Effects of CuO additives and sol-gel technique on NiNb2O6 dielectric ceramics for LTCC application

The effects of CuO additives and sol–gel method synthesis on the sintering behavior, microstructure and the microwave dielectric properties of NiNb2O6 ceramics were investigated systematically. The NiNb2O6 ceramics were synthesized with traditional solid state method and sol–gel method, and the CuO additives were used in the solid state method for comparison. The sintering temperature of NiNb2O6 ceramics with the highest densification can be effectively reduced from about 1275 °C to 1050 and 1100 °C respectively by using CuO additions and sol–gel technique. To study their applicability in low temperature co-fired ceramic technology, dielectric properties have been characterized. The dielectric properties exhibited a significant dependence on the sintering condition, composition and crystal structure of the ceramics. In particular, the 2.5 wt% CuO-doped NiNb2O6 ceramics sintered at 1050 °C have excellent microwave dielectric properties: εr = 21.45, Q × f = 23,531 GHz, τf = −27.9 ppm/°C. While the NiNb2O6 ceramics prepared by sol–gel method obtain microwave dielectric properties as: εr = 19.16, Q × f = 11,149 GHz, τf = −27.3 ppm/°C after sintered at 1100 °C for 2 h

Searching Ultra-compact Pulsar Binaries with Abnormal Timing Behavior

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Structure of hadron resonances with a nearby zero of the amplitude

We discuss the relation between the analytic structure of the scattering amplitude and the origin of an eigenstate represented by a pole of the amplitude.If the eigenstate is not dynamically generated by the interaction in the channel of interest, the residue of the pole vanishes in the zero coupling limit. Based on the topological nature of the phase of the scattering amplitude, we show that the pole must encounter with the Castillejo-Dalitz-Dyson (CDD) zero in this limit. It is concluded that the dynamical component of the eigenstate is small if a CDD zero exists near the eigenstate pole. We show that the line shape of the resonance is distorted from the Breit-Wigner form as an observable consequence of the nearby CDD zero. Finally, studying the positions of poles and CDD zeros of the KbarN-piSigma amplitude, we discuss the origin of the eigenstates in the Lambda(1405) region.Comment: 7 pages, 3 figures, v2: published versio

Vehicle Re-identification in Context

© 2019, Springer Nature Switzerland AG. Existing vehicle re-identification (re-id) evaluation benchmarks consider strongly artificial test scenarios by assuming the availability of high quality images and fine-grained appearance at an almost constant image scale, reminiscent to images required for Automatic Number Plate Recognition, e.g. VeRi-776. Such assumptions are often invalid in realistic vehicle re-id scenarios where arbitrarily changing image resolutions (scales) are the norm. This makes the existing vehicle re-id benchmarks limited for testing the true performance of a re-id method. In this work, we introduce a more realistic and challenging vehicle re-id benchmark, called Vehicle Re-Identification in Context (VRIC). In contrast to existing vehicle re-id datasets, VRIC is uniquely characterised by vehicle images subject to more realistic and unconstrained variations in resolution (scale), motion blur, illumination, occlusion, and viewpoint. It contains 60,430 images of 5,622 vehicle identities captured by 60 different cameras at heterogeneous road traffic scenes in both day-time and night-time. Given the nature of this new benchmark, we further investigate a multi-scale matching approach to vehicle re-id by learning more discriminative feature representations from multi-resolution images. Extensive evaluations show that the proposed multi-scale method outperforms the state-of-the-art vehicle re-id methods on three benchmark datasets: VehicleID, VeRi-776, and VRIC (Available at http://qmul-vric.github.io )

Evaluation of CNN-Based Human Pose Estimation for Body Segment Lengths Assessment

Human pose estimation (HPE) methods based on convolutional neural networks (CNN) have demonstrated significant progress and achieved state-of-the-art results on human pose datasets. In this study, we aimed to assess the perfor-mance of CNN-based HPE methods for measuring anthropometric data. A Vicon motion analysis system as the reference system and a stereo vision system recorded ten asymptomatic subjects standing in front of the stereo vision system in a static posture. Eight HPE methods estimated the 2D poses which were transformed to the 3D poses by using the stereo vision system. Percentage of correct keypoints, 3D error, and absolute error of the body segment lengths are the evaluation measures which were used to assess the results. Percentage of correct keypoints – the stand-ard metric for 2D pose estimation – showed that the HPE methods could estimate the 2D body joints with a minimum accuracy of 99%. Meanwhile, the average 3D error and absolute error for the body segment lengths are 5 cm

Unsupervised Person Re-identification by Deep Learning Tracklet Association

© 2018, Springer Nature Switzerland AG. Most existing person re-identification (re-id) methods rely on supervised model learning on per-camera-pair manually labelled pairwise training data. This leads to poor scalability in practical re-id deployment due to the lack of exhaustive identity labelling of image positive and negative pairs for every camera pair. In this work, we address this problem by proposing an unsupervised re-id deep learning approach capable of incrementally discovering and exploiting the underlying re-id discriminative information from automatically generated person tracklet data from videos in an end-to-end model optimisation. We formulate a Tracklet Association Unsupervised Deep Learning (TAUDL) framework characterised by jointly learning per-camera (within-camera) tracklet association (labelling) and cross-camera tracklet correlation by maximising the discovery of most likely tracklet relationships across camera views. Extensive experiments demonstrate the superiority of the proposed TAUDL model over the state-of-the-art unsupervised and domain adaptation re-id methods using six person re-id benchmarking datasets

L1pred: A Sequence-Based Prediction Tool for Catalytic Residues in Enzymes with the L1-logreg Classifier

To understand enzyme functions, identifying the catalytic residues is a usual first step. Moreover, knowledge about catalytic residues is also useful for protein engineering and drug-design. However, to experimentally identify catalytic residues remains challenging for reasons of time and cost. Therefore, computational methods have been explored to predict catalytic residues. Here, we developed a new algorithm, L1pred, for catalytic residue prediction, by using the L1-logreg classifier to integrate eight sequence-based scoring functions. We tested L1pred and compared it against several existing sequence-based methods on carefully designed datasets Data604 and Data63. With ten-fold cross-validation, L1pred showed the area under precision-recall curve (AUPR) and the area under ROC curve (AUC) of 0.2198 and 0.9494 on the training dataset, Data604, respectively. In addition, on the independent test dataset, Data63, it showed the AUPR and AUC values of 0.2636 and 0.9375, respectively. Compared with other sequence-based methods, L1pred showed the best performance on both datasets. We also analyzed the importance of each attribute in the algorithm, and found that all the scores contributed more or less equally to the L1pred performance

CNx-modified Fe3O4 as Pt nanoparticle support for the oxygen reduction reaction

A novel electrocatalyst support material, nitrogendoped carbon (CNx)-modified Fe3O4 (Fe3O4-CNx), was synthesized through carbonizing a polypyrrole-Fe3O4 hybridized precursor. Subsequently, Fe3O4-CNx-supported Pt (Pt/Fe3O4-CNx) nanocomposites were prepared by reducing Pt precursor in ethylene glycol solution and evaluated for the oxygen reduction reaction (ORR). The Pt/Fe3O4-CNx catalysts were characterized by X-ray diffraction, Raman spectra, X-ray photoelectron spectroscopy, scanning electron microscopy, and transmission electron microscopy. The electrocatalytic activity and stability of the as-prepared electrocatalysts toward ORR were studied by cyclic voltammetry and steady-state polarization measurements. The results showed that Pt/ Fe3O4-CNx catalysts exhibited superior catalytic performance for ORR to the conventional Pt/C and Pt/C-CNx catalysts.Web of Scienc