8,142 research outputs found

    Deep ensemble model-based moving object detection and classification using SAR images

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    In recent decades, image processing and computer vision models have played a vital role in moving object detection on the synthetic aperture radar (SAR) images. Capturing of moving objects in the SAR images is a difficult task. In this study, a new automated model for detecting moving objects is proposed using SAR images. The proposed model has four main steps, namely, preprocessing, segmentation, feature extraction, and classification. Initially, the input SAR image is pre-processed using a histogram equalization technique. Then, the weighted Otsu-based segmentation algorithm is applied for segmenting the object regions from the pre-processed images. When using the weighted Otsu, the segmented grayscale images are not only clear but also retain the detailed features of grayscale images. Next, feature extraction is carried out by gray-level co-occurrence matrix (GLCM), median binary patterns (MBPs), and additive harmonic mean estimated local Gabor binary pattern (AHME-LGBP). The final step is classification using deep ensemble models, where the objects are classified by employing the ensemble deep learning technique, combining the models like the bidirectional long short-term memory (Bi-LSTM), recurrent neural network (RNN), and improved deep belief network (IDBN), which is trained with the features extracted previously. The combined models increase the accuracy of the results significantly. Furthermore, ensemble modeling reduces the variance and modeling method bias, which decreases the chances of overfitting. Compared to a single contributing model, ensemble models perform better and make better predictions. Additionally, an ensemble lessens the spread or dispersion of the model performance and prediction accuracy. Finally, the performance of the proposed model is related to the conventional models with respect to different measures. In the mean-case scenario, the proposed ensemble model has a minimum error value of 0.032, which is better related to other models. In both median- and best-case scenario studies, the ensemble model has a lower error value of 0.029 and 0.015

    Towards Neuromorphic Gradient Descent: Exact Gradients and Low-Variance Online Estimates for Spiking Neural Networks

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    Spiking Neural Networks (SNNs) are biologically-plausible models that can run on low-powered non-Von Neumann neuromorphic hardware, positioning them as promising alternatives to conventional Deep Neural Networks (DNNs) for energy-efficient edge computing and robotics. Over the past few years, the Gradient Descent (GD) and Error Backpropagation (BP) algorithms used in DNNs have inspired various training methods for SNNs. However, the non-local and the reverse nature of BP, combined with the inherent non-differentiability of spikes, represent fundamental obstacles to computing gradients with SNNs directly on neuromorphic hardware. Therefore, novel approaches are required to overcome the limitations of GD and BP and enable online gradient computation on neuromorphic hardware. In this thesis, I address the limitations of GD and BP with SNNs by proposing three algorithms. First, I extend a recent method that computes exact gradients with temporally-coded SNNs by relaxing the firing constraint of temporal coding and allowing multiple spikes per neuron. My proposed method generalizes the computation of exact gradients with SNNs and enhances the tradeoffs between performance and various other aspects of spiking neurons. Next, I introduce a novel alternative to BP that computes low-variance gradient estimates in a local and online manner. Compared to other alternatives to BP, the proposed method demonstrates an improved convergence rate and increased performance with DNNs. Finally, I combine these two methods and propose an algorithm that estimates gradients with SNNs in a manner that is compatible with the constraints of neuromorphic hardware. My empirical results demonstrate the effectiveness of the resulting algorithm in training SNNs without performing BP

    Formation and evolution of vortex rings with weak to moderate swirl and their implications for enhancing vortex ring circulation

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    The formation of swirling vortex rings and their early time evolution, resulting from the controlled discharge of an incompressible fluid into a stationary equivalent fluid bulk, is explored both numerically and experimentally for swirl number S ∈ [0, 1]. For the numerical work, two practically realisable inlet conditions are investigated with swirl simultaneously superposed onto a linear momentum discharge; the corresponding circulation based Reynolds number is 7500. The results reveal that, for S > 1/2, the addition of swirl promotes the breakdown of the leading primary vortex ring structure, giving rise to the striking feature of significant negative vorticity, or opposite sign vorticity (OSV), generation in the region surrounding the primary vortex ring core, whose strength scales with S2. Through a non-linear interaction with the vortex breakdown, the radius of the primary toroidal vortex core is rapidly increased; consequently, the self-induced propagation velocity of the leading ring decreases with S and vortex stretching along the circular primary vortex core increases counteracting viscous diffusion effects. The latter governs the evolution of the peak vorticity intensity and the swirl velocity magnitude in the primary ring core, the circulation growth rate of this ring, as well as the vorticity intensity of the trailing jet and hence its stability. This combination of effects leads to an increased dimensionless kinetic energy for the primary ring with increasing S and results in an almost linearly decreasing circulation based formation number, F. In a rigorous complementary experimental investigation, OSV is observed by introducing swirl using a rotating pipe, varying the time period before the piston stroke to achieve the desired swirl strength at a Reynolds number of 1000. Rotating pipe is found to generate a secondary flow altering the inlet condition. Nevertheless, it is observed, using short periods of pipe rotation and higher angular speed, that it is possible to generate a swirling vortex ring with less OSV production and all the related effects discussed above. The relation between F and the radius of the vortex ring is investigated through manipulation of ring radius growth, achieved through its interaction with a preceding vortex ring. Reducing radius growth, facilitates an increase of the circulation of the vortex ring, which in turn affects its F value

    Minimal information for studies of extracellular vesicles (MISEV2023): From basic to advanced approaches

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    Extracellular vesicles (EVs), through their complex cargo, can reflect the state of their cell of origin and change the functions and phenotypes of other cells. These features indicate strong biomarker and therapeutic potential and have generated broad interest, as evidenced by the steady year-on-year increase in the numbers of scientific publications about EVs. Important advances have been made in EV metrology and in understanding and applying EV biology. However, hurdles remain to realising the potential of EVs in domains ranging from basic biology to clinical applications due to challenges in EV nomenclature, separation from non-vesicular extracellular particles, characterisation and functional studies. To address the challenges and opportunities in this rapidly evolving field, the International Society for Extracellular Vesicles (ISEV) updates its 'Minimal Information for Studies of Extracellular Vesicles', which was first published in 2014 and then in 2018 as MISEV2014 and MISEV2018, respectively. The goal of the current document, MISEV2023, is to provide researchers with an updated snapshot of available approaches and their advantages and limitations for production, separation and characterisation of EVs from multiple sources, including cell culture, body fluids and solid tissues. In addition to presenting the latest state of the art in basic principles of EV research, this document also covers advanced techniques and approaches that are currently expanding the boundaries of the field. MISEV2023 also includes new sections on EV release and uptake and a brief discussion of in vivo approaches to study EVs. Compiling feedback from ISEV expert task forces and more than 1000 researchers, this document conveys the current state of EV research to facilitate robust scientific discoveries and move the field forward even more rapidly

    Tracking unlabeled cancer cells imaged with low resolution in wide migration chambers via U-NET class-1 probability (pseudofluorescence).

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    Cell migration is a pivotal biological process, whose dysregulation is found in many diseases including inflammation and cancer. Advances in microscopy technologies allow now to study cell migration in vitro, within engineered microenvironments that resemble in vivo conditions. However, to capture an entire 3D migration chamber for extended periods of time and with high temporal resolution, images are generally acquired with low resolution, which poses a challenge for data analysis. Indeed, cell detection and tracking are hampered due to the large pixel size (i.e., cell diameter down to 2 pixels), the possible low signal-to-noise ratio, and distortions in the cell shape due to changes in the z-axis position. Although fluorescent staining can be used to facilitate cell detection, it may alter cell behavior and it may suffer from fluorescence loss over time (photobleaching).Here we describe a protocol that employs an established deep learning method (U-NET), to specifically convert transmitted light (TL) signal from unlabeled cells imaged with low resolution to a fluorescent-like signal (class 1 probability). We demonstrate its application to study cancer cell migration, obtaining a significant improvement in tracking accuracy, while not suffering from photobleaching. This is reflected in the possibility of tracking cells for three-fold longer periods of time. To facilitate the application of the protocol we provide WID-U, an open-source plugin for FIJI and Imaris imaging software, the training dataset used in this paper, and the code to train the network for custom experimental settings

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    Enabling Deep Neural Network Inferences on Resource-constraint Devices

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    Department of Computer Science and EngineeringWhile deep neural networks (DNN) are widely used on various devices, including resource-constraint devices such as IoT, AR/VR, and mobile devices, running DNN from resource-constrained devices remains challenging. There exist three approaches for DNN inferences on resource-constraint devices: 1) lightweight DNN for on-device computing, 2) offloading DNN inferences to a cloud server, and 3) split computing to utilize computation and network resources efficiently. Designing a lightweight DNN without compromising the accuracy of DNN is challenging due to a trade-off between latency and accuracy, that more computation is required to achieve higher accuracy. One solution to overcome this challenge is pre-processing to extract and transfer helpful information to achieve high accuracy of DNN. We design the pre-processing, which consists of three processes. The first process of pre-processing is finding out the best input source. The second process is the input-processing which extracts and contains important information for DNN inferences among the whole information gained from the input source. The last process is choosing or designing a suitable lightweight DNN for processed input. As an instance of how to apply the pre-processing, in Sec 2, we present a new transportation mode recognition system for smartphones called DeepVehicleSense, which aims at achieving three performance objectives: high accuracy, low latency, and low power consumption at once by exploiting sound characteristics captured from the built-in microphone while being on candidate transportations. To achieve high accuracy and low latency, DeepVehicleSense makes use of non-linear filters that can best extract the transportation sound samples. For the recognition of five different transportation modes, we design a deep learning-based sound classifier using a novel deep neural network architecture with multiple branches. Our staged inference technique can significantly reduce runtime and energy consumption while maintaining high accuracy for the majority of samples. Offloading DNN inferences to a server is a solution for DNN inferences on resource-constraint devices, but there is one concern about latency caused by data transmission. To reduce transmission latency, recent studies have tried to make this offloading process more efficient by compressing data to be offloaded. However, conventional compression techniques are designed for human beings, so they compress data to be possible to restore data, which looks like the original from the perspective of human eyes. As a result, the compressed data through the compression technique contains redundancy beyond the necessary information for DNN inference. In other words, the most fundamental question on extracting and offloading the minimal amount of necessary information that does not degrade the inference accuracy has remained unanswered. To answer the question, in Sec 3, we call such an ideal offloading semantic offloading and propose N-epitomizer, a new offloading framework that enables semantic offloading, thus achieving more reliable and timely inferences in highly-fluctuated or even low-bandwidth wireless networks. To realize N-epitomizer, we design an autoencoder-based scalable encoder trained to extract the most informative data and scale its output size to meet the latency and accuracy requirements of inferences over a network. Even though our proposed lightweight DNN and offloading framework with the essential information extractor achieve low latency while preserving DNN performance, they alone cannot realize latency-guaranteed DNN inferences. To realize latency-guaranteed DNN inferences, the computational complexity of the lightweight DNN and the compression performance of the encoder for offloading should be adaptively selected according to current computation resources and network conditions by utilizing the DNN's trade-off between computational complexity and DNN performance and the encoder's trade-off between compression performance and DNN performance. To this end, we propose a new framework for latency-guaranteed DNN inferences called LG-DI, which predicts DNN performance degradation given a latency budget in advance and utilizes the better method between the lightweight DNN and offloading with compression. As a result, our proposed framework for DNN inferences can guarantee latency regardless of changes in computation and network resources while maintaining DNN performance as much as possible.ope

    Automated identification and behaviour classification for modelling social dynamics in group-housed mice

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    Mice are often used in biology as exploratory models of human conditions, due to their similar genetics and physiology. Unfortunately, research on behaviour has traditionally been limited to studying individuals in isolated environments and over short periods of time. This can miss critical time-effects, and, since mice are social creatures, bias results. This work addresses this gap in research by developing tools to analyse the individual behaviour of group-housed mice in the home-cage over several days and with minimal disruption. Using data provided by the Mary Lyon Centre at MRC Harwell we designed an end-to-end system that (a) tracks and identifies mice in a cage, (b) infers their behaviour, and subsequently (c) models the group dynamics as functions of individual activities. In support of the above, we also curated and made available a large dataset of mouse localisation and behaviour classifications (IMADGE), as well as two smaller annotated datasets for training/evaluating the identification (TIDe) and behaviour inference (ABODe) systems. This research constitutes the first of its kind in terms of the scale and challenges addressed. The data source (side-view single-channel video with clutter and no identification markers for mice) presents challenging conditions for analysis, but has the potential to give richer information while using industry standard housing. A Tracking and Identification module was developed to automatically detect, track and identify the (visually similar) mice in the cluttered home-cage using only single-channel IR video and coarse position from RFID readings. Existing detectors and trackers were combined with a novel Integer Linear Programming formulation to assign anonymous tracks to mouse identities. This utilised a probabilistic weight model of affinity between detections and RFID pickups. The next task necessitated the implementation of the Activity Labelling module that classifies the behaviour of each mouse, handling occlusion to avoid giving unreliable classifications when the mice cannot be observed. Two key aspects of this were (a) careful feature-selection, and (b) judicious balancing of the errors of the system in line with the repercussions for our setup. Given these sequences of individual behaviours, we analysed the interaction dynamics between mice in the same cage by collapsing the group behaviour into a sequence of interpretable latent regimes using both static and temporal (Markov) models. Using a permutation matrix, we were able to automatically assign mice to roles in the HMM, fit a global model to a group of cages and analyse abnormalities in data from a different demographic

    Aligning Figurative Paintings With Their Sources for Semantic Interpretation

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    This paper reports steps in probing the artistic methods of figurative painters through computational algorithms. We explore a comparative method that investigates the relation between the source of a painting, typically a photograph or an earlier painting, and the painting itself. A first crucial step in this process is to find the source and to crop, standardize and align it to the painting so that a comparison becomes possible. The next step is to apply different low-level algorithms to construct difference maps for color, edges, texture, brightness, etc. From this basis, various subsequent operations become possible to detect and compare features of the image, such as facial action units and the emotions they signify. This paper demonstrates a pipeline we have built and tested using paintings by a renowned contemporary painter Luc Tuymans. We focus in this paper particularly on the alignment process, on edge difference maps, and on the utility of the comparative method for bringing out the semantic significance of a painting
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