Social bookmarking services have recently made it possible for us to register and share our own bookmarks on the web and are attracting attention. The services let us get structured data: (URL, Username, Timestamp, Tag Set). And these data represent user interest in web pages. The number of bookmarks is a barometer of web page value. Some web pages have many bookmarks, but most of those bookmarks may have been posted far in the past. Therefore, even if a web page has many bookmarks, their value is not guaranteed. If most of the bookmarks are very old, the page may be obsolete. In this paper, by focusing on the timestamp sequence of social bookmarkings on web pages, we model their activation levels representing current values. Further, we improve our previously proposed ranking method for web search by introducing the activation level concept. Finally, through experiments, we show effectiveness of the proposed ranking method.

Bag-of-Visual-Words representation has recently become popular for scene classification. However, learning the visual words in an unsupervised manner suffers from the problem when faced these patches with similar appearances corresponding to distinct semantic concepts. This paper proposes a novel supervised learning framework, which aims at taking full advantage of label information to address the problem. Specifically, the Gaussian Mixture Modeling (GMM) is firstly applied to obtain "semantic interpretation" of patches using scene labels. Each scene induces a probability density on the low-level visual features space, and patches are represented as vectors of posterior scene semantic concepts probabilities. And then the Information Bottleneck (IB) algorithm is introduce to cluster the patches into "visual words" via a supervised manner, from the perspective of semantic interpretations. Such operation can maximize the semantic information of the visual words. Once obtained the visual words, the appearing frequency of the corresponding visual words in a given image forms a histogram, which can be subsequently used in the scene categorization task via the Support Vector Machine (SVM) classifier. Experiments on a challenging dataset show that the proposed visual words better perform scene classification task than most existing methods.

In this letter, we develop an analytical model for the drive-thru applications based on the IEEE 802.11p WAVE. The model shows that prioritizing the bitrates via the 802.11e EDCA mechanism leads to significant throughput improvement.

This paper discusses recent trends in the area of low-power, high-performance A/D conversion. We examine survey data collected over the past twelve years to show that the conversion energy of ADCs has halved every two years, while the speed-resolution product has doubled approximately only every four years. A closer inspection on the impact of technology scaling, and developments in ADC design are then presented to explain the observed trends. Finally, we review opportunities in digitally assisted design for the most popular converter architectures.

In this paper, a novel orthogonal frequency-division multiplexing (OFDM) modulator/demodulator based on real-valued discrete Hartley transform (DHT) is presented and implemented for the IEEE 802.11a/g wireless local area network (LAN). Instead of the conventional complex-valued fast Fourier transform (FFT) for OFDM systems, the proposed architecture employs two real-valued fast DHT (FHT) kernels and one post processing unit. By taking advantage of the real-valued operation of FHT, this approach reduces the number of multiplications compared with the radix-2 FFT. The proposed DHT-based modulator/demodulator was designed and fabricated in 0.18-µm CMOS technology with a core area of 928935 µm2. The average power consumption is about 20.16 mW at 20 MHz and 1.8 V supply voltage. Measurement results of the integrated circuit illustrate its superior chip area and power consumption.

In differentiated services, packet classification is used to categorize incoming packets into multiple forwarding classes based on pre-defined filters and make information accessible for quality of service. Although numerous algorithms have presented novel data structures to improve the search performance of packet classification, the performance of these algorithms are usually limited by the characteristics of filter databases. In this paper, we use a different approach of filter preprocessing to enhance the search performance of packet classification. Before generating the searchable data structures, we cluster filters in a bottom-up manner. The procedure of the filter clustering merges filters with high degrees of similarity. The experimental results show that the technique of filter clustering could significantly improve the search performance of Pruned Tuple Space Search, a notable hash-based algorithm. As compared to the prominent existing algorithms, our enhanced Pruned Tuple Space Search also has superior performance in terms of speed and space.

With the big success of 802.11 wireless networks, there have been many proposals addressing end-to-end QoS guarantees in 802.11 WLAN. However, we have found that current end-to-end QoS architectures lack of one or more important properties such as cross-layer interaction, end-to-end integration, reconfigurability and modularity. In this work, we present an end-to-end reference QoS architecture for 802.11 WLAN that encapsulates in an unifying fashion software-based QoS components (mechanisms, algorithms, services), proposed in the literature. To show the usefulness and correctness of the reference architecture, we present three case studies of end-to-end QoS architectures addressing different QoS requirements such as bandwidth and delay with different approaches such as differentiated services and integrated services. We will give an architectural comparison and performance evaluation of these architectures. We believe the reference QoS architectures can help QoS designers to understand the importance and the complexity of various QoS components during the design phase and thus choose these QoS components appropriately.

In this letter, a new CCM material, adding Ni powder to a conventional CCM, for X-band applications is designed and analyzed to improve the SE. To obtain the SE of the fabricated CCM accurately, material constants of the CCM of the permittivity and permeability were extracted using transmission/reflection measurements. Using the material constants derived from the measurement, the SE was calculated and the results were verified using a commercial full-wave three-dimensional electromagnetic wave simulator. The SE of the proposed the CCM was improved by approximately 4 dB in the X band compared to that of a conventional CCM. The CCM proposed in this paper can be applied as a shielding material as well as for housing of various communication systems and electrical instruments.

In this paper, we propose an optimization system with parallel processing for reducing electromagnetic interference (EMI) on electronic control unit (ECU). We adopt simulated annealing (SA), genetic algorithm (GA) and taboo search (TS) to seek optimal solutions, and a Spice-like circuit simulator to analyze common-mode current. Therefore, the proposed system can determine the adequate combinations of the parasitic inductance and capacitance values on printed circuit board (PCB) efficiently and practically, to reduce EMI caused by the common-mode current. Finally, we apply the proposed system to an example circuit to verify the validity and efficiency of the system.

A swing level controlled voltage-mode transmitter is proposed to support a stub series-terminated logic channel with center-tapped termination. This transmitter provides a swing level control to support the diagnostic mode and improve the signal integrity in the absence of the destination termination. By using the variable parallel termination, the proposed transmitter maintains the constant output impedance of the source termination while the swing level is controlled. Also, the series termination using an external resistor is used to reduce the impedance mismatch effect due to the parasitic components of the capacitor and inductor. To verify the proposed transmitter, the voltage-mode driver, which provides eight swing levels with the constant output impedance of about 50 Ω, was implemented using a 70 nm 1-poly 3-metal DRAM process with a 1.5 V supply. The jitter reduction of 54% was measured with the swing level controlled voltage-mode driver in the absence of the destination termination at 1.6-Gb/s.

Reflecting the rapid growth of information technology, the configuration of software applications such as word processors and spreadsheets is both sophisticated and complicated. It is often difficult for users to identify relevant functions in the online manual for a target application. In this paper, we propose a method for question answering that finds functions related to the user's request. To enhance our method, we addressed two "mismatch" problems. The first problem is associated with a mismatch in vocabulary, where the same concept is represented by different words in the manual and in the user's question. The second problem is associated with a mismatch in function. Although the user may have a hypothetical function for a purpose in mind, this purpose can sometimes be accomplished by other functions. To resolve these mismatch problems, we extract terms related to software functions from the Web, so that the user's question can be matched to the relevant function with high accuracy. We demonstrate the effectiveness of our method experimentally.

In this paper, an ultra low power analog front-end for EPCglobal Class 1 Generation 2 RFID tag is presented. The proposed RFID tag removes the need for high frequency clock and counters used in conventional tags, which are the most power hungry blocks. The proposed clock-free decoder employs an analog integrator with an adaptive current source that provides a uniform decoding margin regardless of the data rate and a link frequency extractor based on a relaxation oscillator that generates frequency used for backscattering. A dual supply voltage scheme is also employed to increase the power efficiency of the tag. In order to improve the tolerance of the proposed circuit to environmental variations, a self-calibration circuit is proposed. The proposed RFID analog front-end circuit is designed and simulated in 0.25 µm CMOS, which shows that the power consumption is reduced by an order magnitude compared to the conventional RFID tags, without losing immunity to environmental variations.

Skyline query is very important because it is the basis of many applications, e.g., decision making, user-preference queries. Given an N-dimensional dataset D, a point p is said to dominate another point q if p is better than q in at least one dimension and equal to or better than q in the remaining dimensions. In this paper, we study a generalized problem of skyline query that, users are more interested in the details of the dominant relationship in a dataset, i.e., a point p dominates how many other points and whom they are. We show that the existing framework proposed in can not efficiently solve this problem. We find the interrelated connection between the partial order and the dominant relationship. Based on this discovery, we propose a new data structure, ParCube, which concisely represents the dominant relationship. We propose some effective strategies to construct ParCube. Extensive experiments illustrate the efficiency of our methods.

Free probability theory, which has become a main branch of random matrix theory, is a valuable tool for describing the asymptotic behavior of multiple systems, especially for large matrices. In this paper, using asymptotic free probability theory, a new cooperative scheme for spectrum sensing is proposed, which shows how the asymptotic free behavior of random matrices and the property of Wishart distribution can be used to assist spectrum sensing for cognitive radio. Simulations over Rayleigh fading and AWGN channels demonstrate the proposed scheme has better detection performance than the energy detection techniques and the Maximum-minimum eigenvalue (MME) scheme even for the case of a small sample of observations.

Many knapsack cryptosystems have been proposed but almost all the schemes are vulnerable to lattice attack because of their low density. To prevent the lattice attack, Chor and Rivest proposed a low weight knapsack scheme, which made the density higher than critical density. In Asiacrypt2005, Nguyen and Stern introduced pseudo-density and proved that if the pseudo-density is low enough (even if the usual density is not low enough), the knapsack scheme can be broken by a single call to SVP/CVP oracle. However, the usual density and the pseudo-density are not sufficient to measure the resistance to the lattice attack individually. In this paper, we first introduce the new notion of density D, which naturally unifies the previous two density. Next, we derive conditions for our density so that a knapsack scheme is secure against lattice attack. We obtain a critical bound of density which depends only on the rate of the message length and its Hamming weight. Furthermore, we show that if D<0.8677, the knapsack scheme is solved by lattice attack. Next, we show that the critical bound goes to 1 if the Hamming weight decreases, which means that it is (almost) impossible to construct a low weight knapsack scheme which is supported by an argument of density.

Traditionally, popular synonym acquisition methods are based on the distributional hypothesis, and a metric such as Jaccard coefficients is used to evaluate the similarity between the contexts of words to obtain synonyms for a query. On the other hand, when one tries to compile and clean a thesaurus, one often already has a modest number of synonym relations at hand. Could something be done with a half-built thesaurus alone? We propose the use of spectral methods and discuss their relation to other network-based algorithms in natural language processing (NLP), such as PageRank and Bootstrapping. Since compiling a thesaurus is very laborious, we believe that adding the proposed method to the toolkit of thesaurus constructors would significantly ease the pain in accomplishing this task.

In this paper, we describe a novel model training method for one-to-many eigenvoice conversion (EVC). One-to-many EVC is a technique for converting a specific source speaker's voice into an arbitrary target speaker's voice. An eigenvoice Gaussian mixture model (EV-GMM) is trained in advance using multiple parallel data sets consisting of utterance-pairs of the source speaker and many pre-stored target speakers. The EV-GMM can be adapted to new target speakers using only a few of their arbitrary utterances by estimating a small number of adaptive parameters. In the adaptation process, several parameters of the EV-GMM to be fixed for different target speakers strongly affect the conversion performance of the adapted model. In order to improve the conversion performance in one-to-many EVC, we propose an adaptive training method of the EV-GMM. In the proposed training method, both the fixed parameters and the adaptive parameters are optimized by maximizing a total likelihood function of the EV-GMMs adapted to individual pre-stored target speakers. We conducted objective and subjective evaluations to demonstrate the effectiveness of the proposed training method. The experimental results show that the proposed adaptive training yields significant quality improvements in the converted speech.

This paper proposes a multicast delivery system using base station diversity for cellular systems. Conventional works utilize single wireless link communication to achieve reliable multicast. In cellular systems, received signal intensity declines in cell edge areas. Therefore, wireless terminals in cell edge areas suffer from many transmission errors due to low received signal intensity. Additionally, multi-path fading also causes dynamic fluctuation of received signal intensity. Wireless terminals also suffer from transmission errors due to the multi-path fading. The proposed system utilizes multiple wireless link communication to improve transmission performance. Each wireless terminal communicates with some neighbor base stations, and combines frame information which arrives from different base stations. Numerical results demonstrate that the proposed system can achieve multicast data delivery with a short transmission period and can reduce consumed wireless resource due to retransmission.

This paper presents a wide range DCO with fine discrete tuning steps using a ΣΔ modulation technique for UWB frequency synthesizer. The proposed discrete tuning scheme provides a low effective frequency resolution without any degradation of the phase noise performance. With its three step discrete tunings, the DCO simultaneously has a wide tuning range and fine tuning steps. The frequency synthesizer was implemented using 0.13 µm CMOS technology. The tuning range of the DCO is 5.8-6.8 GHz with an effective frequency resolution of 3.9 kHz. It achieves a measured phase noise of -108 dBc/Hz at 1 MHz offset and a tuning range of 16.8% with the power consumption of 5.9 mW. The figure of merit with the tuning range is -181.5 dBc/Hz.

An arbitrary noise generator (ANG) is based on time-series charging of divided parasitic capacitance (TSDPC) and emulates power supply noise generation in a CMOS digital circuit. A prototype ANG incorporates an array of 32 x 32 6-bit TSDPC cells along with a 128-word vector memory and occupies 2 x 2 mm2 in a 65 nm 1.2 V CMOS technology. Digital noise emulation of functional logic cores such as register arrays is demonstrated with chip-level waveform monitoring at power supply, ground, as well as substrate nodes.