High-resolution time of arrival (TOA) estimation techniques have great promise for the high range resolution required in recently developed radar systems. A widely known super-resolution TOA estimation algorithm for such applications, the multiple-signal classification (MUSIC) in the frequency domain, has been proposed, which exploits an orthogonal relationship between signal and noise eigenvectors obtained by the correlation matrix of the observed transfer function. However, this method suffers severely from a degraded resolution when a number of highly correlated interference signals are mixed in the same range gate. As a solution for this problem, this paper proposes a novel TOA estimation algorithm by introducing a maximum likelihood independent component analysis (MLICA) approach, in which multiple complex sinusoidal signals are efficiently separated by the likelihood criteria determined by the probability density function (PDF) of a complex sinusoid. This MLICA schemes can decompose highly correlated interference signals, and the proposed method then incorporates the MLICA into the MUSIC method, to enhance the range resolution in richly interfered situations. The results from numerical simulations and experimental investigation demonstrate that our proposed pre-processing method can enhance TOA estimation resolution compared with that obtained by the original MUSIC, particularly for lower signal-to-noise ratios.

Training one-class support vector machines (one-class SVMs) involves solving a quadratic programming (QP) problem. By increasing the number of training samples, solving this QP problem becomes intractable. In this paper, we describe a modified Pegasos algorithm for fast training of one-class SVMs. We show that this algorithm is much faster than the standard one-class SVM without loss of performance in the case of linear kernel.

Automatic modulation classification (AMC) is an important function of radio surveillance systems in order to identify unknown signals. Many previous works on AMC have utilized signal cyclostationarity, particularly spectral correlation density (SCD), but many of them fail to address several implementation issues, such as the assumption of perfect knowledge of the symbol rate. In this paper, we discuss several practical issues, e.g. cyclic frequency mismatch, which may affect the SCD, and propose compensation techniques to overcome those issues. We also propose a novel feature extraction technique from the SCD, which utilizes the SCD of not only the original received signal, but also the squared received signal. A symbol rate estimation technique which complements the feature extraction is also proposed. Finally, the classification performance of the system is evaluated through Monte Carlo simulations using a wide variety of modulated signals, and simulation results show that the proposed technique can estimate the symbol rate and classify modulation with a probability of above 0.9 down to SNRs of 5 dB.

Computer-aided diagnosis (CAD) systems on diffuse lung diseases (DLD) were required to facilitate radiologists to read high-resolution computed tomography (HRCT) scans. An important task on developing such CAD systems was to make computers automatically recognize typical pulmonary textures of DLD on HRCT. In this work, we proposed a bag-of-features based method for the classification of six kinds of DLD patterns which were consolidation (CON), ground-glass opacity (GGO), honeycombing (HCM), emphysema (EMP), nodular (NOD) and normal tissue (NOR). In order to successfully apply the bag-of-features based method on this task, we focused to design suitable local features and the classifier. Considering that the pulmonary textures were featured by not only CT values but also shapes, we proposed a set of statistical measures based local features calculated from both CT values and eigen-values of Hessian matrices. Additionally, we designed a support vector machine (SVM) classifier by optimizing parameters related to both kernels and the soft-margin penalty constant. We collected 117 HRCT scans from 117 subjects for experiments. Three experienced radiologists were asked to review the data and their agreed-regions where typical textures existed were used to generate 3009 3D volume-of-interest (VOIs) with the size of 323232. These VOIs were separated into two sets. One set was used for training and tuning parameters, and the other set was used for evaluation. The overall recognition accuracy for the proposed method was 93.18%. The precisions/sensitivities for each texture were 96.67%/95.08% (CON), 92.55%/94.02% (GGO), 97.67%/99.21% (HCM), 94.74%/93.99% (EMP), 81.48%/86.03%(NOD) and 94.33%/90.74% (NOR). Additionally, experimental results showed that the proposed method performed better than four kinds of baseline methods, including two state-of-the-art methods on classification of DLD textures.

For the problem of Indoor Home Scene Classification, this paper proposes the BOW Model of Local Feature Information Gain. The experimental results show that not only the performance is improved but also the computation is reduced. Consequently this method out performs the state-of-the-art approach.

Computer-aided detection (CADe) and diagnosis (CAD) has been a rapidly growing, active area of research in medical imaging. Machine leaning (ML) plays an essential role in CAD, because objects such as lesions and organs may not be represented accurately by a simple equation; thus, medical pattern recognition essentially require “learning from examples.” One of the most popular uses of ML is the classification of objects such as lesion candidates into certain classes (e.g., abnormal or normal, and lesions or non-lesions) based on input features (e.g., contrast and area) obtained from segmented lesion candidates. The task of ML is to determine “optimal” boundaries for separating classes in the multi-dimensional feature space which is formed by the input features. ML algorithms for classification include linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), multilayer perceptrons, and support vector machines (SVM). Recently, pixel/voxel-based ML (PML) emerged in medical image processing/analysis, which uses pixel/voxel values in images directly, instead of features calculated from segmented lesions, as input information; thus, feature calculation or segmentation is not required. In this paper, ML techniques used in CAD schemes for detection and diagnosis of lung nodules in thoracic CT and for detection of polyps in CT colonography (CTC) are surveyed and reviewed.

Automatic modulation classification (AMC) involves extracting a set of unique features from the received signal. Accuracy and uniqueness of the features along with the appropriate classification algorithm determine the overall performance of AMC systems. Accuracy of any modulation feature is usually limited by the blindness of the signal information such as carrier frequency, symbol rate etc. Most papers do not sufficiently consider these impairments and so do not directly target practical applications. The AMC system proposed herein is trained with probable input signals, and the appropriate decision tree should be chosen to achieve robust classification. Six unique features are used to classify eight analog and digital modulation schemes which are widely used by low frequency mobile emergency radios around the globe. The Proposed algorithm improves the classification performance of AMC especially for the low SNR regime.

Capacitive feedback VCOs use capacitors that are connected from the output node to the gate of the tail transistor that acts as a current source. Using such feedback results in modulating the current that is used by the oscillator and therefore changes its cyclostationary noise properties which results in a lower output phase noise. This paper presents a mathematical study of capacitive feedback VCOs in terms of stability and phase noise enhancement to confirm stability and to explain the enhancement in phase noise. The derived expression for the phase noise shows an improvement of 4.4 dB is achievable by using capacitive feedback as long as the VCO stays in the current limited region. Measurement results taken from an actual capacitive feedback VCO implemented in a 65 nm CMOS process also agrees with the analysis and simulation results which further validates the given analysis.

In this study, a novel forest method based on specific random trees (SRT) was proposed for a multiclass classification problem. The proposed SRT was built on one specific class, which decides whether a sample belongs to a certain class. The forest can make a final decision on classification by ensembling all the specific trees. Compared with the original random forest, our method has higher strength, but lower correlation and upper error bound. The experimental results based on 10 different public datasets demonstrated the efficiency of the proposed method.

Player detection is an important part in sports video analysis. Over the past few years, several learning based detection methods using various supervised two-class techniques have been presented. Although satisfactory results can be obtained, a lot of manual labor is needed to construct the training set. To overcome this drawback, this letter proposes a player detection method based on one-class SVM (OCSVM) using automatically generated training data. The proposed method is evaluated using several video clips captured from World Cup 2010, and experimental results show that our approach achieves a high detection rate while keeping the training set construction's cost low.

The cryptographic algorithm called INCrypt32 is a MAC algorithm to authenticate participants, RFID cards and readers, in HID Global's iCLASS systems. HID's iCLASS cards are widely used contactless smart cards for physical access control. Although INCrypt32 is a heart of the security of HID's iCLASS systems, its security has not been evaluated yet since the specification has not been open to public. In this paper, we reveal the specification of INCrypt32 by reverse-engineering iCLASS cards and investigate the security of INCrypt32 with respect to the cryptographic sense. This result is the first work to describe the details of INCrypt32 and the possibility of a secret key (64-bit) recovery in our attack environments. 242 MAC queries are required in the real environment using secure communication protocols. But the required number of MAC queries decreases to 218 if MAC quires for chosen messages with arbitrary length can be requested.

This paper proposes a novel method for object tracking by combining local feature and global template-based methods. The proposed algorithm consists of two stages from coarse to fine. The first stage applies on-line classifiers to match the corresponding keypoints between the input frame and the reference frame. Thus a rough motion parameter can be estimated using RANSAC. The second stage employs kernel-based global representation in successive frames to refine the motion parameter. In addition, we use the kernel weight obtained during the second stage to guide the on-line learning process of the keypoints' description. Experimental results demonstrate the effectiveness of the proposed technique.

In the Naive Bayes classification problem using a vertically partitioned dataset, the conventional scheme to preserve privacy of each partition uses a secure scalar product and is based on the assumption that the data is synchronized amongst common unique identities. In this paper, we attempt to discard this assumption in order to develop a more efficient and secure scheme to perform classification with minimal disclosure of private data. Our proposed scheme is based on the work by Vaidya and Clifton [2], which uses commutative encryption to perform secure set intersection so that the parties with access to the individual partitions have no knowledge of the intersection. The evaluations presented in this paper are based on experimental results, which show that our proposed protocol scales well with large sparse datasets*.

In this paper we consider the two-class classification problem with high-dimensional data. It is important to find a class of distributions such that we cannot expect good performance in classification for any classifier. In this paper, when two population variance-covariance matrices are different, we give a reasonable sufficient condition for distributions such that the misclassification rate converges to the worst value as the dimension of data tends to infinity for any classifier. Our results can give guidelines to decide whether or not an experiment is worth performing in many fields such as bioinformatics.

Histopathology is a microscopic anatomical study of body tissues and widely used as a cancer diagnosing method. Generally, pathologists examine the structural deviation of cellular and sub-cellular components to diagnose the malignancy of body tissues. These judgments may often subjective to pathologists' skills and personal experiences. However, computational diagnosis tools may circumvent these limitations and improve the reliability of the diagnosis decisions. This paper proposes a prostate image classification method by extracting textural behavior using multifractal analysis. Fractal geometry is used to describe the complexity of self-similar structures as a non-integer exponent called fractal dimension. Natural complex structures (or images) are not self-similar, thus a single exponent (the fractal dimension) may not be adequate to describe the complexity of such structures. Multifractal analysis technique has been introduced to describe the complexity as a spectrum of fractal dimensions. Based on multifractal computation of digital imaging, we obtain two textural feature descriptors; i) local irregularity: α and ii) global regularity: f(α). We exploit these multifractal feature descriptors with a texton dictionary based classification model to discriminate cancer/non-cancer tissues of histopathology images of H&E stained prostate biopsy specimens. Moreover, we examine other three feature descriptors; Gabor filter bank, LM filter bank and Haralick features to benchmark the performance of the proposed method. Experiment results indicated that the performance of the proposed multifractal feature descriptor outperforms the other feature descriptors by achieving over 94% of correct classification accuracy.

Characterization of peer-to-peer (P2P) traffic is an essential step to develop workload models towards capacity planning and cyber-threat countermeasure over P2P networks. In this paper, we present a classification scheme for characterizing P2P file-sharing hosts based on transport layer statistical features. The proposed scheme is accessed on a virtualized environment that simulates a P2P-friendly cloud system. The system shows high accuracy in differentiating P2P file-sharing hosts from ordinary hosts. Its tunability regarding monitoring cost, system response time, and prediction accuracy is demonstrated by a series of experiments. Further study on feature selection is pursued to identify the most essential discriminators that contribute most to the classification. Experimental results show that an equally accurate system could be obtained using only 3 out of the 18 defined discriminators, which further reduces the monitoring cost and enhances the adaptability of the system.

A class-F high efficiency GaN power amplifier (PA) for dual band operation at 2.14 GHz and 2.35 GHz is proposed. A novel dual band harmonic-rejection load network, which controls the terminating impedances of the second and third harmonics, and contributes greatly to efficiency improvement of PA, is described. In addition, a matching network which guarantees the high efficiency and gain of PA for the desired dual bands is designed. The proposed load network has the harmonic rejection of more than 24 dB which is sufficient for rejecting harmonics, and an insertion loss of less than 0.11 dB. The dual band matching network for the maximum output power results in the measured highest output power for each operating frequency. The fabricated class-F GaN PA has 43 dBm-65.4% and 43 dBm-63.9% of output power - efficiency at the desired dual frequencies.

Microblogs are a rising social network with distinguishing features such as simplicity and convenience and has already attracted a large number of users and triggered massive information explosion concerning individuals' own statuses and opinions. While sentiment analysis of the messages in microblogs is of great value, most of present studies are on English microblogs and few are on Chinese microblogs. Compared to English, Chinese has its unique expression style, such as no spaces or other word delimiters. Furthermore, Chinese short text also has its own properties. Thus we are inspired to explore effective features for sentiment classification of Chinese short text. In this paper, we propose to study user-related sentiment classification of Chinese microblogs in terms of the statistical and semantic characteristics, and deisgn the corresponding features: ratio of positive words and negative words (PNR), position feature (POS), collocation of verbs (COL), auxiliary words (AU). Then we employ an SVM-based method to classify the sentiment. Experiments show that the features we design is effective in recognizing the sentiment of messages in microblogs.

In this paper, the design and fabrication of a miniaturized class-F 2.5 GHz 8 W power amplifier using a commercially available GaN HEMT bare chip from TriQuint and a Selectively Anodized Aluminum Oxide (SAAO) substrate are presented. The SAAO process was recently proposed and patented by Wavenics Inc., Daejeon, Korea, which provides the fabrication of small size circuit comparable to conventional MMIC and at drastically low cost due to the use of aluminum as a wafer. The advantage of low cost is especially promising for RF components fabrication in commercial applications like mobile communications. The fabricated power amplifier has a compact size of 4.4 4.4 mm2 and shows power added efficiency (PAE) of about 35% and harmonic suppression of above 30 dBc for second and third harmonics at an output power of 39 dBm.

In this paper, we study how to automatically classify mathematical expressions written in MathML (Mathematical Markup Language). It is an essential preprocess to resolve analysis problems originated from multi-meaning mathematical symbols. We first define twelve equation classes based on chapter information of mathematics textbooks and then conduct various experiments. Experimental results show an accuracy of 94.75%, by employing the feature combination of tags, operators, strings, and “identifier & operator” bigram.