Fast Retrieval Technique Research Articles

Fast prostate retrieval in robot-assisted laparoscopic prostatectomy for next-generation biobanking.

Robotic-assisted laparoscopic radical prostatectomy (RALP) has become the most widespread treatment for organ-confined prostate cancer. Here, we describe a fast specimen retrieval technique for RALP to obtain high-quality tissue specimen with minimal warm ischemia time for next-generation biobanking. Here, we show that using fast retrieval technique, short warm ischemia times can be achieved while not increasing the surgical time. Patients undergoing RALP with written informed consent participated in Helsinki Urological Bank study. Previously operated RALP patients and those, who were not willing to participate in the study, served as a control group. The study consisted of 1685 patients, 684 in fast retrieval and 1001 in control group. We developed a novel fast retrieval technique in which fascia is opened for camera port according to the prostate size and a running suture is placed and tightened against the camera port in the beginning of the operation. Immediately after prostate is freed from attachments, suture is loosened and the prostate is extirpated inside the endoscopic bag through the camera port fascial opening, then the fascial suture is again tightened against the camera port and the RALP procedure is completed. The mean warm ischemia times in fast retrieval group were 20min 18s and 22min 30s, respectively, in patients without and with lymphadenectomy. The mean console and surgery times with and without lymphadenectomy were similar in both groups. There were no technique-related complications associated with Fast Retrieval procedure. Tissue integrity test results for the RNA and DNA quality showed good quality for the specimen. Fast retrieval technique can easily and safely be utilized to maximize usefulness of RALP tissue specimen in downstream biobank applications.

A Digital Frequency Adaptive Synchronization Unit for On- and Off-Grid Systems

This paper presents the analysis and implementation details of a frequency adaptive synchronization unit (FASU) capable of extracting the positive, negative, and zero sequence components of a three-phase signal as well as real-time tracking of the amplitude, phase, and frequency of the positive sequence component. The FASU consists of three subsystems: First, a sequence component extractor block based on a fast retrieval technique, second, a zero-crossing peak detection system with dynamic dc compensation and, third, a frequency adaptive multi-input multioutput low-pass finite-impulse response filter. The frequency adaptive filter is able to provide filtering of the positive sequence component over a wide frequency range (40–2000 Hz), in the presence of significant input signal distortion (total harmonic distortion as high as 100%). The system achieves a worst case transient response time of one and a half-cycle of the input period, in the event of input transients such as balanced/unbalanced amplitude sags and swells, balanced/unbalanced phase steps, and positive/negative frequency ramps (up to 700 Hz/s). The proposed system is suitable for use in islanded microgrids, in the aerospace industry, and as a phasor measurement unit. The system is implemented as a proof of concept on a field programmable gate array hardware platform.

Binary code learning via optimal class representations

Hashing is an attracting technique for fast retrieval due to its low storage and computation costs. By hashing, each high-dimensional vector is mapped into a low-dimensional binary code vector and retrieval is performed in the Hamming space. Recently several hashing methods have been proposed, among which, supervised hashing methods have shown great performance by incorporating the supervision information. However, most previous supervised methods simply focused on the pairwise label information of data, and ignored the structure information and relationship within data. To tackle this problem, we propose to learn binary codes by explicitly taking into account class semantic relatedness. Specifically, a set of binary codes is computed according to the intrinsic class similarities in data and serves as the optimal class representations. We show that, by mapping images onto the optimal representation of their corresponding classes, our proposed method outperforms several other state-of-the-art supervised hashing methods in image retrieval on three large-scale datasets.

Adapting indexing trees to data distribution in feature spaces

Fast similarity retrieval is critical for content-based image retrieval systems. Tree indexing is a classical technique for fast retrieval, but the practical performance increase offered by the indexing tree depends on the intrinsic dimension of the data. Data with a low intrinsic dimension can be indexed more efficiently than data with high intrinsic dimension. This suggests that an indexing tree that is adapted to the data distribution may be more efficient. This paper proposes two adaptation procedures that are guaranteed to improve indexing efficiency. The procedures are based on a formula for average number of node tests incurred during the retrieval. The formula clearly shows how indexing performance varies with the distribution of feature points and the query. Greedy and optimal tree adaptation procedures are derived based on the formula. Both procedures explicitly enhance the retrieval performance of indexing trees. The optimally adapted tree carries the mathematical guarantee that it is the best performing tree in a set of possible trees obtained by node elimination. The adaptation procedures are applied to kdb-trees and hierarchical clustering trees for indexing synthetic as well as real data sets in medical image databases. Experimental results validate the claim that adaptation procedures increase retrieval efficiency.

Efficient indexing of interval time sequences

Time sequences, which are ordered sets of observations, have been studied in various database applications. In this paper, we introduce a new class of time sequences where each observation is represented by an interval rather than a number. Such sequences may arise in many situations. For instance, we may not be able to determine the exact value at a time point due to uncertainty or aggregation. Such observation may be represented better by a range of possible values. Similarity search with interval time sequences as both query and data sequences poses a new challenge for research. We first address the issue of (dis)similarity measures for interval time sequences. We choose an L 1 norm-based measure because it effectively quantifies the degree of overlapping and remoteness between two intervals, and is invariant irrespective of the position of an interval when it is enclosed within another interval. We next propose an efficient indexing technique for fast retrieval of similar interval time sequences from large databases. More specifically, we propose: (1) to extract a segment-based feature vector for each sequence, and (2) to map each feature vector to either a point or a hyper-rectangle in a multi-dimensional feature space. We then show how we can use existing multi-dimensional index structures such as the R-tree for efficient query processing. The proposed method guarantees no false dismissals. Experimental results show that, for synthetic and real stock data, it is superior to sequential scanning in performance and scales well with the data size.

Data mining technique for fast retrieval of similar waveforms in Fusion massive databases

Fusion measurement systems generate similar waveforms for reproducible behavior. A major difficulty related to data analysis is the identification, in a rapid and automated way, of a set of discharges with comparable behaviour, i.e. discharges with “similar” waveforms. Here we introduce a new technique for rapid searching and retrieval of “similar” signals. The approach consists of building a classification system that avoids traversing the whole database looking for similarities. The classification system diminishes the problem dimensionality (by means of waveform feature extraction) and reduces the searching space to just the most probable “similar” waveforms (clustering techniques). In the searching procedure, the input waveform is classified in any of the existing clusters. Then, a similarity measure is computed between the input signal and all cluster elements in order to identify the most similar waveforms. The inner product of normalized vectors is used as the similarity measure as it allows the searching process to be independent of signal gain and polarity. This development has been applied recently to TJ-II stellarator databases and has been integrated into its remote participation system.

이미지 시퀀스 데이터베이스에서의 유사성 기반 서브시퀀스 검색

본 논문은 다차원 타임 워핑 거리 함수를 이용하여 유사한 이미지 서브시퀀스를 신속하게 검색할 수 있는 색인 방법을 제안한다. 타임 워핑 거리는 시퀀스들의 길이가 다르거나 샘플링 비율이 다른 많은 응용에서 Lp 거리보다 더욱 적합하다. 우리가 제안한 색인 방법은 디스크 기반의 접미어 트리를 색인 구조체로 채택하고, 유사하지 않은 서브시퀀스를 잘못된 누락 없이 잘 여과하기 위해 하한 거리 함수를 사용한다. 이 방법은 특정 차원의 상대적 가중치를 손쉽게 부여하기 위해 정규화를 적용하고 색인 트리를 압축하기 위해 이산화 과정을 수행한다. 메디컬 이미지와 합성 이미지 시퀀스를 대상으로 한 실험은 본 논문에서 제안한 방법이 naive한 방법보다 우수한 성능을 보이고 대용량의 이미지 시퀸스 데이터베이스로의 확장이 용이함을 입증한다. This paper proposes an indexing technique for fast retrieval of similar image subsequences using the multi-dimensional time warping distance. The time warping distance is a more suitable similarity measure than Lp distance in many applications where sequences may be of different lengths and/or different sampling rates. Our indexing scheme employs a disk-based suffix tree as an index structure and uses a lower-bound distance function to filter out dissimilar subsequences without false dismissals. It applies the normaliration for an easier control of relative weighting of feature dimensions and the discretization to compress the index tree. Experiments on medical and synthetic image sequences verify that the proposed method significantly outperforms the naive method and scales well in a large volume of image sequence databases.

Similarity search of time-warped subsequences via a suffix tree

This paper proposes an indexing technique for fast retrieval of similar subsequences using the time-warping distance. The time-warping distance is a more suitable similarity measure than the Euclidean distance in many applications where sequences may be of different lengths and/or different sampling rates. The proposed indexing technique employs a disk-based suffix tree as an index structure and uses lower-bound distance functions to filter out dissimilar subsequences without false dismissals. To make the index structure compact and hence accelerate the query processing, it converts sequences in the continuous domain into sequences in the discrete domain and stores only a subset of the suffixes whose first values are different from those of the immediately preceding suffixes. Extensive experiments with real and synthetic data sequences revealed that the proposed approach significantly outperforms the sequential scan and LB scan approaches and scales well in a large volume of sequence databases.

SIMILARITY-BASED SUBSEQUENCE SEARCH IN IMAGE SEQUENCE DATABASES

This paper proposes an indexing technique for fast retrieval of similar image subsequences using the multi-dimensional time warping distance. The time warping distance is a more suitable similarity measure as compared to the Lp distance in many applications where sequences may be of different lengths and/or different sampling rates. Our indexing scheme employs a disk-based suffix tree as an index structure and uses a lower-bound distance function to filter out dissimilar subsequence without false dismissals. It applies the normalization for an easier control of relative weighting of feature dimensions and the discretization to compress the index tree. Experiments on medical and synthetic image sequences verified that the proposed method significantly outperforms the naïve method and scales well in a large volume of image sequence databases.

A fast retrieval technique for large graph structures

Graphs are a typical and basic data structure for computer algorithms. A two-dimensional array and a list are well-known as for their representation. These data representations have contrasting features for retrieval time and space efficiency. Aho et al. [3] proposed a method for combining the compactness of the list with the fast retrieval of the array. Applications of Aho's scheme were, however, limited to static data structures because the updating algorithm was not given. By introducing three one-dimensional arrays based on Aho's data structure, called a triple-array structure, this paper proposes a method for updating arcs stored in the triple-array without degrading the worst-case retrieval time complexity 0(1). The triple-array maps arcs into two one-dimensional arrays CHECK and NEXT according to another one-dimensional array BASE. An updating algorithm presented here moves the initial positions of the arcs into other possible positions in the triple-array, by a first-fit strategy which searches th...

Polynomial search

AbstractBy plotting the key value against the record position in a sorted file and then fitting a least squares polynomial through the points, a fast retrieval technique is determined. The target key of a search is inserted in the polynomial and the first access from the file is made on the basis of the evaluation. Since the maximum deviation can be determined, an efficient local search can be made. If the maximum deviation is less than half the size of the file, polynomial searching is more efficient than binary searching. While this method can be applied in many cases, it is most useful in disk oriented file systems where the goal is to minimize the number of accesses even at the expense of some additional calculation.