Search results for: Real time anomaly detection

Authors: Yan-Fang Niu

Abstract:

In view of current IT integration development of SOA, this paper examines AIS design based on SOA, including information sources collection, accounting business process integration and real-time financial reports. The main objective of this exploratory paper is to facilitate AIS research combing the Web Service, which is often ignored in accounting and computer research. It provides a conceptual framework that clarifies the interdependency between SOA and AIS, and also presents the major SOA functions in different areas of AIS

Keywords: AIS, SOA, Web Service

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Authors: Yuki. Akiyama, Hiroaki. Sengoku, Ryosuke. Shibasaki

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In this study, the locations and areas of commercial accumulations were detected by using digital yellow page data. An original buffering method that can accurately create polygons of commercial accumulations is proposed in this paper.; by using this method, distribution of commercial accumulations can be easily created and monitored over a wide area. The locations, areas, and time-series changes of commercial accumulations in the South Kanto region can be monitored by integrating polygons of commercial accumulations with the time-series data of digital yellow page data. The circumstances of commercial accumulations were shown to vary according to areas, that is, highly- urbanized regions such as the city center of Tokyo and prefectural capitals, suburban areas near large cities, and suburban and rural areas.

Keywords: Commercial accumulations, Spatio-temporal analysis, Urban monitoring, Yellow page data

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Authors: Sabrina Gaito, Andrea Greppi, Giuliano Grossi

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In this paper we present a technique to speed up ICA based on the idea of reducing the dimensionality of the data set preserving the quality of the results. In particular we refer to FastICA algorithm which uses the Kurtosis as statistical property to be maximized. By performing a particular Johnson-Lindenstrauss like projection of the data set, we find the minimum dimensionality reduction rate ¤ü, defined as the ratio between the size k of the reduced space and the original one d, which guarantees a narrow confidence interval of such estimator with high confidence level. The derived dimensionality reduction rate depends on a system control parameter β easily computed a priori on the basis of the observations only. Extensive simulations have been done on different sets of real world signals. They show that actually the dimensionality reduction is very high, it preserves the quality of the decomposition and impressively speeds up FastICA. On the other hand, a set of signals, on which the estimated reduction rate is greater than 1, exhibits bad decomposition results if reduced, thus validating the reliability of the parameter β. We are confident that our method will lead to a better approach to real time applications.

Keywords: Independent Component Analysis, FastICA algorithm, Higher-order statistics, Johnson-Lindenstrauss lemma.

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Authors: Mohammed-Aminu Sanda, Emmanuel K. Mawuena

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Frontline workers performing client service duties in finance-oriented firms in most sub-Saharan African countries, such as Ghana, are known to be challenged in the conduct of their activities. The challenge is attributed to clients’ continued demand for real-time services from such workers, despite the introduction of technological interventions to offset the situation. This has caused such frontline workers to experience increases in their work-induced emotions with consequential effects on their work-induced happiness. This study, therefore, explored the effect of frontline workers’ work-induced emotions on their worked-induced happiness when providing tellering services to clients. A cross-sectional design and quantitative technique were used. Data were collected from a sample of 280 frontline workers using questionnaire. Based on the analysis, it was found that an increase in the frontline workers’ work-induced emotions, caused by their feelings of strain, burnout, frustration, and hard work, had consequential effect on their work-induced happiness. This consequential effect was also found to be aggravated by the workers’ senses of being stretched beyond limit, being emotionally drained, and being used up by their work activities. It is concluded that frontline workers in finance-oriented firms can provide quality real-time services to clients without increases in their work-induced emotions, but with enhanced work-induced happiness, when the psychological and physiological emotional factors associated with the challenged work activities are understood and remedied. Management of the firms can use such understanding to redesign the activities of their frontline workers and improve the quality of their service delivery interactivity with clients.

Keywords: Client-service activity, finance industrial sector, frontline workers, work-induced emotion, work-induced happiness.

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Authors: Insung Jung, lockjo Koo, Gi-Nam Wang

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The objective of this paper is to design a model of human vital sign prediction for decreasing prediction error by using two states mapping based time series neural network BP (back-propagation) model. Normally, lot of industries has been applying the neural network model by training them in a supervised manner with the error back-propagation algorithm for time series prediction systems. However, it still has a residual error between real value and prediction output. Therefore, we designed two states of neural network model for compensation of residual error which is possible to use in the prevention of sudden death and metabolic syndrome disease such as hypertension disease and obesity. We found that most of simulations cases were satisfied by the two states mapping based time series prediction model compared to normal BP. In particular, small sample size of times series were more accurate than the standard MLP model. We expect that this algorithm can be available to sudden death prevention and monitoring AGENT system in a ubiquitous homecare environment.

Keywords: Neural network, U-healthcare, prediction, timeseries, computer aided prediction.

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Authors: Vikram Jeganathan, A. V. Sai Balasubramanian, N. Ravi Shankar, S. Subbaraman, R. Rengaraj

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Heating systems are a necessity for regions which brace extreme cold weather throughout the year. To maintain a comfortable temperature inside a given place, heating systems making use of- Hydronic boilers- are used. The principle of a single pipe system serves as a base for their working. It is mandatory for these heating systems to control the room temperature, thus maintaining a warm environment. In this paper, the concept of regulation of the room temperature over a wide range is established by using an Adaptive Fuzzy Controller (AFC). This fuzzy controller automatically detects the changes in the outside temperatures and correspondingly maintains the inside temperature to a palatial value. Two separate AFC's are put to use to carry out this function: one to determine the quantity of heat needed to reach the prospective temperature required and to set the desired temperature; the other to control the position of the valve, which is directly proportional to the error between the present room temperature and the user desired temperature. The fuzzy logic controls the position of the valve as per the requirement of the heat. The amount by which the valve opens or closes is controlled by 5 knob positions, which vary from minimum to maximum, thereby regulating the amount of heat flowing through the valve. For the given test system data, different de-fuzzifier methods have been implemented and the results are compared. In order to validate the effectiveness of the proposed approach, a fuzzy controller has been designed by obtaining a test data from a real time system. The simulations are performed in MATLAB and are verified with standard system data. The proposed approach can be implemented for real time applications.

Keywords: Adaptive fuzzy controller, Hydronic heating system

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Authors: S. Mahalakshmi, B. Senthil Arasu

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As per the recent changes happening in the global policies, climate related changes and the impact it causes across every sector are viewed as green swan events – in essence, climate related changes can happen often and lead to risk and lot of uncertainty, but need to be mitigated instead of considering them as black swan events. This brings about a question on how this risk can be computed, so that the financial institutions can plan to mitigate it. Climate related changes impact all risk types – credit risk, market risk, operational risk, liquidity risk, reputational risk and others. And the models required to compute this have to consider the different industrial needs of the counterparty, as well as the factors that are contributing to this – be it in the form of different risk drivers, or the different transmission channels or the different approaches and the granular form of data availability. This brings out to the suggestion that the climate related changes, though it affects Pillar I risks, will be a Pillar II risk. This has to be modeled specifically based on the financial institution’s actual exposure to different industries, instead of generalizing the risk charge. And this will have to be considered as the additional capital to be met by the financial institution in addition to their Pillar I risks, as well as the existing Pillar II risks. In this paper, we present a risk assessment framework to model and assess climate change risks - for both credit and market risks. This framework helps in assessing the different scenarios, and how the different transition risks affect the risk associated with the different parties. This research paper delves on the topic of increase in concentration of greenhouse gases, that in turn causing global warming. It then considers the various scenarios of having the different risk drivers impacting credit and market risk of an institution, by understanding the transmission channels, and also considering the transition risk. The paper then focuses on the industry that’s fast seeing a disruption: automobile industry. The paper uses the framework to show how the climate changes and the change to the relevant policies have impacted the entire financial institution. Appropriate statistical models for forecasting, anomaly detection and scenario modeling are built to demonstrate how the framework can be used by the relevant agencies to understand their financial risks. The paper also focuses on the climate risk calculation for the Pillar II capital calculations, and how it will make sense for the bank to maintain this in addition to their regular Pillar I and Pillar II capital.

Keywords: Capital calculation, climate risk, credit risk, pillar II risk, scenario modeling.

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Authors: Jihyun Song, Kyeongjoo Kim, Minsoo Lee

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Due to the development of information technology and wireless Internet technology, various data are being generated in various fields. These data are advantageous in that they provide real-time information to the users themselves. However, when the data are accumulated and analyzed, more various information can be extracted. In addition, development and dissemination of boards such as Arduino and Raspberry Pie have made it possible to easily test various sensors, and it is possible to collect sensor data directly by using database application tools such as MySQL. These directly collected data can be used for various research and can be useful as data for data mining. However, there are many difficulties in using the board to collect data, and there are many difficulties in using it when the user is not a computer programmer, or when using it for the first time. Even if data are collected, lack of expert knowledge or experience may cause difficulties in data analysis and visualization. In this paper, we aim to construct a library for sensor data collection and analysis to overcome these problems.

Keywords: Clustering, data mining, DBSCAN, k-means, k-medoids, sensor data.

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Authors: Kavita Khairnar, Shikha Nema

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DS-CDMA system is well known wireless technology. This system suffers from MAI (Multiple Access Interference) caused by Direct Sequence users. Multi-User Detection schemes were introduced to detect the users- data in presence of MAI. This paper focuses on linear multi-user detection schemes used for data demodulation. Simulation results depict the performance of three detectors viz-conventional detector, Decorrelating detector and Subspace MMSE (Minimum Mean Square Error) detector. It is seen that the performance of these detectors depends on the number of paths and the length of Gold code used.

Keywords: Cross Correlation Matrix, MAI, Multi-UserDetection, Multipath Effect.

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Authors: R. Amandi, A. Shahbazi, A. Mohebi, M. Bazargan, Y. Jaberi, P. Emadi, A. Valizade

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The application of Neural Network for disease diagnosis has made great progress and is widely used by physicians. An Electrocardiogram carries vital information about heart activity and physicians use this signal for cardiac disease diagnosis which was the great motivation towards our study. In our work, tachycardia features obtained are used for the training and testing of a Neural Network. In this study we are using Fuzzy Probabilistic Neural Networks as an automatic technique for ECG signal analysis. As every real signal recorded by the equipment can have different artifacts, we needed to do some preprocessing steps before feeding it to our system. Wavelet transform is used for extracting the morphological parameters of the ECG signal. The outcome of the approach for the variety of arrhythmias shows the represented approach is superior than prior presented algorithms with an average accuracy of about %95 for more than 7 tachy arrhythmias.

Keywords: Fuzzy Logic, Probabilistic Neural Network, Tachycardia, Wavelet Transform.

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Authors: Jianqin Qu

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This paper proposes a rigid point set matching algorithm in arbitrary dimensions based on the idea of symmetric covariant function. A group of functions of the points in the set are formulated using rigid invariants. Each of these functions computes a pair of correspondence from the given point set. Then the computed correspondences are used to recover the unknown rigid transform parameters. Each computed point can be geometrically interpreted as the weighted mean center of the point set. The algorithm is compact, fast, and dimension free without any optimization process. It either computes the desired transform for noiseless data in linear time, or fails quickly in exceptional cases. Experimental results for synthetic data and 2D/3D real data are provided, which demonstrate potential applications of the algorithm to a wide range of problems.

Keywords: Covariant point, point matching, dimension free, rigid registration.

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Authors: Sadaf Nawaz, Adnan Ahmed Khan, Asad Mahmood, Chaudhary Farrukh Javed

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Classically, an energy detector is implemented in time domain (TD). However, frequency domain (FD) based energy detector has demonstrated an improved performance. This paper presents a comparison between the two approaches as to analyze their pros and cons. A detailed performance analysis of the classical TD energy-detector and the periodogram based detector is performed. Exact and approximate mathematical expressions for probability of false alarm (Pf) and probability of detection (Pd) are derived for both approaches. The derived expressions naturally lead to an analytical as well as intuitive reasoning for the improved performance of (Pf) and (Pd) in different scenarios. Our analysis suggests the dependence improvement on buffer sizes. Pf is improved in FD, whereas Pd is enhanced in TD based energy detectors. Finally, Monte Carlo simulations results demonstrate the analysis reached by the derived expressions.

Keywords: Cognitive radio, energy detector, periodogram, spectrum sensing.

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Authors: Gustavo Rodrigues, Hans Weber, Larissa Driemeier

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The prevision of post-impact conditions and the behavior of the bodies during the impact have been object of several collision models. The formulation from Hertz’s theory is generally used dated from the 19^th century. These models consider the repulsive force as proportional to the deformation of the bodies under contact and may consider it proportional to the rate of deformation. The objective of the present work is to analyze the behavior of the bodies during impact using the Finite Element Method (FEM) with elastic and plastic material models. The main parameters to evaluate are, the contact force, the time of contact and the deformation of the bodies. An advantage of using the FEM approach is the possibility to apply a plastic deformation to the model according to the material definition: there will be used Johnson–Cook plasticity model whose parameters are obtained through empirical tests of real materials. This model allows analyzing the permanent deformation caused by impact, phenomenon observed in real world depending on the forces applied to the body. These results are compared between them and with the model-based Hertz theory.

Keywords: Collision, finite element method, Hertz’s Theory, impact models.

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Authors: Marc Solé, Francesc Giné, Magda Valls, Nina Bijedic

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What people say on social media has turned into a rich source of information to understand social behavior. Specifically, the growing use of Twitter social media for political communication has arisen high opportunities to know the opinion of large numbers of politically active individuals in real time and predict the global political tendencies of a specific country. It has led to an increasing body of research on this topic. The majority of these studies have been focused on polarized political contexts characterized by only two alternatives. Unlike them, this paper tackles the challenge of forecasting Spanish political trends, characterized by multiple political parties, by means of analyzing the Twitters Users political tendency. According to this, a new strategy, named Tweets Analysis Strategy (TAS), is proposed. This is based on analyzing the users tweets by means of discovering its sentiment (positive, negative or neutral) and classifying them according to the political party they support. From this individual political tendency, the global political prediction for each political party is calculated. In order to do this, two different strategies for analyzing the sentiment analysis are proposed: one is based on Positive and Negative words Matching (PNM) and the second one is based on a Neural Networks Strategy (NNS). The complete TAS strategy has been performed in a Big-Data environment. The experimental results presented in this paper reveal that NNS strategy performs much better than PNM strategy to analyze the tweet sentiment. In addition, this research analyzes the viability of the TAS strategy to obtain the global trend in a political context make up by multiple parties with an error lower than 23%.

Keywords: Political tendency, prediction, sentiment analysis, Twitter.

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Authors: Noriko Hanakawa, Masaki Obana

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A simulation tool is proposed for big-scale projection mapping events. The tool has four main functions based on Mapbox and Unity utilities. The first function is building three-dimensional models of real cities using Mapbox. The second function is movie projections to some buildings in real cities using Unity. The third is a movie sending function from a PC to a virtual projector. The fourth function is mapping movies with fitting buildings. The simulation tool was adapted to a real projection mapping event held in 2019. The event completed, but it faced a severe problem in the movie projection to the target building. Extra tents were set in front of the target building, and the tents became obstacles to the movie projection. The simulation tool developed herein could reconstruct the problems of the event. Therefore, if the simulation tool was developed before the 2019 projection mapping event, the problem of the tents being obstacles could have been avoided using the tool. Moreover, we confirmed that the simulation tool is useful for planning future projection mapping events to avoid various extra equipment obstacles, such as utility poles, planting trees, and monument towers.

Keywords: avoiding obstacles, projection mapping, projector position, real 3D map

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Authors: R. F. Ajayi, U. Sidwaba, U. Feleni, S. F. Douman, E. Nxusani, L. Wilson, C. Rassie, O. Tovide, P. G. L. Baker, S. L. Vilakazi, R. Tshikhudo, E. I. Iwuoha

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Pyrazinamide (PZA) is among the first-line pro-drugs  in the tuberculosis (TB) combination chemotherapy used to treat  Mycobacterium tuberculosis. Numerous reports have suggested that  hepatotoxicity due to pyrazinamide in patients is due to inappropriate  dosing. It is, therefore necessary to develop sensitive and reliable  techniques for determining the PZA metabolic profile of diagnosed  patients promptly and at point-of-care. This study reports the  determination of PZA based on nanobiosensor systems developed  from disuccinimidyl octanedioate modified Cytochrome P450-2E1  (CYP2E1) electrodeposited on gold substrates derivatised with  (poly(8-anilino-1-napthalene sulphonic acid) PANSA/PVP-AgNPs  nanocomposites. The rapid and sensitive amperometric PZA  detection gave a dynamic linear range of 2µM to 16µM revealing a  limit of detection of 0.044µM and a sensitivity of 1.38µA/µM. The  Michaelis-Menten parameters; KM, KM app and IMAX were calculated to  be 6.0µM, 1.41µM and 1.51x10-6 A, respectively, indicating a  nanobiosensor suitable for use in serum.

Keywords: Cytochrome P450-2E1, Disuccinimidyl octanedioate, Pyrazinamide, Tuberculosis.

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Authors: Jonghyun Park, Toan Nguyen Dinh, Gueesang Lee

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In this paper, we present a novel approach to accurately detect text regions including shop name in signboard images with complex background for mobile system applications. The proposed method is based on the combination of text detection using edge profile and region segmentation using fuzzy c-means method. In the first step, we perform an elaborate canny edge operator to extract all possible object edges. Then, edge profile analysis with vertical and horizontal direction is performed on these edge pixels to detect potential text region existing shop name in a signboard. The edge profile and geometrical characteristics of each object contour are carefully examined to construct candidate text regions and classify the main text region from background. Finally, the fuzzy c-means algorithm is performed to segment and detected binarize text region. Experimental results show that our proposed method is robust in text detection with respect to different character size and color and can provide reliable text binarization result.

Keywords: Text detection, edge profile, signboard image, fuzzy clustering.

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Authors: K. Shiba, T. Kaburagi, Y. Kurihara

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With global aging, people who require care, such as people with dementia (PwD), are increasing within many developed countries. And PwDs may wander and unconsciously set foot outdoors, it may lead serious accidents, such as, traffic accidents. Here, round-the-clock monitoring by caregivers is necessary, which can be a burden for the caregivers. Therefore, an automatic wandering detection system is required when an elderly person wanders outdoors, in which case the detection system transmits a ‘moving’ followed by an ‘absence’ state. In this paper, we focus on the transition from the ‘resting’ to the ‘absence’ state, via the ‘moving’ state as one of the wandering transitions. To capture the transition of the three states, our method based on the hidden Markov model (HMM) is built. Using our method, the restraint where the ‘resting’ state and ‘absence’ state cannot be transmitted to each other is applied. To validate our method, we conducted the experiment with 10 subjects. Our results show that the method can classify three states with 0.92 accuracy.

Keywords: Wander, microwave Doppler sensor, respiratory frequency band, the state transition, hidden Markov model.

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Authors: Mayssa Bensalah, Atef Boujelben, Mouna Baklouti, Mohamed Abid

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Image segmentation has a fundamental role in analysis and interpretation for many applications. The automated segmentation of organs and tissues throughout the body using computed imaging has been rapidly increasing. Indeed, it represents one of the most important parts of clinical diagnostic tools. In this paper, we discuss a thorough literature review of recent methods of tumour segmentation from medical images which are briefly explained with the recent contribution of various researchers. This study was followed by comparing these methods in order to define new directions to develop and improve the performance of the segmentation of the tumour area from medical images.

Keywords: Features extraction, image segmentation, medical images, tumour detection.

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Authors: A. T. Boum, L. Bitjoka, N. N. Léandre, S. Bennet

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This paper presents the estimation of the key parameters of a double fed induction machine (DFIM) by the use of the moving horizon estimator (MHE) for control and monitoring purpose. A study was conducted on the behavior of this observer in the presence of some faults which can occur during the operation of the machine. In the first case a stator phase has been suppressed. In the second case the rotor resistance has been multiplied by a factor. The results show a good estimation of different parameters such as rotor flux, rotor speed, stator current with a very small estimation error. The robustness of the observer was also tested in the practical case of DFIM by using another model different from the real one at a constant close. The very small estimation error makes the MHE a good software sensor candidate for monitoring purpose for the DFIM. 

Keywords: Doubly fed induction machine, moving horizon estimator parameters’ estimation.

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Authors: A. Y. Bani Hashim, N. S. A. Ramdan

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Inconsistency in manual inspection is real because humans get tired after some time. Recent trends show that automatic inspection is more appealing for mass production inspections. In such as a case, a robot manipulator seems the best candidate to run a dynamic visual inspection. The purpose of this work is to estimate the optimum workspace where a robot manipulator would perform a visual inspection process onto a work piece where a camera is attached to the end effector. The pseudo codes for the planned path are derived from the number of tool transit points, the delay time at the transit points, the process cycle time, and the configuration space that the distance between the tool and the work piece. It is observed that express start and swift end are acceptable in a robot program because applicable works usually in existence during these moments. However, during the mid-range cycle, there are always practical tasks programmed to be executed. For that reason, it is acceptable to program the robot such as that speedy alteration of actuator displacement is avoided. A dynamic visual inspection system using a robot manipulator seems practical for a work piece with a complex shape.

Keywords: Robot manipulator, Visual inspection, Work piece, Trajectory planning.

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Authors: C. I. L. Justino, A. C. Freitas, T. A. P. Rocha-Santos, A. C. Duarte

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This work relates the development of an optical fiber (OF) sensor for the detection and quantification of single walled carbon nanotubes in aqueous solutions. The developed OF displays a compact design, it requires less expensive materials and equipment as well as low volume of sample (0.2 mL). This methodology was also validated by the comparison of its analytical performance with that of a standard methodology based on ultraviolet-visible spectroscopy. The developed OF sensor follows the general SDS calibration proposed for OF sensors as a more suitable calibration fitting compared with classical calibrations.

Keywords: Optical fiber sensor, single-walled carbon nanotubes, SDS calibration model, UV-Vis spectroscopy

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Authors: Saman Ghaffarian, Ilgın Gökasar

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This paper presents a new automatic vehicle detection method from very high resolution aerial images to measure traffic density. The proposed method starts by extracting road regions from image using road vector data. Then, the road image is divided into equal sections considering resolution of the images. Gradient vectors of the road image are computed from edge map of the corresponding image. Gradient vectors on the each boundary of the sections are divided where the gradient vectors significantly change their directions. Finally, number of vehicles in each section is carried out by calculating the standard deviation of the gradient vectors in each group and accepting the group as vehicle that has standard deviation above predefined threshold value. The proposed method was tested in four very high resolution aerial images acquired from Istanbul, Turkey which illustrate roads and vehicles with diverse characteristics. The results show the reliability of the proposed method in detecting vehicles by producing 86% overall F1 accuracy value.

Keywords: Aerial images, intelligent transportation systems, traffic density measurement, vehicle detection.

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Authors: Juntae Kim, Jaeyoung Park, Kyoungkun Lee, Jong Tae Kim

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The H.264/AVC standard is a highly efficient video codec providing high-quality videos at low bit-rates. As employing advanced techniques, the computational complexity has been increased. The complexity brings about the major problem in the implementation of a real-time encoder and decoder. Parallelism is the one of approaches which can be implemented by multi-core system. We analyze macroblock-level parallelism which ensures the same bit rate with high concurrency of processors. In order to reduce the encoding time, dynamic data partition based on macroblock region is proposed. The data partition has the advantages in load balancing and data communication overhead. Using the data partition, the encoder obtains more than 3.59x speed-up on a four-processor system. This work can be applied to other multimedia processing applications.

Keywords: H.264/AVC, video coding, thread-level parallelism, OpenMP, multimedia

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Authors: Gurinder Singh, Kulbir Singh

Abstract:

Digital multimedia contents such as image, video, and audio can be tampered easily due to the availability of powerful editing softwares. Multimedia forensics is devoted to analyze these contents by using various digital forensic techniques in order to validate their authenticity. Digital image forensics is dedicated to investigate the reliability of digital images by analyzing the integrity of data and by reconstructing the historical information of an image related to its acquisition phase. In this paper, a survey is carried out on the forgery detection by considering the most recent and promising digital image forensic techniques.

Keywords: Computer forensics, multimedia forensics, image ballistics, camera source identification, forgery detection.

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Authors: Mohd Herwan Sulaiman, Omar Aliman, Siti Rafidah Abdul Rahim

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This paper proposes a new methodology for the optimal allocation and sizing of Embedded Generation (EG) employing Real Coded Genetic Algorithm (RCGA) to minimize the total power losses and to improve voltage profiles in the radial distribution networks. RCGA is a method that uses continuous floating numbers as representation which is different from conventional binary numbers. The RCGA is used as solution tool, which can determine the optimal location and size of EG in radial system simultaneously. This method is developed in MATLAB. The effect of EG units- installation and their sizing to the distribution networks are demonstrated using 24 bus system.

Keywords: Embedded generation (EG), load flow study, optimal allocation, real coded genetic algorithm (RCGA).

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Authors: Saswata Chakraborty, R.P.Chatterjee, S. Majumder, Anup Kr. Bhattacharjee

Abstract:

The scattering effect of light in fog improves the difficulty in visibility thus introducing disturbances in transport facilities in urban or industrial areas causing fatal accidents or public harassments, therefore, developing an enhanced fog vision system with radio wave to improvise the way outs of these severe problems is really a big challenge for researchers. Series of experimental studies already been done and more are in progress to know the weather effect on radio frequencies for different ranges. According to Rayleigh scattering Law, the propagating wavelength should be greater than the diameter of the particle present in the penetrating medium. Direct wave RF signal thus have high chance of failure to work in such weather for detection of any object. Therefore an extensive study was required to find suitable region in the RF band that can help us in detecting objects with proper shape. This paper produces some results on object detection using 912 MHz band with successful detection of the persistence of any object coming under the trajectory of a vehicle navigating in indoor and outdoor environment. The developed images are finally transformed to video signal to enable continuous monitoring.

Keywords: RADAR Imaging, Fog vision system, Objectdetection, Jpeg to Mpeg conversion

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Authors: K.Sedhuraman, S.Himavathi, A.Muthuramalingam

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The performance of sensor-less controlled induction motor drive depends on the accuracy of the estimated speed. Conventional estimation techniques being mathematically complex require more execution time resulting in poor dynamic response. The nonlinear mapping capability and powerful learning algorithms of neural network provides a promising alternative for on-line speed estimation. The on-line speed estimator requires the NN model to be accurate, simpler in design, structurally compact and computationally less complex to ensure faster execution and effective control in real time implementation. This in turn to a large extent depends on the type of Neural Architecture. This paper investigates three types of neural architectures for on-line speed estimation and their performance is compared in terms of accuracy, structural compactness, computational complexity and execution time. The suitable neural architecture for on-line speed estimation is identified and the promising results obtained are presented.

Keywords: Sensorless IM drives, rotor speed estimators, artificial neural network, feed- forward architecture, single neuron cascaded architecture.

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Authors: Michael Zimba, Darlison Nyirenda

Abstract:

A novel copy-move image forgery, CMIF, detection method is proposed. The proposed method presents a new approach which relies on electrostatic field theory, EFT. Solely for the purpose of reducing the dimension of a suspicious image, the proposed algorithm firstly performs discrete wavelet transform, DWT, of the suspicious image and extracts only the approximation subband. The extracted subband is then bijectively mapped onto a virtual electrostatic field where concepts of EFT are utilized to extract robust features. The extracted features are invariant to additive noise, JPEG compression, and affine transformation. Finally, same affine transformation selection, SATS, a duplication verification method, is applied to detect duplicated regions. SATS is a better option than the common shift vector method because SATS is insensitive to affine transformation. Consequently, the proposed CMIF algorithm is not only fast but also more robust to attacks compared to the existing related CMIF algorithms. The experimental results show high detection rates, as high as 100% in some cases.

Keywords: Affine transformation, Radix sort, SATS, Virtual electrostatic field.

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Authors: Taiwo. M. Akinmuyisitan, John Cosmas

Abstract:

This paper practically demonstrates the theories and concepts of an Advanced Convolutional Neural Network in the design and development of a scalable artificial intelligence model for the detection of criminal masterminds. The technique uses machine vision algorithms to compute the facial characteristics of suspects and classify actors as criminal or non-criminal faces. The paper proceeds further to compare the results of the error accuracy of two popular custom convolutional pre-trained networks, VGG16 and Resnet50. The result shows that VGG16 is probably more efficient than ResNet50 for the dataset we used.

Keywords: Artificial intelligence, convolutional neural networks, Resnet50, VGG16.

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