The purpose of this study is to develop an automated scheme of carotid artery calcification (CAC) detection on dental panoramic radiographs (DPRs). The CAC is one of the indices for predicting the risk of arteriosclerosis. First, regions of interest (ROIs) that include carotid arteries are determined on the basis of inflection points of the mandibular contour. Initial CAC candidates are detected by using a grayscale top-hat filter and a simple grayscale thresholding technique. Finally, a rule-based approach and a support vector machine to reduce the number of false positive (FP) findings are applied using features such as area, location, and circularity. A hundred DPRs were used to evaluate the proposed scheme. The sensitivity for the detection of CACs was 90% with 4.3 FPs (80% with 1.9 FPs) per image. Experiments show that our computer-aided detection scheme may be useful to detect CACs.

Anomaly-based worm detection is a complement to existing signature-based worm detectors. It detects unknown worms and fills the gap between when a worm is propagated and when a signature is generated and downloaded to a signature-based worm detector. A major obstacle for its deployment to personal computers (PCs) is its high false positive alarms since a typical PC user lacks the skill to handle exceptions flagged by a detector without much knowledge of computers. In this paper, we exploit the feature of personal computers in which the user interacts with many running programs and the features combining various network characteristics. The model of a program's network behaviors is conditioned on the human interactions with the program. Our scheme automates detection of unknown worms with dramatically reduced false positive alarms while not compromising low false negatives, as proved by our experimental results from an implementation on Windows-based PCs to detect real world worms.

Comparable Corpora are valuable resources for many NLP applications, and extensive research has been done on information mining based on comparable corpora in recent years. While there are not enough large-scale available public comparable corpora at present, this paper presents a bi-directional CLIR-based method for creating comparable corpora from two independent news collections in different languages. The original Chinese document collections and English documents collections are crawled from XinHuaNet respectively and formatted in a consistent manner. For each document from the two collections, the best query keywords are extracted to represent the essential content of the document, and then the keywords are translated into the language of the other collection. The translated queries are run against the collection in the same language to pick up the candidate documents in the other language and candidates are aligned based on their publication dates and the similarity scores. Results show that our approach significantly outperforms previous approaches to the construction of Chinese-English comparable corpora.

Spectral subtraction (SS) is an additive noise removal method which is derived in an extensive framework. In spectral subtraction, it is assumed that speech and noise spectra follow Gaussian distributions and are independent with each other. Hence, noisy speech also follows a Gaussian distribution. Spectral subtraction formula is obtained by maximizing the likelihood of noisy speech distribution with respect to its variance. However, it is well known that noisy speech observed in real situations often follows a heavy-tailed distribution, not a Gaussian distribution. In this paper, we introduce a q-Gaussian distribution in the non-extensive statistics to represent the distribution of noisy speech and derive a new spectral subtraction method based on it. We found that the q-Gaussian distribution fits the noisy speech distribution better than the Gaussian distribution does. Our speech recognition experiments using the Aurora-2 database showed that the proposed method, q-spectral subtraction (q-SS), outperformed the conventional SS method.

Face retrieval in news video has been identified as a challenging task due to the huge variations in the visual appearance of the human face. Although several approaches have been proposed to deal with this problem, their extremely high computational cost limits their scalability to large-scale video datasets that may contain millions of faces of hundreds of characters. In this paper, we introduce approaches for face retrieval that are scalable to such datasets while maintaining competitive performances with state-of-the-art approaches. To utilize the variability of face appearances in video, we use a set of face images called face-track to represent the appearance of a character in a video shot. Our first proposal is an approach for extracting face-tracks. We use a point tracker to explore the connections between detected faces belonging to the same character and then group them into one face-track. We present techniques to make the approach robust against common problems caused by flash lights, partial occlusions, and scattered appearances of characters in news videos. In the second proposal, we introduce an efficient approach to match face-tracks for retrieval. Instead of using all the faces in the face-tracks to compute their similarity, our approach obtains a representative face for each face-track. The representative face is computed from faces that are sampled from the original face-track. As a result, we significantly reduce the computational cost of face-track matching while taking into account the variability of faces in face-tracks to achieve high matching accuracy. Experiments are conducted on two face-track datasets extracted from real-world news videos, of such scales that have never been considered in the literature. One dataset contains 1,497 face-tracks of 41 characters extracted from 370 hours of TRECVID videos. The other dataset provides 5,567 face-tracks of 111 characters observed from a television news program (NHK News 7) over 11 years. We make both datasets publically accessible by the research community. The experimental results show that our proposed approaches achieved a remarkable balance between accuracy and efficiency.

MLPDA (Maximum Likelihood Probabilistic Data Association) has attracted a great deal of attention as an effective target track extraction method in high false density environments. However, to extract an accelerated target track on a 2-dimensional plane, the computational load of the conventional MLPDA is extremely high, since it needs to search for the most-likely position, velocity and acceleration of the target in 6-dimensional space. In this paper, we propose VG-MLPDA (Variable Gating MLPDA), which consists of the following two steps. The first step is to search the target's position and velocity among candidates with the assumed acceleration by using variable gates, which take into account both the observation noise and the difference between assumed and true acceleration. The second step is to search the most-likely position, velocity and acceleration using a maximization algorithm while reducing the gate volume. Simulation results show the validity of our method.

In this paper, we present new important privacy goals for on-line matchmaking protocols, which are resistance to off-line dictionary attacks and forward privacy of users' identities and matching wishes. We enhance traditional privacy requirements (e.g., user anonymity, matching-wish authenticity) with our new privacy goals and define the notion of privacy-enhanced matchmaking. We show that previous solutions for on-line matchmaking do not satisfy the new privacy goals and argue that privacy-enhanced matchmaking cannot be provided by solutions to seemingly related problems such as secret handshakes, set intersection, and trust negotiation. We define an adversary model, which captures the key security properties of privacy-enhanced matchmaking, and show that a simple, practical protocol derived by a two-step transformation of a password-based authenticated key exchange counters adversary attacks in a provable manner (in the standard model of cryptographic security).

A mixed storage-type design using flip-flops and latches (FF/latch-based design) has advantages on such as area and power compared to single storage-type design (only flip-flops or latches). Considering FF/latch-based design at high-level synthesis is necessary, because resource binding process significantly affects the quality of resulting circuits. One of the fundamental aspects in FF/latch-based design is that different resource binding solutions could lead to the different numbers of latch-replacable registers. Therefore, as a first step, this paper addresses a datapath design problem in which resource binding and selecting storage-types of registers are simultaneously optimized for datapath area minimization (i.e., latch replacement maximization). An efficient algorithm based on the compatibility path decomposition and an integer linear programming-based exact approach are presented. Experiments confirm the effectiveness of the proposed approaches.

In this paper, we further study linear network error correction code on a multicast network and attempt to establish a connection between linear network error correction codes and representable matroids. We propose a similar but more accurate definition of matroidal error correction network which has been introduced by K. Prasad et al. Moreover, we extend this concept to a more general situation when the given linear network error correction codes have different error correcting capacity at different sinks. More importantly, using a different method, we show that a multicast error correction network is scalar-linearly solvable if and only if it is a matroidal error correction network.

We have proposed a method of identifying superspreaders by flow sampling and a method of filtering legitimate hosts from the identified superspreaders using a white list. However, the problem of how to optimally set parameters of φ, the measurement period length, m*, the identification threshold of the flow count m within φ, and H*, the identification probability for hosts with m=m*, remained unsolved. These three parameters seriously impact the ability to identify the spread of infection. Our contributions in this work are two-fold: (1) we propose a method of optimally designing these three parameters to satisfy the condition that the ratio of the number of active worm-infected hosts divided by the number of all vulnerable hosts is bound by a given upper-limit during the time T required to develop a patch or an anti-worm vaccine, and (2) the proposed method can optimize the identification accuracy of worm-infected hosts by maximally using a limited amount of memory resource of monitors.

In order to execute coherent Doppler processing in a high range-rate scenario, whether it is for detection, estimation or imaging, range walk embedded in target return should be compensated first. In case of a bistatic radar geometry where a transmitter, a receiver and a target can be all moving, the extent of range walk depends on their relative positions and velocities. This paper presents a coherent Doppler processing algorithm to achieve target detection and Doppler frequency estimation of a target under a bistatic radar geometry. This algorithm is based on the assumption that a target has constant Doppler frequency during a coherent processing interval (CPI). Thus, we first show under what condition the assumption could be valid. We next develop an algorithm, along with its implementation procedures where the region of range walk, called a window, is manipulated. Finally, the performance of a proposed algorithm is examined through simulations.

This paper shows a virus scanning engine using two-stage matching. In the first stage, a binary CAM emulator quickly detects a part of the virus pattern, while in the second stage, the MPU detects the full length of the virus pattern. The binary CAM emulator is realized by an index generation unit (IGU) based on row-shift decomposition. The proposed system uses two off-chip SRAMs and a small FPGA. Thus, the cost and the power consumption are lower than the TCAM-based system. The system loaded 1,290,617 ClamAV virus patterns. As for the area and throughput, this system outperforms existing two-stage matching systems using FPGAs.

To provide robust wireless data transmission over fading channels, various schemes which involve the use of relays have been proposed. In some of those schemes, the relay chooses not to forward the received message if its reliability is deemed as too low. Some researchers refer to such schemes as selective decode-and-forward. Our work in this paper falls into such a category. More specifically speaking, the relay in our system is a censorial relay (a relay that performs censorial task). It evaluates the reliability, in terms of log likelihood ratio (LLR), of a received data bit (from the source). If its LLR magnitude is below some preset threshold, then it is censored (i.e. not sent to the destination). When the channel is Rayleigh faded, closed-form bit error rate (BER) expressions for the proposed system are derived for several scenarios. Those scenarios are differentiated by the availability of an energy detector (ED) and the various degrees of knowledge regarding the channel state information (CSI). Aided by those closed-form BER expressions, the system parameters can be efficiently optimized to achieve the minimum BER. Simulation results are observed to closely match theoretical values, as computed by the afore-mentioned closed-form BER expressions. As compared to some existing relay-assisted systems in which censoring is incorporated, the performance of our system is better in terms of BER when the same amount of CSI is exploited.

Semantic concept in an utterance is obtained by a fuzzy matching methods to solve problems such as words' variation induced by automatic speech recognition (ASR), or missing field of key information by users in the process of spoken language understanding (SLU). A two-stage method is proposed: first, we adopt conditional random field (CRF) for building probabilistic models to segment and label entity names from an input sentence. Second, fuzzy matching based on similarity function is conducted between the named entities labeled by a CRF model and the reference characters of a dictionary. The experiments compare the performances in terms of accuracy and processing speed. Dice similarity and cosine similarity based on TF score can achieve better accuracy performance among four similarity measures, which equal to and greater than 93% in F1-measure. Especially the latter one improved by 8.8% and 9% respectively compared to q-gram and improved edit-distance, which are two conventional methods for string fuzzy matching.

This paper proposes a novel high-temperature superconducting dual-band bandpass filter (HTS-DBPF), that employs a broadside coupling structure, in which quarter-wavelength resonators are formed on opposite sides of each substrate. This structure provides a dual-band operation of the BPF and flexibility, in the sense of having a wide range in selecting two center passband frequencies of the HTS-DBPF. This paper employs the ratio of the lower and higher center passband frequencies, α, as a criterion for evaluating the flexibility. The obtained α ranges are from 1 to 4.7, which are the widest for DBPFs for mobile communications applications, to the best knowledge of the authors. This paper presents a 2.4-/2.9-GHz band HTS-DBPF, as an experimental example, using a YBCO film deposited on an MgO substrate. The measured frequency responses of the HTS-DBPF agree with the electromagnetic simulated results. Measurement and simulation results confirm that the proposed filter architecture is effective in configuring a DBPF that can set each center passband frequency widely.

With the technology scaling down, leakage power becomes an important part of total power consumption. The relatively large leakage current weakens the energy recovery capability of adiabatic circuits and reduces its superiority, compared with static CMOS circuits in the field of low-power design. In this paper, we rebuild three types of adiabatic circuits (2N2N2P, IPAL and DCPAL) based on FinFET devices to obtain a large leakage power reduction by rationally utilizing the different operating modes of FinFET devices (SG, LP, and IG). A 16-bit adiabatic adder has been investigated to demonstrate the advantages of FinFET adiabatic circuits. The Predictive Technology Model (PTM) is used for 32-nm bulk MOSFET and FinFET devices and all of the simulations are based on HSPICE. The results evince the proposed FinFET adiabatic circuits have a considerable reduction (more than 60% for SG mode FinFET and more than 80% for LP mode FinFET) of power consumption compared with the bulk MOSFET ones. Furthermore, the FinFET adiabatic circuits also have higher limiting frequency of clock source and better noise immunity.

In this paper, we design a new color space YUskin Vskin from YUV color space, based on the principle of skin color with respect to the change of color temperature. Compared with previous work, this color space proved to be the optimal color space for hand segmentation with linear thresholds. We also propose a novel fingertip detection method based on the concomitance between finger and fingernail. The two techniques together improve the performance of hand contour and fingertip extraction in hand gesture recognition.

Clustering methods are divided into hierarchical clustering, partitioning clustering, and more. K-Means is a method of partitioning clustering. We improve the performance of a K-Means, selecting the initial centers of a cluster through a calculation rather than using random selecting. This method maximizes the distance among the initial centers of clusters. Subsequently, the centers are distributed evenly and the results are more accurate than for initial cluster centers selected at random. This is time-consuming, but it can reduce the total clustering time by minimizing allocation and recalculation. Compared with the standard algorithm, F-Measure is more accurate by 5.1%.

For source camera identification, we propose a method to reconstruct the sensor pattern noise map from a size-reduced query image by minimizing an objective function derived from the observation model. Our method can be applied to multiple queries, and can thus be further improved. Experiments demonstrate the superiority of the proposed method over conventional interpolation-based magnification algorithms.

In this paper, we propose a new class of two dimensional (2D) M-channel (M-ch) non-separable filter banks (FBs) based on cosine modulated filter banks (CMFBs) via a new diagonally modulation scheme. Until now, many researchers have proposed 2D non-separable CMFBs. Nevertheless, efficient direction-selective CMFBs have not been yet. Thanks to our new modulations with diagonal shifts, proposed CMFBs have several frequency supports including direction-selective ones which cannot be realized by conventional ones. In a simulation, we show design examples of proposed CMFBs and their various directional frequency supports.