Three types of monotonicity of averaging functions

Monotonicity with respect to all arguments is fundamental to the definition of aggregation functions, which are one of the basic tools in knowledge-based systems. The functions known as means (or averages) are idempotent and typically are monotone, however there are many important classes of means that are non-monotone. Weak monotonicity was recently proposed as a relaxation of the monotonicity condition for averaging functions. In this paper we discuss the concepts of directional and cone monotonicity, and monotonicity with respect to majority of inputs and coalitions of inputs. We establish the relations between various kinds of monotonicity, and illustrate it on various examples. We also provide a construction method for cone monotone functions.

Representing images using curvilinear feature driven subdivision surfaces.

This paper presents a subdivision-based vector graphics for image representation and creation. The graphics representation is a subdivision surface defined by a triangular mesh augmented with color attribute at vertices and feature attribute at edges. Special cubic B-splines are proposed to describe curvilinear features of an image. New subdivision rules are then designed accordingly, which are applied to the mesh and the color attribute to define the spatial distribution and piecewise-smoothly varying colors of the image. A sharpness factor is introduced to control the color transition across the curvilinear edges. In addition, an automatic algorithm is developed to convert a raster image into such a vector graphics representation. The algorithm first detects the curvilinear features of the image, then constructs a triangulation based on the curvilinear edges and feature attributes, and finally iteratively optimizes the vertex color attributes and updates the triangulation. Compared with existing vector-based image representations, the proposed representation and algorithm have the following advantages in addition to the common merits (such as editability and scalability): 1) they allow flexible mesh topology and handle images or objects with complicated boundaries or features effectively; 2) they are able to faithfully reconstruct curvilinear features, especially in modeling subtle shading effects around feature curves; and 3) they offer a simple way for the user to create images in a freehand style. The effectiveness of the proposed method has been demonstrated in experiments.

Mapping 100 years of Thornthwaite moisture index: Impact of climate change in Victoria, Australia

Geographer C. W. Thornthwaite proposed in 1948 a moisture index called Thornthwaite Moisture Index (TMI) as part of a water balance model for a new classification system for climate. The importance of TMI climatic classification has been recognised in many areas of knowledge and practice worldwide over the last 60 years. However, although past climate research was focused on developing adequate methods for climate classification, current research is more concerned with understanding the patterns of climate change. The use of TMI as an indicator for climate change is still an incipient area of research. The contributions of this paper are twofold. First, it is to fully document a methodology based on geostatistics adopted to produce a time series of TMI maps that are accurate and have high spatial resolution. The state of Victoria, in Australia, over the last century, is used as the case study. Second, by analysing these maps, the paper presents a general evaluation of the spatial patterns found in Victoria related to moisture variability across space and over time. Some potential implications of the verified moisture changes are discussed, and a number of ideas for further development are suggested. © 2014 Institute of Australian Geographers.

Adaptive fuzzy multi-surface sliding control of multiple-input and multiple-output autonomous flight systems

In this study, we proposed an adaptive fuzzy multi-surface sliding control (AFMSSC) for trajectory tracking of 6 degrees of freedom inertia coupled aerial vehicles with multiple inputs and multiple outputs (MIMO). It is shown that an adaptive fuzzy logic-based function approximator can be used to estimate the system uncertainties and an iterative multi-surface sliding control design can be carried out to control flight. Using AFMSSC on MIMO autonomous flight systems creates confluent control that can account for both matched and mismatched uncertainties, system disturbances and excitation in internal dynamics. It is proved that the AFMSSC system guarantees asymptotic output tracking and ultimate uniform boundedness of the tracking error. Simulation results are presented to validate the analysis.

Sensor placement and measurement of wind for water quality studies in urban reservoirs

We study the water quality in an urban district, where the surface wind distribution is an essential input but undergoes high spatial and temporal variations due to the impact of surrounding buildings. In this work, we develop an optimal sensor placement scheme to measure the wind distribution over a large urban reservoir using a limited number of wind sensors. Unlike existing solutions that assume Gaussian process of target phenomena, this study measures the wind that inherently exhibits strong non-Gaussian yearly distribution. By leveraging the local monsoon characteristics of wind, we segment a year into different monsoon seasons that follow a unique distribution respectively. We also use computational fluid dynamics to learn the spatial correlation of wind. The output of sensor placement is a set of the most informative locations to deploy the wind sensors, based on the readings of which we can accurately predict the wind over the entire reservoir in real time. Ten wind sensors are deployed. The in-field measurement results of more than 3 months suggest that the proposed sensor placement and spatial prediction scheme provides accurate wind measurement that outperforms the state-of-the-art Gaussian model based on interpolation-based approaches.

Anonymous credential-based access control scheme for clouds

In the cloud, data is usually stored in ciphertext for security. Attribute-based encryption (ABE) is a popular solution for allowing legal data users to access encrypted data, but it has high overhead and is vulnerable to data leakage. The authors propose an anonymous authorization credential and Lagrange interpolation polynomial-based access control scheme in which an access privilege and one secret share are applied for reconstructing the user's decryption key. Because the credential is anonymously bounded with its owner, only the legal authorized user can access and decrypt the encrypted data without leaking any private information.

Effects of 22ᵒc muscle temperature on voluntary and evoked muscle properties during and after high intensity exercise

The purpose of this study was to investigate the effect of 22 °C local muscle temperature of intact human plantar flexors performing fatiguing contractions on evoked and voluntary contractile properties before and after fatigue. Twelve subjects were tested on plantar flexor voluntary torque, percent muscle activation derived from twitch interpolation, integrated electromyographic (iEMG) activity, and evoked torque and temporal characteristics of maximal twitch and tetanic stimulations before fatigue and 1, 5, and 10 min after intermittent, high-intensity, isometric fatigue under both normothermic and hypothermic conditions. Hypothermic and normothermic changes between time points were analysed by repeated-measures analysis of variance. Normothermic fatigue induced small to large effects (Cohen’s d: 0.29–3.06) on voluntary and evoked contractile properties, whereas most effects of unfatigued hypothermia were limited to rate-dependent processes (Cohen’s d: 0.78–1.70). Most tetanic properties were potentiated 1 min after normothermic fatigue, but remained unchanged by hypothermic fatigue, resulting in significant differences between the two conditions. Soleus iEMG significantly declined 1 min after normothermic fatigue (–29%), but not after hypothermic fatigue. Twitch torque was potentiated by 29% one minute after fatigue while normothermic, but was potentiated by 46% while hypothermic; rate of twitch torque development and time to peak twitch were potentiated by 39% and 10% while normothermic, but 89% and 28% while hypothermic. Although voluntary contractile properties are generally impaired soon after normothermic fatigue, most were not after hypothermic fatigue. Furthermore, evoked contractile properties were generally higher 1 min after hypothermic fatigue. We conclude that the hypothermic condition slows the recovery of potentiated evoked contractile properties back to baseline values.

Geospatial estimation-based auto drift correction in wireless sensor networks

Wireless sensor networks are often deployed in large numbers, over a large geographical region, in order to monitor the phenomena of interest. Sensors used in the sensor networks often suffer from random or systematic errors such as drift and bias. Even if they are calibrated at the time of deployment, they tend to drift as time progresses. Consequently, the progressive manual calibration of such a large-scale sensor network becomes impossible in practice. In this article, we address this challenge by proposing a collaborative framework to automatically detect and correct the drift in order to keep the data collected from these networks reliable. We propose a novel scheme that uses geospatial estimation-based interpolation techniques on measurements from neighboring sensors to collaboratively predict the value of phenomenon being observed. The predicted values are then used iteratively to correct the sensor drift by means of a Kalman filter. Our scheme can be implemented in a centralized as well as distributed manner to detect and correct the drift generated in the sensors. For centralized implementation of our scheme, we compare several krigingand nonkriging-based geospatial estimation techniques in combination with the Kalman filter, and show the superiority of the kriging-based methods in detecting and correcting the drift. To demonstrate the applicability of our distributed approach on a real world application scenario, we implement our algorithm on a network consisting of Wireless Sensor Network (WSN) hardware. We further evaluate single as well as multiple drifting sensor scenarios to show the effectiveness of our algorithm for detecting and correcting drift. Further, we address the issue of high power usage for data transmission among neighboring nodes leading to low network lifetime for the distributed approach by proposing two power saving schemes. Moreover, we compare our algorithm with a blind calibration scheme in the literature and demonstrate its superiority in detecting both linear and nonlinear drifts.

Hybrid threshold adaptable quantum secret sharing scheme with reverse Huffman-Fibonacci-tree coding

With prevalent attacks in communication, sharing a secret between communicating parties is an ongoing challenge. Moreover, it is important to integrate quantum solutions with classical secret sharing schemes with low computational cost for the real world use. This paper proposes a novel hybrid threshold adaptable quantum secret sharing scheme, using an m-bonacci orbital angular momentum (OAM) pump, Lagrange interpolation polynomials, and reverse Huffman-Fibonacci-tree coding. To be exact, we employ entangled states prepared by m-bonacci sequences to detect eavesdropping. Meanwhile, we encode m-bonacci sequences in Lagrange interpolation polynomials to generate the shares of a secret with reverse Huffman-Fibonacci-tree coding. The advantages of the proposed scheme is that it can detect eavesdropping without joint quantum operations, and permits secret sharing for an arbitrary but no less than threshold-value number of classical participants with much lower bandwidth. Also, in comparison with existing quantum secret sharing schemes, it still works when there are dynamic changes, such as the unavailability of some quantum channel, the arrival of new participants and the departure of participants. Finally, we provide security analysis of the new hybrid quantum secret sharing scheme and discuss its useful features for modern applications.