Enhanced Discrete Multi-modal Hashing: More Constraints yet Less Time to Learn (Extended Abstract)

This paper proposes a novel method, Enhanced Discrete Multi-modal Hashing (EDMH), which learns binary codes and hash functions simultaneously from the pairwise similarity matrix of data for large-scale cross-view retrieval. EDMH distinguishes itself from existing methods by considering not just the binarization constraint but also the balance and decorrelation constraints. Although those additional discrete constraints make the optimization problem of EDMH look a lot more complicated, we are actually able to develop a fast iterative learning algorithm in the alternating optimization framework for it, as after introducing a couple of auxiliary variables each subproblem of optimization turns out to have closed-form solutions. It has been confirmed by extensive experiments that EDMH can consistently deliver better retrieval performances than state-of-the-art MH methods at lower computational costs

Dynamic Task Scheduling in Remote Sensing Data Acquisition from Open-Access Data Using CloudSim

With the rapid development of cloud computing and network technologies, large-scale remote sensing data collection tasks are receiving more interest from individuals and small and medium-sized enterprises. Large-scale remote sensing data collection has its challenges, including less available node resources, short collection time, and lower collection efficiency. Moreover, public remote data sources have restrictions on user settings, such as access to IP, frequency, and bandwidth. In order to satisfy users’ demand for accessing public remote sensing data collection nodes and effectively increase the data collection speed, this paper proposes a TSCD-TSA dynamic task scheduling algorithm that combines the BP neural network prediction algorithm with PSO-based task scheduling algorithms. Comparative experiments were carried out using the proposed task scheduling algorithms on an acquisition task using data from Sentinel2. The experimental results show that the MAX-MAX-PSO dynamic task scheduling algorithm has a smaller fitness value and a faster convergence speed

Intra-Urban Levels, Spatial Variability, Possible Sources and Health Risks of PM2.5 Bound Phthalate Esters in Xi'an

Phthalate esters (PAEs) are abundant semi-volatile organic compounds in fine particulate. PM2.5 bound PAEs can inhale into the body with breath, which can cause negative effects to human health. In this study, total of 266 PM2.5 samples dispersed from nineteen communities in Xi'an, were collected at December, 2013, the heavy pollution periods. Most of them are from residential areas, and four of them are in universities. Much high levels of PAEs were obtained in this study, which were from 271.7 to 2134 ng m(-3) (952.6 ng m(-3) on average). DEHP was the dominant species, with an average of 402.4 ng m(-3), and attributed for 42.2% of the total PAEs, followed by BBZP (146.8 ng m(-3) on average) and accounted for 15.4% of the total PAEs. Relative humidity and ventilation coefficient are the two meteorological factors affect the PAEs pollutions during the sampling periods. PAEs showed a declined trend from the urban to suburban. The principal component analysis (PCA) investigated that the release from plasticizer using in vinyl flooring, inks, synthetic leather, adhesives, and food contact wrapping; and emissions from cosmetics and personal care products, varnish, and volatilization from solid waste landfill or sewage sludge from wastewater treatment plant are the main sources for PAEs (86.8% of total PAEs). The daily inhalation and cancer risk assessment displayed that possible risk for all age group persons in this area, and infants are the most susceptible population

Upper Limb Position Tracking with a Single Inertial Sensor Using Dead Reckoning Method with Drift Correction Techniques

Inertial sensors are widely used in human motion monitoring. Orientation and position are the two most widely used measurements for motion monitoring. Tracking with the use of multiple inertial sensors is based on kinematic modelling which achieves a good level of accuracy when biomechanical constraints are applied. More recently, there is growing interest in tracking motion with a single inertial sensor to simplify the measurement system. The dead reckoning method is commonly used for estimating position from inertial sensors. However, significant errors are generated after applying the dead reckoning method because of the presence of sensor offsets and drift. These errors limit the feasibility of monitoring upper limb motion via a single inertial sensing system. In this paper, error correction methods are evaluated to investigate the feasibility of using a single sensor to track the movement of one upper limb segment. These include zero velocity update, wavelet analysis and high-pass filtering. The experiments were carried out using the nine-hole peg test. The results show that zero velocity update is the most effective method to correct the drift from the dead reckoning-based position tracking. If this method is used, then the use of a single inertial sensor to track the movement of a single limb segment is feasible