This paper presents analysis and simple design guideline for ThruChip Interface (TCI) as located by LC-VCO which is used in high-speed SoC. The electromagnetic interference (EMI) from TCI channels to LC-VCO is analyzed and evaluated. The accuracy of the analysis and design guidelines is verified through the test-chip verification.

Distributed compressive video sensing (DCVS) is an emerging low-complexity video coding framework which integrates the merits of distributed video coding (DVC) and compressive sensing (CS). In this paper, we propose a novel rate-distortion optimized DCVS codec, which takes advantage of a rate-distortion optimization (RDO) model based on the estimated correlation noise (CN) between a non-key frame and its side information (SI) to determine the optimal measurements allocation for the non-key frame. Because the actual CN can be more accurately recovered by our DCVS codec, it leads to more faithful reconstruction of the non-key frames by adding the recovered CN to the SI. The experimental results reveal that our DCVS codec significantly outperforms the legacy DCVS codecs in terms of both objective and subjective performance.

This study focuses on modeling the storytelling performance of the participants in a group conversation. Storytelling performance is one of the fundamental communication techniques for providing information and entertainment effectively to a listener. We present a multimodal analysis of the storytelling performance in a group conversation, as evaluated by external observers. A new multimodal data corpus is collected through this group storytelling task, which includes the participants' performance scores. We extract multimodal (verbal and nonverbal) features regarding storytellers and listeners from a manual description of spoken dialog and from various nonverbal patterns, including each participant's speaking turn, utterance prosody, head gesture, hand gesture, and head direction. We also extract multimodal co-occurrence features, such as head gestures, and interaction features, such as storyteller utterance overlapped with listener's backchannel. In the experiment, we modeled the relationship between the performance indices and the multimodal features using machine-learning techniques. Experimental results show that the highest accuracy (R2) is 0.299 for the total storytelling performance (sum of indices scores) obtained with a combination of verbal and nonverbal features in a regression task.

Some diffusive and recurrence properties of Lorentz Lattice Gas Cellular Automata (LLGCA) have been expensively studied in terms of the densities of some of the left/right static/flipping mirrors/rotators. In this paper, for any combination S of these well known scatters, we study the computational complexity of the following problem which we call PERIODICITY on the S-model: given a finite configuration that distributes only those scatters in S, whether a particle visits the starting position periodically or not. Previously, the flipping mirror model and the occupied flipping rotator model have been shown unbounded, i.e. the process is always diffusive [17]. On the other hand, PERIODICITY is shown PSPACE-complete in the unoccupied flipping rotator model [21]. In this paper, we show that PERIODICITY is PSPACE-compete in any S-model that is neither occupied, unbounded, nor static. Particularly, we prove that PERIODICITY in any unoccupied and bounded model containing flipping mirror is PSPACE-complete.

An r-gathering of customers C to facilities F is an assignment A of C to open facilities F' ⊂ F such that r (≥ 2) or more customers are assigned to each open facility. (Each facility needs enough number of customers for its opening.) Then the r-gathering problem finds an r-gathering minimizing a designated cost. Armon gave a simple 3-approximation algorithm for the r-gathering problem and proved that with assumption P ≠ NP the problem cannot be approximated within a factor of less than 3 for any r ≥ 3. The running time of the 3-approximation algorithm is O(|C||F|+r|C|+|C|log|C|)). In this paper we improve the running time of the algorithm by (1) removing the sort in the algorithm and (2) designing a simple but efficient data structure.

MapReduce still suffers from a problem known as skew, where load is unevenly distributed among tasks. Existing solutions follow a similar pattern that estimates the load of each task and then rebalances the load among tasks. However, these solutions often incur heavy overhead due to the load estimation and rebalancing. In this paper, we present DynamicAdjust, a dynamic resource adjustment technique for mitigating skew in MapReduce. Instead of rebalancing the load among tasks, DynamicAdjust adjusts resources dynamically for the tasks that need more computation, thereby accelerating these tasks. Through experiments using real MapReduce workloads on a 21-node Hadoop cluster, we show that DynamicAdjust can effectively mitigate the skew and speed up the job completion time by up to 37.27% compared to the native Hadoop YARN.

This paper proposes an appearance-based novel descriptor for estimating head orientation. Our descriptor is inspired by the Weber-based feature, which has been successfully implemented for robust texture analysis, and the gradient which performs well for shape analysis. To further enhance the orientation differences, we combine them with an analysis of the intensity deviation. The position of a pixel and its intrinsic intensity are also considered. All features are then composed as a feature vector of a pixel. The information carried by each pixel is combined using a covariance matrix to alleviate the influence caused by rotations and illumination. As the result, our descriptor is compact and works at high speed. We also apply a weighting scheme, called Block Importance Feature using Genetic Algorithm (BIF-GA), to improve the performance of our descriptor by selecting and accentuating the important blocks. Experiments on three head pose databases demonstrate that the proposed method outperforms the current state-of-the-art methods. Also, we can extend the proposed method by combining it with a head detection and tracking system to enable it to estimate human head orientation in real applications.

A brain-computer interface (BCI) translates the brain activity into commands to control external devices. P300 speller based character recognition is an important kind of application system in BCI. In this paper, we propose a framework to integrate channel correlation analysis into P300 detection. This work is distinguished by two key contributions. First, a coefficient matrix is introduced and constructed for multiple channels with the elements indicating channel correlations. Agglomerative clustering is applied to group correlated channels. Second, the statistics of central tendency are used to fuse the information of correlated channels and generate virtual channels. The generated virtual channels can extend the EEG signals and lift up the signal-to-noise ratio. The correlated features from virtual channels are combined with original signals for classification and the outputs of discriminative classifier are used to determine the characters for spelling. Experimental results prove the effectiveness and efficiency of the channel correlation analysis based framework. Compared with the state-of-the-art, the recognition rate was increased by both 6% with 5 and 10 epochs by the proposed framework.

Connection Service Providers (CSP) are wishing to increase their Return on Investment (ROI) by utilizing the data assets generated by tracking subscriber behaviors. This results in the ability to apply personalized policies, monitor and control the service traffic to subscribers and gain more revenue through the usage of subscriber data with ad networks. In this paper, a system is proposed to monitor and analyze the Internet access of the subscribers of a regional SP in order to classify the subscribers into interest categories from the Interactive Advertising Bureau (IAB) categories. The study employs the categorization engine to build category vectors for all individuals using Internet services through the subscription. The proposal makes it easy to detect changes in the interests of individuals/subscribers over time.

We have studied ultra-low-power superconductor circuits using adiabatic quantum flux parametron (AQFP) logic. Latches, which store logic data in logic circuits, are indispensable logic elements in the realization of AQFP computing systems. Among them, feedback latches, which hold data by using a feedback loop, have advantages in terms of their wide operation margins and high stability. Their drawbacks are their large junction counts and long latency. In this paper, we propose a majority gate-based feedback latch for AQFP logic with a reduced number of junctions. We designed and fabricated the proposed AQFP latches using a standard National Institute of Advanced Industrial Science and Technology (AIST) process. The measurement results showed that the feedback latches operate with wide operation margins that are comparable with circuit simulation results.

This paper presents a method for registering retinal images. Retinal image registration is crucial for the diagnoses and treatments of various eye conditions and diseases such as myopia and diabetic retinopathy. Retinal image registration is challenging because the images have non-uniform contrasts and intensity distributions, as well as having large homogeneous non-vascular regions. This paper provides a new retinal image registration method by effectively combining expectation maximization principal component analysis based mutual information (EMPCA-MI) with salient features. Experimental results show that our method is more efficient and robust than the conventional EMPCA-MI method.

This paper proposes two linearity enhancement techniques for open-loop amplifiers. One technique is nonlinearity cancellation. An amplifier with reversed nonlinearity is proposed to cascade with a conventional common source amplifier. The product of these two nonlinear gains demonstrates much higher linearity. It achieves a SFDR of 71 dB when differential output range is 600 mV. Compared with the conventional common source amplifier, about 24 dB improvement is achieved. Another proposed technique is gain adapting. An input amplitude detector utilizing second order nonlinearity is combined with a source-degenerated amplifier. It can adjust the gain automatically according to the input amplitude, and compensate the gain compression when the input amplitude becomes larger. A SFDR of 69 dB is realized when the differential output range is 600 mV. An improvement of 23 dB is achieved after gain is adapted. Furthermore, mismatch calibration for the two proposed linearity enhancement techniques is investigated. Finally, comparison between two proposed amplifiers is introduced. The amplifier with nonlinearity cancellation has advantage in large signal range while the amplifier utilizing gain adapting is more competitive on accurate calibration, fast response and low noise.

This paper considers an optimal stabilization problem of quantitative discrete event systems (DESs) under the influence of disturbances. We model a DES by a deterministic weighted automaton. The control cost is concerned with the sum of the weights along the generated trajectories reaching the target state. The region of weak attraction is the set of states of the system such that all trajectories starting from them can be controlled to reach a specified set of target states and stay there indefinitely. An optimal stabilizing controller is a controller that drives the states in this region to the set of target states with minimum control cost and keeps them there. We consider two control objectives: to minimize the worst-case control cost (1) subject to all enabled trajectories and (2) subject to the enabled trajectories starting by controllable events. Moreover, we consider the disturbances which are uncontrollable events that rarely occur in the real system but may degrade the control performance when they occur. We propose a linearithmic time algorithm for the synthesis of an optimal stabilizing controller which is robust to disturbances.

Controlling the peak-to-mean envelope power ratio (PMEPR) of orthogonal frequency-division multiplexed (OFDM) transmissions is a significant obstacle in many low-cost applications of OFDM. An coding approach proposed by H.R. Sadjadpour presents non-square M-QAM symbols as a combination of QPSK and BPSK signals when M=22n+1, and then uses QPSK and BPSK Golay (or Golay-like) sequences with a constant PMEPR to generate M-QAM sequences. This paper proposes a new scheme in which M-QAM sequences are generated by QPSK and BPSK sequences with variable PMEPRs. In other words, this new scheme is a general case of the existing approach. As a result, the code rate of the new sequence is significantly improved, while the upper bound of its PMEPR remains at a comparative level.

We propose an exact algorithm to determine the satisfiability of oblivious read-twice branching programs. Our algorithm runs in $2^{left(1 - Omega(rac{1}{log c}) ight)n}$ time for instances with n variables and cn nodes.

A method based on score level fusion using logistic regression has been developed that uses packet header information to classify Internet applications. Applications are classified not on the basis of the individual flows for each type of application but on the basis of all the flows for each type of application, i.e., the “overall traffic flow.” The overall traffic flow is divided into equal time slots, and the applications are classified using statistical information obtained for each time slot. Evaluation using overall traffic flow generated by five types of applications showed that its true and false positive rates are better than those of methods using feature level fusion.

In this paper, by jointly considering power allocation and network selection, we address the energy efficiency maximization problem in dynamic and heterogeneous wireless networks, where user equipments are typically equipped with multi-homing capability. In order to effectively deal with the dynamics of heterogeneous wireless networks, a stochastic optimization problem is formulated that optimizes the long-term energy efficiency under the constraints of system stability, peak power consumption and average transmission rate. By adopting the parametric approach and Lyapunov optimization, we derive an equivalent optimization problem out of the original problem and then investigate its optimal resource allocation. Then, to reduce the computational complexity, a suboptimal resource allocation algorithm is proposed based on relaxed optimization, which adapts to time-varying channels and stochastic traffic without requiring relevant a priori knowledge. The simulation results demonstrate the theoretical analysis and validate the adaptiveness of our proposed algorithm.

To study the similarity between queries and retrieved masses, we design an interactive CBIR (Content-based Image Retrieval) CADx (Computer-aided Diagnosis) system using relevance feedback for the characterization of breast masses in ultrasound (US) images based on radiologists' visual similarity assessment. The CADx system retrieves masses that are similar to query masses from a reference library based on six computer-extracted features that describe the texture, width-to-height, and posterior shadowing of the mass. The k-NN retrieval with Euclidean distance similarity measure and the Rocchio relevance feedback algorithm (RRF) are used. To train the RRF parameters, the similarities of 1891 image pairs from 62 (31 malignant and 31 benign) masses are rated by 3 MQSA (Mammography Quality Standards Act) radiologists using a 9-point scale (9=most similar). The best RRF parameters are chosen based on 3 observer experiments. For testing, 100 independent query masses (49 malignant and 51 benign) and 121 reference masses on 230 (79 malignant and 151 benign) images were collected. Three radiologists rated the similarity between the query masses and the computer-retrieved masses. Average similarity ratings without and with RRF were 5.39 and 5.64 for the training set and 5.78 and 6.02 for the test set, respectively. Average AUC values without and with RRF were, respectively, 0.86±0.03 and 0.87±0.03 for the training set and 0.91±0.03 and 0.90±0.03 for the test set. On average, masses retrieved using the CBIR system were moderately similar to the query masses based on radiologists' similarity assessments. RRF improved the similarity of the retrieved masses.

In this paper, we describe an improvement of integer factorization of k RSA moduli Ni=piqi (1≤i≤k) with implicit hints, namely all pi share their t least significant bits. May et al. reduced this problem to finding a shortest (or a relatively short) vector in the lattice of dimension k obtained from a given system of k RSA moduli, for which they applied Gaussian reduction or the LLL algorithm. In this paper, we improve their method by increasing the determinant of the lattice obtained from the k RSA moduli. We see that, after our improvement, May et al.'s method works smoothly with higher probability. We further verify the efficiency of our method by computer experiments for various parameters.

Increasing demand in data-traffic has been addressed over the last few years. It is expected that the data-traffic will present the significant part of the total backbone traffic. Accordingly, much more transmission systems will be required to support this growth. A free space optic (FSO) communication is the greatest promising technology supporting high-speed and high-capacity transport networks. It can support multi Gbit/s for few kilometers transmission distance. The benefits of an FSO system are widespread, low cost, flexibility, immunity to electromagnetic field, fast deployment, security, etc. However, it suffers from some drawbacks, which limit the deployment of FSO links. The main drawback in FSO is the degradation in the signal quality because of atmospheric channel impairments. In addition, it is high sensitive for illumination noise coming from external sources such as sun and lighting systems. It is more benefit that FSO and mmWave are operating as a complementary solution that is known as hybrid FSO/mmWave links. Whereas the mmWave is susceptible to heavy rain conditions and oxygen absorption, while fog has no particular effect. This paper will help to better understand the FSO and mmWave technologies and applications operating under various atmospheric conditions. Furthermore, in order to improve the system performance and availability, several modulation schemes will be discussed. In addition to, the hybrid FSO/mmWave with different diversity combining techniques are presented.