Reliable Asynchronous Image Transfer Protocol in Wireless Multimedia Sensor Networks

In the paper, we propose a reliable asynchronous image transfer protocol, RAIT. RAIT applies a double sliding window method to node-to-node transfer, with one sliding window for the receiving queue, which is used to prevent packet loss caused by communication failure between nodes, and another sliding window for the sending queue, which prevents packet loss caused by network congestion. The routing node prevents packet loss between nodes by preemptive scheduling of multiple packets for a given image. RAIT implements a double sliding window method by means of a cross-layer design between the RAIT layer, routing layer, and queue layer. We demonstrate that RAIT guarantees a higher reliability of image transmission compared to the existing protocols

Beam mask and sliding window-facilitated deep learning-based accurate and efficient dose prediction for pencil beam scanning proton therapy

Purpose: To develop a DL-based PBSPT dose prediction workflow with high accuracy and balanced complexity to support on-line adaptive proton therapy clinical decision and subsequent replanning. Methods: PBSPT plans of 103 prostate cancer patients and 83 lung cancer patients previously treated at our institution were included in the study, each with CTs, structure sets, and plan doses calculated by the in-house developed Monte-Carlo dose engine. For the ablation study, we designed three experiments corresponding to the following three methods: 1) Experiment 1, the conventional region of interest (ROI) method. 2) Experiment 2, the beam mask (generated by raytracing of proton beams) method to improve proton dose prediction. 3) Experiment 3, the sliding window method for the model to focus on local details to further improve proton dose prediction. A fully connected 3D-Unet was adopted as the backbone. Dose volume histogram (DVH) indices, 3D Gamma passing rates, and dice coefficients for the structures enclosed by the iso-dose lines between the predicted and the ground truth doses were used as the evaluation metrics. The calculation time for each proton dose prediction was recorded to evaluate the method's efficiency. Results: Compared to the conventional ROI method, the beam mask method improved the agreement of DVH indices for both targets and OARs and the sliding window method further improved the agreement of the DVH indices. For the 3D Gamma passing rates in the target, OARs, and BODY (outside target and OARs), the beam mask method can improve the passing rates in these regions and the sliding window method further improved them. A similar trend was also observed for the dice coefficients. In fact, this trend was especially remarkable for relatively low prescription isodose lines. The dose predictions for all the testing cases were completed within 0.25s

A qualitative approach for online activity recognition

We present a novel qualitative, dynamic length sliding window method which enables a mobile robot to temporally segment activities taking place in live RGB-D video. We demonstrate how activities can be learned from observations by encoding qualitative spatio-temporal relationships between entities in the scene. We also show how a Nearest Neighbour model can recognise activities taking place even if they temporally co-occur. Our system is validated on a challenging dataset of daily living activities

Identification of nonlinear time-varying systems using an online sliding-window and common model structure selection (CMSS) approach with applications to EEG

The identification of nonlinear time-varying systems using linear-in-the-parameter models is investigated. A new efficient Common Model Structure Selection (CMSS) algorithm is proposed to select a common model structure. The main idea and key procedure is: First, generate K 1 data sets (the first K data sets are used for training, and theK 1 th one is used for testing) using an online sliding window method; then detect significant model terms to form a common model structure which fits over all the K training data sets using the new proposed CMSS approach. Finally, estimate and refine the time-varying parameters for the identified common-structured model using a Recursive Least Squares (RLS) parameter estimation method. The new method can effectively detect and adaptively track the transient variation of nonstationary signals. Two examples are presented to illustrate the effectiveness of the new approach including an application to an EEG data set

Detection and characterization of local inverted repeats regularities

To explore the inverted repeats regularities along the genome sequences, we propose a sliding window method to extract the concentration scores of inverted repeats periodic regularities and the total mass of possible inverted repeats pairs. We apply the method to the human genome and locate the regions with the potential for the formation of large number of hairpin/cruciform structures. The number of found windows with periodic regularities is small and the patterns of occurrence are chromosome specific.publishe

Symbolic local information transfer

Recently, the permutation-information theoretic approach has been used in a broad range of research fields. In particular, in the study of highdimensional dynamical systems, it has been shown that this approach can be effective in characterizing global properties, including the complexity of their spatiotemporal dynamics. Here, we show that this approach can also be applied to reveal local spatiotemporal profiles of distributed computations existing at each spatiotemporal point in the system. J. T. Lizier et al. have recently introduced the concept of local information dynamics, which consists of information storage, transfer, and modification. This concept has been intensively studied with regard to cellular automata, and has provided quantitative evidence of several characteristic behaviors observed in the system. In this paper, by focusing on the local information transfer, we demonstrate that the application of the permutation-information theoretic approach, which introduces natural symbolization methods, makes the concept easily extendible to systems that have continuous states. We propose measures called symbolic local transfer entropies, and apply these measures to two test models, the coupled map lattice (CML) system and the Bak-Sneppen model (BS-model), to show their relevance to spatiotemporal systems that have continuous states.Comment: 20 pages, 7 figure