Tolerant pattern recognition: evidence from phonotactic responses in the cricket Gryllus bimaculatus (de Geer)

When the amplitude modulation of species-specific acoustic signals is distorted in the transmission channel, signals become difficult to recognize by the receiver. Tolerant auditory pattern recognition systems, which after having perceived the correct species-specific signal transiently broaden their acceptance of signals, would be advantageous for animals as an adaptation to the constraints of the environment. Using a well-studied cricket species, Gryllus bimaculatus , we analysed tolerance in auditory steering responses to ‘ Odd ’ chirps, mimicking a signal distorted by the transmission channel, and control ‘ Silent ’ chirps by employing a fine-scale open-loop trackball system. Odd chirps on their own did not elicit a phonotactic response. However, when inserted into a calling song pattern with attractive Normal chirps, the females' phonotactic response toward these patterns was significantly larger than to patterns with Silent chirps. Moreover, females actively steered toward Odd chirps when these were presented within a sequence of attractive chirps. Our results suggest that crickets employ a tolerant pattern recognition system that, once activated, transiently allows responses to distorted sound patterns, as long as sufficient natural chirps are present. As pattern recognition modulates how crickets process non-attractive acoustic signals, the finding is also relevant for the interpretation of two-choice behavioural experiments.

Face Recognition Based on Artificial Neural Network: A Review

The face recognition/detection is considered as one of the most popular applications in the field of image processing and biometric pattern recognition systems. Although the face recognition approach improves authentication procedure, nevertheless still many challenges appear due to diversities in human facial expression, image huge size, background complexity, variation in illumination, poses, blurry, etc. Therefore, the face detection procedure is classified as one of the most difficult tasks in computer vision. This research paper tends to address the concept of image processing along with the use of the Artificial Neural Network approach and represent it is a potential capability in enhancing the method of extracting face pattern through an adaption of various ANN topologies. Furthermore, it represents fundamental phases associated with the construction of any facial recognition system. Finally, it provides a general overview of different literature survives that related to face recognition based on the use of different ANN approaches and algorithms

Deep Cross-User Models Reduce the Training Burden in Myoelectric Control

The effort, focus, and time to collect data and train EMG pattern recognition systems is one of the largest barriers to their widespread adoption in commercial applications. In addition to multiple repetitions of motions, including exemplars of confounding factors during the training protocol has been shown to be critical for robust machine learning models. This added training burden is prohibitive for most regular use cases, so cross-user models have been proposed that could leverage inter-repetition variability supplied by other users. Existing cross-user models have not yet achieved performance levels sufficient for commercialization and require users to closely adhere to a training protocol that is impractical without expert guidance. In this work, we extend a previously reported adaptive domain adversarial neural network (ADANN) to a cross-subject framework that requires very little training data from the end-user. We compare its performance to single-repetition within-user training and the previous state-of-the-art cross-subject technique, canonical correlation analysis (CCA). ADANN significantly outperformed CCA for both intact-limb (86.8–96.2%) and amputee (64.1–84.2%) populations. Moreover, the ADANN adaptation computation time was substantially lower than the time otherwise devoted to conducting a full within-subject training protocol. This study shows that cross-user models, enabled by deep-learned adaptations, may be a viable option for improved generalized pattern recognition-based myoelectric control.

Mechanized Significance and Machine Learning: Why It Became Thinkable and Preferable to Teach Machines to Judge the World

The slow and uneven forging of a novel constellation of practices, concerns, and values that became machine learning occurred in 1950s and 1960s pattern recognition research through attempts to mechanize contextual significance that involved building “learning machines” that imitated human judgment by learning from examples. By the 1960s two crises emerged: the first was an inability to evaluate, compare, and judge different pattern recognition systems; the second was an inability to articulate what made pattern recognition constitute a distinct discipline. The resolution of both crises through the problem-framing strategies of supervised and unsupervised learning and the incorporation of statistical decision theory changed what it meant to provide an adequate description of the world even as it caused researchers to reimagine their own scientific self-identities.

Review of Printed Fabric Pattern Segmentation Analysis and Application

Image processing of digital images is one of the essential categories of image transformation in the theory and practice of digital pattern analysis and computer vision. Automated pattern recognition systems are much needed in the textile industry more importantly when the quality control of products is a significant problem. The printed fabric pattern segmentation procedure is carried out since human interaction proves to be unsatisfactory and costly. Hence, to reduce the cost and wastage of time, automatic segmentation and pattern recognition are required. Several robust and efficient segmentation algorithms are established for pattern recognition. In this paper, different automated methods are presented to segregate printed patterns from textiles fabric. This has become necessary because quality product devoid of any disturbances is the ultimate aim of the textile printing industry.

Determination of Informative Features Based on the Criteria of Positive Type

Currently, pattern recognition systems are widely used to solve practical problems. The choice of criterion in pattern recognition problems is fundamental. The choice of informative features depends on the criterion of informativeness. A single criterion has not been developed to select a set of informative features. Therefore, the criterion is selected from the formulation of a practical problem. For each selected criterion, it is necessary to develop special methods and algorithms. For homogeneous criteria, there is no single method.A new method and algorithm for determining sets of informative features based on generalized criteria of a positive order is proposed in this work