16,180 research outputs found

    Audio-visual multi-modality driven hybrid feature learning model for crowd analysis and classification

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    The high pace emergence in advanced software systems, low-cost hardware and decentralized cloud computing technologies have broadened the horizon for vision-based surveillance, monitoring and control. However, complex and inferior feature learning over visual artefacts or video streams, especially under extreme conditions confine majority of the at-hand vision-based crowd analysis and classification systems. Retrieving event-sensitive or crowd-type sensitive spatio-temporal features for the different crowd types under extreme conditions is a highly complex task. Consequently, it results in lower accuracy and hence low reliability that confines existing methods for real-time crowd analysis. Despite numerous efforts in vision-based approaches, the lack of acoustic cues often creates ambiguity in crowd classification. On the other hand, the strategic amalgamation of audio-visual features can enable accurate and reliable crowd analysis and classification. Considering it as motivation, in this research a novel audio-visual multi-modality driven hybrid feature learning model is developed for crowd analysis and classification. In this work, a hybrid feature extraction model was applied to extract deep spatio-temporal features by using Gray-Level Co-occurrence Metrics (GLCM) and AlexNet transferrable learning model. Once extracting the different GLCM features and AlexNet deep features, horizontal concatenation was done to fuse the different feature sets. Similarly, for acoustic feature extraction, the audio samples (from the input video) were processed for static (fixed size) sampling, pre-emphasis, block framing and Hann windowing, followed by acoustic feature extraction like GTCC, GTCC-Delta, GTCC-Delta-Delta, MFCC, Spectral Entropy, Spectral Flux, Spectral Slope and Harmonics to Noise Ratio (HNR). Finally, the extracted audio-visual features were fused to yield a composite multi-modal feature set, which is processed for classification using the random forest ensemble classifier. The multi-class classification yields a crowd-classification accurac12529y of (98.26%), precision (98.89%), sensitivity (94.82%), specificity (95.57%), and F-Measure of 98.84%. The robustness of the proposed multi-modality-based crowd analysis model confirms its suitability towards real-world crowd detection and classification tasks

    Hierarchical Graph Neural Networks for Proprioceptive 6D Pose Estimation of In-hand Objects

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    Robotic manipulation, in particular in-hand object manipulation, often requires an accurate estimate of the object's 6D pose. To improve the accuracy of the estimated pose, state-of-the-art approaches in 6D object pose estimation use observational data from one or more modalities, e.g., RGB images, depth, and tactile readings. However, existing approaches make limited use of the underlying geometric structure of the object captured by these modalities, thereby, increasing their reliance on visual features. This results in poor performance when presented with objects that lack such visual features or when visual features are simply occluded. Furthermore, current approaches do not take advantage of the proprioceptive information embedded in the position of the fingers. To address these limitations, in this paper: (1) we introduce a hierarchical graph neural network architecture for combining multimodal (vision and touch) data that allows for a geometrically informed 6D object pose estimation, (2) we introduce a hierarchical message passing operation that flows the information within and across modalities to learn a graph-based object representation, and (3) we introduce a method that accounts for the proprioceptive information for in-hand object representation. We evaluate our model on a diverse subset of objects from the YCB Object and Model Set, and show that our method substantially outperforms existing state-of-the-art work in accuracy and robustness to occlusion. We also deploy our proposed framework on a real robot and qualitatively demonstrate successful transfer to real settings

    Design and Characterization of Crossbar architecture Velostat-based Flexible Writing Pad

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    Pressure sensors are popular in a large variety of industries. For some applications, it is critical for these sensors to come in a flexible form factor. With the development of new synthetic polymers and novel fabrication techniques, flexible pressure sensing arrays are more easily accessible and can serve a variety of applications. As part of this dissertation, we demonstrate one such application of the same by developing a low-cost flexible writing pad and doing crosstalk analysis on sensors with similar working principles. We present a low-cost, flexible writing pad that uses a 16x16 pressure sensing matrix based on the piezoresistive thin film of velostat. The writing area is 5 cm x 5 cm with an effective pixel area of 0.06 mm^2. A read-out circuit is designed to detect the change in resistance of the velostat pixel using a voltage divider. A microprocessor raster scans through the sensor pixel matrix to obtain a data frame of 256 numbers. This data is processed using techniques like squaring and normalising (S\&N), Gaussian blurring, and adaptive thresholding to generate a more readable output. The writing pad is able to resolve characters larger than 2 cm in length. The flexible writing pad produces legible output while flexed at a bending radius of up to 4 cm. Such flexibility promises to enhance the usability and portability of the writing pad significantly. We noticed that the raw data produced by the writing pad had a lot of crosstalk which we were subsequently able to resolve using the algorithms mentioned above. Such crosstalk has been reported in literature multiple times and is common, especially for sensors of the crossbar architecture.Crosstalk, in a sensor matrix, is the unwanted signal obtained at a sensor pixel that is not directly related to the stimulus. This paper presents a novel approach towards quantifying the crosstalk characteristics of a sensor matrix

    An advanced deep learning models-based plant disease detection: A review of recent research

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    Plants play a crucial role in supplying food globally. Various environmental factors lead to plant diseases which results in significant production losses. However, manual detection of plant diseases is a time-consuming and error-prone process. It can be an unreliable method of identifying and preventing the spread of plant diseases. Adopting advanced technologies such as Machine Learning (ML) and Deep Learning (DL) can help to overcome these challenges by enabling early identification of plant diseases. In this paper, the recent advancements in the use of ML and DL techniques for the identification of plant diseases are explored. The research focuses on publications between 2015 and 2022, and the experiments discussed in this study demonstrate the effectiveness of using these techniques in improving the accuracy and efficiency of plant disease detection. This study also addresses the challenges and limitations associated with using ML and DL for plant disease identification, such as issues with data availability, imaging quality, and the differentiation between healthy and diseased plants. The research provides valuable insights for plant disease detection researchers, practitioners, and industry professionals by offering solutions to these challenges and limitations, providing a comprehensive understanding of the current state of research in this field, highlighting the benefits and limitations of these methods, and proposing potential solutions to overcome the challenges of their implementation

    The State of the Art in Deep Learning Applications, Challenges, and Future Prospects::A Comprehensive Review of Flood Forecasting and Management

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    Floods are a devastating natural calamity that may seriously harm both infrastructure and people. Accurate flood forecasts and control are essential to lessen these effects and safeguard populations. By utilizing its capacity to handle massive amounts of data and provide accurate forecasts, deep learning has emerged as a potent tool for improving flood prediction and control. The current state of deep learning applications in flood forecasting and management is thoroughly reviewed in this work. The review discusses a variety of subjects, such as the data sources utilized, the deep learning models used, and the assessment measures adopted to judge their efficacy. It assesses current approaches critically and points out their advantages and disadvantages. The article also examines challenges with data accessibility, the interpretability of deep learning models, and ethical considerations in flood prediction. The report also describes potential directions for deep-learning research to enhance flood predictions and control. Incorporating uncertainty estimates into forecasts, integrating many data sources, developing hybrid models that mix deep learning with other methodologies, and enhancing the interpretability of deep learning models are a few of these. These research goals can help deep learning models become more precise and effective, which will result in better flood control plans and forecasts. Overall, this review is a useful resource for academics and professionals working on the topic of flood forecasting and management. By reviewing the current state of the art, emphasizing difficulties, and outlining potential areas for future study, it lays a solid basis. Communities may better prepare for and lessen the destructive effects of floods by implementing cutting-edge deep learning algorithms, thereby protecting people and infrastructure

    Technology for Low Resolution Space Based RSO Detection and Characterisation

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    Space Situational Awareness (SSA) refers to all activities to detect, identify and track objects in Earth orbit. SSA is critical to all current and future space activities and protect space assets by providing access control, conjunction warnings, and monitoring status of active satellites. Currently SSA methods and infrastructure are not sufficient to account for the proliferations of space debris. In response to the need for better SSA there has been many different areas of research looking to improve SSA most of the requiring dedicated ground or space-based infrastructure. In this thesis, a novel approach for the characterisation of RSO’s (Resident Space Objects) from passive low-resolution space-based sensors is presented with all the background work performed to enable this novel method. Low resolution space-based sensors are common on current satellites, with many of these sensors being in space using them passively to detect RSO’s can greatly augment SSA with out expensive infrastructure or long lead times. One of the largest hurtles to overcome with research in the area has to do with the lack of publicly available labelled data to test and confirm results with. To overcome this hurtle a simulation software, ORBITALS, was created. To verify and validate the ORBITALS simulator it was compared with the Fast Auroral Imager images, which is one of the only publicly available low-resolution space-based images found with auxiliary data. During the development of the ORBITALS simulator it was found that the generation of these simulated images are computationally intensive when propagating the entire space catalog. To overcome this an upgrade of the currently used propagation method, Specialised General Perturbation Method 4th order (SGP4), was performed to allow the algorithm to run in parallel reducing the computational time required to propagate entire catalogs of RSO’s. From the results it was found that the standard facet model with a particle swarm optimisation performed the best estimating an RSO’s attitude with a 0.66 degree RMSE accuracy across a sequence, and ~1% MAPE accuracy for the optical properties. This accomplished this thesis goal of demonstrating the feasibility of low-resolution passive RSO characterisation from space-based platforms in a simulated environment

    Deep Learning Method for Cell-Wise Object Tracking, Velocity Estimation and Projection of Sensor Data over Time

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    Current Deep Learning methods for environment segmentation and velocity estimation rely on Convolutional Recurrent Neural Networks to exploit spatio-temporal relationships within obtained sensor data. These approaches derive scene dynamics implicitly by correlating novel input and memorized data utilizing ConvNets. We show how ConvNets suffer from architectural restrictions for this task. Based on these findings, we then provide solutions to various issues on exploiting spatio-temporal correlations in a sequence of sensor recordings by presenting a novel Recurrent Neural Network unit utilizing Transformer mechanisms. Within this unit, object encodings are tracked across consecutive frames by correlating key-query pairs derived from sensor inputs and memory states, respectively. We then use resulting tracking patterns to obtain scene dynamics and regress velocities. In a last step, the memory state of the Recurrent Neural Network is projected based on extracted velocity estimates to resolve aforementioned spatio-temporal misalignment.Comment: Preprint submitted to 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC), Macau, China, 7 page

    Fault diagnosis in aircraft fuel system components with machine learning algorithms

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    There is a high demand and interest in considering the social and environmental effects of the component’s lifespan. Aircraft are one of the most high-priced businesses that require the highest reliability and safety constraints. The complexity of aircraft systems designs also has advanced rapidly in the last decade. Consequently, fault detection, diagnosis and modification/ repair procedures are becoming more challenging. The presence of a fault within an aircraft system can result in changes to system performances and cause operational downtime or accidents in a worst-case scenario. The CBM method that predicts the state of the equipment based on data collected is widely used in aircraft MROs. CBM uses diagnostics and prognostics models to make decisions on appropriate maintenance actions based on the Remaining Useful Life (RUL) of the components. The aircraft fuel system is a crucial system of aircraft, even a minor failure in the fuel system can affect the aircraft's safety greatly. A failure in the fuel system that impacts the ability to deliver fuel to the engine will have an immediate effect on system performance and safety. There are very few diagnostic systems that monitor the health of the fuel system and even fewer that can contain detected faults. The fuel system is crucial for the operation of the aircraft, in case of failure, the fuel in the aircraft will become unusable/unavailable to reach the destination. It is necessary to develop fault detection of the aircraft fuel system. The future aircraft fuel system must have the function of fault detection. Through the information of sensors and Machine Learning Techniques, the aircraft fuel system’s fault type can be detected in a timely manner. This thesis discusses the application of a Data-driven technique to analyse the healthy and faulty data collected using the aircraft fuel system model, which is similar to Boeing-777. The data is collected is processed through Machine learning Techniques and the results are comparedPhD in Manufacturin

    Beam scanning by liquid-crystal biasing in a modified SIW structure

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    A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium

    Rapid detection of soil carbonates by means of NIR spectroscopy, deep learning methods and phase quantification by powder Xray diffraction

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    Soil NIR spectral absorbance/reflectance libraries are utilized towards improving agricultural production and analysis of soil properties which are key prerequisite for agroecological balance and environmental sustainability. Carbonates in particular, represent a soil property which is mostly affected even by mild, let alone extreme, changes of environmental conditions during climate change. In this study we propose a rapid and efficient way to predict carbonates content in soil by means of FT NIR reflectance spectroscopy and by use of deep learning methods. We exploited multiple machine learning methods, such as: 1) a MLP Regressor and 2) a CNN and compare their performance with other traditional ML algorithms such as PLSR, Cubist and SVM on the combined dataset of two NIR spectral libraries: KSSL (USDA), a dataset of soil samples reflectance spectra collected nationwide, and LUCAS TopSoil (European Soil Library) which contains soil sample absorbance spectra from all over the European Union, and use them to predict carbonate content on never before seen soil samples. Soil samples in KSSL and in TopSoil spectral libraries were acquired in the spectral region of visNIR, however in this study, only the NIR spectral region was utilized. Quantification of carbonates by means of Xray Diffraction is in good agreement with the volumetric method and the MLP prediction. Our work contributes to rapid carbonates content prediction in soil samples in cases where: 1) no volumetric method is available and 2) only NIR spectra absorbance data are available. Up till now and to the best of our knowledge, there exists no other study, that presents a prediction model trained on such an extensive dataset with such promising results on unseen data, undoubtedly supporting the notion that deep learning models present excellent prediction tools for soil carbonates content.Comment: 39 pages, 5 figure
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