Spectrum sensing using two-stage detection to compensate for reduced primary user duty cycle

Spectrum sensing is considered to be one of the most important tasks in cognitive radio. One of the common assumption among current spectrum sensing detectors is the full presence or complete absence of the primary user within the sensing period. In reality, there are many situations where the primary user signal only occupies a portion of the observed signal and the assumption of primary user duty cycle not necessarily fulfilled. In this paper we show that the true detection performance can degrade from the assumed achievable values when the observed primary user exhibits a certain duty cycle. Therefore, a two-stage detection method incorporating primary user duty cycle that enhances the detection performance is proposed. The proposed detector can improve the probability of detection under low duty cycle at the expense of a small decrease in performance at high duty cycle.

Mixed convection flow of micropolar fluid in an open ended arc-shape cavity

Numerical simulations for mixed convection of micropolar fluid in an open ended arc-shape cavity have been carried out in this study. Computation is performed using the Alternate Direct Implicit (ADI) method together with the Successive Over Relaxation (SOR) technique for the solution of governing partial differential equations. The flow phenomenon is examined for a range of values of Rayleigh number, 102 ≤ Ra ≤ 106, Prandtl number, 7 ≤ Pr ≤ 50, and Reynolds number, 10 ≤ Re ≤ 100. The study is mainly focused on how the micropolar fluid parameters affect the fluid properties in the flow domain. It was found that despite the reduction of flow in the core region, the heat transfer rate increases, whereas the skin friction and microrotation decrease with the increase in the vortex viscosity parameter, Δ.

Unusual scene detection using distributed behaviour model and sparse representation

The ability to detect unusual events in surviellance footage as they happen is a highly desireable feature for a surveillance system. However, this problem remains challenging in crowded scenes due to occlusions and the clustering of people. In this paper, we propose using the Distributed Behavior Model (DBM), which has been widely used in computer graphics, for video event detection. Our approach does not rely on object tracking, and is robust to camera movements. We use sparse coding for classification, and test our approach on various datasets. Our proposed approach outperforms a state-of-the-art work which uses the social force model and Latent Dirichlet Allocation.

Is there an inverted U-shape relationship between job demands and work engagement : the moderating role of social support

Purpose - The purpose of this paper is to examine the possibility of an inverted U-shaped relationship between job demands and work engagement, and whether social support moderates this relationship. Design/methodology/approach – This study uses 307 technical and information technology (IT) managers who responded to an online survey. Multiple regressions are employed to examine linear and curvilinear relationship among variables. Findings – Overall, results support the applicability of the quadratic effect of job demands on employee engagement. However, only supervisor support, not colleague support, moderated the relationship between job demands and work engagement. Originality/value – The paper is the first to shed light on the quadratic effect of job demands on work engagement. The findings have noteworthy implications for managers to design optimal job demands that increase employee engagement.

3D-printing of highly uniform CaSiO3 ceramic scaffolds : preparation, characterization and in vivo osteogenesis

Calcium silicate (CaSiO3, CS) ceramics have received significant attention for application in bone regeneration due to their excellent in vitro apatite-mineralization ability; however, how to prepare porous CS scaffolds with a controllable pore structure for bone tissue engineering still remains a challenge. Conventional methods could not efficiently control the pore structure and mechanical strength of CS scaffolds, resulting in unstable in vivo osteogenesis. The aim of this study is to set out to solve these problems by applying a modified 3D-printing method to prepare highly uniform CS scaffolds with controllable pore structure and improved mechanical strength. The in vivo osteogenesis of the prepared 3D-printed CS scaffolds was further investigated by implanting them in the femur defects of rats. The results show that the CS scaffolds prepared by the modified 3D-printing method have uniform scaffold morphology. The pore size and pore structure of CS scaffolds can be efficiently adjusted. The compressive strength of 3D-printed CS scaffolds is around 120 times that of conventional polyurethane templated CS scaffolds. 3D-Printed CS scaffolds possess excellent apatite-mineralization ability in simulated body fluids. Micro-CT analysis has shown that 3D-printed CS scaffolds play an important role in assisting the regeneration of bone defects in vivo. The healing level of bone defects implanted by 3D-printed CS scaffolds is obviously higher than that of 3D-printed b-tricalcium phosphate (b-TCP) scaffolds at both 4 and 8 weeks. Hematoxylin and eosin (H&E) staining shows that 3D-printed CS scaffolds induce higher quality of the newly formed bone than 3D-printed b-TCP scaffolds. Immunohistochemical analyses have further shown that stronger expression of human type I collagen (COL1) and alkaline phosphate (ALP) in the bone matrix occurs in the 3D-printed CS scaffolds than in the 3D-printed b-TCP scaffolds. Considering these important advantages, such as controllable structure architecture, significant improvement in mechanical strength, excellent in vivo osteogenesis and since there is no need for second-time sintering, it is indicated that the prepared 3D-printed CS scaffolds are a promising material for application in bone regeneration.

Wing shape as a potential discriminator of morphologically similar pest taxa within the Bactrocera dorsalis species complex (Diptera: Tephritidae)

Four morphologically cryptic species of the Bactrocera dorsalis fruit fly complex (B. dorsalis s.s., B. papayae, B. carambolae and B. philippinensis) are serious agricultural pests. As they are difficult to diagnose using traditional taxonomic techniques, we examined the potential for geometric morphometric analysis of wing size and shape to discriminate between them. Fifteen wing landmarks generated size and shape data for 245 specimens for subsequent comparisons among three geographically distinct samples of each species. Intraspecific wing size was significantly different within samples of B. carambolae and B. dorsalis s.s. but not within samples of B. papayae or B. philippinensis. Although B. papayae had the smallest wings (average centroid size=6.002 mm±0.061 SE) and B. dorsalis s.s. the largest (6.349 mm±0.066 SE), interspecific wing size comparisons were generally non-informative and incapable of discriminating species. Contrary to the wing size data, canonical variate analysis based on wing shape data discriminated all species with a relatively high degree of accuracy; individuals were correctly reassigned to their respective species on average 93.27% of the time. A single sample group of B. carambolae from locality 'TN Malaysia' was the only sample to be considerably different from its conspecific groups with regards to both wing size and wing shape. This sample was subsequently deemed to have been originally misidentified and likely represents an undescribed species. We demonstrate that geometric morphometric techniques analysing wing shape represent a promising approach for discriminating between morphologically cryptic taxa of the B. dorsalis species complex.

Fusion of morphological images for airborne target detection

Several track-before-detection approaches for image based aircraft detection have recently been examined in an important automated aircraft collision detection application. A particularly popular approach is a two stage processing paradigm which involves: a morphological spatial filter stage (which aims to emphasize the visual characteristics of targets) followed by a temporal or track filter stage (which aims to emphasize the temporal characteristics of targets). In this paper, we proposed new spot detection techniques for this two stage processing paradigm that fuse together raw and morphological images or fuse together various different morphological images (we call these approaches morphological reinforcement). On the basis of flight test data, the proposed morphological reinforcement operations are shown to offer superior signal to-noise characteristics when compared to standard spatial filter options (such as the close-minus-open and adaptive contour morphological operations). However, system operation characterised curves, which examine detection verses false alarm characteristics after both processing stages, illustrate that system performance is very data dependent.

HMM triangle relative entropy concepts in sequential change detection applied to vision-based dim target manoeuvre detection

The quick detection of abrupt (unknown) parameter changes in an observed hidden Markov model (HMM) is important in several applications. Motivated by the recent application of relative entropy concepts in the robust sequential change detection problem (and the related model selection problem), this paper proposes a sequential unknown change detection algorithm based on a relative entropy based HMM parameter estimator. Our proposed approach is able to overcome the lack of knowledge of post-change parameters, and is illustrated to have similar performance to the popular cumulative sum (CUSUM) algorithm (which requires knowledge of the post-change parameter values) when examined, on both simulated and real data, in a vision-based aircraft manoeuvre detection problem.

Lost in translation (and rotation) : rapid extrinsic calibration for 2D and 3D LIDARs

This paper describes a novel method for determining the extrinsic calibration parameters between 2D and 3D LIDAR sensors with respect to a vehicle base frame. To recover the calibration parameters we attempt to optimize the quality of a 3D point cloud produced by the vehicle as it traverses an unknown, unmodified environment. The point cloud quality metric is derived from Rényi Quadratic Entropy and quantifies the compactness of the point distribution using only a single tuning parameter. We also present a fast approximate method to reduce the computational requirements of the entropy evaluation, allowing unsupervised calibration in vast environments with millions of points. The algorithm is analyzed using real world data gathered in many locations, showing robust calibration performance and substantial speed improvements from the approximations.