Fuzzy Classification of Time Series Data

The problem of classification of time series data is an interesting problem in the field of data mining. Even though several algorithms have been proposed for the problem of time series classification we have developed an innovative algorithm which is computationally fast and accurate in several cases when compared with 1NN classifier. In our method we are calculating the fuzzy membership of each test pattern to be classified to each class. We have experimented with 6 benchmark datasets and compared our method with 1NN classifier.

Exploring Transfer Learning Approaches for Head Pose Classification from Multi-view Surveillance Images

Head pose classification from surveillance images acquired with distant, large field-of-view cameras is difficult as faces are captured at low-resolution and have a blurred appearance. Domain adaptation approaches are useful for transferring knowledge from the training (source) to the test (target) data when they have different attributes, minimizing target data labeling efforts in the process. This paper examines the use of transfer learning for efficient multi-view head pose classification with minimal target training data under three challenging situations: (i) where the range of head poses in the source and target images is different, (ii) where source images capture a stationary person while target images capture a moving person whose facial appearance varies under motion due to changing perspective, scale and (iii) a combination of (i) and (ii). On the whole, the presented methods represent novel transfer learning solutions employed in the context of multi-view head pose classification. We demonstrate that the proposed solutions considerably outperform the state-of-the-art through extensive experimental validation. Finally, the DPOSE dataset compiled for benchmarking head pose classification performance with moving persons, and to aid behavioral understanding applications is presented in this work.

A radial differential mobility analyzer for the size-classification of gas-phase synthesized nanoparticles at low pressures

Differential mobility analyzers (DMAs) are commonly used to generate monodisperse nanoparticle aerosols. Commercial DMAs operate at quasi-atmospheric pressures and are therefore not designed to be vacuum-tight. In certain particle synthesis methods, the use of a vacuum-compatible DMA is a requirement as a process step for producing high-purity metallic particles. A vacuum-tight radial DMA (RDMA) has been developed and tested at low pressures. Its performance has been evaluated by using a commercial NANO-DMA as the reference. The performance of this low-pressure RDMA (LP-RDMA) in terms of the width of its transfer function is found to be comparable with that of other NANO-DMAs at atmospheric pressure and is almost independent of the pressure down to 30 mbar. It is shown that LP-RDMA can be used for the classification of nanometer-sized particles (5-20 nm) under low pressure condition (30 mbar) and has been successfully applied to nanoparticles produced by ablating FeNi at low pressures.

Phylogenetic relationships and classification of thiolases and thiolase-like proteins of Mycobacterium tuberculosis and Mycobacterium smegmatis

Thiolases are enzymes involved in lipid metabolism. Thiolases remove the acetyl-CoA moiety from 3-ketoacyl-CoAs in the degradative reaction. They can also catalyze the reverse Claisen condensation reaction, which is the first step of biosynthetic processes such as the biosynthesis of sterols and ketone bodies. In human, six distinct thiolases have been identified. Each of these thiolases is different from the other with respect to sequence, oligomeric state, substrate specificity and subcellular localization. Four sequence fingerprints, identifying catalytic loops of thiolases, have been described. In this study genome searches of two mycobacterial species (Mycobacterium tuberculosis and Mycobacterium smegmatis), were carried out, using the six human thiolase sequences as queries. Eight and thirteen different thiolase sequences were identified in M. tuberculosis and M. smegmatis, respectively. In addition, thiolase-like proteins (one encoded in the Mtb and two in the Msm genome) were found. The purpose of this study is to classify these mostly uncharacterized thiolases and thiolase-like proteins. Several other sequences obtained by searches of genome databases of bacteria, mammals and the parasitic protist family of the Trypanosomatidae were included in the analysis. Thiolase-like proteins were also found in the trypanosomatid genomes, but not in those of mammals. In order to study the phylogenetic relationships at a high confidence level, additional thiolase sequences were included such that a total of 130 thiolases and thiolase-like protein sequences were used for the multiple sequence alignment. The resulting phylogenetic tree identifies 12 classes of sequences, each possessing a characteristic set of sequence fingerprints for the catalytic loops. From this analysis it is now possible to assign the mycobacterial thiolases to corresponding homologues in other kingdoms of life. The results of this bioinformatics analysis also show interesting differences between the distributions of M. tuberculosis and M. smegmatis thiolases over the 12 different classes. (C) 2014 Elsevier Ltd. All rights reserved.

CLAP: A web-server for automatic classification of proteins with special reference to multi-domain proteins

Background: The function of a protein can be deciphered with higher accuracy from its structure than from its amino acid sequence. Due to the huge gap in the available protein sequence and structural space, tools that can generate functionally homogeneous clusters using only the sequence information, hold great importance. For this, traditional alignment-based tools work well in most cases and clustering is performed on the basis of sequence similarity. But, in the case of multi-domain proteins, the alignment quality might be poor due to varied lengths of the proteins, domain shuffling or circular permutations. Multi-domain proteins are ubiquitous in nature, hence alignment-free tools, which overcome the shortcomings of alignment-based protein comparison methods, are required. Further, existing tools classify proteins using only domain-level information and hence miss out on the information encoded in the tethered regions or accessory domains. Our method, on the other hand, takes into account the full-length sequence of a protein, consolidating the complete sequence information to understand a given protein better. Results: Our web-server, CLAP (Classification of Proteins), is one such alignment-free software for automatic classification of protein sequences. It utilizes a pattern-matching algorithm that assigns local matching scores (LMS) to residues that are a part of the matched patterns between two sequences being compared. CLAP works on full-length sequences and does not require prior domain definitions. Pilot studies undertaken previously on protein kinases and immunoglobulins have shown that CLAP yields clusters, which have high functional and domain architectural similarity. Moreover, parsing at a statistically determined cut-off resulted in clusters that corroborated with the sub-family level classification of that particular domain family. Conclusions: CLAP is a useful protein-clustering tool, independent of domain assignment, domain order, sequence length and domain diversity. Our method can be used for any set of protein sequences, yielding functionally relevant clusters with high domain architectural homogeneity. The CLAP web server is freely available for academic use at http://nslab.mbu.iisc.ernet.in/clap/.

VISUAL OBJECT TRACKING VIA RANDOM FERNS BASED CLASSIFICATION

Designing a robust algorithm for visual object tracking has been a challenging task since many years. There are trackers in the literature that are reasonably accurate for many tracking scenarios but most of them are computationally expensive. This narrows down their applicability as many tracking applications demand real time response. In this paper, we present a tracker based on random ferns. Tracking is posed as a classification problem and classification is done using ferns. We used ferns as they rely on binary features and are extremely fast at both training and classification as compared to other classification algorithms. Our experiments show that the proposed tracker performs well on some of the most challenging tracking datasets and executes much faster than one of the state-of-the-art trackers, without much difference in tracking accuracy.

A model for the classification and survey of clock synchronization protocols in WSNs

Clock synchronization in wireless sensor networks (WSNs) assures that sensor nodes have the same reference clock time. This is necessary not only for various WSN applications but also for many system level protocols for WSNs such as MAC protocols, and protocols for sleep scheduling of sensor nodes. Clock value of a node at a particular instant of time depends on its initial value and the frequency of the crystal oscillator used in the sensor node. The frequency of the crystal oscillator varies from node to node, and may also change over time depending upon many factors like temperature, humidity, etc. As a result, clock values of different sensor nodes diverge from each other and also from the real time clock, and hence, there is a requirement for clock synchronization in WSNs. Consequently, many clock synchronization protocols for WSNs have been proposed in the recent past. These protocols differ from each other considerably, and so, there is a need to understand them using a common platform. Towards this goal, this survey paper categorizes the features of clock synchronization protocols for WSNs into three types, viz, structural features, technical features, and global objective features. Each of these categories has different options to further segregate the features for better understanding. The features of clock synchronization protocols that have been used in this survey include all the features which have been used in existing surveys as well as new features such as how the clock value is propagated, when the clock value is propagated, and when the physical clock is updated, which are required for better understanding of the clock synchronization protocols in WSNs in a systematic way. This paper also gives a brief description of a few basic clock synchronization protocols for WSNs, and shows how these protocols fit into the above classification criteria. In addition, the recent clock synchronization protocols for WSNs, which are based on the above basic clock synchronization protocols, are also given alongside the corresponding basic clock synchronization protocols. Indeed, the proposed model for characterizing the clock synchronization protocols in WSNs can be used not only for analyzing the existing protocols but also for designing new clock synchronization protocols. (C) 2014 Elsevier B.V. All rights reserved.

Exploring Technology Evolution Using Patent Classification: A Case of Cochlear Implant Technology Patents

Understanding technology evolution through periodic landscaping is an important stage of strategic planning in R&D Management. In fields like that of healthcare, where the initial R&D investment is huge and good medical product serve patients better, these activities become crucial. Approximately five percentage of the world population has hearing disabilities. Current hearing aid products meet less than ten percent of the global needs. Patent data and classifications on cochlear implants from 1977-2010, show the landscapes and evolution in the area of such implant. We attempt to highlight emergence and disappearance of patent classes over period of time showing variations in cochlear implant technologies. A network analysis technique is used to explore and capture technology evolution in patent classes showing what emerged or disappeared over time. Dominant classes are identified. The sporadic influence of university research in cochlear implants is also discussed.

A Bacterial Foraging Optimization and Learning Automata Based Feature Selection for Motor Imagery EEG Classification

Selection of relevant features is an open problem in Brain-computer interfacing (BCI) research. Sometimes, features extracted from brain signals are high dimensional which in turn affects the accuracy of the classifier. Selection of the most relevant features improves the performance of the classifier and reduces the computational cost of the system. In this study, we have used a combination of Bacterial Foraging Optimization and Learning Automata to determine the best subset of features from a given motor imagery electroencephalography (EEG) based BCI dataset. Here, we have employed Discrete Wavelet Transform to obtain a high dimensional feature set and classified it by Distance Likelihood Ratio Test. Our proposed feature selector produced an accuracy of 80.291% in 216 seconds.

Damage Detection Sensitivity, Specificity and Classification Data Analysis for SHM Systems Design, Verification and Validation

In this paper, we consider applying derived knowledge base regarding the sensitivity and specificity of damage(s) to be detected by an SHM system being designed and qualified. These efforts are necessary toward developing capabilities in SHM system to classify reliably various probable damages through sequence of monitoring, i.e., damage precursor identification, detection of damage and monitoring its progression. We consider the particular problem of visual and ultrasonic NDE based SHM system design requirements, where the damage detection sensitivity and specificity data definitions for a class of structural components are established. Methodologies for SHM system specification creation are discussed in details. Examples are shown to illustrate how the physics of damage detection scheme limits particular damage detection sensitivity and specificity and further how these information can be used in algorithms to combine various different NDE schemes in an SHM system to enhance efficiency and effectiveness. Statistical and data driven models to determine the sensitivity and probability of damage detection (POD) has been demonstrated for plate with varying one-sided line crack using optical and ultrasonic based inspection techniques.

Framework for morphometric classification of cells in imaging flow cytometry

Imaging flow cytometry is an emerging technology that combines the statistical power of flow cytometry with spatial and quantitative morphology of digital microscopy. It allows high-throughput imaging of cells with good spatial resolution, while they are in flow. This paper proposes a general framework for the processing/classification of cells imaged using imaging flow cytometer. Each cell is localized by finding an accurate cell contour. Then, features reflecting cell size, circularity and complexity are extracted for the classification using SVM. Unlike the conventional iterative, semi-automatic segmentation algorithms such as active contour, we propose a noniterative, fully automatic graph-based cell localization. In order to evaluate the performance of the proposed framework, we have successfully classified unstained label-free leukaemia cell-lines MOLT, K562 and HL60 from video streams captured using custom fabricated cost-effective microfluidics-based imaging flow cytometer. The proposed system is a significant development in the direction of building a cost-effective cell analysis platform that would facilitate affordable mass screening camps looking cellular morphology for disease diagnosis. Lay description In this article, we propose a novel framework for processing the raw data generated using microfluidics based imaging flow cytometers. Microfluidics microscopy or microfluidics based imaging flow cytometry (mIFC) is a recent microscopy paradigm, that combines the statistical power of flow cytometry with spatial and quantitative morphology of digital microscopy, which allows us imaging cells while they are in flow. In comparison to the conventional slide-based imaging systems, mIFC is a nascent technology enabling high throughput imaging of cells and is yet to take the form of a clinical diagnostic tool. The proposed framework process the raw data generated by the mIFC systems. The framework incorporates several steps: beginning from pre-processing of the raw video frames to enhance the contents of the cell, localising the cell by a novel, fully automatic, non-iterative graph based algorithm, extraction of different quantitative morphological parameters and subsequent classification of cells. In order to evaluate the performance of the proposed framework, we have successfully classified unstained label-free leukaemia cell-lines MOLT, K562 and HL60 from video streams captured using cost-effective microfluidics based imaging flow cytometer. The cell lines of HL60, K562 and MOLT were obtained from ATCC (American Type Culture Collection) and are separately cultured in the lab. Thus, each culture contains cells from its own category alone and thereby provides the ground truth. Each cell is localised by finding a closed cell contour by defining a directed, weighted graph from the Canny edge images of the cell such that the closed contour lies along the shortest weighted path surrounding the centroid of the cell from a starting point on a good curve segment to an immediate endpoint. Once the cell is localised, morphological features reflecting size, shape and complexity of the cells are extracted and used to develop a support vector machine based classification system. We could classify the cell-lines with good accuracy and the results were quite consistent across different cross validation experiments. We hope that imaging flow cytometers equipped with the proposed framework for image processing would enable cost-effective, automated and reliable disease screening in over-loaded facilities, which cannot afford to hire skilled personnel in large numbers. Such platforms would potentially facilitate screening camps in low income group countries; thereby transforming the current health care paradigms by enabling rapid, automated diagnosis for diseases like cancer.

A Novel Approach for Multispectral Satellite Image Classification Based on the Bat Algorithm

Among the multiple advantages and applications of remote sensing, one of the most important uses is to solve the problem of crop classification, i.e., differentiating between various crop types. Satellite images are a reliable source for investigating the temporal changes in crop cultivated areas. In this letter, we propose a novel bat algorithm (BA)-based clustering approach for solving crop type classification problems using a multispectral satellite image. The proposed partitional clustering algorithm is used to extract information in the form of optimal cluster centers from training samples. The extracted cluster centers are then validated on test samples. A real-time multispectral satellite image and one benchmark data set from the University of California, Irvine (UCI) repository are used to demonstrate the robustness of the proposed algorithm. The performance of the BA is compared with two other nature-inspired metaheuristic techniques, namely, genetic algorithm and particle swarm optimization. The performance is also compared with the existing hybrid approach such as the BA with K-means. From the results obtained, it can be concluded that the BA can be successfully applied to solve crop type classification problems.

A mode-shape classification technique for robust speech rate estimation and syllable nuclei detection

Acoustic feature based speech (syllable) rate estimation and syllable nuclei detection are important problems in automatic speech recognition (ASR), computer assisted language learning (CALL) and fluency analysis. A typical solution for both the problems consists of two stages. The first stage involves computing a short-time feature contour such that most of the peaks of the contour correspond to the syllabic nuclei. In the second stage, the peaks corresponding to the syllable nuclei are detected. In this work, instead of the peak detection, we perform a mode-shape classification, which is formulated as a supervised binary classification problem - mode-shapes representing the syllabic nuclei as one class and remaining as the other. We use the temporal correlation and selected sub-band correlation (TCSSBC) feature contour and the mode-shapes in the TCSSBC feature contour are converted into a set of feature vectors using an interpolation technique. A support vector machine classifier is used for the classification. Experiments are performed separately using Switchboard, TIMIT and CTIMIT corpora in a five-fold cross validation setup. The average correlation coefficients for the syllable rate estimation turn out to be 0.6761, 0.6928 and 0.3604 for three corpora respectively, which outperform those obtained by the best of the existing peak detection techniques. Similarly, the average F-scores (syllable level) for the syllable nuclei detection are 0.8917, 0.8200 and 0.7637 for three corpora respectively. (C) 2016 Elsevier B.V. All rights reserved.