Sensor task allocation plays a great role in military, environmental science, medical health, transportation and other fields. In order to make rational use of limited sensor resources, a multi-sensor multi-target task allocation method based on an improved firefly algorithm (FA) is proposed. In the algorithm, the initial position of firefly individual in firefly algorithm is optimized to speed up the search optimization procedure. In the process of constructing efficiency function, position constraints, sensor monitoring ability constraints and target threat degree constraints are considered comprehensively, leading to a more realistic multi-sensor multi-target task allocation algorithm. The analytic hierarchy process (AHP) is used to construct the target threat measure. The simulation results show that the proposed algorithm is more efficient than the standard particle swarm optimization algorithm (PSO) and the standard FA, that is, the sensor task allocation is more reasonable, and the task allocation time cost is also shorter than the other two algorithms.
This paper presents a statistical reconstruction algorithm for dual-energy (DE) CT of polychromatic x-ray source. Each
pixel in the imaged object is assumed to be composed of two basis materials (i.e., bone and soft tissue) and a penalizedlikelihood
objective function is developed to determine the densities of the two basis materials. Two penalty terms are
used respectively to penalize the bone density difference and the soft tissue density difference in neighboring pixels. A
gradient ascent algorithm for monochromatic objective function is modified to maximize the polychromatic penalizedlikelihood
objective function using the convexity technique. In order to reduce computation consumption, the
denominator of the update step is pre-calculated with reasonable approximation replacements. Ordered-subsets method is
applied to speed up the iteration. Computer simulation is implemented to evaluate the penalized-likelihood algorithm.
The results indicate that this statistical method yields the best quality image among the tested methods and has a good
noise property even in a lower photon count.
Optical sensing of specific molecular target using near-infrared light has been recognized to be the crucial technology,
have changing human's future. The imaging of Fluorescence Molecular Tomography is the most novel technology in
optical sensing. It uses near-infrared light(600-900nm) as instrument and utilize fluorochrome as probe to take noncontact
three-dimensional imaging for live molecular targets and to exhibit molecular process in vivo. In order to solve
the problem of forward simulation in FMT, this paper mainly introduces a new simulation modeling. The modeling
utilizes Monte Carlo method and is implemented in C++ programming language. Ultimately its accuracy has been
testified by comparing with analytic solutions and MOSE from University of Iowa and Chinese Academy of Science.
The main characters of the modeling are that it can simulate both of bioluminescent imaging and FMT and take analytic
calculation and support more than one source and CCD detector simultaneously. It can generate sufficient and proper
data and pre-preparation for the study of fluorescence molecular tomography.
In this paper, we propose a novel method for beam hardening correction in polychromatic transmission tomography. A
family of polynomials is firstly determined in a training phase, which forms a complete set in the sense of X-ray physics
of medical diagnostic imaging. In particular, every polynomial in the set is indexed by a beam hardening factor, i.e.
effective atomic number, which is further assigned to specific X-ray penetrating path. In order to successfully accomplish
the assignation in an imaging phase, another polynomial is adopted to formulize the mapping relationship between the
index of polynomial family and the area density ratio of bone tissue. Here, the area density ratio of bone tissue is
calculated after the pre reconstructed image being segmented into soft tissue and bone regions. The mapping polynomial
is iteratively approximated by a dedicated HL Consistency (HLC) based nonlinear algorithm. The characteristics of this
method include that the polynomial family can cover the variations of both high potential and effective filter of X-ray
tube, the beam hardening correction in the imaging phase can adapt the content variations of objects being imaged, and
the correction effect is also sophisticated even bowtie filter exists. Performance analysis and related computer simulation
show that our HLC based correction is much robust than traditional bone correction to the variants of scale factor lambda0.