ABSTRACT
We report the water adsorption/desorption behavior and dynamic magnetic properties of the Pt-Cl chain complex [{[Pt(en)2 ][PtCl2 (en)2 ]}3 ][{(MnCl5 )Cl3 }2 ] â 12H2 O (1). Upon heating 1 in a vacuum, we obtained the dehydrated form [{[Pt(en)2 ][PtCl2 (en)2 ]}3 ][{(MnCl5 )Cl3 }2 ] (1DH). The framework structures of 1 and 1DH are identical, and both complexes underwent slow magnetic relaxation. However, the magnetic relaxation times for 1DH were shorter than those for 1, meaning that the dynamic magnetic properties were controlled upon water vapor adsorption/desorption. From detailed analyses of the dynamic magnetic behavior, a phonon-bottleneck effect contributes to the magnetic relaxation processes. We discuss the mechanism for the changes in the magnetic relaxation processes upon dehydration in terms of the heat capacity and thermal conductivity.
ABSTRACT
Accurately distinguishing aerial photographs from different categories is a promising technique in computer vision. It can facilitate a series of applications, such as video surveillance and vehicle navigation. In this paper, a new image kernel is proposed for effectively recognizing aerial photographs. The key is to encode high-level semantic cues into local image patches in a weakly supervised way, and integrate multimodal visual features using a newly developed hashing algorithm. The flowchart can be elaborated as follows. Given an aerial photo, we first extract a number of graphlets to describe its topological structure. For each graphlet, we utilize color and texture to capture its appearance, and a weakly supervised algorithm to capture its semantics. Thereafter, aerial photo categorization can be naturally formulated as graphlet-to-graphlet matching. As the number of graphlets from each aerial photo is huge, to accelerate matching, we present a hashing algorithm to seamlessly fuze the multiple visual features into binary codes. Finally, an image kernel is calculated by fast matching the binary codes corresponding to each graphlet. And a multi-class SVM is learned for aerial photo categorization. We demonstrate the advantage of our proposed model by comparing it with state-of-the-art image descriptors. Moreover, an in-depth study of the descriptiveness of the hash-based graphlet is presented.
ABSTRACT
We propose perceptually guided photo retargeting, which shrinks a photo by simulating a human's process of sequentially perceiving visually/semantically important regions in a photo. In particular, we first project the local features (graphlets in this paper) onto a semantic space, wherein visual cues such as global spatial layout and rough geometric context are exploited. Thereafter, a sparsity-constrained learning algorithm is derived to select semantically representative graphlets of a photo, and the selecting process can be interpreted by a path which simulates how a human actively perceives semantics in a photo. Furthermore, we learn the prior distribution of such active graphlet paths (AGPs) from training photos that are marked as esthetically pleasing by multiple users. The learned priors enforce the corresponding AGP of a retargeted photo to be maximally similar to those from the training photos. On top of the retargeting model, we further design an online learning scheme to incrementally update the model with new photos that are esthetically pleasing. The online update module makes the algorithm less dependent on the number and contents of the initial training data. Experimental results show that: 1) the proposed AGP is over 90% consistent with human gaze shifting path, as verified by the eye-tracking data, and 2) the retargeting algorithm outperforms its competitors significantly, as AGP is more indicative of photo esthetics than conventional saliency maps.
ABSTRACT
In order to eliminate the intrinsic noise due to special structure of the wrist, a new curve fitting algorithm based on singular value decomposition (SVD) was developed to increase the measurement accuracy. This algorithm could be subdivided into SVD and curve fitting algorithm (SCFA). SVD was used to extract the dominant component of oscillation waves at wrist. Then oscillation amplitudes of dominant component and cuff pressure were used to determine arterial blood pressure (ABP) with curve fitting algorithm. To test the performance of SCFA, 45 subjects underwent the ABP measurement with different methods. The correlation coefficient between the pooled blood pressure measured by the auscultation and those by SCFA was 0.96. Comparison the results of SCFA with those of traditional curve fitting algorithm (TCFA), we found that the proposed SCFA could be used to reduce the partial intrinsic interference and efficiently improve the accuracy of the ABP at wrist.
