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.     

Meter-scale large area LED-embedded light fabric for the application of fabric ceilings in rooms

We have developed a meter-scale light emitting diode (LED)-embedded light fabric and its weaving machine for application to a light device for fabric ceilings, which have recently become desired for lightweight safe ceilings in Japan and other countries with frequent earthquakes. The LED fabric structure is 1.2-m-wide woven fabric that has 5-mm-wide LED chip-mounted printed circuit board (PCB) tapes as wefts. LEDs are mounted on the tape of PCBs with a reel-to-reel chip mounting system. Then, the LED-mounted tapes are woven with a developed automatic looming machine that aligns the weft with an accuracy of 0.9 mm, which is suitable for precise arrangement of LEDs and wiring to power supply. A 1.2 × 1.2 m LED-embedded light fabric weighing 320 g/m² was woven. The luminance of the LED fabric is 353 lx at a distance of 1 m, which is the luminance of conventional office lighting. The temperature increase of LEDs without a rigid cooling aluminum plate is only 5.8 °C, and the LED fabric is flexible enough to sustain 1,000 bends down to a radius of 3 mm. This LED fabric and its weaving technology will lead to light devices that have lightweight, large area, and high flexibility for fabric ceilings, walls, and other large areas in homes and offices.     

Comparison of spherical and non-spherical particles in microchannels under dielectrophoretic force

This paper focuses on the computational and experimental study of dielectrophoretic (DEP) force based manipulation of spherical and non-spherical particles by taking into consideration of both electrokinetic effects and particle hydrodynamics. The model is first validated with conventional dipole moment theory. The movements of a spherical polystyrene particle and a rod-shape particle under a non-uniform electric field created by a pair of non-symmetrical electrodes in a microfluidic channel are studied, and a good agreement between the simulation and experimental results is obtained. Both experimental and simulation results reveal that the rod-shape particle experiences larger DEP force and moves faster than spherical particle with a similar mass. It was also interestingly found that the shape-dependent DEP force distribution on the microscale rod particle results in its unique behavior, which cannot be captured by traditional DEP theory.     

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.