A novel MEMS tunable antenna with wide tuning range of frequency

In this paper a novel MEMS tunable CPW antenna with wide tuning range of frequency is presented. The antenna’s frequency tuning range increment is achieved by loading three novel large tuning range capacitors at radiation edge of antenna patch. Two techniques are employed for increasing the capacitors tuning range. First, dual gap technique is used to overcome the pull-in limitation and then two lateral beams are added in order to parallel movement of capacitive plate which increases the capacitance value. The simulation result shows that the resonance frequency tuning range of antenna increased from 1.96 GHz loaded by traditional capacitor to 6.89 GHz using the new capacitor structure. Also in resonance frequency, the antenna has a good impedance matching with transmission line even in high capacitance values.     

Accurate sensing of scene geo-context via mobile visual localization

Image geo-tagging has drawn a great deal of attention in recent years. The geographic information associated with images can be used to promote potential applications such as location recognition or virtual navigation. In this paper, we propose a novel approach for accurate mobile image geo-tagging in urban areas. The approach is able to provide a comprehensive set of geo-context information based on the current image, including the real location of the camera and the viewing angle, as well as the location of the captured scene. Moreover, the parsed building facades and their geometric structures can also be estimated. First, for the image to be geo-tagged, we perform partial duplicate image retrieval to filter crowd-sourced images capturing the same scene. We then employ the structure-from-motion technique to reconstruct a sparse 3D point cloud of the scene. Meanwhile, the geometric structure of the query image is analyzed to extract building facades. Finally, by combining the reconstructed 3D scene model and the extracted structure information, we can register the camera location and viewing direction to a real-world map. The captured building location and facade orientation are also aligned. The effectiveness of the proposed system is demonstrated by experiment results.     

Finding logos in real-world images with point-context representation-based region search

Finding logos in the real-world images is a challenging task due to their small size, simple shape, less texture and clutter background. In this paper, through visual logo analysis with different types of features, we propose a novel framework for finding visual logos in the real-world images. First, we exploit the contextual shape and patch information around feature points, merge them into a combined feature representation (point-context). Considering the characteristics of logos, this kind of fusion is an effective enhancement for the discriminability of single point features. Second, to eliminate the interference of the complex and noisy background, we transfer the logo recognition to a region-to-image search problem by segmenting real-world images into region trees. A weak geometric constraint based on regions is encoded into an inverted file structure to accelerate the search process. Third, we apply global features to refine initial results in the re-ranking stage. Finally, we combine each region score both in max-response and accumulate-response mode to obtain the final results. Performances of the proposed approach are evaluated on both our CASIA-LOGO dataset and the standard Flickr logos 27 dataset. Experiments and comparisons show that our approach is superior to the state-of-the-art approaches.     

Nonlocal variational image segmentation models on graphs using the Split Bregman

Variational functionals such as Mumford-Shah and Chan-Vese methods have a major impact on various areas of image processing. After over 10 years of investigation, they are still in widespread use today. These formulations optimize contours by evolution through gradient descent, which is known for its overdependence on initialization and the tendency to produce undesirable local minima. In this paper, we propose an image segmentation model in a variational nonlocal means framework based on a weighted graph. The advantages of this model are twofold. First, the convexity global minimum (optimum) information is taken into account to achieve better segmentation results. Second, the proposed global convex energy functionals combine nonlocal regularization and local intensity fitting terms. The nonlocal total variational regularization term based on the graph is able to preserve the detailed structure of target objects. At the same time, the modified local binary fitting term introduced in the model as the local fitting term can efficiently deal with intensity inhomogeneity in images. Finally, we apply the Split Bregman method to minimize the proposed energy functional efficiently. The proposed model has been applied to segmentation of real medical and remote sensing images. Compared with other methods, the proposed model is superior in terms of both accuracy and efficient.     

Analytical study on cantilever resonance type magnet-integrated sensor device for micro-magnetic field detection

A beam-shaped cantilever resonance type magnetic sensor device has been proposed with a micro magnet. Two structural designs, named as design 1 and design 2, have been comparatively analyzed using ANSYS in order to obtain larger frequency shifts (higher magnetism sensitivity) due to the applied exterior magnetic field. The analytical results show that, in the range of 0–10 mT, the frequency shifts are small, while under 100 mT, a relatively larger frequency shift of about 30 Hz can be theoretically obtained. The power consumption of the proposed devices has been further theoretically studied for preliminary understanding. Using the well-known displacement equations, the estimated power consumption is around 0.21 μW, which is very lower than that of the reported magnetic field sensors. This implies that it is possible to fabricate higher sensitive magnetic field sensor with lower power consumption.     

Think,feel, bid: the impact of environmental conditions on the role of bidders’ cognitive and affective processes in auction bidding

Environmental conditions and the interplay of cognitive and affective processes both exert influences on bidding behavior. This paper brings the above together, considering how the (external) auction environment determines the impact of (internal) cognitive and affective processes on bidding behavior, assessed in comparison to the optimal bid. Two aspects of the auction environment were considered, namely auction dynamics (low: first-price sealed-bid auction, high: Dutch auction) and value uncertainty (low, high). In a laboratory experiment, we assess bidders’ cognitive workload and emotional arousal through physiological measurements. We find that higher auction dynamics increase the impact of emotional arousal on bid deviations, but not that of cognitive workload. Higher value uncertainty, conversely, increases the impact of cognitive workload on bid deviations, but not that of emotional arousal. Taken together, the auction environment is a critical factor in understanding the nature of the underlying decision process and its impact on bids.     

Quantum commitments from complexity assumptions

Bit commitment schemes are at the basis of modern cryptography. Since information-theoretic security is impossible both in the classical and in the quantum regime, we examine computationally secure commitment schemes. In this paper we study worst-case complexity assumptions that imply quantum bit commitment schemes. First, we show that QSZK \({\not\subseteq}\) QMA implies a computationally hiding and statistically binding auxiliary-input quantum commitment scheme. We then extend our result to show that the much weaker assumption QIP \({\not\subseteq}\) QMA (which is weaker than PSPACE \({\not\subseteq}\) PP) implies the existence of auxiliary-input commitment schemes with quantum advice. Finally, to strengthen the plausibility of the separation QSZK \({\not\subseteq}\) QMA, we find a quantum oracle relative to which honest-verifier QSZK is not contained in QCMA, the class of languages that can be verified using a classical proof in quantum polynomial time.     

Factors of low individual degree polynomials

Kaltofen (Randomness in computation, vol 5, pp 375–412, 1989) proved the remarkable fact that multivariate polynomial factorization can be done efficiently, in randomized polynomial time. Still, more than twenty years after Kaltofen’s work, many questions remain unanswered regarding the complexity aspects of polynomial factorization, such as the question of whether factors of polynomials efficiently computed by arithmetic formulas also have small arithmetic formulas, asked in Kopparty et al. (2014), and the question of bounding the depth of the circuits computing the factors of a polynomial. We are able to answer these questions in the affirmative for the interesting class of polynomials of bounded individual degrees, which contains polynomials such as the determinant and the permanent. We show that if \({P(x_{1},\ldots,x_{n})}\) is a polynomial with individual degrees bounded by r that can be computed by a formula of size s and depth d, then any factor \({f(x_{1},\ldots, x_{n})}\) of \({P(x_{1},\ldots,x_{n})}\) can be computed by a formula of size \({\textsf{poly}((rn)^{r},s)}\) and depth d + 5. This partially answers the question above posed in Kopparty et al. (2014), who asked if this result holds without the dependence on r. Our work generalizes the main factorization theorem from Dvir et al. (SIAM J Comput 39(4):1279–1293, 2009), who proved it for the special case when the factors are of the form \({f(x_{1}, \ldots, x_{n}) \equiv x_{n} - g(x_{1}, \ldots, x_{n-1})}\). Along the way, we introduce several new technical ideas that could be of independent interest when studying arithmetic circuits (or formulas).