Automated measurement of quality of mucosa inspection for colonoscopy

With 655,000 deaths worldwide per year, colorectal cancer it is the third most common form of cancer and the third leading cause of cancer-related death in the Western world. Colonoscopy is currently the preferred screening modality for prevention of colorectal cancer, in which a tiny camera is inserted into the colon to look for early signs of colorectal cancer. A recent systematic review calculated a 22% miss rate for all colonoscopic neoplasia, being 2.1% for advanced lesions. This could be attributed to factors such as inadequate endoscope withdrawal time, poor range of motion of the endoscope, and general endoscopist experience. Therefore the demand for quality control for colonoscopic procedures is increasing, and many researchers have been taking efforts in this area. In this paper, we first presented a novel technique - Colon Center Axis Determination Technique for Non-dark Lumen Images, and the performance evaluation result demonstrates that this technique enables a more accurate view mode classification for all kind of images. Secondly, we proposed two novel approaches to help objectively measure the quality of colonoscopy. A set of objective metrics has been proposed, and preliminary analysis result shows the spiral number during whole procedure/withdrawal phase has a relatively strong positive association with the ground truth circumferential inspection score. The other approach is using association rule mining knowledge to determine patterns of colon inspection. The preliminary result demonstrates that endoscopists with good and relatively poor inspection skill have different inspection patterns, and thus using patterns to assess colonoscopy quality would be anther feasible and promising method

Multi-Timescale Control and Communications with Deep Reinforcement Learning -- Part II: Control-Aware Radio Resource Allocation

In Part I of this two-part paper (Multi-Timescale Control and Communications with Deep Reinforcement Learning -- Part I: Communication-Aware Vehicle Control), we decomposed the multi-timescale control and communications (MTCC) problem in Cellular Vehicle-to-Everything (C-V2X) system into a communication-aware Deep Reinforcement Learning (DRL)-based platoon control (PC) sub-problem and a control-aware DRL-based radio resource allocation (RRA) sub-problem. We focused on the PC sub-problem and proposed the MTCC-PC algorithm to learn an optimal PC policy given an RRA policy. In this paper (Part II), we first focus on the RRA sub-problem in MTCC assuming a PC policy is given, and propose the MTCC-RRA algorithm to learn the RRA policy. Specifically, we incorporate the PC advantage function in the RRA reward function, which quantifies the amount of PC performance degradation caused by observation delay. Moreover, we augment the state space of RRA with PC action history for a more well-informed RRA policy. In addition, we utilize reward shaping and reward backpropagation prioritized experience replay (RBPER) techniques to efficiently tackle the multi-agent and sparse reward problems, respectively. Finally, a sample- and computational-efficient training approach is proposed to jointly learn the PC and RRA policies in an iterative process. In order to verify the effectiveness of the proposed MTCC algorithm, we performed experiments using real driving data for the leading vehicle, where the performance of MTCC is compared with those of the baseline DRL algorithms

Multi-Timescale Control and Communications with Deep Reinforcement Learning -- Part I: Communication-Aware Vehicle Control

An intelligent decision-making system enabled by Vehicle-to-Everything (V2X) communications is essential to achieve safe and efficient autonomous driving (AD), where two types of decisions have to be made at different timescales, i.e., vehicle control and radio resource allocation (RRA) decisions. The interplay between RRA and vehicle control necessitates their collaborative design. In this two-part paper (Part I and Part II), taking platoon control (PC) as an example use case, we propose a joint optimization framework of multi-timescale control and communications (MTCC) based on Deep Reinforcement Learning (DRL). In this paper (Part I), we first decompose the problem into a communication-aware DRL-based PC sub-problem and a control-aware DRL-based RRA sub-problem. Then, we focus on the PC sub-problem assuming an RRA policy is given, and propose the MTCC-PC algorithm to learn an efficient PC policy. To improve the PC performance under random observation delay, the PC state space is augmented with the observation delay and PC action history. Moreover, the reward function with respect to the augmented state is defined to construct an augmented state Markov Decision Process (MDP). It is proved that the optimal policy for the augmented state MDP is optimal for the original PC problem with observation delay. Different from most existing works on communication-aware control, the MTCC-PC algorithm is trained in a delayed environment generated by the fine-grained embedded simulation of C-V2X communications rather than by a simple stochastic delay model. Finally, experiments are performed to compare the performance of MTCC-PC with those of the baseline DRL algorithms

Whole-body Dynamic Collision Avoidance with Time-varying Control Barrier Functions

Recently, there has been increasing attention in robot research towards the whole-body collision avoidance. In this paper, we propose a safety-critical controller that utilizes time-varying control barrier functions (time varying CBFs) constructed by Robo-centric Euclidean Signed Distance Field (RC-ESDF) to achieve dynamic collision avoidance. The RC-ESDF is constructed in the robot body frame and solely relies on the robot's shape, eliminating the need for real-time updates to save computational resources. Additionally, we design two control Lyapunov functions (CLFs) to ensure that the robot can reach its destination. To enable real-time application, our safety-critical controller which incorporates CLFs and CBFs as constraints is formulated as a quadratic program (QP) optimization problem. We conducted numerical simulations on two different dynamics of an L-shaped robot to verify the effectiveness of our proposed approach

Experimental characterization of operational regimes in low aspect-ratio CFB risers

This work discusses operational regimes in CFB-furnaces by means of mapping the relation between fluidization velocity, riser pressure drop and external solids circulation. A cold CFB unit with riser dimensions 0.8 × 0.12 × 8.5 m is used, thus yielding a riser height-to-width aspect ratio of 10.6, similar to that of typical CFB boilers (1). The overall flow regime established in the unit has been shown to be representative for large-scale CFB boilers, while providing the opportunity to investigate a wide range of operational parameters, including such that are not easily applicable to real boilers. The operational conditions covered fluidization velocities from 0.2 to 5 m/s and riser pressure drop from 1.3 to 11 kPa, which resulted in solids net circulation up to 50 kg/m2s. The riser is equipped with 25 pressure transducers in order to provide resolve relevant gradients in the vertical distribution of solids concentration. The solids net circulation is measured during operation by using a valve in the cyclone dipleg which, when closed, acts as a gas distributor. In the present work, three air-distributor plates were used, covering pressure-drop characteristics similar to what is typically used in lab-units, bubbling fluidized bed boilers and circulating fluidized bed boilers. The paper presents results which relate fluidization velocity, riser pressure drop and external solids circulation under different operational regimes ranging from bubbling conditions to pneumatic transport. The results are compared with data from industrial boilers. REFERENCES F. Johnsson, W. Zhang and B. Leckner. Characteristics of the formation of particle wall-layers in CFB boilers. In Proceedings of the second international conference on multiphase flow (pp. FB1-25). The Japan Society of Multiphase Flow Nagoy

On incremental global update support in cooperative database systems

OzGateway is a cooperative database system designed for integrating heterogeneous existing information systems into an interoperable environment. It also aims to provide a gatewway for legacy information system migration. This paper summarises the problems and results of multidatabase transaction management research. In supporting global updates in OzGateway in an evolutionary way, we introduce a classification of multidatabase transactions and discuss the problems in each category. The architecture of OzGateway and the design of the global transaction manager and servers are presented