Total Body Photography (TBP) is becoming a useful screening tool for patients at high risk for skin cancer. While much progress has been made, existing TBP systems can be further improved for automatic detection and analysis of suspicious skin lesions, which is in part related to the resolution and sharpness of acquired images. This paper proposes a novel shape-aware TBP system automatically capturing full-body images while optimizing image quality in terms of resolution and sharpness over the body surface. The system uses depth and RGB cameras mounted on a 360-degree rotary beam, along with 3D body shape estimation and an in-focus surface optimization method to select the optimal focus distance for each camera pose. This allows for optimizing the focused coverage over the complex 3D geometry of the human body given the calibrated camera poses. We evaluate the effectiveness of the system in capturing high-fidelity body images. The proposed system achieves an average resolution of 0.068 mm/pixel and 0.0566 mm/pixel with approximately 85% and 95% of surface area in-focus, evaluated on simulation data of diverse body shapes and poses as well as a real scan of a mannequin respectively. Furthermore, the proposed shape-aware focus method outperforms existing focus protocols (e.g. auto-focus). We believe the high-fidelity imaging enabled by the proposed system will improve automated skin lesion analysis for skin cancer screening.

Speech enhancement aims to recover clean speech from complex noise backgrounds. This paper proposes a novel information processing module dubbed dynamic scene perception module (DSPM) that can help existing systems to accommodate various complex scenarios. The inspiration of DSPM is based on the observation that different regions of the noisy spectrum in different scenarios have different enhancing requirements. Concretely, DSPM consists of two parts, one for dynamic scene estimation, and the other for adaptive region perception. In particular, the scene estimator utilizes a spectrum-energy-based attention mechanism to obtain the coefficients of each convolution kernel. Then, at each position’ the region perceptron chooses the corresponding kernels by considering the requirements of the current region (preserve vocals or suppress noise). Systematic evaluations on the TIMIT corpus and Voice Bank + DEMAND demonstrate the effectiveness of our method. Compared with the existing systems, our proposed method achieved better performance under various SNR conditions and complex noise scenarios.

Path inference aims to reveal missing paths given a few number of GPS samples associated with a moving object by exploiting the topology of road network and statistical information of historical GPS trajectories, and plays a vital role in data preprocessing of location based information services. But, in practice path inference severely suffers from the data sparsity as well as the randomness of drivers path selection behaviors. In this paper, we propose a novel Bayesian path inference model subject to spatiotemporal constraints by taking into account the drivers path selection behaviors. To be specific, the problem of path inference is cast as the problem of searching K most probable candidate paths according to the joint posterior selection probabilities of candidate paths. When estimating model parameters, we use the frequency of each road segment in the historical GPS trajectories instead of that of road segment transfers to mitigate the influence of data sparsity. In addition, both spatiotemporal constraints and probability thresholds are introduced to narrow the search space, which significantly improves the time efficiency. The experiments are conducted using practical data and show that the proposed model is significantly superior to three existing popular models. When the GPS sampling interval varies from 1 minute to 5 minutes, the accuracy of the proposed method is 0.94, 0.91, 0.86, 0.80 and 0.74, and the Jaccard similarity 0.89, 0.85, 0.83, 0.80 and 0.75 respectively, the average improvement in accuracy rises from 3.68% to 18.69% and that in the Jaccard similarity from 4.56% to 18.42%.

Trajectory clustering is an important task in trajectory data mining. Grouping a trajectory dataset into clusters based on the similarity between vehicle trajectories is conducive to revealing the movement pattern of the vehicles. For trajectory clustering in a real urban road network environment, the existing methods have some deficiencies, including high time complexity in measuring the distance between trajectory sequences and poor clustering performance. This paper proposes a method for clustering vehicle traj ectories in the urban road network environment based on a word vector model. First, the original Global Positioning System (GPS) trajectory data of vehicles are converted into road segment sequences by using the urban road segment information contained in the GPS trajectory data (after map-matching). Then, the spatial properties, such as the geographical location, time and moving direction of vehicles in the original trajectory sequence, are expressed by road segments and their order. Next, the road segment sequences are converted into traj ectory segment eigenvectors with fixed dimensions using the doc2vec model, in this way, the calculation efficiency of similarity between traj ectory segments of different lengths is improved. Finally, the vehicle tracks are clustered according to the distances between the traj ectory segment eigenvectors using the hierarchical clustering method. The result of a simulation based on taxi trajectory data gathered in a real urban road network shows that the proposed method was superior to the traditional clustering methods based on trajectory space-time distance, improving the silhouette index by 10%-25%, and reducing the clustering time by two orders of magnitude.

Motivated by the network tomography, in this paper, we present a novel methodology to estimate link travel time distributions (TTDs) using end-to-end (E2E) measurements detected by the limited traffic detectors at or near the road intersections. As it is not necessary to monitor the traffic in each link, the proposed estimator can be readily implemented in real life. The technical contributions of this paper are as follows: First, we employ the kernel density estimator (KDE) to model link travel times instead of parametric models, e.g., Gaussian distribution. It is able to capture the dynamic of link travel times that vary with the change of road conditions. The model parameters are estimated with the proposed C-shortest path algorithm, K-means-based algorithm, as well as expectation maximization (EM) algorithm. Second, to reduce the complexity of parameter estimation, we further propose a Q-opt and an X-means -based algorithm. Finally, we validate our proposed method using a dataset consisting of 3.0e +07 GPS trajectories collected by the taxicabs in Xi'an, China. With the metrics of Kullback Leibler and Kolmogorov-Smirnov test, the experimental results show that the link TTDs obtained from our proposed model are in excellent agreement with the empirical distributions, provided that ~70% of the intersections are equipped with traffic detectors.

The matrix converter has many advantages in wind power system applications. Matrix converter is compact and highly efficient because it directly converts power from AC generator to the grid without intermediate DC bus while conventional back-to-back converter system requires a lot of electrolytic capacitors in DC link bus which are bulky and have short life-time. Matrix converter has both motoring and regenerative power flow maintaining low harmonics current to the grid. It is required for wind power converter to provide reactive power to the grid, which is one of the most important characteristics for wind farms to stabilize the power system during and after grid failure. In this paper, an enhanced fault ride through capability of high power matrix converter for wind power system is proposed. The proposed ride through technique during grid voltage drop is explained, and it is verified by simulation and experiment results.