Seam carving, which preserves semantically important image content during resizing process, has been actively researched in recent years. This paper proposes a novel forensic technique to detect the trace of seam carving. We exploit the energy bias and noise level of images under analysis to reliably unveil the evidence of seam carving. Furthermore, we design a detector investigating the relationship among neighboring pixels to estimate the inserted seams. Experimental results from a large set of test images indicates the superior performance of the proposed methods for both seam carving and seam insertion.

This study proposes a feature-level fusion method that uses finger veins (FVs) and finger dorsal texture (FDT) for personal authentication based on orientation selection (OS). The orientation codes obtained by the filters correspond to different parts of an image (foreground or background) and thus different orientations offer different levels of discrimination performance. We have conducted an orientation component analysis on both FVs and FDT. Based on the analysis, an OS scheme is devised which combines the discriminative orientation features of both modalities. Our experiments demonstrate the effectiveness of the proposed method.

In this paper, a novel White-RGB (WRGB) color filter array-based imaging system for cell phone is presented to reduce noise and reproduce color in low illumination. The core process is based on adaptive diagonal color separation to recover color components from a white signal using diagonal reference blocks and location-based color ratio estimation in the luminance space. The experiments, which are compared with the RGB and state-of-the-art WRGB approaches, show that our imaging system performs well for various spatial frequency images and color restoration in low-light environments.

Software engineering education at universities plays an increasingly important role as software quality is becoming essential in realizing a safe and dependable society. This paper proposes a practical state transition model (Practical-STM) based on the Organizational Expectancy Model for the improvement of software process education based on the Personal Software Process (PSP) from a motivation point of view. The Practical-STM treats an individual trainee of the PSP course as a state machine, and formalizes a motivation process of a trainee using a set of states represented by factors regarding motivation and a set of operations carried out by course instructors. The state transition function of this model represents the features or characteristics of a trainee in terms of motivation. The model allows a formal description of the states of a trainee in terms of motivation and the educational actions of the instructors in the PSP course. The instructors are able to decide effective and efficient actions to take toward the trainees objectively by presuming a state and a state transition function of the trainees formally. Typical patterns of state transitions from an initial state to a final state, which is called a scenario, are useful for inferring possible transitions of a trainee and taking proactive operations from a motivation point of view. Therefore, the model is useful not only for improving the educational effect of the PSP course, but also for the standardization of the course management and the quality management of the instructors.

k-NN classification has been applied to classify normal tissues in MR images. However, the intensity inhomogeneity of MR images forces conventional k-NN classification into significant misclassification errors. This letter proposes a new interleaved method, which combines k-NN classification and bias field estimation in an energy minimization framework, to simultaneously overcome the limitation of misclassifications in conventional k-NN classification and correct the bias field of observed images. Experiments demonstrate the effectiveness and advantages of the proposed algorithm.

This paper presents a method for automatic rectification of design bugs in processors. Given a golden sequential instruction-set architecture model of a processor and its erroneous detailed cycle-accurate model at the micro-architecture level, we perform symbolic simulation and property checking combined with concrete simulation iteratively to detect the buggy location and its corresponding fix. We have used the truth-table model of the function that is required for correction, which is a very general model. Moreover, we do not represent the truth-table explicitly in the design. We use, instead, only the required minterms, which are obtained from the output of our backend formal engine. This way, we avoid adding any new variable for representing the truth-table. Therefore, our correction model is scalable to the number of inputs of the truth-table that could grow exponentially. We have shown the effectiveness of our method on a complex out-of-order superscalar processor supporting atomic execution of instructions. Our method reduces the model size for correction by 6.0x and total correction time by 12.6x, on average, compared to our previous work.

As the recent growth of online social network services such as Facebook and Twitter, people are able to easily share information with each other by writing posts or commenting for another's posts. In this paper, we firstly suggest a method of discovering information flows of posts on Facebook and their underlying contexts by incorporating process mining and text mining techniques. Based on comments collected from Facebook, the experiment results illustrate how the proposed method can be applied to analyze information flows and contexts of posts on social network services.

The numbers of files in file systems have increased dramatically in recent years. Office workers spend much time and effort searching for the documents required for their jobs. To reduce these costs, we propose a new method for recommending files and operations on them. Existing technologies for recommendation, such as collaborative filtering, suffer from two problems. First, they can only work with documents that have been accessed in the past, so that they cannot recommend when only newly generated documents are inputted. Second, they cannot easily handle sequences involving similar or differently ordered elements because of the strict matching used in the access sequences. To solve these problems, such minor variations should be ignored. In our proposed method, we introduce the concepts of abstract files as groups of similar files used for a similar purpose, abstract tasks as groups of similar tasks, and frequent abstract workflows grouped from similar workflows, which are sequences of abstract tasks. In experiments using real file-access logs, we confirmed that our proposed method could extract workflow patterns with longer sequences and higher support-count values, which are more suitable as recommendations. In addition, the F-measure for the recommendation results was improved significantly, from 0.301 to 0.598, compared with a method that did not use the concepts of abstract tasks and abstract workflows.

In recent years, with the popularization of video collection devices and the development of the Internet, it is easy to copy original digital videos and distribute illegal copies quickly through the Internet. It becomes a critical task to uphold copyright laws, and this problem will require a technical solution. Therefore, as a challenging problem, copy detection or video identification becomes increasingly important. The problem addressed here is to identify a given video clip in a given set of video sequences. In this paper, an extension to the video identification approach based on video tomography is presented. First, the feature extraction process is modified to enhance the reliability of the shot signature with its size unchanged. Then, a new similarity measurement between two shot signatures is proposed to address the problem generated by the original approach when facing the query shot with a short length. In addition, the query scope is extended from one shot only to one clip (several consecutive shots) by giving a new definition of similarity between two clips and describing a search algorithm which can save much of the computation cost. Experimental results show that the proposed approach is more suitable for identifying shots with short lengths than the original approach. The clip query approach performs well in the experiment and it also shows strong robustness to data loss.

Graph pattern mining has played important roles in network analysis and information retrieval. However, temporal characteristics of networks have not been estimated sufficiently. We propose time graph pattern mining as a new concept of graph mining reflecting the temporal information of a network. We conduct two case studies of time graph pattern mining: extensively discussed topics on blog sites and a book recommendation network. Through examination of case studies, we ascertain that time graph pattern mining has numerous possibilities as a novel means for information retrieval and network analysis reflecting both structural and temporal characteristics.

Travel recommendation and travel diary generation applications can benefit significantly from methods that infer the durations and locations of visits from travelers' GPS data. However, conventional inference methods, which cluster GPS points on the basis of their spatial distance, are not suited to inferring visit durations. This paper presents a pace-based clustering method to infer visit locations and durations. The method contributes two novel techniques: (1) It clusters GPS points logged during visits by considering the speed and applying a probabilistic density function for each trip. Consequently, it avoids clustering GPS points that are near but unrelated to visits. (2) It also includes additional GPS points in the clusters by considering their temporal sequence. As a result, it is able to complete the clusters with GPS points that are far from the visits but are logged during the visits, caused, for example, by GPS noise indoors. The results of an experimental evaluation comparing our proposed method with three published inference methods indicate that our proposed method infers the duration of a visit with an average error rate of 8.7%, notably outperforming the other methods.

In this paper, a new technique is proposed to reduce the frequency of cell search by user equipment (UE) in the presence of femtocells. A new common signal (CS) and a separate set of primary synchronization signals (PSSs) are employed to facilitate efficient cell search in a next-geration LTE-based system. The velocity of the UE is also utilized to determine cell search mode. A slow UE recognizes the presence of femtocells using the CS, so that it can make separate searches for macrocells and femtocells. A fast UE will not search for femtocells since the coverage of femtocells is restricted to a small region. The fast UE detects the macrocell boundary using the PSSs transmitted from neighboring macrocells, so that it can search for macrocells only at the macrocell boundary. The effects of CS and UE velocity on the number of cell searches are analyzed. The performance of the proposed technique is evaluated by computer simulations.

A two-stage classifier is proposed that identifies criminal charges and a range of punishments given a set of case facts and attributes. Our supervised-learning model focuses only on the offences against life and body section of the criminal law code of Thailand. The first stage identifies a set of diagnostic issues from the case facts using a set of artificial neural networks (ANNs) modularized in hierarchical order. The second stage extracts a set of legal elements from the diagnostic issues by employing a set of C4.5 decision tree classifiers. These linked modular networks of ANNs and decision trees form an effective system in terms of determining power and the ability to trace or infer the relevant legal reasoning behind the determination. Isolated and system-integrated experiments are conducted to measure the performance of the proposed system. The overall accuracy of the integrated system can exceed 90%. An actual case is also demonstrated to show the effectiveness of the proposed system.

We review our recent achievements in monolithic 3D-ICs and flexible electronics based on single-grain Si TFTs that are fabricated inside a single-grain with a low-temperature process. Based on pulsed-laser crystallization and submicron sized cavities made in the substrate, amorphous-Si precursor film was converted into poly-Si having grains that are formed on predetermined positions. Using the method called µ-Czochralski process and LPCVD a-Si precursor film, two layers of the SG Si TFT layers with the grains having a diameter of 6µm were vertically stacked with a maximum process temperature of 550°C. Mobility for electrons and holes were 600cm2/Vs and 200cm2/Vs, respectively. As a demonstration of monolithic 3D-ICs, the two SG-TFT layers were successfully implemented into CMOS inverter, 3D 6T-SRAM and single-grain lateral PIN photo-diode with in-pixel amplifier. The SG Si TFTs were applied to flexible electronics. In this case, the a-Si precursor was prepared by doctor-blade coating of liquid-Si based on pure cyclopentasilane (CPS) on a polyimide (PI) substrate with maximum process temperature of 350°C. The µ-Czochralski process provided location-controlled Si grains with a diameter of 3µm and mobilities of 460 and 121cm2/Vs for electrons and holes, respectively, were obtained. The devices on PI were transferred to a plastic foil which can operate with a bending diameter of 6mm. Those results indicate that the SG TFTs are attractive for their use in both monolithic 3D-ICs and flexible electronics.

As one of the popular social media that many people turn to in recent years, collaborative encyclopedia Wikipedia provides information in a more “Neutral Point of View” way than others. Towards this core principle, plenty of efforts have been put into collaborative contribution and editing. The trajectories of how such collaboration appears by revisions are valuable for group dynamics and social media research, which suggest that we should extract the underlying derivation relationships among revisions from chronologically-sorted revision history in a precise way. In this paper, we propose a revision graph extraction method based on supergram decomposition in the document collection of near-duplicates. The plain text of revisions would be measured by its frequency distribution of supergram, which is the variable-length token sequence that keeps the same through revisions. We show that this method can effectively perform the task than existing methods.

This paper proposes a radiation-hardened phase-locked loop (RH-PLL) with a switchable dual modular redundancy (DMR) structure. After radiation strikes, unhardened PLLs suffer clock perturbations. Conventional RH-PLLs have been proposed to reduce recovery time after perturbation. However, this recovery still requires tens of clock cycles. Our proposal involves ‘detecting’ and ‘switching’, rather than ‘recovering’ from clock perturbation. Detection speed is crucial for robust perturbation-immunity. We identify types of clock perturbation and then propose a set of detectors to detect each type. With this method, the detectors guarantee high-speed detection that leads to perturbation-immune switching from a radiated clock to an undistorted clock. The proposed RH-PLL was fabricated and then verified with a radiation test on real silicon.

In this letter, we present a new scheme to find small fundamental instantons (SFIs) of regular low-density parity-check (LDPC) codes for the linear programming (LP) decoding over the binary symmetric channel (BSC). Based on the fact that each instanton-induced graph (IIG) contains at least one short cycle, we determine potential instantons by constructing possible IIGs which contain short cycles and additional paths connected to the cycles. Then we identify actual instantons from potential ones under the LP decoding. Simulation results on some typical LDPC codes show that our scheme is effective, and more instantons can be obtained by the proposed scheme when compared with the existing instanton search method.

This letter investigates the relationship between antenna position and data communication performance in a magnetic resonance wireless power transfer (MRWPT) system. In MRWPT information such as the types of equipments, the required amount of electrical power, or the timing of power transfer should be exchanged. It is assumed here that power transfer coils in the MRWPT system are employed as antennas for data communication. The frequency characteristics of the antennas change due to coil displacements. The power transfer coils are modeled as a band pass filter (BPF) and the frequency characteristics of the filter are presented in this letter. The characteristics of the filter are derived through circuit simulation and resulting data communication performance is evaluated. Numerical results obtained through computer simulation show that the bit error late (BER) performance can be improved by controlling the center frequency of the communication link.

We propose a method for downsizing line pictures to generate pixel line arts. In our method, topological properties such as connectivity of lines and segments are preserved by allowing slight distortion in the form of objects in input images. When input line pictures are painted with colors, the number of colors is preserved by our method.

This paper proposes a novel and an efficient method termed hypersphere sampling to estimate the circuit yield of low-failure probability with a large number of variable sources. Importance sampling using a mean-shift Gaussian mixture distribution as an alternative distribution is used for yield estimation. Further, the proposed method is used to determine the shift locations of the Gaussian distributions. This method involves the bisection of cones whose bases are part of the hyperspheres, in order to locate probabilistically important regions of failure; the determination of these regions accelerates the convergence speed of importance sampling. Clustering of the failure samples determines the required number of Gaussian distributions. Successful static random access memory (SRAM) yield estimations of 6- to 24-dimensional problems are presented. The number of Monte Carlo trials has been reduced by 2-5 orders of magnitude as compared to conventional Monte Carlo simulation methods.