Leader election and group management in vehicular ad hoc network

As automobiles become more intelligent, research on the Vehicular Ad Hoc Network (VANET) also becomes more important. Leader election is an important piece of the puzzle that can be utilized to solve many other problems in VANET. However, most existing literatures either focus on Virtual Traffic Light (VTL) application or leader election in regular ad hoc networks. In this thesis, we focus on creating a generalized algorithm for leader election in VANET and designing a group management mechanism to address various scenarios. In addition, simulations are conducted to evaluate performance of proposed algorithms

Deep learning based on co-registered ultrasound and photoacoustic imaging improves the assessment of rectal cancer treatment response

Identifying complete response (CR) after rectal cancer preoperative treatment is critical to deciding subsequent management. Imaging techniques, including endorectal ultrasound and MRI, have been investigated but have low negative predictive values. By imaging post-treatment vascular normalization using photoacoustic microscopy, we hypothesize that co-registered ultrasound and photoacoustic imaging will better identify complete responders. In this study, we use

Palm-vein classification based on principal orientation features.

Personal recognition using palm-vein patterns has emerged as a promising alternative for human recognition because of its uniqueness, stability, live body identification, flexibility, and difficulty to cheat. With the expanding application of palm-vein pattern recognition, the corresponding growth of the database has resulted in a long response time. To shorten the response time of identification, this paper proposes a simple and useful classification for palm-vein identification based on principal direction features. In the registration process, the Gaussian-Radon transform is adopted to extract the orientation matrix and then compute the principal direction of a palm-vein image based on the orientation matrix. The database can be classified into six bins based on the value of the principal direction. In the identification process, the principal direction of the test sample is first extracted to ascertain the corresponding bin. One-by-one matching with the training samples is then performed in the bin. To improve recognition efficiency while maintaining better recognition accuracy, two neighborhood bins of the corresponding bin are continuously searched to identify the input palm-vein image. Evaluation experiments are conducted on three different databases, namely, PolyU, CASIA, and the database of this study. Experimental results show that the searching range of one test sample in PolyU, CASIA and our database by the proposed method for palm-vein identification can be reduced to 14.29%, 14.50%, and 14.28%, with retrieval accuracy of 96.67%, 96.00%, and 97.71%, respectively. With 10,000 training samples in the database, the execution time of the identification process by the traditional method is 18.56 s, while that by the proposed approach is 3.16 s. The experimental results confirm that the proposed approach is more efficient than the traditional method, especially for a large database

63×63 Gaussian-Radon filters at the directions of (a) 0°, (b) 30°, (c) 60°, (d) 90°, (e) 120°, and (f) 150°.

<p>63×63 Gaussian-Radon filters at the directions of (a) 0°, (b) 30°, (c) 60°, (d) 90°, (e) 120°, and (f) 150°.</p

Palm–vein images at major directions of (a) 0°, (b) 30°, (c) 60°, (d) 90°, (e) 120°, and (f) 150° in PolyU, CASIA, and the database of this study.

<p>Palm–vein images at major directions of (a) 0°, (b) 30°, (c) 60°, (d) 90°, (e) 120°, and (f) 150° in PolyU, CASIA, and the database of this study.</p

Palm–vein images in the database of this study.

<p>Palm–vein images in the database of this study.</p

Comparison between traditional and proposed methods in three different databases.

<p>Comparison between traditional and proposed methods in three different databases.</p

Performance of palm vein classification via different coding methods using PolyU, CASIA, and the database for this study.

<p>Performance of palm vein classification via different coding methods using PolyU, CASIA, and the database for this study.</p

The response time of identification process for one test sample by different coding methods at different database sizes.

<p>The response time of identification process for one test sample by different coding methods at different database sizes.</p