Testing a Firefly-Inspired Synchronization Algorithm in a Complex Wireless Sensor Network

Data acquisition is the foundation of soft sensor and data fusion. Distributed data acquisition and its synchronization are the important technologies to ensure the accuracy of soft sensors. As a research topic in bionic science, the firefly-inspired algorithm has attracted widespread attention as a new synchronization method. Aiming at reducing the design difficulty of firefly-inspired synchronization algorithms for Wireless Sensor Networks (WSNs) with complex topologies, this paper presents a firefly-inspired synchronization algorithm based on a multiscale discrete phase model that can optimize the performance tradeoff between the network scalability and synchronization capability in a complex wireless sensor network. The synchronization process can be regarded as a Markov state transition, which ensures the stability of this algorithm. Compared with the Miroll and Steven model and Reachback Firefly Algorithm, the proposed algorithm obtains better stability and performance. Finally, its practicality has been experimentally confirmed using 30 nodes in a real multi-hop topology with low quality links

Improving Image Quality of Bronchial Arteries with Virtual Monochromatic Spectral CT Images.

OBJECTIVE:To evaluate the clinical value of using monochromatic images in spectral CT pulmonary angiography to improve image quality of bronchial arteries. METHODS:We retrospectively analyzed the chest CT images of 38 patients who underwent contrast-enhanced spectral CT. These images included a set of 140kVp polychromatic images and the default 70keV monochromatic images. Using the standard Gemstone Spectral Imaging (GSI) viewer on an advanced workstation (AW4.6,GE Healthcare), an optimal energy level (in keV) for obtaining the best contrast-to-noise ratio (CNR) for the artery could be automatically obtained. The signal-to-noise ratio (SNR), CNR and objective image quality score (1-5) for these 3 image sets (140kVp, 70keV and optimal energy level) were obtained and, statistically compared. The image quality score consistency between the two observers was also evaluated using Kappa test. RESULTS:The optimal energy levels for obtaining the best CNR were 62.58±2.74keV.SNR and CNR from the 140kVp polychromatic, 70keV and optimal keV monochromatic images were (16.44±5.85, 13.24±5.52), (20.79±7.45, 16.69±6.27) and (24.9±9.91, 20.53±8.46), respectively. The corresponding subjective image quality scores were 1.97±0.82, 3.24±0.75, and 4.47±0.60. SNR, CNR and subjective scores had significant difference among groups (all p0.80). CONCLUSIONS:Virtual monochromatic images at approximately 63keV in dual-energy spectral CT pulmonary angiography yielded the best CNR and highest diagnostic confidence for imaging bronchial arteries

Comparison of signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR) and Subjective image quality score for bronchial artery among images of three energy levels.

<p>Comparison of signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR) and Subjective image quality score for bronchial artery among images of three energy levels.</p

Scoring criteria for bronchial artery.

<p>Scoring criteria for bronchial artery.</p

A 68 years old male patient with cancer in the right lung.

<p>(A) Axial image with region-of-interest indications for measuring CT number and standard deviation: bronchial artery blood vessels (blue), adjacent tissues in mediastinum (green), and subcutaneous fat on chest wall (purple). (B) Plot of contrast-to-noise ratio(CNR) as function of photon energy showing the optimal energy level of 63keV to obtain the highest CNR for the bronchial artery. (C) Volume-rendering (VR) 140kVp image with image quality score of 3. (D) VR image at 70 keV with image quality score of 4. (E) VR image at the optimal 63 keV with image quality score of 5.</p