BITS Faculty Publications

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    Evolutionary computation-based self-supervised learning for image processing: a big data-driven approach to feature extraction and fusion for multispectral object detection
    (Springer, 2024-09) Chamola, Vinay
    The image object recognition and detection technology are widely used in many scenarios. In recent years, big data has become increasingly abundant, and big data-driven artificial intelligence models have attracted more and more attention. Evolutionary computation has also provided a powerful driving force for the optimization and improvement of deep learning models. In this paper, we propose an image object detection method based on self-supervised and data-driven learning. Differ from other methods, our approach stands out due to its innovative use of multispectral data fusion and evolutionary computation for model optimization. Specifically, our method uniquely combines visible light images and infrared images to detect and identify image targets. Firstly, we utilize a self-supervised learning method and the AutoEncoder model to perform high-dimensional feature extraction on the two types of images. Secondly, we fuse the extracted features from the visible light and infrared images to detect and identify objects. Thirdly, we introduce a model parameter optimization method using evolutionary learning algorithms to enhance model performance. Validation on public datasets shows that our method achieves comparable or superior performance to existing methods.
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    Distributed information fusion for secure healthcare
    (Elsevier, 2024) Rajput, Amitesh Singh
    Recent years have seen a significant increase in the demand for cutting-edge healthcare systems. With the rising potential of artificial intelligence and big data technology, all sectors, especially the healthcare sector, have been greatly supported. Huge amounts of privacy-sensitive clinical data are being generated from several sources. While processing these enormous amounts of diverse healthcare data, the problem is that the data are heterogeneous. The data vary with respect to the patient population, environment, data source, size, complexity, medical procedures, and treatment protocols at individual medical centers. This creates the need for a central knowledge base in the healthcare setting. Federated learning-based fusion techniques can turn out to be beneficial to acquire knowledge from these distributed data. This will bring the distributed data together into a single view that can help hospitals and health workers to obtain new insights and helps secure patients' personal information and safeguards them from information leakage. If the distribution of data among the classes is skewed or biased, the distribution is said to be imbalanced. This chapter discusses problems associated with imbalanced and heterogeneous healthcare data and their effects on machine learning models and proposes methods to improve data fairness in a distributed healthcare system using federated learning
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    Pedestrian Augmented Reality Navigator
    (MDPI, 2023-02) Mahapatra, Tanmaya
    Navigation is often regarded as one of the most-exciting use cases for Augmented Reality (AR). Current AR Head-Mounted Displays (HMDs) are rather bulky and cumbersome to use and, therefore, do not offer a satisfactory user experience for the mass market yet. However, the latest-generation smartphones offer AR capabilities out of the box, with sometimes even pre-installed apps. Apple’s framework ARKit is available on iOS devices, free to use for developers. Android similarly features a counterpart, ARCore. Both systems work well for small spatially confined applications, but lack global positional awareness. This is a direct result of one limitation in current mobile technology. Global Navigation Satellite Systems (GNSSs) are relatively inaccurate and often cannot work indoors due to the restriction of the signal to penetrate through solid objects, such as walls. In this paper, we present the Pedestrian Augmented Reality Navigator (PAReNt) iOS app as a solution to this problem. The app implements a data fusion technique to increase accuracy in global positioning and showcases AR navigation as one use case for the improved data. ARKit provides data about the smartphone’s motion, which is fused with GNSS data and a Bluetooth indoor positioning system via a Kalman Filter (KF). Four different KFs with different underlying models have been implemented and independently evaluated to find the best filter. The evaluation measures the app’s accuracy against a ground truth under controlled circumstances. Two main testing methods were introduced and applied to determine which KF works best. Depending on the evaluation method, this novel approach improved the accuracy by 57% (when GPS and AR were used) or 32% (when Bluetooth and AR were used) over the raw sensor data.
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    Nonlinear Dynamical Systems, Their Stability, and Chaos : Lecture notes from the FLOW-NORDITA Summer School on Advanced Instability Methods for Complex Flows, Stockholm, Sweden, 2013
    (Springer, 2022-01) Marathe, Amol
    In condition monitoring, accurate fault identification is an essential task for designing a proper maintenance strategy. Misalignment is one of the main faults in rotary machinery, because 70% of the failure occurs due to misalignment. Conventionally, the diagnosis of misalignment is carried out through vibration measurements. Especially, the presence of strong 2x vibration peak is generally accepted. Both angular and parallel misalignment shows peak at 2x, therefore, distinguishing misalignment type by using vibration signals alone is a difficult activity. This paper discusses classification of misalignment i.e., angular and parallel by using a diagnostic medium such as the acoustic emission and the rotor vibration signal. Vibro-acoustic sensors are used to collect data from the misaligned rotor system at two different loading, three different speed and three defect severity conditions. Time domain features are extracted and graded according to their significance using t test (One-way ANOVA) technique. Extracted features are used to train different algorithms. The outcome obtained using support vector machine (SVM) is 100% accurate. Vibro-acoustic sensor data fusion technique is employed to classify various forms of misalignment under different operating conditions. This work also intended to explore using a small amount of training data using different algorithms. The proposed method outperforms fault classification using vibration signal and acoustic signal separately.
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    Ensemble Subspace Discriminant Classifiers for Misalignment Fault Classification Using Vibro-acoustic Sensor Data Fusion
    (Springer, 2022-05) Jalan, Arun Kumar
    Misalignment is one of the major reasons for rotating machinery breakdown. Conventionally, misalignment diagnosis is done by the occurrence of a strong peak at 2x running speed is widely accepted.
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    Support Vector Machine for Misalignment Fault Classification Under Different Loading Conditions Using Vibro-Acoustic Sensor Data Fusion
    (Springer, 2022-01) Jalan, Arun Kumar; Marathe, Amol
    In condition monitoring, accurate fault identification is an essential task for designing a proper maintenance strategy. Misalignment is one of the main faults in rotary machinery, because 70% of the failure occurs due to misalignment. Conventionally, the diagnosis of misalignment is carried out through vibration measurements. Especially, the presence of strong 2x vibration peak is generally accepted. Both angular and parallel misalignment shows peak at 2x, therefore, distinguishing misalignment type by using vibration signals alone is a difficult activity. This paper discusses classification of misalignment i.e., angular and parallel by using a diagnostic medium such as the acoustic emission and the rotor vibration signal. Vibro-acoustic sensors are used to collect data from the misaligned rotor system at two different loading, three different speed and three defect severity conditions. Time domain features are extracted and graded according to their significance using t test (One-way ANOVA) technique. Extracted features are used to train different algorithms. The outcome obtained using support vector machine (SVM) is 100% accurate. Vibro-acoustic sensor data fusion technique is employed to classify various forms of misalignment under different operating conditions. This work also intended to explore using a small amount of training data using different algorithms. The proposed method outperforms fault classification using vibration signal and acoustic signal separately.
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    Classification of Ball Bearing Faults Using Vibro-Acoustic Sensor Data Fusion
    (Springer, 2019-04) Jalan, Arun Kumar
    This paper presents the novel technique for fault diagnosis of bearing by fusion of two different sensors: Vibration based and acoustic emission-based sensor. The diagnosis process involves the following steps: Data Acquisition and signal processing, Feature extraction, Classification of features, High-level data fusion and Decision making. Experiments are carried out upon test bearings with a fusion of sensors to obtain signals in time domain. Then, signal indicators for each signal have been calculated. Classifier called K-nearest neighbor (KNN) has been used for classification of fault conditions. Then, high-level sensor fusion was carried out to gain useful data for fault classification. The decision-making step allows understanding that vibration-based sensors are helpful in detecting inner race and outer race defect whereas the acoustic-based sensor is more useful for ball defects detection. These studies based on fusion helps to detect all the faults of rolling bearing at an early stage.