Impulsive noise interference is a significant problem for the Integrated Services Digital Broadcasting for Terrestrial (ISDB-T) receivers due to its effect on the orthogonal frequency division multiplexing (OFDM) signal. In this paper, an adaptive scheme to suppress the effect of impulsive noise is proposed. The impact of impulsive noise can be detected by using the guard band in the frequency domain; furthermore the position information of the impulsive noise, including burst duration, instantaneous power and arrived time, can be estimated as well. Then a time-domain window function with adaptive parameters, which are decided in terms of the estimated information of the impulsive noise and the carrier-to-noise ratio (CNR), is employed to suppress the impulsive interference. Simulation results confirm the validity of the proposed scheme, which improved the bit error rate (BER) performance for the ISDB-T receivers in both AWGN channel and Rayleigh fading channel.

This paper presents a novel chunk selection strategy for peer-to-peer video streaming, called enr-first selection policy, which simultaneously considers both block rarity and playback deadline. The policy intends to boost overall network-wide streaming performance, while ensuring good playback continuity of individual peers. The efficacy of the proposed scheme is validated through our performance evaluation study that demonstrates a substantial gain.

In this paper, we propose an SNMP-aware web cache design that has two main objectives: (1) to avoid overload of network devices by SNMP requests, and (2) guaranteeing the monitoring time granularity of SNMP Object Identifiers (OID) for a large scale network such as the Internet. To meet these objectives, a cache is built into an RESTful active proxy, called Tambourine, which is the gateway for accessing management information through the Internet. Tambourine changes the landscape of traditional SNMP monitoring by allowing the Internet users to monitor closed-domain network devices through translating requests in HTTP into SNMP. However, the typical web cache algorithm can not be used in Tambourine due to two main reasons: (1) SNMP is not a cache-aware protocol and therefore can not provide Tambourine with the caching rules that need to be applied, and (2) the cache in Tambourine needs to accommodate two SNMP monitoring patterns: periodic and on-demand polling. In order for efficient periodic polling, SNMP traffic is reduced by a multi-TTL cache and user (or Manager)-side aggregation. For efficient on-demand polling, four-state transition is used to categorize OIDs into dynamic and static objects, each of which is allocated an optimum TTL. To provide users with a proper time stamp, the cache time stamp is included in the response to the users' request. Our experiments show that our cache design gives the staleness of 0 and a bounded number of SNMP requests even when the number of users' requests goes to infinity.

Scene-context plays an important role in scene analysis and object recognition. Among various sources of scene-context, we focus on scene-context scale, which means the effective scale of local context to classify an image pixel in a scene. This paper presents random forests based image categorization using the scene-context scale. The proposed method uses random forests, which are ensembles of randomized decision trees. Since the random forests are extremely fast in both training and testing, it is possible to perform classification, clustering and regression in real time. We train multi-scale texton forests which efficiently provide both a hierarchical clustering into semantic textons and local classification in various scale levels. The scene-context scale can be estimated by the entropy of the leaf node in the multi-scale texton forests. For image categorization, we combine the classified category distributions in each scale and the estimated scene-context scale. We evaluate on the MSRC21 segmentation dataset and find that the use of the scene-context scale improves image categorization performance. Our results have outperformed the state-of-the-art in image categorization accuracy.

The brain-machine interface (BMI) is a new method for man-machine interface, which enables us to control machines and to communicate with others, without input devices but directly using brain signals. Previously, we successfully developed a real time control system for operating a robot arm using brain-machine interfaces based on the brain surface electrodes, with the purpose of restoring motor and communication functions in severely disabled people such as amyotrophic lateral sclerosis patients. A fully-implantable wireless system is indispensable for the clinical application of invasive BMI in order to reduce the risk of infection. This system includes many new technologies such as two 64-channel integrated analog amplifier chips, a Bluetooth wireless data transfer circuit, a wirelessly rechargeable battery, 3 dimensional tissue-fitting high density electrodes, a titanium head casing, and a fluorine polymer body casing. This paper describes key features of the first prototype of the BMI system for clinical application.

Nearest neighbor search (NNS) among large-scale and high-dimensional vectors plays an important role in recent large-scale multimedia search applications. This paper proposes an optimized multiple codebook construction method for an approximate NNS scheme based on product quantization, where sets of residual sub-vectors are clustered according to their distribution and the codebooks for product quantization are constructed from these clusters. Our approach enables us to adaptively select the number of codebooks to be used by trading between the search accuracy and the amount of memory available.

This paper proposes a new method that reduces the computational cost of the phase-only correlation (POC)-based methods for displacement estimation in old film sequences. Conventional POC-based methods calculate all the points of the POC and only use the highest peak of the POC and its neighboring points to estimate the displacement with subpixel accuracy. Our proposed method reduces the computational cost by calculating the POC in a small region, instead of all the points of the POC. The proposed method combines a displacement pre-estimation with a modified inverse discrete Fourier transform (IDFT). The displacement pre-estimation uses the 1-D POCs of frame projections to pre-estimate the displacement with pixel accuracy and chooses a small region in the POC including the desired points for displacement estimation. The modified IDFT is then used to calculate the points in this small region for displacement estimation. Experimental results show that use of the proposed method can effectively reduce the computational cost of the POC-based methods without compromising the accuracy.

Although a large number of query processing algorithms in spatial network database (SNDB) have been studied, there exists little research on route-based queries. Since moving objects move only in spatial networks, route-based queries, like in-route nearest neighbor (IRNN), are essential for Location-based Service (LBS) and Telematics applications. However, the existing IRNN query processing algorithm has a problem in that it does not consider time and space constraints. Therefore, we, in this paper, propose IRNN query processing algorithms which take both time and space constraints into consideration. Finally, we show the effectiveness of our IRNN query processing algorithms considering time and space constraints by comparing them with the existing IRNN algorithm.

The IP Multimedia Subsystem (IMS) establishes a session between end terminals as a client/server application in the Next Generation Network (NGN). These days, many application services are being provided as Web services. In this letter, we propose a new NGN architecture conforming to the architectural styles of Representational State Transfer (REST), which is a Web service technology for solving interoperability and traffic concentration problems in the Session Initiation Protocol (SIP).

This paper proposes a region-of-interest (ROI) based H.264 encoder and the VLSI architecture of the ROI detection algorithm. In ROI based video coding system, pre-processing unit to detect ROI should only introduce low computational complexity overhead due to the low power requirement. The Macroblocks (MBs) in ROIs are detected sequentially in the same order of H.264 encoding to satisfy the MB level pipelining of ROI detector and H.264 encoder. ROI detection is performed in a novel estimation-and-verification process with an ROI contour template. Proposed architecture can be configured to detect either single ROI or multiple ROIs in each frame and the throughput of single detection mode is 5.5 times of multiple detection mode. 98.01% and 97.89% of MBs in ROIs can be detected in single and multiple detection modes respectively. Hardware cost of proposed architecture is only 4.68 k gates. Detection speed is 753 fps for CIF format video at the operation frequency of 200 MHz in multiple detection mode with power consumption of 0.47 mW. Compared with previous fast ROI detection algorithms for video coding application, the proposed architecture obtains more accurate and smaller ROI. Therefore, more efficient ROI based computation complexity and compression efficiency optimization can be implemented in H.264 encoder.

An advanced interest point detector is proposed to improve the Hessian-Matrix based detector of the SURF algorithm. Round-like shapes are utilized as the filter shape to calculate of the Hessian determinant. Dxy can be acquired from approximate round areas, while the regions for computing Dyy or Dxx are designed with the consideration to symmetry and a balance of pixel number. Experimental results indicate that the proposed method has higher repeatability than the one used in SURF, especially in the aspects of rotation and viewpoint, due to the centrosymmetry of the proposed filter shapes. The results of image matching also show that more precision can be gained with the application of proposed detector.

Web APIs are offered in many Web sites for Ajax and mashup, but they have been developed independently since no reusable database component has been specifically created for Web applications. In this paper, we propose WAPDB, a distributed database management system for the rapid development of Web applications. WAPDB is designed on Atom, a set of Web API standards, and provides several of the key features required for Web applications, including efficient access control, an easy extension mechanism, and search and statistics capabilities. By introducing WAPDB, developers are freed from the need to implement these features as well as Web API processing. In addition, its design totally follows the REST architectural style, which gives uniformity and scalability to applications. We develop a proof-of-concept application with WAPDB, and find that it offers great cost effectiveness with no significant impact on performance; in our experiments, the development cost is reduced to less than half with the overhead (in use) of response times of just a few msec.

Broadband antennas have various applications in digital terrestrial television (DTV) services. Compact broadband antennas are required for arranging in long and narrow space along the rim of a laptop display. A leaky-wave antenna using the composite right/left-handed transmission line (CRLH-TL) is one of the candidates for achieving the broadband antenna. However, there are not enough to design guideline of small leaky wave antennas using the CRLH-TL for UHF band. In this paper, a CRLH-TL comprising a ladder network is proposed for broadband and simple structure. The paper also discusses the design of a leaky-wave antenna with the CRLH-TL operating in the DTV band. The relation between the operating bandwidth and attenuation constant of the CRLH-TL is discussed. An antenna that can be accommodated in the limited and narrow space available in mobile terminals has to be designed. Hence, the effects of the number of cells and a finite ground plane are discussed with the purpose of achieving the miniaturization of the antenna. In this study, the transmission and radiation characteristics of the fabricated antennas are measured. The gain of the fabricated antenna is confirmed to remain almost constant even when the operating frequency is varied. The maximum gain and operating band achieved in this study are approximately -0.6 dBi and about 54%, respectively.

This paper presents a novel algorithm for Nearest Neighbor (NN) classifier. NN classification is a well-known method of pattern classification having the following properties: * it performs maximum-margin classification and achieves less than twice the ideal Bayesian error, * it does not require knowledge of pattern distributions, kernel functions or base classifiers, and * it can naturally be applied to multiclass classification problems. Among the drawbacks are A) inefficient memory use and B) ineffective pattern classification speed. This paper deals with the problems A and B. In most cases, NN search algorithms, such as k-d tree, are employed as a pattern search engine of the NN classifier. However, NN classification does not always require the NN search. Based on this idea, we propose a novel algorithm named k-d decision tree (KDDT). Since KDDT uses Voronoi-condensed prototypes, it consumes less memory than naive NN classifiers. We have confirmed that KDDT is much faster than NN search-based classifier through a comparative experiment (from 9 to 369 times faster than NN search based classifier). Furthermore, in order to extend applicability of the KDDT algorithm to high-dimensional NN classification, we modified it by incorporating Gabriel editing or RNG editing instead of Voronoi condensing. Through experiments using simulated and real data, we have confirmed the modified KDDT algorithms are superior to the original one.

People are being inundated under enormous volumes of information and they often dither about making the right choices from these. Interactive user support by information service system such as concierge services will effectively assist such people. However, human-machine interaction still lacks naturalness and thoughtfulness despite the widespread utilization of intelligent systems. The system needs to estimate user's interest to improve the interaction and support the choices. We propose a novel approach to estimating the interest, which is based on the relationship between the dynamics of user's eye movements, i.e., the endogenous control mode of saccades, and machine's proactive presentations of visual contents. Under a specially-designed presentation phase to make the user express the endogenous saccades, we analyzed the timing structures between the saccades and the presentation events. We defined resistance as a novel time-delay feature representing the duration a user's gaze remains fixed on the previously presented content regardless of the next event. In experimental results obtained from 10 subjects, we confirmed that resistance is a good indicator for estimating the interest of most subjects (75% success in 28 experiments on 7 subjects). This demonstrated a higher accuracy than conventional estimates of interest based on gaze duration or frequency.

The availability of IP networks has increased its importance due to the evolving use of real-time and mission-critical applications on IP networks. Methods for preparing alternate routing tables that can be used for fast restoration from link failures have been investigated. In such methods, each node has to compute a number of alternate routing tables in advance since they have to prepare for each potential failure. The resulting huge number of alternate routing tables has prevented these methods from being deployed. In this paper, we propose a method for reducing the number of alternate routing tables for link failure. It analyzes three types of shortest path trees on the basis of link-state information. We show that the number of alternate routing tables can be reduced to 1/100, on average, from that with the conventional method, and that they are small enough to be stored in the memory of IP routers.

A circuit technique to correct Vdd/PM distortion and improve efficiency as supply modulation of cascode class-E PAs has been proposed. The experimental result shows that the phase distortion can be improved from 20 degrees to 5 degrees. Moreover, a system co-simulation result demonstrated that the EVM can be improved from -17 dB to -19 dB.

This paper proposes a novel crest factor reduction (CFR) algorithm applicable to currently deployed W-CDMA base stations. The peak-to-average ratio (PAR) reduction of the multiple carrier mixed signal, namely CFR, has been an issue in order to convey the benefit of using low-cost power amplifiers. The simple final clipping method (SFCM) as a conventional method has been widely utilized due to its simplicity and effectiveness. However, the SFCM degrades the adjacent channel leakage ratio (ACLR) characteristic as well as the signal quality indicated by either the error vector magnitude (EVM) or the peak code domain error (PCDE). Conventionally, in order to alleviate this undesired deterioration, extra channel filtering and signal quality enhancement followed by CFR might be processed in an open-loop style. Alternatively, to perform CFR by maintaining the PAR as low as possible subject to satisfying the prescribed ACLR and EVM/PCDE performance, this paper introduces the prediction filter dependent peak reduction (PFDPR) process collaboratively working with dynamic waterfilling-based code domain compensation (DWCDC). To verify the superiority of the proposed CFR algorithm, tentative simulations are conducted while maintaining the rules of legitimate W-CDMA base station test specifications.

In this paper, we propose a routing method for 2-layer ball grid array packages that generates a routing pattern satisfying a design rule. In our proposed method, the routing structure on each layer is restricted while keeping most of feasible patterns to efficiently obtain a feasible routing pattern. A routing pattern that satisfies the design rule is formulated as a mixed integer linear programming. In experiments with seven data, we obtain a routing pattern such that satisfies the design rule within a practical time by using a mixed integer linear programming solver.

This paper addresses problems appearing in restoration algorithms based on utilizing both Tikhonov and bilateral total variation (BTV) regularization. The former regularization assumes that prior information has Gaussian distribution which indeed fails at edges, while the later regularization highly depends on the selected bilateral filter's parameters. To overcome these problems, we propose a locally adaptive regularization. In the proposed algorithm, we use general directional regularization functions with adaptive weights. The adaptive weights are estimated from local patches based on the property of the partially restored image. Unlike Tikhonov regularization, it can avoid smoothness across edges by using adaptive weights. In addition, unlike BTV regularization, the proposed regularization function doesn't depend on parameters' selection. The convexity conditions as well as the convergence conditions are derived for the proposed algorithm.