In measuring TV ratings, some features can be significant at a certain time, whereas they can be meaningless in other time periods. Because the importance of features can change, a model capturing the time changing relevance is required in order to estimate TV ratings more accurately. Therefore, we focus on the time-awareness of features, particularly the time when the words of tweets are used. We develop a correlation-based, time-aware feature selection algorithm which finds the optimal time period of each feature, and the estimation method using e-SVR based on top-n-features that are ordered by correlation. We identify that the correlation values between features and TV ratings vary according to the time of postings - before and after the broadcast time. This implies that the relevance of features can change according to the time of the tweets. Experimental results indicate that the proposed method has better performance compared with the method based on count-based features. This result implies that understanding the time-dependency of features can be helpful in improving the accuracy of measuring TV ratings.

Internet traffic classification is an essential step for stable service provision. The payload signature classifier is considered a reliable method for Internet traffic classification but is prohibitively computationally expensive for real-time handling of large amounts of traffic on high-speed networks. In this paper, we describe several design techniques to minimize the search space of traffic classification and improve the processing speed of the payload signature classifier. Our suggestions are (1) selective matching algorithms based on signature type, (2) signature reorganization using hierarchical structure and traffic locality, and (3) early packet sampling in flow. Each can be applied individually, or in any combination in sequence. The feasibility of our selections is proved via experimental evaluation on traffic traces of our campus and a commercial ISP. We observe 2 to 5 times improvement in processing speed against the untuned classification system and Snort Engine, while maintaining the same level of accuracy.

This paper proposes a novel method to segment the object of a specific class based on a rough detection window (such as Deformable Part Model (DPM) in this paper), which is robust to the positions of the bounding boxes. In our method, the DPM is first used to generate the root and part windows of the object. Then a set of object part candidates are generated by randomly sampling windows around the root window. Furthermore, an undirected graph (the minimum spanning tree) is constructed to describe the spatial relationships between the part windows. Finally, the object is segmented by grouping the part proposals on the undirected graph, which is formulated as an energy function minimization problem. A novel energy function consisting of the data term and the smoothness term is designed to characterize the combination of the part proposals, which is globally minimized by the dynamic programming on a tree. Our experimental results on challenging dataset demonstrate the effectiveness of the proposed method.

A discriminative reference-based method for scene image categorization is presented in this letter. Reference-based image classification approach combined with K-SVD is approved to be a simple, efficient, and effective method for scene image categorization. It learns a subspace as a means of randomly selecting a reference-set and uses it to represent images. A good reference-set should be both representative and discriminative. More specifically, the reference-set subspace should well span the data space while maintaining low redundancy. To automatically select reference images, we adapt affinity propagation algorithm based on data similarity to gather a reference-set that is both representative and discriminative. We apply the discriminative reference-based method to the task of scene categorization on some benchmark datasets. Extensive experiment results demonstrate that the proposed scene categorization method with selected reference set achieves better performance and higher efficiency compared to the state-of-the-art methods.

With the increase of the number of wireless sensing or metering devices, the collection of sensing data using wireless communication becomes an important part of a smart grid system. Cognitive radio technology can be used to facilitate the deployment of smart grid systems. In this paper, we propose a data collection and dissemination framework for cognitive radio smart grid systems to fully utilize wireless resources while maintaining a reliably connected and efficient topology for each channel. In the proposed framework, each sensor node selects a channel considering the primary user (PU) channel utilization and network connectivity. In this way, the data collection and dissemination can be performed with a high reliability and short delay while avoiding a harmful effect on primary users. We use computer simulations to evaluate the proposed framework.

To secure a wireless sensor and actuator network (WSAN) in cyber-physical systems, trust management framework copes with misbehavior problem of nodes and stimulate nodes to cooperate with each other. The existing trust management frameworks can be classified into reputation-based framework and trust establishment framework. There, however, are still many problems with these existing trust management frameworks, which remain unsolved, such as frangibility under possible attacks. To design a robust trust management framework, we identify the attacks to the existing frameworks, present the countermeasures to them, and propose a hybrid trust management framework (HTMF) to construct trust environment for WSANs in the paper. HTMF includes second-hand information and confidence value into trustworthiness evaluation and integrates the countermeasures into the trust formation. We preform extensive performance evaluations, which show that the proposed HTMF is more robust and reliable than the existing frameworks.

In this paper, we propose a modified-error adaptive feedback active noise control (ANC) system using a linear prediction filter. The proposed ANC system is advantageous in terms of the rate of convergence, while maintaining stability, because it can reduce narrowband noise while suppressing disturbance, including wideband components. The estimation accuracy of the noise control filter in the conventional system is degraded because the disturbance corrupts the input signal to the noise control filter. A solution of this problem is to utilize a linear prediction filter. The linear prediction filter is utilized for the modified-error feedback ANC system to suppress the wideband disturbance because the linear prediction filter can separate narrowband and wideband noise. Suppressing wideband noise is important for the head-mounted ANC system we have already proposed for reducing the noise from a magnetic resonance imaging (MRI) device because the error microphones are located near the user's ears and the user's voice consequently corrupts the input signal to the noise control filter. Some simulation and experimental results obtained using a digital signal processor (DSP) demonstrate that the proposed feedback ANC system is superior to a conventional feedback ANC system in terms of the estimation accuracy and the rate of convergence of the noise control filter.

An efficient subcarrier allocation scheme of a supporting cell is proposed to recover the communication of faulty cell users in an OFDM-based wireless system. With the proposed subcarrier allocation scheme, the number of subcarriers allocated to faulty cell users is maximized while the average throughput of supporting cell users is maintained at a desired level. To find the maximum number of subcarriers allocated to faulty cell users, the average throughput of the subcarrier with the k-th smallest channel gain in a subcarrier group is derived by an inductive method. It is shown by simulation that the proposed subcarrier allocation scheme can provide more subcarriers to faulty cell users than the random selection subcarrier allocation scheme.

A path loss model for low antenna heights below surrounding buildings in residential areas is presented to contribute to the construction of VHF band wireless systems. The model is constructed on the basis of measurement results at 167.65MHz, near center frequency at VHF band. Path loss characteristics in the middle VHF band are compared to those in bands above UHF. The dominant paths in bands above UHF include propagation paths below surrounding buildings, such as paths along roads. However, in the middle VHF band, these paths are instantly attenuated because their 1st Fresnel zone radius is larger than the average building height or road width. The dominant path in the middle VHF band is the over-roof propagation path, and the 1st Fresnel zone of the path is shielded by the buildings and the ground surface. The proposed path loss model has two features. First, it derives the effective height of the ground surface from the terrain profile of the buildings and the ground surface. Second, it uses formulas of a two-path model to take the shielding of the 1st Fresnel zone into account. Finally, it is shown that the proposed model is able to predict the path loss measurement results more accurately than the conventional model.

As the electromagnetic (EM) environment is becoming increasingly diverse, it is essential to estimate specific absorption rates (SARs) and temperature elevations of pregnant females and their fetuses under various exposure situations. This study presents calculated SARs and temperature elevations in a fetus exposed to EM waves. The calculations involved numerical models for the anatomical structures of a pregnant Japanese woman at gestational stages of 13, 18, and 26 weeks; the EM source was a wireless portable terminal placed close to the abdomen of the pregnant female model. The results indicate that fetal SARs and temperature elevations are closely related to the position of the fetus relative to the EM source. We also found that, although the fetal SAR caused by a half-wavelength dipole antenna is sometimes comparable to or slightly more than the International Commission Non-Ionizing Radiation Protection guidelines, it is lower than the guideline level in more realistic situations, such as when a planar inverted-F antenna is used. Furthermore, temperature elevations were significantly below the threshold set to prevent the child from being born with developmental disabilities.

According to the match-degree between lightpaths, an HC-sharing approach is proposed to assign wavelength for an arriving transmission request for dynamic traffic in LOBS-based datacenter networks. The simulation results demonstrate that the proposed approach can provide lower block probability than other approaches for both unicast and multicast transmissions.

We have improved a compact model for the injection-enhancedinsulated-gate bipolar transistor for inverter circuit simulation. The holeaccumulation of floating-base region and potential change are modeled. It turned out that negative capacitance which occurs by floating-base region has the dependence of frequency. It is necessary to consider the frequency dependence of the total gate capacitance for transient simulation. We analyzed the relationship between negative gate capacitance and current rise rate at the switch turn-on timing and device structure. The development model simulation result is well reproduced $I_{ extrm{c}}$ and $V_{ extrm{ce}}$ of measurement data, and the switching loss calculation accuracy is improved.

This paper analyzes the transmission performances of visible light communication (VLC) based on unipolar orthogonal frequency division multiplexing (OFDM), which is compatible with intensity modulation and direct detection (IM/DD). Three existing unipolar OFDM schemes, namely DC biased optical OFDM (DCO-OFDM), asymmetrically clipped optical OFDM (ACO-OFDM), and flip-OFDM are investigated and compared. While these three schemes have been analyzed for indoor optical wireless communication (OWC) subject to the limitation on the transmit optical power, they have not been carefully investigated and compared for VLC when a large transmit power is available due to the illumination requirement, and the signal dynamic range (DR) becomes the main limitation. For the analysis, DR expressions of DCO-OFDM, ACO-OFDM, and flip-OFDM signals are first derived. Then, the bit error rate (BER) expression of each unipolar OFDM scheme is derived in terms of the DR. For data rates in the range of 1-10Mbps, under the system parameters based on typical indoor environments, DCO-OFDM is observed to outperform the other two schemes. This superiority of DCO-OFDM is in contrast with previously reported results that indicate the attractiveness of ACO-OFDM and flip-OFDM over DCO-OFDM when the transmit optical power is the main limitation. Finally, light dimming is considered to identify the illumination level below which DCO-OFDM loses this superiority.

In recent years, ray space (or light field in other literatures) photography has become popular in the area of computer vision and image processing, and the capture of a ray space has become more significant to these practical applications. In order to handle the huge data problem in the acquisition stage, original data are compressively sampled in the first place and completely reconstructed later. In this paper, in order to achieve better reconstruction quality and faster reconstruction speed, we propose a statistically weighted model in the reconstruction of compressively sampled ray space. This model can explore the structure of ray space data in an orthogonal basis, and integrate this structure into the reconstruction of ray space. In the experiment, the proposed model can achieve much better reconstruction quality for both 2D image patch and 3D image cube cases. Especially in a relatively low sensing ratio, about 10%, the proposed method can still recover most of the low frequency components which are of more significance for representation of ray space data. Besides, the proposed method is almost as good as the state-of-art technique, dictionary learning based method, in terms of reconstruction quality, and the reconstruction speed of our method is much faster. Therefore, our proposed method achieves better trade-off between reconstruction quality and reconstruction time, and is more suitable in the practical applications.

The bilateral filter (BF) is a nonlinear and low-pass filter which can smooth an image while preserving detail structures. However, the filer is time consuming for real-time processing. In this paper, we bring forward a fresh idea that bilateral filtering can be accelerated by a multigrid (MG) scheme. Our method is based on the following two facts. a) The filtering result by a BF with a large kernel size on the original resolution can be approximated by applying a small kernel sized (3×3) version on the lower resolution many times on the premise of visual acceptance. Early work has shown that a BF can be viewed as nonlinear diffusion. The desired filtering result is actually an intermediate status of the diffusion process. b) Iterative linear equation techniques are sufficiently mature to cope with the nonlinear diffusion equation, which can be accelerated by the MG scheme. Experimental results with both simulated data sets and real sets are provided, and the new method is demonstrated to achieve almost twice the speed of the state-of-the-art. Compared with previous efforts for finding a generalized representation to link bilateral filtering and nonlinear diffusion by adaptive filtering, a novel relationship between nonlinear diffusion and bilateral filtering is explored in this study by focusing attention on numerical calculus.

A simple, low reflection, and highly-efficient pilot-plant scale microwave irradiation reactor for woody biomass pretreatment was fabricated. Pretreatment is an essential process for effective bioethanol production. The fabricated reactor consists of 8 microwave irradiators which are attached to a metal pipe. The woody biomass mixture which contains water and organic acid flows through the metal pipe and is heated by microwaves at a total power of 12,kW. To design the microwave irradiators, we used a 3D Finite Element Method (FEM) simulator, which was based on the measured complex permittivity data of the woody biomass mixture. The simulation results showed that the reflection coefficient $|S_{11}|$ from the reactor was less than -30,dB when the woody biomass mixture temperature was between 30$^{circ}$C and 90$^{circ}$C. Finally, we experimentally confirmed that the fabricated irradiation reactor yielded a microwave absorption efficiency of 79%.

Provisioning multiple paths can improve fault tolerance and transport capability of multi-routing in wireless networks. Disjoint paths can improve the diversity of paths and further reduce the risk of simultaneous link failure and network congestion. In this paper we first address a many-to-one disjoint-path problem (MOND) for multi-path routing in a multi-hop wireless network. The objective of this problem is to maximize the minimum number of disjoint paths of every source to the destination. We prove that it is NP-hard to obtain k disjoint paths for every source when k ≥ 3. To solve this problem efficiently, we propose a heuristic algorithm called TOMAN based on network flow theory. Experimental results demonstrate that it outperforms three related algorithms.

Strongly deterministic multithreading provides determinism for multithreaded programs even in the presence of data races. A common way to guarantee determinism for data races is to isolate threads by buffering shared memory accesses. Unfortunately, buffering all shared accesses is prohibitively costly. We propose an approach called DRDet to efficiently make data races deterministic. DRDet leverages the insight that, instead of buffering all shared memory accesses, it is sufficient to only buffer memory accesses involving data races. DRDet uses a sound data-race detector to detect all potential data races. These potential data races, along with all accesses which may access the same set of memory objects, are flagged as data-race-involved accesses. Unsurprisingly, the imprecision of static analyses makes a large fraction of shared accesses to be data-race-involved. DRDet employs two optimizations which aim at reducing the number of accesses to be sent to query alias analysis. We implement DRDet on CoreDet, a state-of-the-art deterministic multithreading system. Our empirical evaluation shows that DRDet reduces the overhead of CoreDet by an average of 1.6X, without weakening determinism and scalability.

This paper aims to achieve a high-quality exposure level quantification of whole-body average-specific absorption rates (WBA-SARs) for small animals in a medium-size reverberation chamber (RC). A two-step method, which incorporates the finite-difference time-domain (FDTD) numerical solutions with electric field measurements in an RC-type exposure system, has been used as an evaluation method to determine the whole-body exposure level in small animals. However, there is little data that quantitatively demonstrate the validity and accuracy of this method in an RC up to now. In order to clarify the validity of the two-step method, we compare the physical quantities in terms of electric field strength and WBA-SARs by using a direct numerical assessment method known as the method of moments (MoM) with ten homogenous gel phantoms placed in an RC with 2GHz exposure. The comparison results show that the relative errors between the two-step method and the MoM approach are approximately below 10%, which reveals the validity and usefulness of the two-step technique. Finally, we perform a dosimetric analysis of the WBA-SARs for anatomical mouse models with the two-step method and determine the input power related to our developed RC-exposure system to achieve a target exposure level in small animals.