayesian methods, learning theory, sensor networks, knowledge modelling and knowledge based systems, artificial intelligence, buildings

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Authors: Xinyuan Liu, Xiaojun Jing, Yuan He, Junsheng Mu

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Aspect-based Sentiment Analysis (ABSA) has attracted much attention due to its capacity to determine the sentiment polarity of the certain aspect in a sentence. In previous works, great significance of the interaction between aspect and sentence has been exhibited in ABSA. In consequence, a Multi-Level Attentional Networks (MLAN) is proposed. MLAN consists of four parts: Embedding Layer, Encoding Layer, Multi-Level Attentional (MLA) Layers and Final Prediction Layer. Among these parts, MLA Layers including Aspect Level Attentional (ALA) Layer and Interactive Attentional (ILA) Layer is the innovation of MLAN, whose function is to focus on the important information and obtain multiple levels’ attentional weighted representation of aspect and sentence. In the experiments, MLAN is compared with classical TD-LSTM, MemNet, RAM, ATAE-LSTM, IAN, AOA, LCR-Rot and AEN-GloVe on SemEval 2014 Dataset. The experimental results show that MLAN outperforms those state-of-the-art models greatly. And in case study, the works of ALA Layer and ILA Layer have been proven to be effective and interpretable.

Keywords: deep learning, aspect-based sentiment analysis, attention, natural language processing

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Authors: Tallataf Rasheed, Adnan Rashdi, Ahmad Naeem Akhtar

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The accurate spectrum sensing is a fundamental requirement of dynamic spectrum access for deployment of Cognitive Radio Network (CRN). To acheive this requirement a Reliability factors based Fuzzy Logic (RFL) Scheme for Spectrum Sensing has been proposed in this paper. Cognitive Radio User (CRU) predicts the presence or absence of Primary User (PU) using energy detector and calculates the Reliability factors which are SNR of sensing node, threshold of energy detector and decision difference of each node with other nodes in a cooperative spectrum sensing environment. Then the decision of energy detector is combined with Reliability factors of sensing node using Fuzzy Logic. These Reliability Factors used in RFL Scheme describes the reliability of decision made by a CRU to improve the local spectrum sensing. This Fuzzy combining scheme provides the accuracy of decision made by sensornode. The simulation results have shown that the proposed technique provide better PU detection probability than existing Spectrum Sensing Techniques.

Keywords: cognitive radio, spectrum sensing, energy detector, reliability factors, fuzzy logic

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Authors: Sreedevi P. Chakyar, Jolly Andrews, V. P. Joseph

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A compact method for measuring the relative permittivity of a dielectric material at different temperatures using a single circular Split Ring Resonator (SRR) metamaterial unit working as a test probe is presented in this paper. The dielectric constant of a material is dependent upon its temperature and the LC resonance of the SRR depends on its dielectric environment. Hence, the temperature of the dielectric material in contact with the resonator influences its resonant frequency. A single SRR placed between transmitting and receiving probes connected to a Vector Network Analyser (VNA) is used as a test probe. The dependence of temperature between 30 ^oC and 60 ^oC on resonant frequency of SRR is analysed. Relative permittivities ‘ε’ of test samples for different temperatures are extracted from a calibration graph drawn between the relative permittivity of samples of known dielectric constant and their corresponding resonant frequencies. This method is found to be an easy and efficient technique for analysing the temperature dependent permittivity of different materials.

Keywords: metamaterials, negative permeability, permittivity measurement techniques, split ring resonators, temperature dependent dielectric constant

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Authors: Anita Kushwaha

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We propose a new evolutionary computational model called Artificial Reproduction System which is based on the complex process of meiotic reproduction occurring between male and female cells of the living organisms. Artificial Reproduction System is an attempt towards a new computational intelligence approach inspired by the theoretical reproduction mechanism, observed reproduction functions, principles and mechanisms. A reproductive organism is programmed by genes and can be viewed as an automaton, mapping and reducing so as to create copies of those genes in its off springs. In Artificial Reproduction System, the binding mechanism between male and female cells is studied, parameters are chosen and a network is constructed also a feedback system for self regularization is established. The model then applies Mendel’s law of inheritance, allele-allele associations and can be used to perform data analysis of imbalanced data, multivariate, multiclass and big data. In the experimental study Artificial Reproduction System is compared with other state of the art classifiers like SVM, Radial Basis Function, neural networks, K-Nearest Neighbor for some benchmark datasets and comparison results indicates a good performance.

Keywords: bio-inspired computation, nature- inspired computation, natural computing, data mining

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Authors: Hanbey Hazar, Hakan Gul

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In this study, Thermal Barrier Coating (TBC) application is performed in order to reduce the engine emissions. Piston, exhaust, and intake valves of a single-cylinder four-cycle gasoline engine were coated with chromium carbide (Cr3C2) at a thickness of 300 µm by using the Plasma Spray coating method which is a TBC method. Gasoline engine was converted into an LPG system. The study was conducted in 4 stages. In the first stage, the piston, exhaust, and intake valves of the gasoline engine were coated with Cr3C2. In the second stage, gasoline engine was converted into the LPG system and the emission values in this engine were recorded. In the third stage, the experiments were repeated under the same conditions with a standard (uncoated) engine and the results were recorded. In the fourth stage, data obtained from both engines were loaded on Artificial Neural Networks (ANN) and estimated values were produced for every revolution. Thus, mathematical modeling of coated and uncoated engines was performed by using ANN. While there was a slight increase in exhaust gas temperature (EGT) of LPG engine due to TBC, carbon monoxide (CO) values decreased.

Keywords: LPG fuel, thermal barrier coating, artificial neural network, mathematical modelling

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Authors: Josef Wolfartsberger, Michael Heiml, Georg Schwarz, Sabrina Egger

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Growing individualization and higher numbers of variants in industrial assembly products raise the complexity of manufacturing processes. Technical assistance systems considering both procedural and human factors allow for an increase in product quality and a decrease in required learning times by supporting workers with precise working instructions. Due to varying needs of workers, the presentation of working instructions leads to several challenges. This paper presents an approach for a multi-modal visualization application to support assembly work of complex parts. Our approach is integrated within an interconnected assistance system network and supports the presentation of cloud-streamed textual instructions, images, videos, 3D animations and audio files along with multi-modal user interaction, customizable UI, multi-platform support (e.g. tablet-PC, TV screen, smartphone or Augmented Reality devices), automated text translation and speech synthesis. The worker benefits from more accessible and up-to-date instructions presented in an easy-to-read way.

Keywords: assembly, assistive technologies, augmented reality, manufacturing, visualization

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Authors: H. Parveen Begam, M. A. Maluk Mohamed

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Grid computing is an environment that allows sharing and coordinated use of diverse resources in dynamic, heterogeneous and distributed environment using Virtual Organization (VO). Security is a critical issue due to the open nature of the wireless channels in the grid computing which requires three fundamental services: authentication, authorization, and encryption. The privacy and anonymity are considered as an important factor while communicating over publicly spanned network like web. To ensure a high level of security we explored an extension of onion routing, which has been used with dynamic token exchange along with protection of privacy and anonymity of individual identity. To improve the performance of encrypting the layers, the elliptic curve cryptography is used. Compared to traditional cryptosystems like RSA (Rivest-Shamir-Adelman), ECC (Elliptic Curve Cryptosystem) offers equivalent security with smaller key sizes which result in faster computations, lower power consumption, as well as memory and bandwidth savings. This paper presents the estimation of the performance improvements of onion routing using ECC as well as the comparison graph between performance level of RSA and ECC.

Keywords: grid computing, privacy, anonymity, onion routing, ECC, RSA

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Authors: Maria Spanoudaki, Pauldy C. J. Otermans, Dev Aditya

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Relationships form an integral part of our everyday wellbeing. In this study, the focus is on Inner Wellbeing which can be described as an individuals' thoughts and feelings about what they can do and be. Relationships can come in many forms and can be divided into Social Connections (thoughts and feelings about the social network people can establish and rely on), and Close Relationships (thoughts and feeling about the emotional support people can receive from significant others or their close, intimate circle). The purpose of this study is to compare the Social Connections and Close Relationship dimensions of Inner Wellbeing during the COVID-19 pandemic between the UK and India. 392 participants in the UK and 205 participants India completed an online questionnaire using the Inner Wellbeing scale. Factor analyses showed that the construct of Inner Wellbeing can be described as one factor for the UK sample whereas it can be described as two factors (one focusing on positive items and one focusing on negative items) for the Indian sample. Results showed that Social Connections were significantly during COVID-19 in the UK compared to India, whereas there is no significant difference for Close Relationships. The implications on relationships and wellbeing are discussed in detail.

Keywords: social networks, relationship maintenance, relationship satisfaction, COVID-19

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Authors: Sandra Cuenca, Steven Chango, Fabian Chamba, Alexandra Vaca

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This project addresses a representation of a virtual environment based on the analysis of the RSRP (Reference Signal Received Power) obtained by the Network Cell Info Lite application at the Escuela Superior Politécnica de Chimborazo (ESPOCH) considering the open areas of the Business Administration Department in the 4G LTE Frequency (band 2) of Claro Telephony at a frequency of 1967. 5 MHz, where measurements were performed from 17:00 UTC-05:00. The indicators required for the simulation of the environment designed in sketchup were focused especially on the power levels obtained where it was possible to represent the scenario with real power values obtained in each concentric radius of a total of 3 campaigns of 200 samples each, where the values vary between 84.6 dBm to 115.5 dBm having average power values for each of the 23 radiuses which are introduced in a virtual environment, allowing users to immerse themselves in it, where they can explore 3D virtual environments, generating a color scale from 0 to 10 with red being the weakest signal and green the signal with the best intensity.

Keywords: virtualization, LTE, radios, power intensity levels colors, mobile signal reception power

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Authors: Priyadarshini Alok

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The present situation in India envisages that because of the surge in population and the economy, cities are expected to spill over to hinterland areas. The consumption-led factors such as land, labor, etc. will be boosted. Hence, the need for regional connectivity becomes obligatory. But, there is enormous pressure upon the land; proving itself through rising traffic congestion, roads, and railway accidents. Air transport is practical, but due to decreasing availability of land, this will not be a wise solution. What with the introduction of seaplanes in the country which was once the vital asset in the world prior to Second World War. Maldives has proved it. Seaplanes offer natural landing site and are time and cost-efficient. Seaplanes in accordance with UDAN can prove to be the solution in linking various regions with other states. This research paper aims to offer the feasibility analysis along with site justification of the potential areas in the state of Andhra Pradesh, India; for the operation of seaplanes. The standards are taken from the US Department of Transportation, Federal Aviation Administration for the analysis. The conflation of Seaplanes with UDAN will offer an alternate mode of air connectivity, strengthen the transport network by simulation of connectivity to unserved and under-served areas and boost the nation's economy.

Keywords: connectivity, seaplanes, transport, UDAN

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Authors: Waheeda Jabbar, Ali Gul, Rida Noor, Sania Kurd, Saba Gulzar

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Hundreds of miners die from mining accidents each year due to poisonous gases found underground mining areas. This paper proposed an idea to protect the precious lives of mining workers. A supervising system is designed which is based on ZigBee wireless technique along with the smart protective helmets to detect real-time surveillance and it gives early warnings on presence of different poisonous gases in order to save mineworkers from any danger caused by these poisonous gases. A wireless sensor network is established using ZigBee wireless technique by integrating sensors on the helmet, apart from this helmet have embedded heartbeat sensor to detect the pulse rate and be aware of the physical or mental strength of a mineworker to increase the potential safety. Radio frequency identification (RFID) technology is used to find the location of workers. A ZigBee based base station is set-upped to control the communication. The idea is implemented and results are verified through experiment.

Keywords: Arduino, gas sensor (MQ7), RFID, wireless ZigBee

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Authors: Keigh D. Guinto, Ma. Romina M. Santos

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One of the most catastrophic natural disasters in the Philippines is floods. Twelve (12) million people reside in Metro Manila, National Capital Region (NCR), prone to flooding. A flood can cause widespread devastation resulting in damaged properties and infrastructures and loss of life. By using the analytical hierarchy process, six (6) parameters were selected, namely elevation, slope, lithology, distance from the river, river network density, and flow accumulation. Ranking of these parameters demonstrates that distance from the river with 25.31% and river density with 17.30% ranked the highest causative factor to flooding. This is followed by flow accumulation with 16.72%, elevation with 15.33%, slope with 13.53%, and the least flood causative factor is lithology with 11.8%. The generated flood susceptibility map of Mandaluyong has three (3) classes: high susceptibility, moderate susceptibility, and low susceptibility. The flood susceptibility map generated in this study can be used as an aid for planning flood mitigation, land use planning, and general public awareness. This study can also be used for emergency management and can be applied in the disaster risk management of Mandaluyong.

Keywords: analytical hierarchy process, assessment, flood, geographic information system

Procedia PDF Downloads 210

Authors: Kazuaki Shiraishi, Narumitsu Asai, Tsukasa Kitahara, Sosuke Mieno, Takaharu Kameoka

Abstract:

The purpose of this research is to facilitate productivity advances in agriculture. To accomplish this, we developed an automatic three-dimensional (3D) recording system for growth of field crops that consists of a number of inexpensive modules: a very low-cost stereo camera, a couple of ZigBee wireless modules, a Raspberry Pi single-board computer, and a third generation (3G) wireless communication module. Our system uses an inexpensive Web stereo camera in order to keep total costs low. However, inexpensive video cameras record low-resolution images that are very noisy. Accordingly, in order to resolve these problems, we adopted a deep learning method. Based on the results of extended period of time operation test conducted without the use of an external power supply, we found that by using Super-Resolution Convolutional Neural Network method, our system could achieve a balance between the competing goals of low-cost and superior performance. Our experimental results showed the effectiveness of our system.

Keywords: 3D plant data, automatic recording, stereo camera, deep learning, image processing

Procedia PDF Downloads 275

Authors: Atanu K Samanta, Asim Ali Khan

Abstract:

Due to the acquisition of huge amounts of brain tumor magnetic resonance images (MRI) in clinics, it is very difficult for radiologists to manually interpret and segment these images within a reasonable span of time. Computer-aided diagnosis (CAD) systems can enhance the diagnostic capabilities of radiologists and reduce the time required for accurate diagnosis. An intelligent computer-aided technique for automatic detection of a brain tumor through MRI is presented in this paper. The technique uses the following computational methods; the Level Set for segmentation of a brain tumor from other brain parts, extraction of features from this segmented tumor portion using gray level co-occurrence Matrix (GLCM), and the Artificial Neural Network (ANN) to classify brain tumor images according to their respective types. The entire work is carried out on 50 images having five types of brain tumor. The overall classification accuracy using this method is found to be 98% which is significantly good.

Keywords: brain tumor, computer-aided diagnostic (CAD) system, gray-level co-occurrence matrix (GLCM), tumor segmentation, level set method

Procedia PDF Downloads 515

Authors: Elias Nemer, Greg Vines

Abstract:

Time-frequency transformation and spectral representations of audio signals are commonly used in various machine learning applications. Training networks on frequency features such as the Mel-Spectrogram or Cochleogram have been proven more effective and convenient than training on-time samples. In practical realizations, these features are created on a different processor and/or pre-computed and stored on disk, requiring additional efforts and making it difficult to experiment with different features. In this paper, we provide a PyTorch framework for creating various spectral features as well as time-frequency transformation and time-domain filter-banks using the built-in trainable conv1d() layer. This allows computing these features on the fly as part of a larger network and enabling easier experimentation with various combinations and parameters. Our work extends the work in the literature developed for that end: First, by adding more of these features and also by allowing the possibility of either starting from initialized kernels or training them from random values. The code is written as a template of classes and scripts that users may integrate into their own PyTorch classes or simply use as is and add more layers for various applications.

Keywords: neural networks Mel-Spectrogram, chromagram, cochleogram, discrete Fourrier transform, PyTorch conv1d()

Procedia PDF Downloads 237

Authors: Bangaly Kaba

Abstract:

In the global information society, the importance of the Internet cannot be overemphasized. Africa needs access to the powerful information and communication tools of the Internet in order to obtain the resources and efficiency essential for sustainable development. Unfortunately, in 2013, the data from Internetworldstats showed only 15% of African populations have access to Internet. This relative low Internet penetration rate signals a problem that may threaten the economic development, governmental efficiency, and ultimately the global competitiveness of African countries. Many initiatives were undertaken to bring the benefits of the global information revolution to the people of Africa, through connection to the Internet and other Global Information Infrastructure technologies. The purpose is to understand differences between socio-economically advantaged and disadvantaged internet users. From that, we will determine what prevents disadvantaged groups from benefiting from Internet usage. Data were collected through a survey from Internet users in Ivory Coast. The results reveal that Personal network exposure, Self-efficacy and Availability are the key drivers of continued use intention for the socio-economically disadvantaged group. The theoretical and practical implications are also described.

Keywords: digital inequality, internet, integrative model, socio-economically advantaged and disadvantaged, use continuance, Africa

Procedia PDF Downloads 471