Improve Discrimination Ability Research Articles

Discrimination of similar characters using nonlinear normalization based on regional importance measure

Discrimination of confusing characters is very important in recognition of character sets containing a multitude of similar characters. Confusing characters have very similar shapes and are separated by only a small difference. For a successful discrimination, we need to focus on that difference. However, the small difference can be reduced or even lost during the feature extraction process. In such a case, further analysis after the feature extraction rarely succeeds. This paper proposes a discriminative nonlinear normalization algorithm to improve discrimination ability. The proposed method emphasizes the difference between confusing characters. It measures the importance of each region in the discrimination of confusing characters. Then, it resamples the image according to the regional importance measure. As a result, it expands important regions but shrinks less important regions. Since it emphasizes important regions in the preprocessing step, it does not suffer from the information loss during the feature extraction. In experiments, the proposed method successfully detected and expanded important regions. In handwritten Hangul recognition, the proposed method outperformed other two recently developed pair-wise discrimination methods. On SERI95a data set, it improved the recognition rate from 87.69 to 90.11 %, achieving a 19.66 % error reduction rate.

SAR TARGET CLASSIFICATION USING BAYESIAN COMPRESSIVE SENSING WITH SCATTERING CENTERS FEATURES

The emerging fleld of compressed sensing provides sparse reconstruction, which has demonstrated promising results in the areas of signal processing and pattern recognition. In this paper, a new approach for synthetic aperture radar (SAR) target classiflcation is proposed based on Bayesian compressive sensing (BCS) with scattering centers features. Scattering centers features is extracted as a l1-norm sparse problem on the basis of SAR observation physical model, which can improve discrimination ability compared with original SAR image. Using an overcomplete dictionary constructed by training samples, BCS is utilized to design targets classifler. For target classiflcation performance evaluation, the proposed method is compared with several state-of-art methods through experiments on Moving and Stationary Target Acquisition and Recognition (MSTAR) public release database. Experiment results illustrate the efiectiveness and robustness of the proposed approach.

Gonadal hormones and voluntary exercise interact to improve discrimination ability in a set-shift task.

Exercise has been demonstrated to improve multiple facets of health, including cognitive function. Rodent studies have suggested that exercise has robust effects on the hippocampus and on tasks that require the hippocampus. However, studies of the effects of exercise in humans often focus on the benefits to cognitive processes that engage areas outside of the hippocampus, such as executive function. Additionally, when exercise's cognitive benefits are examined, consideration of both males and females, and gonadal hormones, is rarely made. Here we looked at the interaction of gonadal hormones and exercise in terms of the ability of male and female rats to learn to discriminate rewarded from unrewarded arms in a T maze based on either brightness (white vs. black) or texture (rough vs. smooth) and then to set-shift (a measure of executive function), where this required discrimination is based on the opposite dimension. Gonadectomized or intact males and females had access to running wheels for 2 weeks before being tested. Intact males and females given access to unlocked running wheels performed better at the initial discrimination (Set 1) compared with intact males and females with locked running wheels but not at the set shift (Set 2). No advantage of exercise was observed in gonadectomized rats.

Fluorescence resonance energy transfer dye-labeled probe for fluorescence-enhanced DNA detection: An effective strategy to greatly improve discrimination ability toward single-base mismatch

In this article, we report on the first use of fluorescence resonance energy transfer (FRET) dye-labeled probe for fluorescence resonance enhanced DNA detection to greatly improve discrimination ability toward single-base mismatch using conjugation polymer poly(p-phenylenediamine) nanobelts (PNs) as a sensing platform. The suggested FRET dye-labeled probe contains a 5-carboxyfluorescein (FAM) group at 5′ end of the oligomer as a donor and a 6-carboxy-X-rhodamine (ROX) attached to a modified cytosine (C) base as an acceptor, which were separated by three bases. The general concept used in this DNA assay is based on adsorption of the FRET dye-labeled single-stranded DNA (ssDNA) probe by PN, which is accompanied by substantial fluorescence quenching and disappearance of FRET. The subsequent specific hybridization with its target forms a double-stranded DNA (dsDNA), resulting in desorption of the hybridized duplex from PN surface accompanied by reoccurrence of FRET and fluorescence recovery. It suggests that the discrimination ability of this FRET probe based system toward single-base mismatch is about 5.2 times that of the system based on single dye-labeled probe based system.

Decoding of grasping information from neural signals recorded using peripheral intrafascicular interfaces

BackgroundThe restoration of complex hand functions by creating a novel bidirectional link between the nervous system and a dexterous hand prosthesis is currently pursued by several research groups. This connection must be fast, intuitive, with a high success rate and quite natural to allow an effective bidirectional flow of information between the user's nervous system and the smart artificial device. This goal can be achieved with several approaches and among them, the use of implantable interfaces connected with the peripheral nervous system, namely intrafascicular electrodes, is considered particularly interesting.MethodsThin-film longitudinal intra-fascicular electrodes were implanted in the median and ulnar nerves of an amputee's stump during a four-week trial. The possibility of decoding motor commands suitable to control a dexterous hand prosthesis was investigated for the first time in this research field by implementing a spike sorting and classification algorithm.ResultsThe results showed that motor information (e.g., grip types and single finger movements) could be extracted with classification accuracy around 85% (for three classes plus rest) and that the user could improve his ability to govern motor commands over time as shown by the improved discrimination ability of our classification algorithm.ConclusionsThese results open up new and promising possibilities for the development of a neuro-controlled hand prosthesis.

A Novel Speech/Music Discrimination Using Feature Dimensionality Reduction

In this paper, we propose an improved speech/music discrimination method based on a feature combination and dimensionality reduction approach. To improve discrimination ability, we use a feature based on spectral duration analysis and employ the hierarchical dimensionality reduction (HDR) method to reduce the effect of correlated features. Through various kinds of experiments on speech and music, it is shown that the proposed method showed high discrimination results when compared with conventional methods.

Cooperative Inversion of Time Domain Electromagnetic and Magnetometer Data for The Discrimination of Unexploded Ordnance

Magnetic and electromagnetic induction (EMI) surveys are the primary techniques used for unexploded ordnance (UXO) remediation projects. Magnetometry is a valuable geophysical tool for UXO detection because of the ease of data acquisition and its ability to detect relatively deep targets. However, magnetic data can have large false alarm rates caused by geological noise, and there is an inherent non-uniqueness when trying to determine the orientation, size and shape of a target. EMI surveys, on the other hand, are relatively immune to geologic noise and are more diagnostic for target shape and size but have a reduced depth of investigation. We aim to improve discrimination ability by developing an interpretation method that takes advantage of the strengths of both techniques. We consider cooperative inversion, where information from the inversion of one type of data is used as a constraint for inverting another. We compare the confidence with which we can discriminate UXO from non-UXO targets when inverting the data sets cooperatively, to results from individual inversions. Examples are given of the application of the methodology to time domain electromagnetic induction (TEM) and magnetic data sets collected at the Yuma Proving Ground UXO Standardized Test Site calibration grid and the Former Camp Sibert.

Zur Stabilität der Lautdiskriminationsfähigkeit im Vorschulalter

Several tests have been developed to probe phoneme discrimination. However, little is known about the stability of discrimination abilities in preschool children. We investigated the test-retest reliability of a newly developed test material. In a prospective study, 30 preschool children (5.1-6.3 years old) were tested with a time lag between test 1 and test 2 of 1 month. Correlational analysis was carried out using 60 minimal pairs consisting of real words and nonsense syllables. The choice of items took into consideration their frequency in the basic vocabulary as well as phoneme properties such as contrast and position. High Cronbach's alpha were found for all subscales. For the whole item list, Cronbach's alpha was 0.994. No significant gender specific differences were found. These data indicate a high stability of phoneme discrimination abilities in preschool children. Based on these results, the test material can be reliably used to evaluate therapeutic strategies to improve discrimination ability.

The Evolution of Mimicry under Constraints

The resemblance between mimetic organisms and their models varies from near perfect to very crude. One possible explanation, which has received surprisingly little attention, is that evolution can improve mimicry only at some cost to the mimetic organism. In this article, an evolutionary game theory model of mimicry is presented that incorporates such constraints. The model generates novel and testable predictions. First, Batesian mimics that are very common and/or mimic very weakly defended models should evolve either inaccurate mimicry (by stabilizing selection) or mimetic polymorphism. Second, Batesian mimics that are very common and/or mimic very weakly defended models are more likely to evolve mimetic polymorphism if they encounter predators at high rates and/or are bad at evading predator attacks. The model also examines how cognitive constraints acting on signal receivers may help determine evolutionarily stable levels of mimicry. Surprisingly, improved discrimination abilities among signal receivers may sometimes select for less accurate mimicry.

Comparing Cooccurrence Probabilities and Markov Random Fields for Texture Analysis of SAR Sea Ice Imagery

This paper compares the discrimination ability of two texture analysis methods: Markov random fields (MRFs) and gray-level cooccurrence probabilities (GLCPs). There exists limited published research comparing different texture methods, especially with regard to segmenting remotely sensed imagery. The role of window size in texture feature consistency and separability as well as the role in handling of multiple textures within a window are investigated. Necessary testing is performed on samples of synthetic (MRF generated), Brodatz, and synthetic aperture radar (SAR) sea ice imagery. GLCPs are demonstrated to have improved discrimination ability relative to MRFs with decreasing window size, which is important when performing image segmentation. On the other hand, GLCPs are more sensitive to texture boundary confusion than MRFs given their respective segmentation procedures.

Copying in Drawing: The Importance of Adequate Visual Analysis versus the Ability to Utilize Drawing Rules

RAND, COLLEEN WRIGHT. Copying in Drawing: The Importance of Adequate Visual Analysis versus the Ability to Utilize Drawing Rules. CHILD DEVELOPMENT, 1973, 44, 47-53. Copying in drawing requires many skills. 2 theoretically important abilities are visual analysis and drawingrule utilization. In this experiment, training methods designed to improve preschool children's ability to analyze figures and to utilize drawing rules were compared in terms of their effects on figure discrimination and copying accuracy. Drawing-rule utilization resulted in improved copying accuracy but did not improve discrimination ability, while training in visual analysis resulted in improved discrimination ability but not improved copies. Further, increased improvement in figure discrimination was related to decreased copying accuracy. It was concluded that drawing rules are essential and that adequate visual analysis is a necessary but not sufficient prerequisite to the production of accurate copies.