A Modified Shared-tree Multicast Routing Protocol in Ad Hoc Network

Mobile ad hoc network is a wireless mobile network that does not have any base station or other central control infrastructure. Design of efficient multicast routing protocols in such network is challenging, especially when the mobile hosts move faster. Shared tree routing protocol is a widely used multicast routing protocol in ad hoc network. However, there are problems in end-to-end delay and network throughput for this protocol. In this paper, we propose a protocol to improve the inherent problem of large end-to-end delay in shared tree method as a modification to the existing multicast Ad hoc On-demand Distance Vector (MAODV) routing for low mobility network. The protocol uses n-hop local ring search to establish new forwarding path and limit flooding region. We then propose an extension to our proposed protocol, which uses periodic route discovery message to improve the network throughput for high mobility network. Simulation results demonstrate the improvement with average end-to-end delay in low mobility case as well as high packet delivery ratio in high mobility cas

A Fault-Tolerant Multicast Routing Protocol for Mobile Ad–Hoc Networks

In this work, we propose an efficient multicast routing protocol for mobile ad hoc networks. To achieve high efficiency with low channel and storage overhead, the proposed protocol employs the following mechanism: (1) on-demand invocation of route setup and route maintenance process to avoid periodical control packet transmissions, thus reducing channel overhead, (2) creation of “forwarding group” to forward multicast packets, thus reducing storage overhead, (3) exploration of multiple possible routes from a single flooded query to reduce the frequency of route discovery, thus further reducing channel overhead, (4) a new route setup mechanism that allows a newly joining node to find the nearest forwarding node to minimize the number of added forwarding nodes, thus further reducing storage overhead. To provide the capability of fault tolerance, we introduce the alternate route together with the primary route. We observe that for multicasting the channel and storage overheads of the presented approach are less than those of the DVMRP approach. Also, the channel overhead is less than that in the FGMP approach for multicasting in low mobility scenario, while the storage overheads are the same in the presented approach and in the FGMP approach

Need for objective task-based evaluation of AI-based segmentation methods for quantitative PET

Artificial intelligence (AI)-based methods are showing substantial promise in segmenting oncologic positron emission tomography (PET) images. For clinical translation of these methods, assessing their performance on clinically relevant tasks is important. However, these methods are typically evaluated using metrics that may not correlate with the task performance. One such widely used metric is the Dice score, a figure of merit that measures the spatial overlap between the estimated segmentation and a reference standard (e.g., manual segmentation). In this work, we investigated whether evaluating AI-based segmentation methods using Dice scores yields a similar interpretation as evaluation on the clinical tasks of quantifying metabolic tumor volume (MTV) and total lesion glycolysis (TLG) of primary tumor from PET images of patients with non-small cell lung cancer. The investigation was conducted via a retrospective analysis with the ECOG-ACRIN 6668/RTOG 0235 multi-center clinical trial data. Specifically, we evaluated different structures of a commonly used AI-based segmentation method using both Dice scores and the accuracy in quantifying MTV/TLG. Our results show that evaluation using Dice scores can lead to findings that are inconsistent with evaluation using the task-based figure of merit. Thus, our study motivates the need for objective task-based evaluation of AI-based segmentation methods for quantitative PET

Design of High Performance Distributed Snapshot/Recovery Algorithms for Ring Networks

In this work, we have presented non-blocking checkpointing and recovery algorithms for bidirectional networks. We have deviated from the conventional approach of taking first temporary checkpoints and then converting them to permanent ones by processes (as followed by any coordinated checkpointing scheme). Thus, the proposed coordinated checkpointing algorithm allows processes to take permanent checkpoints directly without taking temporary checkpoints and whenever a process is busy, it takes a checkpoint after completing its current procedure. We have shown that the presented algorithms take much less time for their execution and use much less number of control messages (and hence much less number of interrupts to a process) when compared to a noted recent work [4]

YOLO-FaceV2: A Scale and Occlusion Aware Face Detector

In recent years, face detection algorithms based on deep learning have made great progress. These algorithms can be generally divided into two categories, i.e. two-stage detector like Faster R-CNN and one-stage detector like YOLO. Because of the better balance between accuracy and speed, one-stage detectors have been widely used in many applications. In this paper, we propose a real-time face detector based on the one-stage detector YOLOv5, named YOLO-FaceV2. We design a Receptive Field Enhancement module called RFE to enhance receptive field of small face, and use NWD Loss to make up for the sensitivity of IoU to the location deviation of tiny objects. For face occlusion, we present an attention module named SEAM and introduce Repulsion Loss to solve it. Moreover, we use a weight function Slide to solve the imbalance between easy and hard samples and use the information of the effective receptive field to design the anchor. The experimental results on WiderFace dataset show that our face detector outperforms YOLO and its variants can be find in all easy, medium and hard subsets. Source code in https://github.com/Krasjet-Yu/YOLO-FaceV

Fully automated 3D segmentation of dopamine transporter SPECT images using an estimation-based approach

Quantitative measures of uptake in caudate, putamen, and globus pallidus in dopamine transporter (DaT) brain SPECT have potential as biomarkers for the severity of Parkinson disease. Reliable quantification of uptake requires accurate segmentation of these regions. However, segmentation is challenging in DaT SPECT due to partial-volume effects, system noise, physiological variability, and the small size of these regions. To address these challenges, we propose an estimation-based approach to segmentation. This approach estimates the posterior mean of the fractional volume occupied by caudate, putamen, and globus pallidus within each voxel of a 3D SPECT image. The estimate is obtained by minimizing a cost function based on the binary cross-entropy loss between the true and estimated fractional volumes over a population of SPECT images, where the distribution of the true fractional volumes is obtained from magnetic resonance images from clinical populations. The proposed method accounts for both the sources of partial-volume effects in SPECT, namely the limited system resolution and tissue-fraction effects. The method was implemented using an encoder-decoder network and evaluated using realistic clinically guided SPECT simulation studies, where the ground-truth fractional volumes were known. The method significantly outperformed all other considered segmentation methods and yielded accurate segmentation with dice similarity coefficients of ~ 0.80 for all regions. The method was relatively insensitive to changes in voxel size. Further, the method was relatively robust up to +/- 10 degrees of patient head tilt along transaxial, sagittal, and coronal planes. Overall, the results demonstrate the efficacy of the proposed method to yield accurate fully automated segmentation of caudate, putamen, and globus pallidus in 3D DaT-SPECT images