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Because the implicit Runge-Kutta method is hard to use, in addition, there is the lack of precision analysis for the implicit Runge-Kutta methods, three classic four-stage implicit Runge-Kutta methods are used to compare their calculation accuracy and the sensitivity of calculation step, these results provided reference for the selection of four-stage implicit Runge-Kutta methods.
In traditional DV-Hop localization algorithm, the average estimation hop distance is greater influence for the distribution of beacon nodes, which causes high positioning error. A new algorithm is proposed to improve this disadvantage. The new improved algorithm uses the hyperbolic method to locate the unknown node position. This way eliminates the cumulative error and improves the positioning accuracy...
Graphs are a powerful way to model interactions and relationships in data from a wide variety of application domains. In this setting, entities represented by vertices at the 'center' of the graph are often more important than those associated with vertices on the 'fringes'. For example, central nodes tend to be more critical in the spread of information or disease and play an important role in clustering/community...
Classification of data points in a data stream is a fundamentally different set of challenges than data mining on static data. While streaming data is often placed into the context of "Big Data" (or more specifically "Fast Data") wherein one-pass algorithms are used, true data streams offer additional hurdles due to their dynamic, evolving, and non-stationary nature. During the...
The widespread adoption of ubiquitous devices does not only facilitate the connection of billions of people, but has also fuelled a culture of sharing rich, high resolution locations through check-ins. Despite the profusion of GPS and WiFi driven location prediction techniques, the sparse and random nature of check-in data generation have ushered diverse problems, which have prompted the prediction...
Representational competence (RC), defined as "the ability to simultaneously process and integrate multiple external representations (MERs) in a domain", is a marker of expertise in science and engineering. However, the cognitive mechanisms underlying this ability and how this ability develops in learners, is poorly understood. In this paper, we report a fully controllable interface, designed...
A recently introduced data density based approach to clustering, known as Data Density based Clustering has been presented which automatically determines the number of clusters. By using the Recursive Density Estimation for each point the number of calculations is significantly reduced in offline mode and, further, the method is suitable for online use. The Data Density based Clustering method however...
Ranking objects is an essential problem in recommendation systems. Since comparing two objects is the simplest type of queries in order to measure the relevance of objects, the problem of aggregating pair wise comparisons to obtain a global ranking has been widely studied. In order to learn a ranking model, a training set of queries as well as their correct labels are supplied and a machine learning...
Labeled data, in real world, is quite scarce compared with unlabeled data. Manual annotation is usually expensive and inefficient. Active learning paradigm is used to handle this problem by identifying the most informative instances to annotate. In this paper, we proposed a new active learning algorithm based on nonparallel support vector machine. Numeric experiment shows the effective performance...
Building accurate classifiers is difficult when using data that is skewed or imbalanced which is typical of real world data sets. Two popular approaches that have been applied for improving classification accuracy and statistical comparisons of imbalanced data sets are: synthetic minority over-sampling technique (SMOTE) and propensity score matching (PSM). A novel sampling approach is introduced referred...
In cognitive radio networks, spectrum sensing plays a crucial role in the discovery of spectrum opportunities for secondary systems (or unlicensed systems). The performance of spectrum sensing is characterized by both accuracy and efficiency, and more importantly the time taken to make a decision and also the complexity involved in doing so. In this work we propose a simple detection technique based...
Localisation is one of the most important applications for wireless sensor networks since the locations of the sensor nodes are critical to both network operations and most application level tasks. Numerous techniques for localisation of sensor nodes that make use of the Received Signal Strength Indicator (RSSI) have been proposed because of the simplicity and low cost of implementation. However,...
Radio Frequency Identification (RFID) technology brings a revolutionary change in warehouse management by automatically monitoring and tracking. Considering the misplaced and newly added tags, fast identifying such unknown tags is of paramount importance, especially in large-scale RFID systems. Unlike existing work, this paper proposes a fast Physical-layer Unknown Tag Identification (PUTI) protocol...
Recommendation systems have become extremely common in recent years due to the ubiquity of information across various applications. Online entertainment (e.g., Netflix), E-commerce (e.g., Amazon, Ebay) and publishing services such as Google News are all examples of services which use recommender systems. Recommendation systems are rapidly evolving in these years, but these methods have fallen short...
This paper presents a novel switched-capacitor (SC) DC-DC converter that incorporates three conversion topologies. At 100 kHz, the design is proposed to cater to wide input range, of 2.5 V to 4.5 V. Excellent accuracy and output regulation capabilities are obtained with output ripple voltage and error of < 6 mV and < 1.3%, respectively, when operated with a fixed output voltage of 1 V. A proportional-integral...
This paper presents a new feature selection technique based on rough sets and bat algorithm (BA). BA is attractive for feature selection in that bats will discover best feature combinations as they fly within the feature subset space. Compared with GAs, BA does not need complex operators such as crossover and mutation, it requires only primitive and simple mathematical operators, and is computationally...
This paper presents a new time-mode duty-cycle-modulation-based high accuracy temperature sensor. Different from the well-known ΣΔADC based read-out structure, this temperature sensor's architecture utilizes a temperature-dependent oscillator to convert the temperature information into temperature related time-mode parameter, which means that the large power consumption of ΣΔADCs can be mitigated...
In the software development, defects affect quality and cost in an adverse way. Therefore, various studies have been proposed defect prediction techniques. Most of current defect prediction approaches use past project data for building prediction models. That is, these approaches are difficult to apply new development projects without past data. In this study, we focus on the cross project prediction...
Background: Information in bug reports is implicit and therefore difficult to comprehend. To extract its meaning, some processes are required. Categorizing bug reports is a technique that can help in this regard. It can be used to help in the bug reports management or to understand the underlying structure of the desired project. However, most researches in this area are focusing on a supervised learning...
Spreadsheets are by far the most used programs that are written by end-users. They often build the basis for decisions in companies and governmental organizations and therefore they have a high impact on our daily life. Ensuring correctness of spreadsheets is thus an important task. But what happens after detecting a faulty behavior? This question has not been sufficiently answered. Therefore, we...
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