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Removing physical scanners on mobile devices has come to be one of the most recent trends, adopting in-display scanners becomes the solution of mobile device authorization applications. As the characteristics of the image captured from in-display scanners were quite different from physical scanners, the criteria of the image quality should be reconsidered to maintain the effectiveness. This paper proposes an image quality analysis technique for optical-based fingerprint on display (FoD) applications, we evaluated the method on our own dataset with 11520 images, and proof that it is truly useful to fingerprint images captured from optical-based in-display scanners.

The trajectory tracking and turning intention prediction of vehicles at intersections are a vital part of smart traffic safety. With the height limitation, traffic objects are often obscured by other vehicles that easily results in blind spots for the visual sensing system. This paper summarizes the author's practice of a roadside unit composed of monitors and radar sensors to track and predict behavioral intentions of traffic objects, and develop a stable system based on the fusion of radar and image sensing information to reduce the danger caused by the steering of other vehicles that are not predicted by the driving sight and the blind angle of the on-board sensor. The roadside unit is installed at the intersection to collect vehicle data on the road, such as position, speed, and direction. An artificial neural network based on LSTM-GAN is used to process data and predict vehicle turning intention. The research case shows that the proposed model has about 91% prediction accuracy.

License plate detection has an important role to play in intelligent transportation systems. It aims at locating the license plate and accurate region information in the image. In recent years, there are lots of efficient detection methods based on the application of deep convolutional networks proposed. Although these approaches have presented promising results, these methods use horizontal or nearly horizontal bounding boxes to locate the object which is inappropriate for multi- oriented object in the real world. In this paper, a new license plate detection framework has been proposed for oriented license plate detection which combines the oriented region with a deep learning method to recognize oriented license plate detection. The proposed framework contains three sub- networks for extracting, generating and prediction proposals. Experimental results for license plate detection from the Chinese City Parking Dataset demonstrate that the proposed approach has good performance under specific scenarios.

Numerous vehicle detection methods have been proposed to obtain trustworthy traffic data for the development of intelligent traffic systems. Most of these methods perform sufficiently well under common scenarios, such as sunny or cloudy days, however, the detection accuracy drastically decreases under various bad weather conditions, such as rainy days or days with glare, which normally happens during sunset. This study proposes a vehicle detection system with a visibility complementation module that improves detection accuracy under various bad weather conditions. Furthermore, the proposed system can be implemented without retraining the deep learning models for object detection under different weather conditions. The complementation of the visibility was obtained through the use of a dark channel prior and a convolutional encoder&ndash, decoder deep learning network with dual residual blocks to resolve different effects from different bad weather conditions. We validated our system on multiple surveillance videos by detecting vehicles with the You Only Look Once (YOLOv3) deep learning model and demonstrated that the computational time of our system could reach 30 fps on average, moreover, the accuracy increased not only by nearly 5% under low-contrast scene conditions but also 50% under rainy scene conditions. The results of our demonstrations indicate that our approach is able to detect vehicles under various bad weather conditions without the need to retrain a new model.

Vehicle re-identification is a popular issue in intelligent traffic research. A lot of proposed method achieve vehicle re-identification by recognizing their license plate, because of the uniqueness. However, license plate can be stolen and pinned to different vehicles by criminals to hide their identities. In addition, the license plate number might be covered by dirt or stain, even hided from different viewpoints, makes the character recognition result might be wrong or unrecognized. To get more robust re-identification result, not only the license plate should be considered, but also the appearance. In this paper, we adopt Siamese Convolutional Neural Network structure, take license plate and vehicle appearance as input, come up with a neural network for vehicle re-identification task. We validate our proposed method on VeRi-776 dataset, and proof that it can deal with vehicle re-identification task well, even under variant viewpoints scenarios.

Foreseeing the positions of the surrounding objects is a critical technique to autonomous vehicles. Referring to the positions predicted by the trajectories, moving vehicles could avoid possible collisions. Many trajectory prediction methods about have been proposed, from traditional hand-crafted approaches to Recurrent Neural Network (RNN) models. Although the proposed methods can achieve satisfactory results on publicly available human-trajectory datasets, there are still exist some problems, such as predicting positions in obstacles, sharp turns that are not socially acceptable. We adopted the concepts of social GAN to propose a new model that take the social forces, scene information and motion direction into account when predicting trajectories. We demonstrate the performance of our proposed methods by adopting several videos from Stanford Drone Dataset.

Dynamic voltage and frequency scaling (DVFS) is a well-known method for saving energy consumption. Several DVFS studies have applied learning-based methods to implement the DVFS prediction model instead of complicated mathematical models. This paper proposes a lightweight learning-directed DVFS method that involves using counter propagation networks to sense and classify the task behavior and predict the best voltage/frequency setting for the system. An intelligent adjustment mechanism for performance is also provided to users under various performance requirements. The comparative experimental results of the proposed algorithms and other competitive techniques are evaluated on the NVIDIA JETSON Tegra K1 multicore platform and Intel PXA270 embedded platforms. The results demonstrate that the learning-directed DVFS method can accurately predict the suitable central processing unit (CPU) frequency, given the runtime statistical information of a running program, and achieve an energy savings rate up to 42%. Through this method, users can easily achieve effective energy consumption and performance by specifying the factors of performance loss.

This study proposes a vision-based nightime surrounding nighttime vehicle detection system implemented by automatic multilevel thresholding method, denoise, connected component labeling, head light matching method, and head light tracking and classification. It is able to detect head light position, and get the vehicle position. Testing by four videos that we have record while we were driving, for side view, accuracy are all higher than 90%, and the time cost are all higher than 140 frames per second; for rear view, accuracy are all higher than 90%, and the time cost are all higher than 85 frames per second. By this system, we can make sure that there is any vehicle surrounding or not. If there is, it will decide which kind of vehicle it is, and notice the driver with different level, to supply more information and longer reaction time for driver, in order to decrease accident rate, increase driving safety.

In this paper, Haar-like patterns and a classification machine were employed for fingerprint feature extraction. Although the Gabor filter has high accuracy for feature extraction under a wide angle representation range, the full range of the filter is not required. Moreover, fingerprints have clear direction field representation. Due to these two key factors, this paper uses modified Haar-like patterns to create near-circular patterns for acceptable feature detection. A support vector machine classifier is used to detect features. In a performance comparison that used 15 general Haar-like patterns as a benchmark, the proposed algorithm was able to reduce the average computation required by approximately 50% without sacrificing accuracy. Thus, the proposed method is suitable for real-world applications.

Most object detection models cannot achieve satisfactory performance under nighttime and other insufficient illumination conditions, which may be due to the collection of data sets and typical labeling conventions. Public data sets collected for object detection are usually photographed with sufficient ambient lighting. However, their labeling conventions typically focus on clear objects and ignore blurry and occluded objects. Consequently, the detection performance levels of traditional vehicle detection techniques are limited in nighttime environments without sufficient illumination. When objects occupy a small number of pixels and the existence of crucial features is infrequent, traditional convolutional neural networks (CNNs) may suffer from serious information loss due to the fixed number of convolutional operations. This study presents solutions for data collection and the labeling convention of nighttime data to handle various types of situations, including in-vehicle detection. Moreover, the study proposes a specifically optimized system based on the Faster region-based CNN model. The system has a processing speed of 16 frames per second for 500 &times, 375-pixel images, and it achieved a mean average precision (mAP) of 0.8497 in our validation segment involving urban nighttime and extremely inadequate lighting conditions. The experimental results demonstrated that our proposed methods can achieve high detection performance in various nighttime environments, such as urban nighttime conditions with insufficient illumination, and extremely dark conditions with nearly no lighting. The proposed system outperforms original methods that have an mAP value of approximately 0.2.

In this paper, treatment and reuse of wastewater generated from Pb-Zn sulfide Ore flotation with butyl xanthate and amino dithiophosphate as collectors were studied respectively. The impact of directly reused wastewater on galena flotation was investigated. The results indicate that lower recovery was achieved when the wastewater was directly reused in the process of galena flotation whereas 90% of recovery was achieved as the wastewater was treated by ozone, activated carbon and resins before reusing.

To clarify the variation in soil microbial respiration (SMR) in Jiuduansha wetland during different succession stages, the SMR of five typical zones was evaluated. The results showed that the SMR during different successional stages of vegetation varied significantly (P

The main objectives of the research described in the present paper are to develop a semi-analysis model of water clarity for case 2 waters without inputting the absorption and scattering coefficient, which are not easy to be obtained for offshore marine areas so far. Based on the Zsd (Secchi depth)inversion theory, a simple semi-analysis spectra model was established for offshore seawater clarity by analyzing the relationship between vertical diffuse attenuation coefficient K(d) (490) and the beam attenuation coefficient c(490) with remote sensing reflectance. This semi-analysis spectra model needed two band reflectance ratios on- ly, while tidal correction was produced for this model to improve the precision of the retrieving results. The semi-analysis spectra model was applied to ASD hyperspectral reflectance data measured in the Pearl River Estuary Ecological Zone (October 21, 23, 2012, November 2, 2012; N=20) and the Xuwen Coral Reef Protection Zone (January 13, 14, 2013, N=25) which covered different water body of tidal times and different pollution sources. The results indicated that the changing tendency of predicted values was consistent with the synchronous measurement values after comparing them. However, water clarity calculated by the ASD hyperspectral reflectance measured in spring tidal time, generated 0. 4 m deviation compared with in-situ water clarity, while water clarity calculated by the ASD hyperspectral reflectance measured in neap tidal time is close to the in-situ water clarity. So the tidal correction coefficient of 0.4 was further applied for the model. After modification, the coefficient of determination between the inversed and measured water clarity was 0. 663, the average absolute error was 0. 14 m and the average relative error was 19.5%. Research demonstrated that this semi-analysis inversion algorithm just needs two band reflectance ratio to complete the inversion of water clarity, which is simple and works relatively well for lower clarity (less than 2 meters) waters compared to He' (2004) and Doron' (2011) algorithms.

By the methods of field survey and laboratory analysis, an investigation was conducted on the seasonal dynamics of biomass and carbon storage of Phragmites australis and Spartina alterniflora dominated vegetation belts in the Jiuduan Shoal Wetland of Yangtze Estuary, East China in 2010-2012. The organic carbon storage of the biomass (including aboveground part, underground part, and standing litter) of the two plants was the highest in autumn and the lowest in spring. The average carbon storage of the biomass of S. alterniflora per unit area (445.81 g x m(-2)) was much higher than that of P. australis (285.52 g x m(-2)), and the average carbon storage of the standing litter of S. alterniflora (315.28 g x m(-2)) was also higher than that of P. australia (203.15 g x m(-2)). However, the organic carbon storage in the surface soil (0-30 cm) under P. australis community (1048.62 g x m(-2)) was almost as twice times as that under S. alterniflora community (583.33 g x m(-2)). Overall, the carbon accumulation ability of P. australis community (3212.96 g x m(-2)) was stronger than that of the S. alterniflora community (2730.42 g x m(-2)). Therefore, it is of significance to protect the P. australis community in terms of carbon sequestration at the salt marsh.