GenSelfDiff-HIS: Generative Self-Supervision Using Diffusion for Histopathological Image Segmentation

Histopathological image segmentation is a laborious and time-intensive task, often requiring analysis from experienced pathologists for accurate examinations. To reduce this burden, supervised machine-learning approaches have been adopted using large-scale annotated datasets for histopathological image analysis. However, in several scenarios, the availability of large-scale annotated data is a bottleneck while training such models. Self-supervised learning (SSL) is an alternative paradigm that provides some respite by constructing models utilizing only the unannotated data which is often abundant. The basic idea of SSL is to train a network to perform one or many pseudo or pretext tasks on unannotated data and use it subsequently as the basis for a variety of downstream tasks. It is seen that the success of SSL depends critically on the considered pretext task. While there have been many efforts in designing pretext tasks for classification problems, there haven't been many attempts on SSL for histopathological segmentation. Motivated by this, we propose an SSL approach for segmenting histopathological images via generative diffusion models in this paper. Our method is based on the observation that diffusion models effectively solve an image-to-image translation task akin to a segmentation task. Hence, we propose generative diffusion as the pretext task for histopathological image segmentation. We also propose a multi-loss function-based fine-tuning for the downstream task. We validate our method using several metrics on two publically available datasets along with a newly proposed head and neck (HN) cancer dataset containing hematoxylin and eosin (H\&E) stained images along with annotations. Codes will be made public at https://github.com/PurmaVishnuVardhanReddy/GenSelfDiff-HIS.git

Wavelet-based Burst Event Detection and Localization in Water Distribution Systems

In this paper we present techniques for detecting and locating transient pipe burst events in water distribution systems. The proposed method uses multiscale wavelet analysis of high rate pressure data recorded to detect transient events. Both wavelet coefficients and Lipschitz exponents provide additional information about the nature of the signal feature detected and can be used for feature classification. A local search method is proposed to estimate accurately the arrival time of the pressure transient associated with a pipe burst event. We also propose a graph-based localization algorithm which uses the arrival times of the pressure transient at different measurement points within the water distribution system to determine the actual location (or source) of the pipe burst. The detection and localization performance of these algorithms is validated through leak-off experiments performed on the WaterWiSe@SG wireless sensor network test bed, deployed on the drinking water distribution system in Singapore. Based on these experiments, the average localization error is 37.5 m. We also present a systematic analysis of the sources of localization error and show that even with significant errors in wave speed estimation and time synchronization the localization error is around 56 m.Singapore-MIT Alliance for Research and Technolog

Tracking 3D seismic horizons with a new, hybrid tracking algorithm

We introduce a new algorithm for tracking 3D seismic horizons. The algorithm combines an inversion-based, seismic-dip flattening technique with conventional, similarity-based auto-tracking. The inversion part of the algorithm aims to minimize the error between horizon dips and computed seismic dips. After each cycle in the inversion loop, more seeds are added to the horizon by the similarity-based auto-tracker. In the example data set, the algorithm is first used to quickly track a set of framework horizons, each guided by a small set of user-picked seed positions. Next, the intervals bounded by the framework horizons are infilled to generate a dense set of horizons, a.k.a. HorizonCube. This is done under supervision of a human interpreter in a similar manner. The results show that the algorithm behaves better than unconstrained flattening techniques in intervals with trackable events. Inversion-based algorithms generate continuous horizons with no holes to be filled post-tracking with a gridding algorithm and no loop-skips (jumping to the wrong event) that need to be edited as is standard practice with auto-trackers. As editing is a time-consuming process, creating horizons with inversion-based algorithms tends to be faster than conventional auto-tracking. Horizons created with the proposed algorithm follow seismic events more closely than horizons generated with the inversion-only algorithm and fault crossings are sharper

Distributed Sensor Network Localization with Inaccurate Anchor Positions and Noisy Distance Information

The goal of the sensor network localization problem is to de-termine positions of all the sensor nodes in a network given certain pairwise noisy distance measurements and inaccurate anchor node positions. A two-step distributed localization approach based on second-order cone programming (SOCP) relaxation is presented. In the first step, the sensor nodes determine their positions based on local information and in the second step, the anchor nodes refine their positions us-ing information from the neighboring nodes. Our numeri-cal study shows that the sensor and anchor positions cannot be estimated in a single step; the sensors must be estimated first for the results to converge. The second step enables an-chors which are in the convex hull of their neighbors to refine their positions. Extensive simulation results with inaccurate anchor positions and noisy distance measurements are pre-sented. These illustrate the robustness of the algorithm and the performance gains achievable in terms ofproblem size re-duction, computational efficiency and localization accuracy. Index Terms- Distributed algorithms, Relaxation meth-ods, Convex optimization, Positioning, Localizatio

Water Main Burst Event Detection and Localization

In this paper we present a technique for detecting and locating burst events in pipelines. The proposed method uses wavelet analysis of the high-rate pressure data to detect pipe burst events. Multiscale wavelet analysis of the pressure signal will be shown to be robust to impulsive noise encountered in the physical phenomena under observation. The wavelet coefficients also allow us to obtain additional information about the nature of the signal feature detected, which can used for further feature classification. A local search method is also proposed to accurately determine the arrival time of the pressure front associated with the burst event. The detection performance of these algorithms is verified through leak-off experiments performed on the WaterWiSe@SG test bed deployed on the water distribution system in Singapore. We also propose a graph-based search algorithm which uses the arrival times of the pressure front at different locations within the water distribution system to determine the actual location of the pipe burst event

Water Distribution System Analysis

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WaterWiSe@SG : a testbed for continuous monitoring of the water distribution system in Singapore

This paper describes the development of WaterWiSe@ SG, a wireless sensor network to enable real-time monitoring of a water distribution network in Singapore. The overall project is directed towards three main goals: 1) the application of a low cost wireless sensor network for high data rate, on-line monitoring of hydraulic parameters within a large urban water distribution system; 2) the development of systems to enable remote detection of leaks and prediction of pipe burst events; 3) the integrated monitoring of hydraulic and water quality parameters. In this paper we will describe the current state of the WaterWiSe@SG testbed, and report on experimentation we have performed with respect to leak detection and localization. Furthermore, we describe how we have assimilated real time pressure and flow measurements from the sensor network into hydraulic models that are used to improve state estimation for the network. Finally, we discuss the future plans for the project