Search results for: Error Detection Rate

Authors: Muhammd Hassan Khalil, Xu Jiadong

Abstract:

Early breast cancer detection is an emerging field of research as it can save the women infected by malignant tumors. Microwave breast imaging is based on the electrical property contrast between healthy and malignant tumor. This contrast can be detected by use of microwave energy with an array of antennas that illuminate the breast through coupling medium and by measuring the scattered fields. In this paper, author has been presented the design and simulation results of the bowtie antenna. This bowtie antenna is designed for the detection of breast cancer detection.

Keywords: Breast cancer detection, Microwave Imaging, Tomography.

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Authors: Pankaj K. Gupta, Krishnan V. Pagalthivarthi

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The present work compares the performance of three turbulence modeling approach (based on the two-equation k -ε model) in predicting erosive wear in multi-size dense slurry flow through rotating channel. All three turbulence models include rotation modification to the production term in the turbulent kineticenergy equation. The two-phase flow field obtained numerically using Galerkin finite element methodology relates the local flow velocity and concentration to the wear rate via a suitable wear model. The wear models for both sliding wear and impact wear mechanisms account for the particle size dependence. Results of predicted wear rates using the three turbulence models are compared for a large number of cases spanning such operating parameters as rotation rate, solids concentration, flow rate, particle size distribution and so forth. The root-mean-square error between FE-generated data and the correlation between maximum wear rate and the operating parameters is found less than 2.5% for all the three models.

Keywords: Rotating channel, maximum wear rate, multi-sizeparticulate flow, k −ε turbulence models.

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Authors: Witcha Chimphlee, Abdul Hanan Abdullah, Mohd Noor Md Sap, Siriporn Chimphlee, Surat Srinoy

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It is important problems to increase the detection rates and reduce false positive rates in Intrusion Detection System (IDS). Although preventative techniques such as access control and authentication attempt to prevent intruders, these can fail, and as a second line of defence, intrusion detection has been introduced. Rare events are events that occur very infrequently, detection of rare events is a common problem in many domains. In this paper we propose an intrusion detection method that combines Rough set and Fuzzy Clustering. Rough set has to decrease the amount of data and get rid of redundancy. Fuzzy c-means clustering allow objects to belong to several clusters simultaneously, with different degrees of membership. Our approach allows us to recognize not only known attacks but also to detect suspicious activity that may be the result of a new, unknown attack. The experimental results on Knowledge Discovery and Data Mining-(KDDCup 1999) Dataset show that the method is efficient and practical for intrusion detection systems.

Keywords: Network and security, intrusion detection, fuzzy cmeans, rough set.

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Authors: Mohammadali Tavakoli, Atefeh Heydari, Zuriati Ismail, Naomie Salim

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With the increasing number of people reviewing products online in recent years, opinion sharing websites has become the most important source of customers’ opinions. Unfortunately, spammers generate and post fake reviews in order to promote or demote brands and mislead potential customers. These are notably destructive not only for potential customers, but also for business holders and manufacturers. However, research in this area is not adequate, and many critical problems related to spam detection have not been solved to date. To provide green researchers in the domain with a great aid, in this paper, we have attempted to create a highquality framework to make a clear vision on review spam-detection methods. In addition, this report contains a comprehensive collection of detection metrics used in proposed spam-detection approaches. These metrics are extremely applicable for developing novel detection methods.

Keywords: Fake reviews, Feature collection, Opinion spam, Spam detection.

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Authors: Sanjoy Das, Akash Arya, Rishi Pal Singh

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Vehicular Ad hoc Network is basically the solution of several problems associated while vehicles are plying on the road. In this paper, we have focused on the detection of imposter node while it has stolen the ID's of the authenticated vehicle in the network. The purpose is to harm the network through imposter messages. Here, we have proposed a protocol namely Imposter Detection based on Location (IDBL), which will store the location coordinate of the each vehicle as the key of the authenticity of the message so that imposter node can be detected. The imposter nodes send messages from a stolen ID and show that it is from an authentic node ID. So, to detect this anomaly, the first location is checked and observed different from original vehicle location. This node is known as imposter node. We have implemented the algorithm through JAVA and tested various types of node distribution and observed the detection probability of imposter node.

Keywords: Authentication, detection, IDBL protocol, imposter node, node detection.

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Authors: Lenka Krulikovská, Jaroslav Polec, Tomáš Hirner

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In this paper we present a novel method, which reduces the computational complexity of abrupt cut detection. We have proposed fast algorithm, where the similarity of frames within defined step is evaluated instead of comparing successive frames. Based on the results of simulation on large video collection, the proposed fast algorithm is able to achieve 80% reduction of needed frames comparisons compared to actually used methods without the shot cut detection accuracy degradation.

Keywords: Abrupt cut, fast algorithm, shot cut detection, Pearson correlation coefficient.

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Authors: E. Dabbour, S. M. Easa

Abstract:

Most of the collision warning systems currently available in the automotive market are mainly designed to warn against imminent rear-end and lane-changing collisions. No collision warning system is commercially available to warn against imminent turning collisions at intersections, especially for left-turn collisions when a driver attempts to make a left-turn at either a signalized or non-signalized intersection, conflicting with the path of other approaching vehicles traveling on the opposite-direction traffic stream. One of the major factors that lead to left-turn collisions is the human error and misjudgment of the driver of the turning vehicle when perceiving the speed and acceleration of other vehicles traveling on the opposite-direction traffic stream; therefore, using a properly-designed collision warning system will likely reduce, or even eliminate, this type of collisions by reducing human error. This paper introduces perceptual framework for a proposed collision warning system that can detect imminent left-turn collisions at intersections. The system utilizes a commercially-available detection sensor (either a radar sensor or a laser detector) to detect approaching vehicles traveling on the opposite-direction traffic stream and calculate their speeds and acceleration rates to estimate the time-tocollision and compare that time to the time required for the turning vehicle to clear the intersection. When calculating the time required for the turning vehicle to clear the intersection, consideration is given to the perception-reaction time of the driver of the turning vehicle, which is the time required by the driver to perceive the message given by the warning system and react to it by engaging the throttle. A regression model was developed to estimate perception-reaction time based on age and gender of the driver of the host vehicle. Desired acceleration rate selected by the driver of the turning vehicle, when making the left-turn movement, is another human factor that is considered by the system. Another regression model was developed to estimate the acceleration rate selected by the driver of the turning vehicle based on driver-s age and gender as well as on the location and speed of the nearest approaching vehicle along with the maximum acceleration rate provided by the mechanical characteristics of the turning vehicle. By comparing time-to-collision with the time required for the turning vehicle to clear the intersection, the system displays a message to the driver of the turning vehicle when departure is safe. An application example is provided to illustrate the logic algorithm of the proposed system.

Keywords: Collision warning systems, intelligent transportationsystems, vehicle safety.

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Authors: Waqas Ahmed, S Sajjad Haider Zaidi

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For the past couple of decades Weak signal detection is of crucial importance in various engineering and scientific applications. It finds its application in areas like Wireless communication, Radars, Aerospace engineering, Control systems and many of those. Usually weak signal detection requires phase sensitive detector and demodulation module to detect and analyze the signal. This article gives you a preamble to intrusion detection system which can effectively detect a weak signal from a multiplexed signal. By carefully inspecting and analyzing the respective signal, this system can successfully indicate any peripheral intrusion. Intrusion detection system (IDS) is a comprehensive and easy approach towards detecting and analyzing any signal that is weakened and garbled due to low signal to noise ratio (SNR). This approach finds significant importance in applications like peripheral security systems.

Keywords: Data Acquisition, fast frequency transforms, Lab VIEW software, weak signal detection.

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Authors: Abdulaziz Alsadhan, Naveed Khan

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In recent years intrusions on computer network are the major security threat. Hence, it is important to impede such intrusions. The hindrance of such intrusions entirely relies on its detection, which is primary concern of any security tool like Intrusion detection system (IDS). Therefore, it is imperative to accurately detect network attack. Numerous intrusion detection techniques are available but the main issue is their performance. The performance of IDS can be improved by increasing the accurate detection rate and reducing false positive. The existing intrusion detection techniques have the limitation of usage of raw dataset for classification. The classifier may get jumble due to redundancy, which results incorrect classification. To minimize this problem, Principle component analysis (PCA), Linear Discriminant Analysis (LDA) and Local Binary Pattern (LBP) can be applied to transform raw features into principle features space and select the features based on their sensitivity. Eigen values can be used to determine the sensitivity. To further classify, the selected features greedy search, back elimination, and Particle Swarm Optimization (PSO) can be used to obtain a subset of features with optimal sensitivity and highest discriminatory power. This optimal feature subset is used to perform classification. For classification purpose, Support Vector Machine (SVM) and Multilayer Perceptron (MLP) are used due to its proven ability in classification. The Knowledge Discovery and Data mining (KDD’99) cup dataset was considered as a benchmark for evaluating security detection mechanisms. The proposed approach can provide an optimal intrusion detection mechanism that outperforms the existing approaches and has the capability to minimize the number of features and maximize the detection rates.

Keywords: Particle Swarm Optimization (PSO), Principle component analysis (PCA), Linear Discriminant Analysis (LDA), Local Binary Pattern (LBP), Support Vector Machine (SVM), Multilayer Perceptron (MLP).

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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: G. Sandhya Devi, G. Suvarna Kumar, S. Chandini

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This research work aims to develop a system that will analyze and identify students who indulge in malpractices/suspicious activities during the course of an academic offline examination. Automated Video Surveillance provides an optimal solution which helps in monitoring the students and identifying the malpractice event immediately. This work is organized into three modules. The first module deals with performing an impersonation check using a PCA-based face recognition method which is done by cross checking his profile with the database. The presence or absence of the student is even determined in this module by implementing an image registration technique wherein a grid is formed by considering all the images registered using the frontal camera at the determined positions. Second, detecting such facial malpractices in which a student gets involved in conversation with another, trying to obtain unauthorized information etc., based on the threshold range evaluated by considering his/her mouth state whether open or closed. The third module deals with identification of unauthorized material or gadgets used in the examination hall by training the positive samples of the object through various stages. Here, a top view camera feed is analyzed to detect the suspicious activities. The system automatically alerts the administration when any suspicious activities are identified, thereby reducing the error rate caused due to manual monitoring. This work is an improvement over our previous work published in identifying suspicious activities done by examinees in an offline examination.

Keywords: Impersonation, image registration, incrimination, object detection, threshold evaluation.

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Authors: D. Ouahdi, R. Ladjeroud, I. Habi

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The distance protection mainly the impedance relay which is considered as the main protection for transmission lines can be subjected to impedance measurement error which is, mainly, due to the fault resistance and to the power fluctuation. Thus, the impedance relay may not operate for a short circuit at the far end of the protected line (case of the under reach) or operates for a fault beyond its protected zone (case of overreach). In this paper, an approach to fault detection by a distance protection, which distinguishes between the faulty conditions and the effect of overload operation mode, has been developed. This approach is based on the symmetrical components; mainly the negative sequence, and it is taking into account both the effect of fault resistance and the overload situation which both have an effect upon the reliability of the protection in terms of dependability for the former and security for the latter.

Keywords: Distance Protection, Fault Detection, negative sequence, overload, Transmission line.

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Authors: Rajesh Joseph Abraham, D. Das, Amit Patra

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This paper is concerned with the application of small rating Capacitive Energy Storage units for the improvement of Automatic Generation Control of a multiunit multiarea power system. Generation Rate Constraints are also considered in the investigations. Integral Squared Error technique is used to obtain the optimal integral gain settings by minimizing a quadratic performance index. Simulation studies reveal that with CES units, the deviations in area frequencies and inter-area tie-power are considerably improved in terms of peak deviations and settling time as compared to that obtained without CES units.

Keywords: Automatic Generation Control, Capacitive EnergyStorage, Integral Squared Error.

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Authors: Seyed Nima Tayarani Bathaie

Abstract:

In this paper, an image-receiving system was designed and implemented through optimization of object detection algorithms using Haar features. This optimized algorithm served as face and eye detection separately. Then, cascading them led to a clear image of the user. Utilization of this feature brought about higher security by preventing fraud. This attribute results from the fact that services will be given to the user on condition that a clear image of his face has already been captured which would exclude the inappropriate person. In order to expedite processing and eliminating unnecessary ones, the input image was compressed, a motion detection function was included in the program, and detection window size was confined.

Keywords: Face detection algorithm, Haar features, Security of ATM.

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Authors: Kaveh Shahi, Helmi Z. M. Shafri, Ebrahim Taherzadeh

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In remote sensing, shadow causes problems in many applications such as change detection and classification. It is caused by objects which are elevated, thus can directly affect the accuracy of information. For these reasons, it is very important to detect shadows particularly in urban high spatial resolution imagery which created a significant problem. This paper focuses on automatic shadow detection based on a new spectral index for multispectral imagery known as Shadow Detection Index (SDI). The new spectral index was tested on different areas of WorldView-2 images and the results demonstrated that the new spectral index has a massive potential to extract shadows with accuracy of 94% effectively and automatically. Furthermore, the new shadow detection index improved road extraction from 82% to 93%.

Keywords: Spectral index, shadow detection, remote sensing images, WorldView-2.

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Authors: Lenka Krulikovská, Jaroslav Polec, Michal Martinovič

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In this paper we propose a method which improves the efficiency of video coding. Our method combines an adaptive GOP (group of pictures) structure and the shot cut detection. We have analyzed different approaches for shot cut detection with aim to choose the most appropriate one. The next step is to situate N frames to the positions of detected cuts during the process of video encoding. Finally the efficiency of the proposed method is confirmed by simulations and the obtained results are compared with fixed GOP structures of sizes 4, 8, 12, 16, 32, 64, 128 and GOP structure with length of entire video. Proposed method achieved the gain in bit rate from 0.37% to 50.59%, while providing PSNR (Peak Signal-to-Noise Ratio) gain from 1.33% to 0.26% in comparison to simulated fixed GOP structures.

Keywords: Adaptive GOP structure, video coding, video content, shot cut detection.

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Authors: M. Kowalski, M. Kastek, M. Szustakowski

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Multispectral screening systems are becoming more popular because of their very interesting properties and applications. One of the most significant applications of multispectral screening systems is prevention of terrorist attacks. There are many kinds of threats and many methods of detection. Visual detection of objects hidden under clothing of a person is one of the most challenging problems of threats detection. There are various solutions of the problem; however, the most effective utilize multispectral surveillance imagers. The development of imaging devices and exploration of new spectral bands is a chance to introduce new equipment for assuring public safety. We investigate the possibility of long lasting detection of potentially dangerous objects covered with various types of clothing. In the article we present the results of comparative studies of passive imaging in three spectrums – visible, infrared and terahertz.

Keywords: Infrared, image processing, object detection, screening camera, terahertz.

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Authors: Dewan Md. Farid, Jerome Darmont, Nouria Harbi, Nguyen Huu Hoa, Mohammad Zahidur Rahman

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In this paper, a new learning approach for network intrusion detection using naïve Bayesian classifier and ID3 algorithm is presented, which identifies effective attributes from the training dataset, calculates the conditional probabilities for the best attribute values, and then correctly classifies all the examples of training and testing dataset. Most of the current intrusion detection datasets are dynamic, complex and contain large number of attributes. Some of the attributes may be redundant or contribute little for detection making. It has been successfully tested that significant attribute selection is important to design a real world intrusion detection systems (IDS). The purpose of this study is to identify effective attributes from the training dataset to build a classifier for network intrusion detection using data mining algorithms. The experimental results on KDD99 benchmark intrusion detection dataset demonstrate that this new approach achieves high classification rates and reduce false positives using limited computational resources.

Keywords: Attributes selection, Conditional probabilities, information gain, network intrusion detection.

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Authors: A. Fayad, Q. Alqhazaly, T. Cinkler

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In light of the substantial increase in end-user requirements and the incessant need of network operators to upgrade the capabilities of access networks, in this paper, the performance of the different modulation formats on eight-channels Time and Wavelength Division Multiplexing Passive Optical Network (TWDM-PON) transmission system has been examined and compared. Limitations and features of modulation formats have been determined to outline the most suitable design to enhance the data rate and transmission reach to obtain the best performance of the network. The considered modulation formats are On-Off Keying Non-Return-to-Zero (NRZ-OOK), Carrier Suppressed Return to Zero (CSRZ), Duo Binary (DB), Modified Duo Binary (MODB), Quadrature Phase Shift Keying (QPSK), and Differential Quadrature Phase Shift Keying (DQPSK). The performance has been analyzed by varying transmission distances and bit rates under different channel spacing. Furthermore, the system is evaluated in terms of minimum Bit Error Rate (BER) and Quality factor (Qf) without applying any dispersion compensation technique, or any optical amplifier. Optisystem software was used for simulation purposes.

Keywords: Bit Error Rate, BER, Carrier Suppressed Return to Zero, CSRZ, Duo Binary, DB, Differential Quadrature Phase Shift Keying, DQPSK, Modified Duo Binary, MODB, On-Off Keying Non-Return-to-Zero, NRZ-OOK, Quality factor, Qf, Time and Wavelength Division Multiplexing Passive Optical Network, TWDM-PON.

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Authors: S. Swain, P. S Khuntia

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In this paper, the unstable angle of attack of a FOXTROT aircraft is controlled by using Genetic Algorithm based flight controller and the result is compared with the conventional techniques like Tyreus-Luyben (TL), Ziegler-Nichols (ZN) and Interpolation Rule (IR) for tuning the PID controller. In addition, the performance indices like Mean Square Error (MSE), Integral Square Error (ISE), and Integral Absolute Time Error (IATE) etc. are improved by using Genetic Algorithm. It was established that the error by using GA is very less as compared to the conventional techniques thereby improving the performance indices of the dynamic system.

Keywords: Angle of Attack, Genetic Algorithm, Performance Indices, PID Controller.

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Authors: Chaiwat Tantarangsee

Abstract:

The purposes of this study are 1) to study the effects of participatory error correction process and 2) to find out the students’ satisfaction of such error correction process. This study is a Quasi Experimental Research with single group, in which data is collected 5 times preceding and following 4 experimental studies of participatory error correction process including providing coded indirect corrective feedback in the students’ texts with error treatment activities. Samples include 52 2nd year English Major students, Faculty of Humanities and Social Sciences, Suan Sunandha Rajabhat University. Tool for experimental study includes the lesson plan of the course; Reading and Writing English for Academic Purposes II, and tools for data collection include 5 writing tests of short texts and a questionnaire. Based on formative evaluation of the students’ writing ability prior to and after each of the 4 experiments, the research findings disclose the students’ higher scores with statistical difference at 0.00. Moreover, in terms of the effect size of such process, it is found that for mean of the students’ scores prior to and after the 4 experiments; d equals 0.6801, 0.5093, 0.5071, and 0.5296 respectively. It can be concluded that participatory error correction process enables all of the students to learn equally well and there is improvement in their ability to write short texts. Finally the students’ overall satisfaction of the participatory error correction process is in high level (Mean = 4.39, S.D. = 0.76).

Keywords: Coded indirect corrective feedback, participatory error correction process, error treatment.

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Authors: Hyun-Koo Kim, Young-Nam Shin, Sa-gong Kuk, Ju H. Park, Ho-Youl Jung

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This paper presents an effective traffic lights detection method at the night-time. First, candidate blobs of traffic lights are extracted from RGB color image. Input image is represented on the dominant color domain by using color transform proposed by Ruta, then red and green color dominant regions are selected as candidates. After candidate blob selection, we carry out shape filter for noise reduction using information of blobs such as length, area, area of boundary box, etc. A multi-class classifier based on SVM (Support Vector Machine) applies into the candidates. Three kinds of features are used. We use basic features such as blob width, height, center coordinate, area, area of blob. Bright based stochastic features are also used. In particular, geometric based moment-s values between candidate region and adjacent region are proposed and used to improve the detection performance. The proposed system is implemented on Intel Core CPU with 2.80 GHz and 4 GB RAM and tested with the urban and rural road videos. Through the test, we show that the proposed method using PF, BMF, and GMF reaches up to 93 % of detection rate with computation time of in average 15 ms/frame.

Keywords: Night-time traffic light detection, multi-class classification, driving assistance system.

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Authors: J. Kavitha, S. Sowmyayani, P. Arockia Jansi Rani

Abstract:

In this paper, a shot boundary detection method is presented using octagon square search pattern. The color, edge, motion and texture features of each frame are extracted and used in shot boundary detection. The motion feature is extracted using octagon square search pattern. Then, the transition detection method is capable of detecting the shot or non-shot boundaries in the video using the feature weight values. Experimental results are evaluated in TRECVID video test set containing various types of shot transition with lighting effects, object and camera movement within the shots. Further, this paper compares the experimental results of the proposed method with existing methods. It shows that the proposed method outperforms the state-of-art methods for shot boundary detection.

Keywords: Content-based indexing and retrieval, cut transition detection, discrete wavelet transform, shot boundary detection, video source.

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Authors: Tesnim Charrad, Kaouther Nouira, Ahmed Ferchichi

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In order to reduce the number of deaths due to heart problems, we propose the use of Hierarchical Temporal Memory Algorithm (HTM) which is a real time anomaly detection algorithm. HTM is a cortical learning algorithm based on neocortex used for anomaly detection. In other words, it is based on a conceptual theory of how the human brain can work. It is powerful in predicting unusual patterns, anomaly detection and classification. In this paper, HTM have been implemented and tested on ECG datasets in order to detect cardiac anomalies. Experiments showed good performance in terms of specificity, sensitivity and execution time.

Keywords: HTM, Real time anomaly detection, ECG, Cardiac Anomalies.

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Authors: Abdenour Bounsiar, Edith Grall, Pierre Beauseroy

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In this paper we are interested in classification problems with a performance constraint on error probability. In such problems if the constraint cannot be satisfied, then a rejection option is introduced. For binary labelled classification, a number of SVM based methods with rejection option have been proposed over the past few years. All of these methods use two thresholds on the SVM output. However, in previous works, we have shown on synthetic data that using thresholds on the output of the optimal SVM may lead to poor results for classification tasks with performance constraint. In this paper a new method for supervised classification with rejection option is proposed. It consists in two different classifiers jointly optimized to minimize the rejection probability subject to a given constraint on error rate. This method uses a new kernel based linear learning machine that we have recently presented. This learning machine is characterized by its simplicity and high training speed which makes the simultaneous optimization of the two classifiers computationally reasonable. The proposed classification method with rejection option is compared to a SVM based rejection method proposed in recent literature. Experiments show the superiority of the proposed method.

Keywords: rejection, Chow's rule, error-reject tradeoff, SupportVector Machine.

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Authors: Moad Alhamaty , Ali Yazdian , Fathi Al-qadasi

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A common way to elude the signature-based Network Intrusion Detection System is based upon changing a recognizable attack to an unrecognizable one via the IDS. For example, in order to evade sign accommodation with intrusion detection system markers, a hacker spilt the payload packet into many small pieces or hides them within messages. In this paper we try to model the main fragmentation attack and create a new module in the intrusion detection architecture system which recognizes the main fragmentation attacks through verification of integrity checking of TCP packet in order to prevent elusion of the system and also to announce the necessary alert to the system administrator.

Keywords: Intrusion detection system, Evasion techniques, Fragmentation attacks, TCP Packet integrity.

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Authors: Guezouli Larbi, Belkacem Soundes

Abstract:

The automation of the text detection process can help the human in his driving task. Its application can be very useful to help drivers to have more information about their environment by facilitating the reading of road signs such as directional signs, events, stores, etc. In this paper, a system consisting of two stages has been proposed. In the first one, we used pseudo-Zernike moments to pinpoint areas of the image that may contain text. The architecture of this part is based on three main steps, region of interest (ROI) detection, text localization, and non-text region filtering. Then, in the second step, we present a convolutional neural network architecture (On-Road Text Detection Network - ORTDN) which is considered as a classification phase. The results show that the proposed framework achieved ≈ 35 fps and an mAP of ≈ 90%, thus a low computational time with competitive accuracy.

Keywords: Text detection, CNN, PZM, deep learning.

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Authors: Kjersti Engan, Karl Skretting, Jostein Herredsvela, Thor Ole Gulsrud

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Texture classification is an important image processing task with a broad application range. Many different techniques for texture classification have been explored. Using sparse approximation as a feature extraction method for texture classification is a relatively new approach, and Skretting et al. recently presented the Frame Texture Classification Method (FTCM), showing very good results on classical texture images. As an extension of that work the FTCM is here tested on a real world application as detection of abnormalities in mammograms. Some extensions to the original FTCM that are useful in some applications are implemented; two different smoothing techniques and a vector augmentation technique. Both detection of microcalcifications (as a primary detection technique and as a last stage of a detection scheme), and soft tissue lesions in mammograms are explored. All the results are interesting, and especially the results using FTCM on regions of interest as the last stage in a detection scheme for microcalcifications are promising.

Keywords: detection, mammogram, texture classification, dictionary learning, FTCM

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Authors: Ruili Zhou, Yuesheng Zhu

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In this paper, a new robust audio fingerprinting algorithm in MP3 compressed domain is proposed with high robustness to time scale modification (TSM). Instead of simply employing short-term information of the MP3 stream, the new algorithm extracts the long-term features in MP3 compressed domain by using the modulation frequency analysis. Our experiment has demonstrated that the proposed method can achieve a hit rate of above 95% in audio retrieval and resist the attack of 20% TSM. It has lower bit error rate (BER) performance compared to the other algorithms. The proposed algorithm can also be used in other compressed domains, such as AAC.

Keywords: Audio Fingerprinting, MP3, Modulation Frequency, TSM

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Authors: Abhijit Mitra

Abstract:

A special case of floating point data representation is block floating point format where a block of operands are forced to have a joint exponent term. This paper deals with the finite wordlength properties of this data format. The theoretical errors associated with the error model for block floating point quantization process is investigated with the help of error distribution functions. A fast and easy approximation formula for calculating signal-to-noise ratio in quantization to block floating point format is derived. This representation is found to be a useful compromise between fixed point and floating point format due to its acceptable numerical error properties over a wide dynamic range.

Keywords: Block floating point, Roundoff error, Block exponent dis-tribution fuction, Signal factor.

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