Pattern Recognition Properties Research Articles

A novel fault classification‐based fault‐tolerant control for two degree of freedom helicopter systems

SummaryFault detection and diagnosis (FDD) plays an essential role in identifying and isolating various faults in a system. In general, fault detection is attained by monitoring the extent of matching between the actual operating condition and an analytical model prediction. This process aids in achieving enhanced performance, and for operating the system within the acceptable bounds. In this article, a neural network‐based classification method and fuzzy‐based control strategy are adapted to perform FDD on a two degree of freedom (2DoF) helicopter system. The operating voltage, pitch, and yaw outputs of the 2DoF helicopter system were considered for developing the algorithm. The signal processing properties of the discrete wavelet transform and pattern recognition properties of a multilayer perceptron neural network are adapted to design the classification algorithm. The developed algorithm improves training and testing efficiency. In order to reinstate the normal operation of the system, the classifier output is integrated with a hybrid fuzzy‐proportional integral derivative controller. This control technique enhances the 2DoF helicopter response as the time taken by the pitch and yaw angle to settle trajectory is reduced. The results depicted validate the efficiency of the projected approach.

Designer DNA architecture offers precise and multivalent spatial pattern-recognition for viral sensing and inhibition.

DNA, when folded into nanostructures with a specific shape, is capable of spacing and arranging binding sites into a complex geometric pattern with nanometre precision. Here we demonstrate a designer DNA nanostructure that can act as a template to display multiple binding motifs with precise spatial pattern-recognition properties, and that this approach can confer exceptional sensing and potent viral inhibitory capabilities. A star-shaped DNA architecture, carrying five molecular beacon-like motifs, was constructed to display ten dengue envelope protein domain III (ED3)-targeting aptamers into a two-dimensional pattern precisely matching the spatial arrangement of ED3 clusters on the dengue (DENV) viral surface. The resulting multivalent interactions provide high DENV-binding avidity. We show that this structure is a potent viral inhibitor and that it can act as a sensor by including a fluorescent output to report binding. Our molecular-platform design strategy could be adapted to detect and combat other disease-causing pathogens by generating the requisite ligand patterns on customized DNA nanoarchitectures.

Replicated Pattern Formation and Recognition Properties of 2,4-Dichlorophenoxyacetic Acid-Imprinted Polymers Using Colloidal Silica Array Molds.

Surface imprinting is an effective and simple method to fabricate and retain imprinted templates and recognizable nanocavities after template extraction. The imprinted effects can be controlled depending on the surface morphological changes. In general, a planar film has a limited area compared to a structured film with relatively higher surface-to-volume (S/V) ratio (A/A0), leading to the conventional sensing response upon the functionality of monomers in a fixed chemical composition. To increase the limited sensing properties and develop simple fabrication of porous arrays on a large area, we herein demonstrate the 2,4-dichlorophenoxyacetic acid (2,4-D, herbicide)-imprinted porous thin film lithographically patterned using photopolymerization and silica colloidal array as a master mold, derived by a unidirectional rubbing method. The resonant frequency changes with respect to the adsorption of 2,4-D molecules on a template-extracted porous poly(MAA-co-EGDMA) (MIP) film in a 10−1 mM aqueous solution of 2,4-D for 1 h, and when compared to the planar MIP film, the higher sensing response (Δf = −283 ± 7 Hz ≈ 1543 ± 38 ng/cm2) appears on the porous MIP film due to the specific recognition toward the more accessible templated cavities of the structured porous array, indicating an imprinting effect (If) value of 3.5. In addition, a higher selectivity for 2,4-D was also displayed on the porous MIP film compared to other herbicides. From these results, it was revealed that these improved sensing properties can be determined from the effects of various parameters (template functionality, film structuring, hydroxyl groups of silica colloids, etc.).

Involvement of a versatile pattern recognition receptor, apolipophorin-III in prophenoloxidase activation and antibacterial defense of the Chinese oak silkworm, Antheraea pernyi

Apolipophorin-III (apoLp-III) is an exchangeable apolipoprotein found in many insect species and functions as a lipid transport vehicle. Recent studies have shown that apoLp-III is a multifunctional molecule involved in not only lipid transportation but also innate immune responses. In the present study, the pattern recognition properties of Antheraea pernyi apoLp-III were investigated. Recombinant Ap-apoLp-III was bound to different species of microbes and further study showed the rAp-apoLp-III is capable of interacting with pathogen associated molecular patterns (PAMPs) on the microbial cell surface. In addition, an Ap-apoLp-III/PAMP mixture stimulated the prophenoloxidase (PPO) activation of A. pernyi hemolymph in vitro, to a greater extent than PAMP alone while Ap-apoLp-III itself failed to activate the PPO system, indicating that Ap-apoLp-III up-regulates PPO activation by combining with PAMP. After pathogen invasion following an injection of Staphylococcus aureus, RNAi-mediated silencing of apoLp-III decreased the transcriptional abundance of three antimicrobial peptide genes. These data suggest that apoLp-III is a versatile pattern recognition receptor and may play important roles in the innate immune responses of Antheraea pernyi.

Automatic builder of class diagram (ABCD): an application of UML generation from functional requirements

SummarySoftware development life cycle is a structured process, including the definition of user requirements specification, the system design, and programming. The design task comprises the transfer of natural language specifications into models. The class diagram of Unified Modeling Language has been considered as one of the most useful diagrams. It is a formal description of user's requirements and serves as inputs to the developers. The automated extraction of UML class diagram from natural language requirements is a highly challenging task. This paper explains our vision of an automated tool for class diagram generation from user requirements expressed in natural language. Our new approach amalgamates the statistical and pattern recognition properties of natural language processing techniques. More than 1000 patterns are defined for the extraction of the class diagram concepts. Once these concepts are captured, an XML Metadata Interchange file is generated and imported with a Computer‐Aided Software Engineering tool to build the corresponding UML class diagram. Copyright © 2015 John Wiley & Sons, Ltd.

Abstract 3283: The role of formyl peptide receptor 1 (FPR1) in neuroblastoma tumorigenesis

Abstract The formyl peptide receptor 1 (FPR1) is a G protein-coupled receptor with pattern recognition properties and is mainly expressed by myeloid cells. It is involved in a broad range of host defense mechanisms and a variety of host-derived agonists of FPR1 have been identified, including formyl peptides released from disrupted mitochondria of necrotic cells. In the present study, we demonstrate the expression of FPR1 in 7 different neuroblastoma cell lines and in primary tumors. Furthermore, FPR1 is expressed at increased levels in high stage tumors. Addition of the FPR1 agonist N-formyl-L-methionyl-L-leucyl-L-phenylalanine (fMLP) to neuroblastoma cells in vitro caused enhanced proliferative activity, increase of intracellular calcium response and activation of STAT3 and MAPK/ERK signal transduction pathways. All these signal transduction events were abrogated by the use of Cyclosporin H, a specific FPR1 antagonist. To assess the significance of this receptor in vivo, we developed a set of neuroblastoma cell clones with different expression levels of FPR1. Xenograft models showed that cells with overexpression of the receptor developed tumors significantly faster compared to control group. Our findings so far suggest that FPR1 may play a significant role in neuroblastoma tumorigenesis and that therapeutic intervention of the FPR1 pathway may be an important clinical strategy in neuroblastoma therapy. Citation Format: Igor Snapkov, Carl Otto Öqvist, Yngve Anton Figenschau, Per Kogner, John Inge Johnsen, Baldur Sveinbjørnsson. The role of formyl peptide receptor 1 (FPR1) in neuroblastoma tumorigenesis. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 3283. doi:10.1158/1538-7445.AM2015-3283

Innate immune humoral factors, C1q and factor H, with differential pattern recognition properties, alter macrophage response to carbon nanotubes

Interaction between the complement system and carbon nanotubes (CNTs) can modify their intended biomedical applications. Pristine and derivatised CNTs can activate complement primarily via the classical pathway which enhances uptake of CNTs and suppresses pro-inflammatory response by immune cells. Here, we report that the interaction of C1q, the classical pathway recognition molecule, with CNTs involves charge pattern and classical pathway activation that is partly inhibited by factor H, a complement regulator. C1q and its globular modules, but not factor H, enhanced uptake of CNTs by macrophages and modulated the pro-inflammatory immune response. Thus, soluble complement factors can interact differentially with CNTs and alter the immune response even without complement activation. Coating CNTs with recombinant C1q globular heads offers a novel way of controlling classical pathway activation in nanotherapeutics. Surprisingly, the globular heads also enhance clearance by phagocytes and down-regulate inflammation, suggesting unexpected complexity in receptor interaction. From the Clinical EditorCarbon nanotubes (CNTs) maybe useful in the clinical setting as targeting drug carriers. However, it is also well known that they can interact and activate the complement system, which may have a negative impact on the applicability of CNTs. In this study, the authors functionalized multi-walled CNT (MWNT), and investigated the interaction with the complement pathway. These studies are important so as to gain further understanding of the underlying mechanism in preparation for future use of CNTs in the clinical setting.

A sodium channel variant in Aedes aegypti as a candidate pathogen sensor for viral-associated molecular patterns

Recent work demonstrated that a splice variant of a human macrophage voltage-gated sodium channel expressed on endosomes acts as an intracellular sensor for dsRNA, a viral-associated molecular pattern. Here our goal was to identify a candidate gene in a clinically relevant invertebrate model with related cellular and pattern recognition properties. The para gene in drosophila and other insects encodes voltage-gated sodium channels with similar electrophysiological properties to those found in vertebrate excitable membranes. A database search revealed that the AAEL006019 gene in Aedes aegypti, the yellow fever mosquito, encodes a voltage-gated sodium channel that is distinct from genes that encode para-like sodium channels. As compared to para-like channels, the protein products from this gene have deletions in the N-terminus and in the DII-DIII linker region. When over-expressed in an Aedes aegypti cell line, CCL-125, the AAEL006019 channel demonstrated cytoplasmic expression on vesicular-like organelles. Electrophysiologic analysis revealed that the channel mediates small inward currents that are enhanced by synthetic mimics of viral-derived ssRNA, R848 and ORN02, but not the dsRNA mimic, poly I:C. R848 treatment of CCL-125 cells that express high levels of the channels led to increased expression of RelA and Ago2, two mediators of insect innate immunity. These results suggest that the AAEL006019 channel acts as an intracellular pathogen sensor for ssRNA molecular patterns.

Abstract 3976: Expression of formyl peptide receptor 1 (FPR1) in neuroblastoma: Implications in tumorigenesis

Abstract The formyl peptide receptor 1 (FPR1) is a G protein-coupled receptor with pattern recognition properties. It is mainly expressed by myeloid cells and is involved in a broad range of host defense mechanisms. However, a variety of host-derived agonists of FPR1 have been identified, including formyl peptides released from disrupted mitochondria of necrotic cells. In the present study, we demonstrate that FPR1 is expressed in neuroblastoma cell lines and primary tumors and high expression is correlated with poor overall survival. Addition of the FPR1 agonist N-formyl-L-methionyl-L-leucyl-L-phenylalanine (fMLP) to neuroblastoma cells in vitro causes enhanced proliferative activity, increase of intracellular calcium response and activation of STAT3 and MAPK/Erk signaling pathways. Currently, clonal neuroblastoma cell line with inducible activity of FPR1 gene is under testing in a mouse model to assess the significance of this receptor in vivo. Our findings so far suggest that FPR1 may play a significant role in neuroblastoma tumorigenesis and that therapeutic intervention of the FPR1 pathway may become an important clinical strategy in neuroblastoma therapy. Citation Format: Igor Snapkov, Per Kogner, John-Inge Johnsen, Baldur Sveinbjørnsson. Expression of formyl peptide receptor 1 (FPR1) in neuroblastoma: Implications in tumorigenesis. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 3976. doi:10.1158/1538-7445.AM2014-3976

EEG-based event detection using optimized echo state networks with leaky integrator neurons.

This study investigates the classification ability of linear and nonlinear classifiers on biological signals using the electroencephalogram (EEG) and examines the impact of architectural changes within the classifier in order to enhance the classification. Consequently, artificial events were used to validate a prototype EEG-based microsleep detection system based around an echo state network (ESN) and a linear discriminant analysis (LDA) classifier. The artificial events comprised infrequent 2-s long bursts of 15 Hz sinusoids superimposed on prerecorded 16-channel EEG data which provided a means of determining and optimizing the accuracy of overall classifier on `gold standard' events. The performance of this system was tested on different signal-to-noise amplitude ratios (SNRs) ranging from 16 down to 0.03. Results from several feature selection/reduction and pattern classification modules indicated that training the classifier using a leaky-integrator neuron ESN structure yielded highest classification accuracy. For datasets with a low SNR of 0.3, training the leaky-neuron ESN using only those features which directly correspond to the underlying event, resulted in a phi correlation of 0.92 compared to 0.37 that employed principal component analysis (PCA). On the same datasets, other classifiers such as LDA and simple ESNs using PCA performed weakly with a correlation of 0.05 and 0 respectively. These results suggest that ESNs with leaky neuron architectures have superior pattern recognition properties. This, in turn, may reflect their superior ability to exploit differences in state dynamics and, hence, provide superior temporal characteristics in learning.

Distributed coupling complexity in a weakly coupled oscillatory network with associative properties

We present a novel architecture of an oscillatory neural network capable of performing pattern recognition tasks. Two established strategies for obtaining associative properties in oscillatory networks invoke either a physical, time constant or a global, dynamical all-to-all coupling. Our network distributes the complexity of the coupling between the spatial and the temporal domain. Instead of physical connections or a global connection with frequency components, each of the N oscillators receives an individual coupling signal which is composed of N − 1 frequency components. We demonstrate that such a network can be built with analog electronic oscillators and possesses reliable pattern recognition properties. Theoretical analysis shows that the scalability is in fact superior to the dynamic global coupling approach, while its physical complexity is greatly reduced compared to the individual time constant coupling.

Logistic Neural Networks: Their chaotic and pattern recognition properties

The goal of this paper is to catalog the chaotic and Pattern Recognition (PR) properties of a network of Logistic Neurons (LNs). Over the last few years, the field of Chaotic Neural Networks (CNNs) has been extensively studied because of their potential applications in PR, Associative Memory (AM), optimization, multi-value content addressing and image processing. The research in chaos theory has thus expanded to report numerous neural models that, by virtue of their inter-connections, yield chaotic behavior. Recently, the Adachi Neural Network (AdNN) and its variants have been shown to yield an entire spectrum of properties including chaotic, quasi-chaotic, PR and AM as its/their parameters change. To simplify the AdNN model and to also investigate the design philosophy of the CNN model, in this paper, we consider the consequences of networking a set of LNs, each of which is founded on principles of the Logistic map. By appropriately defining the input/output characteristics of a fully connected network of LNs, and by defining their set of weights and output functions, we have succeeded in designing a Logistic Neural Network (LNN). Although the LNN is much simpler than other CNNs such as the AdNN, it possesses some of those properties mentioned above. The chaotic properties of a single-neuron have been formally proven using the theory of Lyapunov analysis and by examining its Jacobian matrix. As far as we know, the results presented here, that the LNN can also demonstrate both AM and PR properties, are unreported, and we submit that it can, hopefully, lead to a new method of PR and AM.

The entire range of Chaotic pattern recognition properties possessed by the Adachi neural network1

Decision Theory and Pattern Recognition (PR) are inter-related. Indeed, if a practitioner is faced with a set of decisions and is required to make one based on the current “state of the world”, the problem is easily mapped into a PR problem where one

Iris matching using multi-dimensional artificial neural network

Iris recognition is one of the most widely used biometric technique for personal identification. This identification is achieved in this work by using the concept that, the iris patterns are statistically unique and suitable for biometric measurements. In this study, a novel method of recognition of these patterns of an iris is considered by using a multi-dimensional artificial neural network. The proposed technique has the distinct advantage of using the entire resized iris as an input at once. It is capable of excellent pattern recognition properties as the iris texture is unique for every person used for recognition. The system is trained and tested using two publicly available databases (CASIA and UBIRIS). The proposed approach shows significant promise and potential for improvements, compared with the other conventional matching techniques with regard to time and efficiency of results.

Bridging the fuzzy, neural and evolutionary paradigms for automatic target recognition

Multilayer perceptron neural networks possess pattern recognition properties that make them well suited for use in automatic target recognition systems. Their application is hindered, however, by the lack of a training algorithm, which reliably finds a nearly global optimal set of weights in a relatively short time. The approach presented here is based on implementation of genetic algorithms and fuzzy logic in training the proposed hybrid architecture. Compared to other approaches, it offers the following three main advantages. The neuro-computing technique is capable of fast and adaptive distortion-invariant pattern recognition for rapidly changing targets. On the other hand, genetic algorithms and fuzzy logic offer very sophisticated configuration control, which combines the results of previous computations with the external operating environment. Third, it allows us to significantly improve the reliability of object detection in the input scene with respect to associated distortions at no additional computational cost. This paper examines using genetic algorithms as an efficient way to train a feedforward neural net, the inputs for which are provided by a fuzzy front end, to be applied to automatic target recognition. The system is tested using actual laser detection and range data as training data and the results of the analysis show that the proposed system results in a much faster convergence on a weight set, and a high rate of successful recognition.

Fingerprint matching using multi-dimensional ANN

Fingerprint matching is one of the most widely used biometric technique for personal identification. This identification is achieved in this work by using the concept that every fingerprint has a unique pattern of distribution of the minutiae points. In this paper, a new method of recognition of this pattern of distribution of the minutiae points of an enhanced image is considered by using a multi-dimensional artificial neural network (MDANN). The proposed technique has the distinct advantage of using the entire resized minutiae image as an input at once. It is capable of excellent pattern recognition properties as the distribution of the minutiae points are used directly for recognition. The proposed approach shows significant promise and potential for improvement, compared with the other conventional matching techniques with regard to time and efficiency of results.

Adachi-Like Chaotic Neural Networks Requiring Linear-Time Computations by Enforcing a Tree-Shaped Topology

The Adachi neural network (AdNN) is a fascinating neural network (NN) which has been shown to possess chaotic properties, and to also demonstrate associative memory (AM) and pattern recognition (PR) characteristics. Variants of the AdNN have also been used to obtain other PR phenomena, and even blurring. An unsurmountable problem associated with the AdNN and the variants referred to above is that all of them require a quadratic number of computations. This is essentially because the NNs in each case are completely connected graphs. In this paper, we consider how the computations can be significantly reduced by merely using a linear number of computations. To achieves this, we extract from the original completely connected graph one of its spanning trees. We then address the problem of computing the weights for this spanning tree. This is done in such a manner that the modified tree-based NN has approximately the same input-output characteristics, and thus the new weights are themselves calculated using a gradient-based algorithm. By a detailed experimental analysis, we show that the new linear-time AdNN-like network possesses chaotic and PR properties for different settings. As far as we know, such a tree-based AdNN has not been reported, and the results given here are novel.

Adaptive Resonance Theory 1 (ART1) Neural Network Based Horizontal and Vertical Classification of 0-9 Digits Recognition

This study describes the Adaptive Resonance Theory 1 (ART1), an efficient algorithm that emulates the self-organizing pattern recognition and hypothesis testing properties of the ART neural network architecture for horizontal and vertical classification of 0-9 digits recognition. In our approach the ART1 model can self-organize in real time producing stable and clear recognition while getting input patterns beyond those originally stored. It can also preserve its previously learned knowledge while keeping its ability to learn new input patterns that can be saved in such a fashion that the stored patterns cannot be destroyed or forgotten. A parameter called the attentional vigilance parameter determines how fine the categories will be. If vigilance increases or decreases due to environmental control feedback, then the system automatically searches for and learns fine recognition categories.

Combinatorial templates generated by dip-pen nanolithography for the formation of two-dimensional particle arrays.

Lattices of single polystyrene particles were constructed by using a combinatorial approach to analyze particle pattern recognition properties. Dip-pen nanolithography was used to generate chemical templates of 16-thiohexadecanoic acid on a gold surface to study the two-dimensional assembly of amine- and amidine-modified particles.

Flexibility in a Single Behavioral Variable of Drosophila

The flexibility of behavior is so rich, and its components are so exquisitely interwoven, that one may be well advised to turn to an isolated behavioral module for study. Gill withdrawal inAplysia, the proboscis extension reflex in the honeybee, and lid closure in mammals are such examples. We have chosen yawing, a single component of flight orientation in Drosophila melanogaster, for this approach. A specialty of this preparation is that the behavioral output can be reduced beyond the single module by one further step. It can be studied in tethered animals in which all turns are blocked while the differentially beating wings still provide the momentum. These intended yaw turns are measured by a torque meter to which the fly is hooked. The fly is held horizontally as if cruising at high speed. The head is glued to the thorax. It can bend its abdomen, extend its proboscis, and move its legs but cannot shift its direction of gaze or its orientation in space. Evidently, a fly hardly ever encounters this bizarre situation in the wild. We describe here the flexibility in this single behavioral variable. It provides insights into the relation between classical and operant conditioning, the processing of and interactions between the conditioned visual stimuli, early visual memory, visual pattern recognition, selective attention, and several other experience-dependent properties of visual orientation behavior. We start with a brief summary of visual flight control at the torque meter.