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The principal environmental factors influencing the monthly dynamics of phytoplankton were examined from April to October in 2016 in 12 stations of Lake Wuliangsuhai (northwest China). Interestingly, in this lake, nutrient did not exhibit to be limiting for the phytoplankton growth. From April to October, the volume of total nitrogen (TN) range 0.66~22.22 mg/L, maximum volume was in W1 station in October; total phosphorus (TP) range 0.008~0.339 mg/L, maximum volume was in W2 station in April; NH4–N range 0.11~1.74mg/L, maximum volume was in W12 station in October. The most critical forcing factors were water quality physicochemical parameter variables, mainly water temperature (WT), salinity (Sal), conductivity (CON), total dissolved solids (TDS), pondus hydrogenii (pH), and water transparency (SD). The highest phytoplankton density (2.99 × 108 cells/L) was recorded in station W12 in June, and the lowest phytoplankton density (4.84 × 105 cells/L) in station W8 in May, in conditions of increasing water temperature in summer and lower water temperature in spring. Conversely, the highest species number (61 species) exhibited in station W5 in September, and the lowest species number (6 species) exhibited in station W1 in May. The physicochemical parameter variables, mainly WT, Sal, CON, TDS, pH, and SD, had a significant and measurable effect not only on the development of phytoplankton density but also on the monthly dynamics of the phytoplankton community. Our results revealed that the species number and density of the phytoplankton could serve as biological water quality indicators, which would give overall descriptions of water quality in Wuliangsuhai Lake.

Animal detection plays a very vital role in wildlife protection and many other real life applications. In this paper, we focus on face detection of Golden monkeys who live in Qinling Mountains, Shaanxi province, China, and present a relatively complete face detection algorithm to detect these monkeys’ faces, which mainly includes three parts: the location of the monkeys’ bodies, the detection of th+e suspicious facial skin and the accurate detection of the true faces. Firstly, regional color quantization is proposed to quantize the HSV color space for the nature images with different sizes, and we can get the areas of the monkeys’ bodies according to the color distribution of the monkeys’ hair in the histogram of the quantized color space. Then the areas of suspicious facial skin can be extracted from these areas of the monkeys’ bodies. Further, we propose incremental self-paced curriculum learning (ISPCL) to detect the true monkeys’ faces accurately. In our method, regional color quantization can increase the color differences between the background and the monkeys’ hair, so that the segmented results have fewer background pixels. Besides, the basic idea of the incremental learning is introduced into the training process of SPCL, which is to simulate the process in which human learn something from easy samples to hard ones, this idea is able to improve the performances of face detection. The experimental results demonstrate that the proposed algorithm can locate the monkeys’ bodies in the images with different sizes, and can detect their faces effectively. This research lays a foundation for face recognition and behavior analysis of golden monkeys.

Chinese calligraphy is a unique visual art, and and is one of the material basis of China’s traditional cultural heritage. However, time had caused the old calligraphy works to weathering and damages, so it is necessary to utilize advanced technologies to protect those works. One of those technologies is digital imaging, and the obtained images by digital imaging can preserve the visual information of calligraphy works better, furthermore, they can be used in further researches. While the basic works for those researches are to extract the artistic features which include two elements, i.e., form and spirit. However, most of the existing methods only extract the form and ignore the characters’ spirit, especially they are insensitive to the slight variation in complex ink strokes. To solve these problems, this paper proposes an extraction method based on regional guided flter (RGF) with reference images, which is generated by KNN matting and used as the input image for RGF. Since RGF is sensitive to the slight variation of ink, so the detailed information of the inside of strokes can be detected better. Besides, unlike the past works, which filter the whole strokes, RGF filters the inside of strokes and edges in different windows respectively, which results in that the edges are preserved accurately. Results from a deployment of several famous Chinese calligraphy works demonstrate that our method can extract more accurate and complete form and spirit with lower error rate.

Efficient energy consumption is crucial for energy constrained networks such as Wireless Sensor Networks (WSN). Using a mobile sink to collect the data of the nodes is a good method to balance the energy level of the nodes and prolong the lifetime of the whole network. For the mobile sink, an efficient path planning can make the mobile sink visit significantly more nodes during a limited period and shorten the latency of information gathering. Considering the communication range of the nodes, we can deduce this routing problem as a special case of traveling salesman problem with neighborhoods (TSPN), which is a NP-hard problem [1]. In this paper, we propose a novel routing design algorithm based on Variable Dimension Particle Swarm Optimization (VD-PSO). In this algorithm, every feasible path solution of TSPN is expressed as a particle. Each dimension of the particle is the coordinates of a rendezvous point (RP, the point where the mobile sink stays to gather data). The dimensionality of the particle is equal to the number of the rendezvous points in the path. Using the evolutionary method of the particles, we can derive the optimal path of the mobile sink. Simulation results show that the proposed algorithm has fast convergence speed, and the result is quite approximate to the optimal solution.

Device-free localization has long been playing a key role in the anti-intrusion applications. However, current multi-target localization solutions mainly use uneconomical equipment, which are not cost-efficient for large-scale scenarios . Moreover, they only consider the line-of-sight (LoS) path signals distorted by the targets, hence can't exactly pinpoint the locations when faced with multipath effects and non-line-of-sight (NLoS), which are typical in real-world deployments. In this paper, we propose FISCP, a fine-grained device-free positioning system for multiple targets working in sparse deployments. The RFID passive tags are employed, which are much cheaper than other devices for localization. Meanwhile, unlike past approaches, which ignore the multipath effects or even take multipath as detrimental, FISCP exploits the dynamic distortion in multipath caused by the targets and considers the distortion as fingerprints for localization. We make a prototype system for FISCP using the commercial off-the-shelf products, including RFID systems and omnidirectional antennas, and develop a software program for the RFID systems. All the experiments are conducted in the deployments where the distance interval between each pair of tags is 1.2 m, and the deployments are sparse with respect to the short communication range of passive RFID systems (from a few meters up to tens of meters). The results of our experiments demonstrate that FISCP is effective in multi-target localization with low localization errors of 0.33 m in average.

Wireless localization technology has become an important research area of pervasive computing applications. This paper presents $$DE ^{2 }$$DE2 (direction estimation and distance estimation), a wireless localization approach that is responsive to the scenario where only a single beacon is available to locate multiple position-unknown targets. Unlike the previous work which needs a priori knowledge of the scenario during the training phase by manual recording (taking significant human efforts), or uses a certain number of beacons during the localization phase to reach a certain accuracy (leading to high energy consumption and reducing network lifecycle), $$DE ^{2 }$$DE2 leverages a single beacon without many prior human efforts to locate multiple targets. The intuition underlying $$DE ^{2 }$$DE2 is that, direction and distance constraints between an unknown position and the single beacon are adequate to determine the unknown position. When a person rotates around an RF receiver (the single beacon), the human body acts as a signal-blocking obstacle. It causes the signal from a transmitter (position-unknown) to the single receiver to attenuate in a certain scope. The blocking effect caused by human body can be utilized to obtain the direction constraint between the unknown position and the single beacon. And we call the signal strength which is perceived by the receiver during the person's rotation as rotating received signal strength (RSS). Moreover, a corresponding distance constraint is also concluded in the rotating RSS according to the RF propagation model. $$DE ^{2 }$$DE2 pushes the limit of minimum beacons needed for localization without much pre-configuration effort. To demonstrate the utility of $$DE ^{2 }$$DE2, we implement $$DE ^{2 }$$DE2 in real-world single beacon wireless networks. The results show that these applications can significantly benefit from $$DE ^{2 }$$DE2.

Two variants of permanent magnet (PM) based integrated starter generator (ISG) machines were compared for use during development of a new hybrid electric vehicle (HEV). Such a system design generally has high efficiency requirements, mainly regarding high power/weight ratio at minimal size. another criteria is high reliability for high-speed operation. In a study of Ricardo, two types of ISG were compared: one with permanent magnets placed on the surface of the rotor and the other with magnets integrated within the rotor core.