Search results for: elliptic curve digital signature algorithm

Authors: Malika Yaici, Assia Arab, Betitra Yakouben, Samia Zermani

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Ubiquitous computing is nowadays a reality through the networking of a growing number of computing devices. It allows providing users with context aware information and services in a heterogeneous environment, anywhere and anytime. Selection of the best context-aware service, between many available services and providers, is a tedious problem. In this paper, a service selection method based on Constraint Satisfaction Problem (CSP) formalism is proposed. The services are considered as variables and domains; and the user context, preferences and providers characteristics are considered as constraints. The Backtrack algorithm is used to solve the problem to find the best service and provider which matches the user requirements. Even though this algorithm has an exponential complexity, but its use guarantees that the service, that best matches the user requirements, will be found. A comparison of the proposed method with the existing solutions finishes the paper.

Keywords: ubiquitous computing, services selection, constraint satisfaction problem, backtrack algorithm

Procedia PDF Downloads 245

Authors: Cesar Hernández, Diego Giral, Fernando Santa

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The spectral handoff is important in cognitive wireless networks to ensure an adequate quality of service and performance for secondary user communications. This work proposes a benchmarking of performance of the three spectrum handoff models: VIKOR, SAW and MEW. Four evaluation metrics are used. These metrics are, accumulative average of failed handoffs, accumulative average of handoffs performed, accumulative average of transmission bandwidth and, accumulative average of the transmission delay. As a difference with related work, the performance of the three spectrum handoff models was validated with captured data of spectral occupancy in experiments realized at the GSM frequency band (824 MHz-849 MHz). These data represent the actual behavior of the licensed users for this wireless frequency band. The results of the comparative show that VIKOR Algorithm provides 15.8% performance improvement compared to a SAW Algorithm and, 12.1% better than the MEW Algorithm.

Keywords: cognitive radio, decision making, MEW, SAW, spectrum handoff, VIKOR

Procedia PDF Downloads 438

Authors: Wahyu Purnama Putra, Artono Isharanto

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Thoracic trauma is trauma that hits the thoracic wall or intrathoracic organs, either due to blunt trauma or sharp trauma. Thoracic trauma often causes impaired ventilation-perfusion due to damage to the lung parenchyma. This results in impaired tissue oxygenation, which is one of the causes of acute respiratory distress syndrome (ARDS). These changes are caused by the release of pro-inflammatory mediators, plasmatic proteins, and proteases into the alveolar space associated with ongoing edema, as well as oxidative products that ultimately result in severe inhibition of the surfactant system. This study aims to predict the incidence of acute respiratory distress syndrome (ARDS) through human neutrophil elastase levels. This study examines the relationship between plasma elastase levels as a predictor of the incidence of ARDS in thoracic trauma patients in Malang. This study is an observational cohort study. Data analysis uses the Pearson correlation test and ROC curve (receiver operating characteristic curve). It can be concluded that there is a significant (p= 0.000, r= -0.988) relationship between elastase levels and BGA-3. If the value of elastase levels is limited to 23.79 ± 3.95, the patient will experience mild ARDS. While if the value of elastase levels is limited to 57.68 ± 18.55, in the future, the patient will experience moderate ARDS. Meanwhile, if the elastase level is between 107.85 ± 5.04, the patient will likely experience severe ARDS. Neutrophil elastase levels correlate with the degree of severity of ARDS incidence.

Keywords: ARDS, human neutrophil elastase, severity, thoracic trauma

Procedia PDF Downloads 149

Authors: Bâlc Denisa-Maria

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This paper aims to present some ways of preserving a language heritage in the global era. Teaching a second language to foreign students does not imply only teaching the grammar and the vocabulary in order to reach the 4 skills, but it means constant work on developing strategies to make the students aware of the heritage that the language they learn has. Teachers and professors need to be aware of the fact that language is in constant change, they need to adjust their techniques to the digital era, but they also have to be aware of the changes, the good and the bad parts of globalizations. How is it possible to preserve the patrimony of a certain language in a globalized era? What transformations does a language face in time? What does it mean to preserve the heritage of a language through L2 teaching? What makes a language special? What impact does it have on the foreign students? How can we, as teachers, preserve the heritage of our language? Would it be everything about books, films, music, cultural events or what else? How is it possible to include digital programs in your teaching and preserving the patrimony of a language at the same time? How does computational linguistics help us in teaching a certain language? All these questions will be tackled during the essay, with special accent on the definition of a language heritage, the new perspectives for teachers/ professors, everything in a multimodal and complex way of presenting the context. The objectives of this research are: - to present some ways of preserving the heritage of a certain language against globalization - to illustrate what preservation means for L2 teaching - to encourage teachers to be aware of their language patrimony The main contributions of my research are on moving the discussion of preserving a certain language patrimony in the context of L2 teaching.

Keywords: preservation, globalization, language heritage, L2 teaching

Procedia PDF Downloads 62

Authors: Qiu Chen, Koji Kotani, Feifei Lee, Tadahiro Ohmi

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In this paper, we propose an improved face recognition algorithm using histogram-based features in spatial and frequency domains. For adding spatial information of the face to improve recognition performance, a region-division (RD) method is utilized. The facial area is firstly divided into several regions, then feature vectors of each facial part are generated by Binary Vector Quantization (BVQ) histogram using DCT coefficients in low frequency domains, as well as Local Binary Pattern (LBP) histogram in spatial domain. Recognition results with different regions are first obtained separately and then fused by weighted averaging. Publicly available ORL database is used for the evaluation of our proposed algorithm, which is consisted of 40 subjects with 10 images per subject containing variations in lighting, posing, and expressions. It is demonstrated that face recognition using RD method can achieve much higher recognition rate.

Keywords: binary vector quantization (BVQ), DCT coefficients, face recognition, local binary patterns (LBP)

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Authors: Rodolfo Lorbieski, Silvia Modesto Nassar

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Many supervised machine learning tasks require decision making across numerous different classes. Multi-class classification has several applications, such as face recognition, text recognition and medical diagnostics. The objective of this article is to analyze an adapted method of Stacking in multi-class problems, which combines ensembles within the ensemble itself. For this purpose, a training similar to Stacking was used, but with three levels, where the final decision-maker (level 2) performs its training by combining outputs from the tree-based pair of meta-classifiers (level 1) from Bayesian families. These are in turn trained by pairs of base classifiers (level 0) of the same family. This strategy seeks to promote diversity among the ensembles forming the meta-classifier level 2. Three performance measures were used: (1) accuracy, (2) area under the ROC curve, and (3) time for three factors: (a) datasets, (b) experiments and (c) levels. To compare the factors, ANOVA three-way test was executed for each performance measure, considering 5 datasets by 25 experiments by 3 levels. A triple interaction between factors was observed only in time. The accuracy and area under the ROC curve presented similar results, showing a double interaction between level and experiment, as well as for the dataset factor. It was concluded that level 2 had an average performance above the other levels and that the proposed method is especially efficient for multi-class problems when compared to binary problems.

Keywords: stacking, multi-layers, ensemble, multi-class

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Authors: Ferinar Moaidi, Mahdi Moaidi

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Environmental issues and the ever-increasing in demand of electrical energy make it necessary to have distributed generation (DG) resources in the power system. In this research, in order to realize the goals of reducing losses and improving the voltage profile in a microgrid, the allocation and sizing of DGs have been used. The proposed Genetic Algorithm (GA) is described from the array of artificial intelligence methods for solving the problem. The algorithm is implemented on the IEEE 33 buses network. This study is presented in two scenarios, primarily to illustrate the effect of location and determination of DGs has been done to reduce losses and improve the voltage profile. On the other hand, decisions made with the one-level assumptions of load are not universally accepted for all levels of load. Therefore, in this study, load modelling is performed and the results are presented for multi-levels load state.

Keywords: distributed generation, genetic algorithm, microgrid, load modelling, loss reduction, voltage improvement

Procedia PDF Downloads 143

Authors: Mohammad Tarek Al Muallim, Ozhan Duzenli, Ceyhun Ilguy

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Passive sonar is a method for detecting acoustic signals in the ocean. It detects the acoustic signals emanating from external sources. With passive sonar, we can determine the bearing of the target only, no information about the range of the target. Target Motion Analysis (TMA) is a process to estimate the position and speed of a target using passive sonar information. Since bearing is the only available information, the TMA technique called Bearing-only TMA. Many TMA techniques have been developed. However, until now, there is not a very effective method that could be used to always track an unknown target and extract its moving trace. In this work, a design of effective Bearing-only TMA Algorithm is done. The measured bearing angles are very noisy. Moreover, for multi-beam sonar, the measurements is quantized due to the sonar beam width. To deal with this, modified gain extended Kalman filter algorithm is used. The algorithm is fine-tuned, and many modules are added to improve the performance. A special validation gate module is used to insure stability of the algorithm. Many indicators of the performance and confidence level measurement are designed and tested. A new method to detect if the target is maneuvering is proposed. Moreover, a reactive optimal observer maneuver based on bearing measurements is proposed, which insure converging to the right solution all of the times. To test the performance of the proposed TMA algorithm a simulation is done with a MATLAB program. The simulator program tries to model a discrete scenario for an observer and a target. The simulator takes into consideration all the practical aspects of the problem such as a smooth transition in the speed, a circular turn of the ship, noisy measurements, and a quantized bearing measurement come for multi-beam sonar. The tests are done for a lot of given test scenarios. For all the tests, full tracking is achieved within 10 minutes with very little error. The range estimation error was less than 5%, speed error less than 5% and heading error less than 2 degree. For the online performance estimator, it is mostly aligned with the real performance. The range estimation confidence level gives a value equal to 90% when the range error less than 10%. The experiments show that the proposed TMA algorithm is very robust and has low estimation error. However, the converging time of the algorithm is needed to be improved.

Keywords: target motion analysis, Kalman filter, passive sonar, bearing-only tracking

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Authors: Van Huong Mai, Kanokwan Nithiratphat, Adool Booncham

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The Movement of Contemporary Life consisted of two purposes, which were to study the movement and development of the modern life and to create the visual arts, which were paintings expressed via the form of apartment buildings was used from mixed media (digital printing and acrylic painting on canvas) which conveyed the rapid pace of modern life leading to diverse movements in viewer’s feeling. The operation of this creation was collected field data, documentary data, and influence from creative work. The data analysis was analyzed in order to theme, form, technique, and process to satisfy of concept and special character of the pieces.

Keywords: movement, contemporary life, visual art, acrylic painting, digital art, urban space

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Authors: Miroslav Popelka, Jakub Nožička

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In this paper we deal with using Lego Mindstorms in simulation of robotic systems with respect to cost reduction. Lego Mindstorms kit contains broad variety of hardware components which are required to simulate, program and test the robotics systems in practice. Algorithm programming went in development environment supplied together with Lego kit as in programming language C# as well. Algorithm following the line, which we dealt with in this paper, uses theoretical findings from area of controlling circuits. PID controller has been chosen as controlling circuit whose individual components were experimentally adjusted for optimal motion of robot tracking the line. Data which are determined to process by algorithm are collected by sensors which scan the interface between black and white surfaces followed by robot. Based on discovered facts Lego Mindstorms can be considered for low-cost and capable kit to simulate real robotics systems.

Keywords: LEGO Mindstorms, PID controller, low-cost robotics systems, line follower, sensors, programming language C#, EV3 Home Edition Software

Procedia PDF Downloads 375

Authors: Xun Li, Haojie Wang

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Federated learning is a kind of distributed training and centralized training mode, which is of great value in the protection of user privacy. In order to solve the problem that the model is vulnerable to backdoor attacks in federated learning, a backdoor attack detection method based on a weighted k-medoids algorithm is proposed. First of all, this paper collates the update parameters of the client to construct a vector group, then uses the principal components analysis (PCA) algorithm to extract the corresponding feature information from the vector group, and finally uses the improved k-medoids clustering algorithm to identify the normal and backdoor update parameters. In this paper, the backdoor is implanted in the federation learning model through the model replacement attack method in the simulation experiment, and the update parameters from the attacker are effectively detected and removed by the defense method proposed in this paper.

Keywords: federated learning, backdoor attack, PCA, k-medoids, backdoor defense

Procedia PDF Downloads 114

Authors: Hosuk Shin, Young-Hyun Seo, Eunhak Lee, Seung-Young Kho

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Currently, in Seoul, users have the privilege to avoid riding crowded buses with the installation of Bus Information System (BIS). BIS has three levels of on-board bus ridership level information (spacious, normal, and crowded). However, there are flaws in the system due to it being real time which could provide incomplete information to the user. For example, a bus comes to the station, and on the BIS it shows that the bus is crowded, but on the stop that the user is waiting many people get off, which would mean that this station the information should show as normal or spacious. To fix this problem, this study predicts the bus ridership level using smart card data to provide more accurate information about the passenger ridership level on the bus. An Artificial Neural Network (ANN) is an interconnected group of nodes, that was created based on the human brain. Forecasting has been one of the major applications of ANN due to the data-driven self-adaptive methods of the algorithm itself. According to the results, the ANN algorithm was stable and robust with somewhat small error ratio, so the results were rational and reasonable.

Keywords: smartcard data, ANN, bus, ridership

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Authors: Ilona Buchem, Carolin Gellner

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Over the last few years, the call for the support of senior learners in the development of their digital literacy has become prevalent, mainly due to the progression towards ageing societies paired with advances in digitalisation in all spheres of life, including e-health and electronic patient record (EPA). While major research efforts in supporting senior learners in developing digital literacy have been invested so far in e-learning focusing on knowledge acquisition and cognitive tasks, little research exists in learning models which target virtual mentoring and coaching with the help of pedagogical agents and address the social dimensions of learning. Research from studies with students in the context of formal education has already provided methods for designing intelligent virtual agents in support of personalised learning. However, this research has mostly focused on cognitive skills and has not yet been applied to the context of mentoring/coaching of senior learners, who have different characteristics and learn in different contexts. In this paper, we describe how insights from previous research can be used to develop an intelligent virtual learning coach (agent) for senior learners with a focus on building the social relationship between the agent and the learner and the key task of the agent to socialize learners to the larger context of digital literacy with a focus on electronic health records. Following current approaches to mentoring and coaching, the agent is designed not to enhance and monitor the cognitive performance of the learner but to serve as a trusted friend and advisor, whose role is to provide one-to-one guidance and support sharing of experiences among learners (peers). Based on literature review and synopsis of research on virtual agents and current coaching/mentoring models under consideration of the specific characteristics and requirements of senior learners, we describe the design framework which was applied to design an intelligent virtual learning coach as part of the e-learning system for digital literacy of senior learners in the ePA-Coach project founded by the German Ministry of Education and Research. This paper also presents the results from the evaluation study, which compared the use of the first prototype of the virtual learning coach designed according to the design framework with a voice narration in a multimedia learning environment with senior learners. The focus of the study was to validate the agent design in the context of the persona effect (Lester et al., 1997). Since the persona effect is related to the hypothesis that animated agents are perceived as more socially engaging, the study evaluated possible impacts of agent coaching in comparison with voice coaching on motivation, engagement, experience, and digital literacy.

Keywords: virtual learning coach, virtual mentor, pedagogical agent, senior learners, digital literacy, electronic health records

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Authors: Surendra Choudhary, Sapan Tiwari

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The driver has two fundamental duties i) controlling the position of the vehicle along the longitudinal and lateral direction of movement ii) roadway width. Both of these duties are interdependent and are concurrently referred to as two-dimensional driver behavior. One of the main problems facing driver behavior modeling is to identify the parameters for describing the exemplary driving conduct and car maneuver under distinct traffic circumstances. Still, to date, there is no well-accepted theory that can comprehensively model the 2-D driver conduct (longitudinal and lateral). The primary objective of this research is to explore the vehicle's lateral longitudinal behavior in the heterogeneous condition of traffic on horizontal curves as well as the effect of road geometry on dynamic traffic parameters, i.e., car velocity and lateral placement. In this research, with their interrelationship, a thorough assessment of dynamic car parameters, i.e., speed, lateral acceleration, and turn radius. Also, horizontal curve road parameters, i.e., curvature radius, pavement friction, are performed. The dynamic parameters of the various types of car drivers are gathered using a VBOX GPS-based tool with high precision. The connection between dynamic car parameters and curve geometry is created after the removal of noise from the GPS trajectories. The major findings of the research are that car maneuvers with higher than the design limits of speed, acceleration, and lateral deviation on the studied curves of the highway. It can become lethal if the weather changes from dry to wet.

Keywords: geometry, maneuverability, terrain, trajectory, VBOX

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Authors: Nikolaos Papadakis, Constantinos Condaxakis, Konstantinos Savvakis

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The aim of this paper is to determine the elastic properties (elastic modulus and Poisson ratio) of a composite material based on noncontacting imaging methods. More specifically, the significantly reduced cost of digital cameras has given the opportunity of the high reliability of low-cost strain measurement. The open source platform Ncorr is used in this paper which utilizes the method of digital image correlation (DIC). The use of digital image correlation in measuring strain uses random speckle preparation on the surface of the gauge area, image acquisition, and postprocessing the image correlation to obtain displacement and strain field on surface under study. This study discusses technical issues relating to the quality of results to be obtained are discussed. [0]8 fabric glass/epoxy composites specimens were prepared and tested at different orientations 0[o], 30[o], 45[o], 60[o], 90[o]. Each test was recorded with the camera at a constant frame rate and constant lighting conditions. The recorded images were processed through the use of the image processing software. The parameters of the test are reported. The strain map output which is obtained through strain measurement using Ncorr is validated by a) comparing the elastic properties with expected values from Classical laminate theory, b) through finite element analysis.

Keywords: composites, Ncorr, strain map, videoextensometry

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Authors: R. F. Vieira, S. A. Figueiredo, O. M. Freitas, V. F. Domingues, C. Delerue-Matos

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The presence of sulphur compounds like hydrogen sulphide and mercaptans is one of the reasons for waste-water treatment and waste management being associated with odour emissions. In this context having a quantifying method for these compounds helps in the optimization of treatment with the goal of their elimination, namely biofiltration processes. The aim of this study was the development of a method for quantification of odorous gases in waste treatment plants air samples. A method based on head space solid phase microextraction (HS-SPME) coupled with gas chromatography - flame photometric detector (GC-FPD) was used to analyse H2S and Metil Mercaptan (MM). The extraction was carried out with a 75-μm Carboxen-polydimethylsiloxane fiber coating at 22 ºC for 20 min, and analysed by a GC 2010 Plus A from Shimadzu with a sulphur filter detector: splitless mode (0.3 min), the column temperature program was from 60 ºC, increased by 15 ºC/min to 100 ºC (2 min). The injector temperature was held at 250 ºC, and the detector at 260 ºC. For calibration curve a gas diluter equipment (digital Hovagas G2 - Multi Component Gas Mixer) was used to do the standards. This unit had two input connections, one for a stream of the dilute gas and another for a stream of nitrogen and an output connected to a glass bulb. A 40 ppm H2S and a 50 ppm MM cylinders were used. The equipment was programmed to the selected concentration, and it automatically carried out the dilution to the glass bulb. The mixture was left flowing through the glass bulb for 5 min and then the extremities were closed. This method allowed the calibration between 1-20 ppm for H2S and 0.02-0.1 ppm and 1-3.5 ppm for MM. Several quantifications of air samples from inlet and outlet of a biofilter operating in a waste management facility in the north of Portugal allowed the evaluation the biofilters performance.

Keywords: biofiltration, hydrogen sulphide, mercaptans, quantification

Procedia PDF Downloads 476

Authors: Muhammad Ashfaq

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Main theme: This prime focus of this study is on the role of digital technology in crime prevention, with special focus on Cellular Forensic Unit, Capital City Police Peshawar-Khyber Pakhtunkhwa-Pakistan. Objective(s) of the study: The prime objective of this study is to provide statistics, strategies and pattern of analysis used for crime prevention in Cellular Forensic Unit of Capital City Police Peshawar, Khyber Pakhtunkhwa-Pakistan. Research Method and Procedure: Qualitative method of research has been used in the study for obtaining secondary data from research wing and Information Technology (IT) section of Peshawar police. Content analysis was the method used for the conduction of the study. This study is delimited to Capital City Police and Cellular Forensic Unit Peshawar-KP, Pakistan. information technologies. Major finding(s): It is evident that the old traditional approach will never provide solutions for better management in controlling crimes. The best way to control crimes and promotion of proactive policing is to adopt new technologies. The study reveals that technology have transformed police more effective and vigilant as compared to traditional policing. The heinous crimes like abduction, missing of an individual, snatching, burglaries and blind murder cases are now traceable with the help of technology. Recommendation(s): From the analysis of the data, it is reflected that Information Technology (IT) expert should be recruited along with research analyst to timely assist and facilitate operational as well as investigation units of police.A mobile locator should be Provided to Cellular Forensic Unit to timely apprehend the criminals .Latest digital analysis software should be provided to equip the Cellular Forensic Unit.

Keywords: crime prevention, digital technology, pakistan, police

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Authors: Milad Yousefi, Moslem Yousefi, Ricarpo Poley, Amer Nordin Darus

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Design optimization of heat exchangers is a very complicated task that has been traditionally carried out based on a trial-and-error procedure. To overcome the difficulties of the conventional design approaches especially when a large number of variables, constraints and objectives are involved, a new method based on a well-stablished evolutionary algorithm, particle swarm optimization (PSO), weighted sum approach and a novel constraint handling strategy is presented in this study. Since, the conventional constraint handling strategies are not effective and easy-to-implement in multi-objective algorithms, a novel feasibility-based ranking strategy is introduced which is both extremely user-friendly and effective. A case study from industry has been investigated to illustrate the performance of the presented approach. The results show that the proposed algorithm can find the near pareto-optimal with higher accuracy when it is compared to conventional non-dominated sorting genetic algorithm II (NSGA-II). Moreover, the difficulties of a trial-and-error process for setting the penalty parameters is solved in this algorithm.

Keywords: Heat exchanger, Multi-objective optimization, Particle swarm optimization, NSGA-II Constraints handling.

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Authors: Vaishali D. Khairnar

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The main aim is to provide lower-latency and highly reliable communication facilities for vehicles in the automobile industry; vehicle-to-everything (V2X) communication basically intends to increase expressway road security and its effectiveness. The Ultra-Reliable Low-Latency Communications (URLLC) algorithm and cellular networks are applied in combination with Mobile Broadband (MBB). This is particularly used in express way safety-based driving applications. Expressway vehicle drivers (humans) will communicate in V2X systems using the sixth-generation (6G) communication systems which have very high-speed mobility features. As a result, we need to determine how to ensure reliable and consistent wireless communication links and improve the quality to increase channel gain, which is becoming a challenge that needs to be addressed. To overcome this challenge, we proposed a unique multi-user scheduling algorithm for ultra-massive multiple-input multiple-output (MIMO) systems using 6G. In wideband wireless network access in case of high traffic and also in medium traffic conditions, moreover offering quality-of-service (QoS) to distinct service groups with synchronized contemporaneous traffic on the highway like the Mumbai-Pune expressway becomes a critical problem. Opportunist MAC (OMAC) is a way of proposing communication across a wireless communication link that can change in space and time and might overcome the above-mentioned challenge. Therefore, a multi-user scheduling algorithm is proposed for MIMO systems using a cross-layered MAC protocol to achieve URLLC and high reliability in V2X communication.

Keywords: ultra-reliable low latency communications, vehicle-to-everything communication, multiple-input multiple-output systems, multi-user scheduling algorithm

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Authors: Faci Youcef

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In the present work, an experimental study is carried out using the digital image correlation (DIC) technique to analyze the damage and behavior of woven composite carbon/epoxy under tensile loading. The tension mechanisms associated with failure modes of bolted joints in advanced composites are studied, as well as displacement distribution and strain distribution. The evolution value of bolt angle inclination during tensile tests was studied. In order to compare the distribution of displacements and strains along the surface, figures of image mapping are made. Several factors that are responsible for the failure of fiber-reinforced polymer composite materials are observed. It was found that strain concentrations observed in the specimens can be used to identify full-field damage onset and to monitor damage progression during loading. Moreover, there is an interaction between laminate pattern, laminate thickness, fastener size and type, surface strain concentrations, and out-of-plane displacement. Conclusions include a failure analysis associated with bolt angle inclinations and supported by microscopic visualizations of the composite specimen. The DIC results can be used to develop and accurately validate numerical models.

Keywords: Carbone, woven, damage, digital image, bolted joint, the inclination of angle

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Authors: Deepak Kumar, Debasish Deb, Reena Mamgain

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Radar signal processing requires high number crunching capability. Most often this is achieved using multiprocessor platform. Though multiprocessor platform provides the capability of meeting the real time computational challenges, the architecture of the same along with mapping of the algorithm on the architecture plays a vital role in efficiently using the platform. Towards this, along with standard performance metrics, few additional metrics are defined which helps in evaluating the multiprocessor platform along with the algorithm mapping. A generic multiprocessor architecture can not suit all the processing requirements. Depending on the system requirement and type of algorithms used, the most suitable architecture for the given problem is decided. In the paper, we study different architectures and quantify the different performance metrics which enables comparison of different architectures for their merit. We also carried out case study of different architectures and their efficiency depending on parallelism exploited on algorithm or data or both.

Keywords: radar signal processing, multiprocessor architecture, efficiency, load imbalance, buffer requirement, pipeline, parallel, hybrid, cluster of processors (COPs)

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Authors: Spanova Yerkezhan, Amantay Ayan, Alimzhanova Laura

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This article discusses the concept of rebranding and its relationship to cybersecurity. Rebranding is the process of changing the appearance and image of a company or organization in order to appeal to new customers or change the perception of a company. It can be a powerful tool for businesses looking to renew their reputation or expand into new markets. In today's digital age, companies increasingly rely on technology and the internet to conduct business; rebranding can also present significant cybersecurity risks. This is because a rebranding effort can create new vulnerabilities for companies, particularly in terms of their online presence. This article explores the potential hazards associated with rebranding and provides recommendations for mitigating those risks. It also highlights the importance of considering cybersecurity in the rebranding process and how it can be integrated into the overall strategy for a successful and secure rebranding.

Keywords: rebranding, cybersecurity, cyberattack, logo, vulnerability

Procedia PDF Downloads 166

Authors: H. Hotait, X. Chiementin, L. Rasolofondraibe

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This paper presents a continuous work to detect the abnormal state in the rolling bearing by studying the vibration signature analysis and calculation of the remaining useful life. To achieve these aims, two methods; the first method is the classification to detect the degradation state by the AOM-OPTICS (Acousto-Optic Modulator) method. The second one is the prediction of the degradation state using least-squares support vector regression and then compared with the linear degradation model. An experimental investigation on ball-bearing was conducted to see the effectiveness of the used method by applying the acquired vibration signals. The proposed model for predicting the state of bearing gives us accurate results with the experimental and numerical data.

Keywords: bearings, automatization, optimization, prognosis, classification, defect detection

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Authors: Samig Ibayev

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This research addresses the problem of illegal child abuse content on the Twitter platform bypassing filtering systems and appearing before users from a social-anthropological perspective. Although the wide access opportunities provided by social media platforms to their users are beneficial in many ways, it is seen that they contain gaps that pave the way for the spread of harmful and illegal content. The aim of the study is to examine the inadequacies of the current content filtering mechanisms of the Twitter platform, to understand the psychological effects of young users unintentionally encountering such content and the social dimensions of this situation. The research was conducted with a qualitative approach and was conducted using digital ethnography, content analysis and user experiences on the Twitter platform. Digital ethnography was used to observe the frequency of child abuse content on the platform and how these contents were presented. The content analysis method was used to reveal the gaps in Twitter's current filtering mechanisms. In addition, detailed information was collected on the extent of psychological effects and how the perception of trust in social media changed through interviews with young users exposed to such content. The main contributions of the research are to highlight the weaknesses in the content moderation and filtering mechanisms of social media platforms, to reveal the negative effects of illegal content on users, and to offer suggestions for preventing the spread of such content. As a result, it is suggested that platforms such as Twitter should improve their content filtering policies in order to increase user security and fulfill their social responsibilities. This research aims to create significant awareness about social media content management and ethical responsibilities on digital platforms.

Keywords: Twitter, child exploitation, content filtering, digital ethnography, social anthropology

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Authors: Mandeep Kaur, Fatehgarh Sahib

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The purpose of economic load dispatch is to allocate the required load demand between the available generation units such that the cost of operation is minimized. It is an optimization problem to find the most economical schedule of the generating units while satisfying load demand and operational constraints. The multiobjective optimization problem in which the engineer’s goal is to maximize or minimize not a single objective function but several objective functions simultaneously. The purpose of multiobjective problems in the mathematical programming framework is to optimize the different objective functions. Many approaches and methods have been proposed in recent years to solve multiobjective optimization problems. Weighting method has been applied to convert multiobjective optimization problems into scalar optimization. MATLAB 7.10 has been used to write the code for the complete algorithm with the help of genetic algorithm (GA). The validity of the proposed method has been demonstrated on a three-unit power system.

Keywords: economic load dispatch, genetic algorithm, generating units, multiobjective optimization, weighting method

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Authors: Samba Raju, Chiluveru, Manoj Tripathy

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Intelligibility is an essential characteristic of a speech signal, which is used to help in the understanding of information in speech signal. Background noise in the environment can deteriorate the intelligibility of a recorded speech. In this paper, we presented a simple variance subtracted - variable level discrete wavelet transform, which improve the intelligibility of speech. The proposed algorithm does not require an explicit estimation of noise, i.e., prior knowledge of the noise; hence, it is easy to implement, and it reduces the computational burden. The proposed algorithm decides a separate decomposition level for each frame based on signal dominant and dominant noise criteria. The performance of the proposed algorithm is evaluated with speech intelligibility measure (STOI), and results obtained are compared with Universal Discrete Wavelet Transform (DWT) thresholding and Minimum Mean Square Error (MMSE) methods. The experimental results revealed that the proposed scheme outperformed competing methods

Keywords: discrete wavelet transform, speech intelligibility, STOI, standard deviation

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Authors: Tommy Krieger, Klaus Gruendler

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We present a novel approach for measuring democracy, which enables a very detailed and sensitive index. This method is based on Support Vector Machines, a mathematical algorithm for pattern recognition. Our implementation evaluates 188 countries in the period between 1981 and 2011. The Support Vector Machines Democracy Index (SVMDI) is continuously on the 0-1-Interval and robust to variations in the numerical process parameters. The algorithm introduced here can be used for every concept of democracy without additional adjustments, and due to its flexibility it is also a valuable tool for comparison studies.

Keywords: democracy, democracy index, machine learning, support vector machines

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Authors: Almahd Alshereef, Ahmed Alkilany, Hammad Said, Azuraliza Abu Bakar

Abstract:

From time to time, electrical power grid is directed by the National Electricity Operator to conduct load shedding, which involves hours' power outages on the area of this study, Southern Electrical Grid of Libya (SEGL). Load shedding is conducted in order to alleviate pressure on the National Electricity Grid at times of peak demand. This approach has chosen a set of categories to study load-shedding problem considering the effect of the demand priorities on the operation of the power system during emergencies. Classification of category region for load shedding problem is solved by a new algorithm (the harmony algorithm) based on the "random generation list of category region", which is a possible solution with a proximity degree to the optimum. The obtained results prove additional enhancements compared to other heuristic approaches. The case studies are carried out on SEGL.

Keywords: optimization, harmony algorithm, load shedding, classification

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Authors: Francesca Schiavello

Abstract:

This work aims to create a hybrid algorithm, combining Quantum Approximate Optimization Algorithm (QAOA) with an Evolutionary Algorithm (EA) in the place of traditional gradient based optimization processes. QAOA’s were first introduced in 2014, where, at the time, their algorithm performed better than the traditional best known classical algorithm for Max-cut graphs. Whilst classical algorithms have improved since then and have returned to being faster and more efficient, this was a huge milestone for quantum computing, and their work is often used as a benchmarking tool and a foundational tool to explore variants of QAOA’s. This, alongside with other famous algorithms like Grover’s or Shor’s, highlights to the world the potential that quantum computing holds. It also presents the reality of a real quantum advantage where, if the hardware continues to improve, this could constitute a revolutionary era. Given that the hardware is not there yet, many scientists are working on the software side of things in the hopes of future progress. Some of the major limitations holding back quantum computing are the quality of qubits and the noisy interference they generate in creating solutions, the barren plateaus that effectively hinder the optimization search in the latent space, and the availability of number of qubits limiting the scale of the problem that can be solved. These three issues are intertwined and are part of the motivation for using EAs in this work. Firstly, EAs are not based on gradient or linear optimization methods for the search in the latent space, and because of their freedom from gradients, they should suffer less from barren plateaus. Secondly, given that this algorithm performs a search in the solution space through a population of solutions, it can also be parallelized to speed up the search and optimization problem. The evaluation of the cost function, like in many other algorithms, is notoriously slow, and the ability to parallelize it can drastically improve the competitiveness of QAOA’s with respect to purely classical algorithms. Thirdly, because of the nature and structure of EA’s, solutions can be carried forward in time, making them more robust to noise and uncertainty. Preliminary results show that the EA algorithm attached to QAOA can perform on par with the traditional QAOA with a Cobyla optimizer, which is a linear based method, and in some instances, it can even create a better Max-Cut. Whilst the final objective of the work is to create an algorithm that can consistently beat the original QAOA, or its variants, due to either speedups or quality of the solution, this initial result is promising and show the potential of EAs in this field. Further tests need to be performed on an array of different graphs with the parallelization aspect of the work commencing in October 2023 and tests on real hardware scheduled for early 2024.

Keywords: evolutionary algorithm, max cut, parallel simulation, quantum optimization

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Authors: Pooja Rani, G. S. Mahapatra, S. K. Pandey

Abstract:

In this paper, we propose a novel approach of Neural Network and Particle Swarm Optimization methods for software reliability prediction. We first explain how to apply compound function in neural network so that we can derive a Flexible Logistic (S-shaped) Growth Curve (FLGC) model. This model mathematically represents software failure as a random process and can be used to evaluate software development status during testing. To avoid trapping in local minima, we have applied Particle Swarm Optimization method to train proposed model using failure test data sets. We drive our proposed model using computational based intelligence modeling. Thus, proposed model becomes Neuro-Particle Swarm Optimization (NPSO) model. We do test result with different inertia weight to update particle and update velocity. We obtain result based on best inertia weight compare along with Personal based oriented PSO (pPSO) help to choose local best in network neighborhood. The applicability of proposed model is demonstrated through real time test data failure set. The results obtained from experiments show that the proposed model has a fairly accurate prediction capability in software reliability.

Keywords: software reliability, flexible logistic growth curve model, software cumulative failure prediction, neural network, particle swarm optimization

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