Radar Cross Section (RCS) can be obtained from near-field data by using near-field to far-field RCS transformation methods. Phase errors in near-field data cause the degradation of the prediction accuracy. In order to overcome the difficulty, we propose the far-field RCS prediction method from one-dimensional intensity data in near-field. The proposed method is derived by extending the phase retrieval method based on the Gerchberg-Saxton algorithm with the use of the relational expression between near-fields and scattering coefficients. The far-field RCS can be predicted from the intensity data of scattered fields measured at two different ranges. The far-field RCS predicted by the proposed method approximately coincides with the computed one. The proposed method also has significant advantages of simple and efficient algorithm. The proposed method is valuable from a practical point of view.

A phase-retrieval method is applied to the quasioptical feed system of the offset Cassegrain antenna of the Superconducting Submillimeter-Wave Limb-Emission Sounder (JEM/SMILES) to be aboard the International Space Station for evaluating the beam alignment by estimating the phase pattern from the beam amplitude pattern measurements. As the result, the application of the phase retrieval method is demonstrated to be effective for measuring and evaluating the quasioptical antenna feed system. It is also demonstrated that the far-field radiation pattern of the antenna main reflector can be estimated from the phase-retrieved beam pattern of the feed system.

This paper investigates suitable indexing techniques to enable efficient content-based audio retrieval in large acoustic databases. To make an index-based retrieval mechanism applicable to audio content, we investigate the design of Locality Sensitive Hashing (LSH) and the partial sequence comparison. We propose a fast and efficient audio retrieval framework of query-by-content and develop an audio retrieval system. Based on this framework, four different audio retrieval schemes, LSH-Dynamic Programming (DP), LSH-Sparse DP (SDP), Exact Euclidian LSH (E2LSH)-DP, E2LSH-SDP, are introduced and evaluated in order to better understand the performance of audio retrieval algorithms. The experimental results indicate that compared with the traditional DP and the other three compititive schemes, E2LSH-SDP exhibits the best tradeoff in terms of the response time, retrieval accuracy and computation cost.

Automated capture and retrieval of experiences at home is interesting due to the wide variety and personal significance of such experiences. We present a system for retrieval and summarization of continuously captured multimedia data from Ubiquitous Home, a two-room house consisting of a large number of cameras and microphones. Data from pressure based sensors on the floor are analyzed to segment footsteps of different persons. Video and audio handover are implemented to retrieve continuous video streams corresponding to moving persons. An adaptive algorithm based on the rate of footsteps summarizes these video streams. A novel method for audio segmentation using multiple microphones is used for video retrieval based on sounds with high accuracy. An experiment, in which a family lived in this house for twelve days, was conducted. The system was evaluated by the residents who used the system for retrieving their own experiences; we report and discuss the results.

We propose a query language based on extended regular expressions. This language extends texts with text-generating macros. These macros make it possible to define languages in a compressed, elegant way. This paper also extends queries with linear implications and additive (classical) conjunctions. To be precise, it allows goals of the form D —ο G and G1&G2 where D is a text or a macro and G is a query. The first goal is solved by adding D to the current text and then solving G. This goal is flexible in controlling the current text dynamically. The second goal is solved by solving both G1 and G2 from the current text. This goal is particularly useful for internet search.

In this paper, we propose two weighting techniques to improve performances of query expansion in biomedical document retrieval, especially when a short biomedical term in a query is expanded with its synonymous multi-word terms. When a query contains synonymous terms of different lengths, a traditional IR model highly ranks a document containing a longer terminology because a longer terminology has more chance to be matched with a query. However, such preference is clearly inappropriate and it often yields an unsatisfactory result. To alleviate the bias weighting problem, we devise a method of normalizing the weights of query terms in a long multi-word biomedical term, and a method of discriminating terms by using inverse terminology frequency which is a novel statistics estimated in a query domain. The experiment results on MEDLINE corpus show that our two simple techniques improve the retrieval performance by adjusting the inadequate preference for long multi-word terminologies in an expanded query.

Expansion of imagination is crucial for lively creativity. However, such expansion is sometimes rather difficult and an environment which supports creativity is required. Because people can attain higher creativity by using words with a thematic relation rather than words with a taxonomical relation, we tried to extract word lists having thematic relations among words. We first extracted word lists from domain specific documents by utilizing inclusive relations between words based on a modifiee/modifier relationship in documents. Next, from the extracted word lists, we removed the word lists having taxonomical relations so as to obtain only word lists having thematic relations. Finally, based on the assumption what kind of knowledge a person can associate when he/she looks at a set of words correlates with how the word set is effective in creativity support, we examined whether the word lists direct us to informative pages on the Web for verifying the availability of our extracted word lists.

We propose a method to retrieve similar and duplicate images from a JPEG (Joint Photographic Image Group) image database. Similarity level is decided based on the DCT (Discrete Cosine Transform) coefficients signs. The method is simple and fast because it uses the DCT coefficients signs as features, which can be obtained directly after partial decoding of JPEG bitstream. The method is robust to JPEG compression, in which similarity level of duplicate images, i.e., images that are compressed from the same original images with different compression ratios, is not disguised due to JPEG compression. Simulation results showed the superiority of the method compared to previous methods in terms of computational complexity and robustness to JPEG compression.

This paper proposes a new method for realizing the web page recommendation system by sharing users' web browse history on an anonymous P2P network. Our scheme creates a user profile, a summary of the user's web browse trends, by analyzing the contents of the web pages browsed. The scheme then provides a P2P network to exchange web browse histories so as to create mutual web page recommendations. The novelty of our method lies in its P2P network formulation; it is formulated in a way so that users having similar user profiles are automatically connected, yet their user profiles are protected from being disclosed to other users. The proposed method intentionally distributes bogus user profiles on the P2P network, while not harming the efficiency of the web browse history sharing process.

This letter proposes a group-based distributed air index (called GDI) using two-leveled groups by partitioning the identifiers of data items to reduce the size of the index. GDI provides both global and local views of data items and multiple pointers to data items in a single access to an index. Simulation results show that GDI outperforms the existing index in terms of multiple data access, energy conservation and data waiting time.

A new phase retrieval method using an active contour model (snake) for image reconstruction is proposed. The proposed method reconstructs a target image by retrieving the phase from the magnitude of its Fourier transform and the measured area of the image. In general, the measured area is different from the true area where the target image exists. Thus a snake, which can extract the shape of the target image, is utilized to renew the measured area. By processing this renewal iteratively, the area obtained by the snake converges to the true area and as a result the proposed method can accurately reconstruct a target image even when the measured area is different from the true area. Experimental results show the effectiveness of the proposed method.

Data retrieval is used to obtain a particular data item from a database. A user requests an item in the database from a database server by sending a query, and obtains the item from an answer to the query. Security requirements of data retrieval include protecting the privacy of the user, the secrecy of the database, and the consistency of answers. In this paper, a data retrieval scheme which satisfies all the security requirements is defined and an efficient construction is proposed. In the proposed construction, the size of a query and an answer is O((log N)2), and the size of data published by the database server when the database is updated is only O(1). The proposed construction uses the Merkle tree, a commitment scheme, and Oblivious Transfer. The proof of the security is given under the assumption that the used cryptographic schemes are secure.

Recently, cars are equipped with a lot of sensors for safety driving. We have been trying to store the driving-scene video with such sensor data and to detect the change of scenery of streets. Detection results can be used for building historical database of town scenery, automatic landmark updating of maps, and so forth. In order to compare images to detect changes, image retrieval taken at nearly identical locations is required as the first step. Since Global Positioning System (GPS) data essentially contain some noises, we cannot rely only on GPS data for our image retrieval. Therefore, we have developed an image retrieval algorithm employing edge-histogram-based image features in conjunction with hierarchical search. By using edge histograms projected onto the vertical and horizontal axes, the retrieval has been made robust to image variation due to weather change, clouds, obstacles, and so on. In addition, matching cost has been made small by limiting the matching candidates employing the hierarchical search. Experimental results have demonstrated that the mean retrieval accuracy has been improved from 65% to 76% for the front-view images and from 34% to 53% for the side-view images.

This paper describes a flexible strategy to generate candidate answers for factoid questions in Question Answering (QA) systems. Most QA systems have predefined the conceptual categories for candidate answers. But if the conceptual category of answers to any question is not prepared in the QA system, it is hard to extract correct answers to that question. Therefore, we propose an extraction method for IS-A relation patterns which describe relations between the nominal target concepts of question and candidate answers. The extracted IS-A relation patterns can be used for questions with an unexpected target concept.

With today's advances in peer-to-peer (P2P) techniques, a lot of non-document content has become searchable and usable. In the near future, since a huge amount of content will be distributed over the networks, not only index server searching but also P2P searching will become important because of its scalability and robustness. Typical P2P content searching services have some problems, such as low search precision ratio, significant increase in traffic and inundations of malicious content such as viruses. We propose a P2P content searching method in which a query is effectively forwarded only to peers that have indices of content semantically similar to the wanted content but not forwarded to the same peer repeatedly. It is based on the ideas of content addressable network (CAN) topology and a vector space method where vectors have a variable length. It maps non-document content to a vector space based on users' evaluations and manages the vector space or routes queries using the CAN topology control. The effectiveness of our method is shown by both analytical estimations and simulation experiments. The simulations clarified that our method is effective at improving the precision and recall ratios while reducing the amount of traffic compared with Gnutella flooding, the vector space method in which vector lengths are fixed (similar to the pSearch method), and Chord. In particular, when there was a lot of malicious content, our method exhibited a higher precision ratio than other methods.

File-sharing Peer-to-Peer systems are effective for autonomous data retrieval and provision over the networks. However, the early data retrieval schemes such as Gnutella and Local Indices have low performance and large overhead. In order to solve weakness of early schemes, this paper proposes a dynamic scheme for data retrieval and provision, in which indices are adaptively allocated in appropriate nodes to variation of traffic patterns caused by query messages. The simulation experimental results show that the proposed scheme has good performance with reasonable overhead even when the traffic patterns vary as time proceeds.

This letter proposes a fast k nearest neighbors search algorithm based on the wavelet transform. This technique exploits the important information of the approximation coefficients of the transform coefficient vector, from which we obtain two crucial inequalities that can be used to reject those vectors for which it is impossible to be k nearest neighbors. The computational complexity for searching for k nearest neighbors can be largely reduced. Experimental results on texture classification verify the effectiveness of our algorithm.

The noise robustness of an audio fingerprinting system is one of the most important issues in music information retrieval by the content-based audio identification technique. In a real environment, sound recordings are commonly distorted by channel and background noise. Recently, Philips published a robust and efficient audio fingerprinting system for audio identification. To extract a robust and efficient audio fingerprint, Philips applied the first derivative (differential) to the frequency-time sequence of the perceptual filter-bank energies. In practice, however, the noise robustness of Philips' audio fingerprinting scheme is still insufficient. In this paper, we introduce an extension method of the audio fingerprinting scheme for the enhancement of noise robustness. As an alternative to frequency filtering, a type of band-pass filter, instead of a high-pass filter, is used to achieve robustness to background noise in a real situation. Our experimental results show that the proposed filter improves the noise robustness in audio identification.

A great deal of research has been made to model the vagueness and uncertainty in information retrieval. One such research is fuzzy ranking models, which have been showing their superior performance in handling the uncertainty involved in the retrieval process. However, these conventional fuzzy ranking models have a limited ability to incorporate the user preference when calculating the rank of documents. To address this issue, in this study we develop a new fuzzy ranking model based on the user preference. Through the experiments on the TREC-2 collection of Wall Street Journal documents, we show that the proposed method outperforms the conventional fuzzy ranking models.

According to the Gestalt principals, this paper presents a recognition method of grouping areas in trademark images modeling features for measuring the attraction degree between couples of image components. This investigation would be used for content-based image retrieval from the view of mirroring human perception for images. Depending on variability in human perception for trademark images, the proposed method finds grouping areas by calculating Mahalanobis distance with the features to every combination of two components in images. The features are extracted from every combination of two components in images, and the features represent proximity, shape similarity, and closure between two components. In addition, changing combination of the features, plural grouping patterns are output. Besides, this paper shows the efficiency and limits of the proposed method from experimental results. In the experiments, 104 participants have perceived grouping patterns to 74 trademark images and the human perceptions have been compared with outputs by the proposed method for the 74 images.