Reversible data hiding in encrypted images based on multi-level encryption and block histogram modification

Multimedia Tools and Applications - In recent years there has been significant interest in reversible data hiding, and also in particular, reversible data hiding in encrypted images (RDH-EI). This...     

“技能-能力-思维”三层次大学计算机基础教学

当前因受“狭议工具论”错误认识的影响致使非计算机专业大学计算机基础教育受到冲击,计算机基础课程的学时也被缩减.针对这一问题,提出了以计算思维培养为核心,“技能-能力-计算思维”三层次培养目标的教学改革.对课程内容进行了面向知识能力、计算思维教学的内容重构与组织,给出了具体的教学内容模块,同时探讨了计算思维培养教学方法.     

Performance evaluation of a dental handpiece in simulation of clinical finishing using a novel 2DOF in vitro apparatus

This paper reports on the performance evaluation of a dental handpiece in simulation of clinical finishing using a novel two-degrees-of-freedom (2DOF) in vitro apparatus. The instrumented apparatus consisted of a two-dimensional computer-controlled coordinate worktable carrying a dental handpiece, a piezoelectric force dynamometer, and a high-speed data acquisition and signal conditioning system for simulating the clinical operations and monitoring the dental finishing processes. The performance of the dental handpiece was experimentally evaluated with respect to rotational speed, torque, and specific finishing energy under the applied clinical finishing conditions. The results show that the rotational speeds of the dental handpiece decreased by increasing either the depth of cut or the feed rate at a constant clinically applied air pressure and water flowrate. They also decreased when increasing both the tangential and normal finishing forces. The specific finishing energy decreased with an increase in either depth of cut or feed rate, while the finishing torque increased as either the depth of cut or the feed rate was increased. Implications of these results were to provide guidance for proper applications of dental handpieces in clinical practice.     

电炉法CS_2废气的治理

简介了电炉法CS2 废气吸附的原理及工艺流程 ,开辟了H2 S利用的新途径 ,具有较好的环境效益和经济效益     

Acoustic emission from gas-filled coal under triaxial compression

Acoustic emission (AE) experiments have been performed on gas-saturated coal specimens under conventional triaxial compression. The AE characteristics were investigated for a methane gas flow through the coal specimen. One AE parameter, AE count, when normalized by the total count number was used to represent the damage evolution in the gassy coal. It is shown that this AE parameter is a reasonable indicator for damage occurring within the coal specimen since its envelope has almost the same shape as the complete stress–strain curve, except for a short time delay. In addition, the change in AE count is highly consistent with the change in coal permeability. Test results also show that methane containing coal emits a small number of AE events before entering the yield stage. AE activity gradually increases during the yield process up to the peak stress. The lowest permeability corresponds to the highest AE activity, implying failure will soon occur. An AE based constitutive model was constructed and the theoretical results agree well with those of experiments.     

Effects of AMF on soil enzyme activity and carbon sequestration capacity in reclaimed mine soil

A series of pot experiments and field trials were carried out to evaluate the effects of arbuscular mycorrhizal fungi (AMF) on activities of soil enzymes and carbon sequestration capacity in reclaimed mine soil. A complex substrate of coal gangue, fly ash and sludge was used as reclaimed mine soil, and ryegrass was planted with AMF inoculation to construct a plant-complex substrate-microbe ecological restoration system. The changes to the soil organic carbon (SOC), activities of soil enzymes and glomalin-related soil protein (GRSP) were measured and the effects of AMF on activities of soil enzymes and carbon sequestration capacity in reclaimed mine soil were analyzed. The results show that the contents of GRSP (total glomalin (TG) and easily extractable glomalin (EEG)), SOC and activities of enzymes increased, and the increments were higher in the AMF inoculation treated plant-complex substrate-microbe ecological restoration systems than those with no AMF inoculated treatments after 12 months of ryegrass growth. TG, EEG and soil enzyme activity have a significant positive correlation, and the correlative coefficient was 0.427–0.573; SOC and TG, EEG have a significant positive correlation (p < 0.01), indicating that AMF plays an important role in carbon sequestration of reclaimed mine soils.     

Cytoplasm and nucleus segmentation in cervical smear images using Radiating GVF Snake

A Radiating Gradient Vector Flow (RGVF) Snake aiming at accurate extraction of both the nucleus and cytoplasm from a single-cell cervical smear image is proposed. After preprocessing, the areas in the image are roughly clustered into nucleus, cytoplasm and the background by a spatial K-means clustering algorithm. After initial contours are extracted, the image is segmented using RGVF. RGVF involves a new edge map computation method and a stack-based refinement, and is thus robust to contaminations and can effectively locate the obscure boundaries. The boundaries can also be correctly traced even if there are interferences near the cytoplasm and nucleus regions. Experiments performed on the Herlev dataset, which contains 917 images show the effectiveness of the proposed algorithm.     

Relevance feature mapping for content-based multimedia information retrieval

This paper presents a novel ranking framework for content-based multimedia information retrieval (CBMIR). The framework introduces relevance features and a new ranking scheme. Each relevance feature measures the relevance of an instance with respect to a profile of the targeted multimedia database. We show that the task of CBMIR can be done more effectively using the relevance features than the original features. Furthermore, additional performance gain is achieved by incorporating our new ranking scheme which modifies instance rankings based on the weighted average of relevance feature values. Experiments on image and music databases validate the efficacy and efficiency of the proposed framework.     

Tracking mobile users in wireless networks via semi-supervised colocalization

Recent years have witnessed the growing popularity of sensor and sensor-network technologies, supporting important practical applications. One of the fundamental issues is how to accurately locate a user with few labeled data in a wireless sensor network, where a major difficulty arises from the need to label large quantities of user location data, which in turn requires knowledge about the locations of signal transmitters or access points. To solve this problem, we have developed a novel machine learning-based approach that combines collaborative filtering with graph-based semi-supervised learning to learn both mobile users' locations and the locations of access points. Our framework exploits both labeled and unlabeled data from mobile devices and access points. In our two-phase solution, we first build a manifold-based model from a batch of labeled and unlabeled data in an offline training phase and then use a weighted k-nearest-neighbor method to localize a mobile client in an online localization phase. We extend the two-phase colocalization to an online and incremental model that can deal with labeled and unlabeled data that come sequentially and adapt to environmental changes. Finally, we embed an action model to the framework such that additional kinds of sensor signals can be utilized to further boost the performance of mobile tracking. Compared to other state-of-the-art systems, our framework has been shown to be more accurate while requiring less calibration effort in our experiments performed on three different testbeds.