Efficient Approach for Maximizing Lifespan in Wireless Sensor Networks by Using Mobile Sinks

Recently, sink mobility has been shown to be highly beneficial in improving network lifetime in wireless sensor networks (WSNs). Numerous studies have exploited mobile sinks (MSs) to collect sensed data in order to improve energy efficiency and reduce WSN operational costs. However, there have been few studies on the effectiveness of MS operation on WSN closed operating cycles. Therefore, it is important to investigate how data is collected and how to plan the trajectory of the MS in order to gather data in time, reduce energy consumption, and improve WSN network lifetime. In this study, we combine two methods, the cluster‐head election algorithm and the MS trajectory optimization algorithm, to propose the optimal MS movement strategy. This study aims to provide a closed operating cycle for WSNs, by which the energy consumption and running time of a WSN is minimized during the cluster election and data gathering periods. Furthermore, our flexible MS movement scenarios achieve both a long network lifetime and an optimal MS schedule. The simulation results demonstrate that our proposed algorithm achieves better performance than other well‐known algorithms.     

ETARM: an efficient top-k association rule mining algorithm

Mining association rules plays an important role in data mining and knowledge discovery since it can reveal strong associations between items in databases. Nevertheless, an important problem with traditional association rule mining methods is that they can generate a huge amount of association rules depending on how parameters are set. However, users are often only interested in finding the strongest rules, and do not want to go through a large amount of rules or wait for these rules to be generated. To address those needs, algorithms have been proposed to mine the top-k association rules in databases, where users can directly set a parameter k to obtain the k most frequent rules. However, a major issue with these techniques is that they remain very costly in terms of execution time and memory. To address this issue, this paper presents a novel algorithm named ETARM (Efficient Top-k Association Rule Miner) to efficiently find the complete set of top-k association rules. The proposed algorithm integrates two novel candidate pruning properties to more effectively reduce the search space. These properties are applied during the candidate selection process to identify items that should not be used to expand a rule based on its confidence, to reduce the number of candidates. An extensive experimental evaluation on six standard benchmark datasets show that the proposed approach outperforms the state-of-the-art TopKRules algorithm both in terms of runtime and memory usage.     

(Φ,Φ<Superscript>*</Superscript>) Image Decomposition Models and Minimization Algorithms

We propose in this paper minimization algorithms for image restoration using dual functionals and dual norms. In order to extract a clean image u from a degraded version f=Ku+n (where f is the observation, K is a blurring operator and n represents additive noise), we impose a standard regularization penalty Φ(u)=∫ φ(|Du|)dx<∞ on u, where φ is positive, increasing and has at most linear growth at infinity. However, on the residual f−Ku we impose a dual penalty Φ^*(f−Ku)<∞, instead of the more standard fidelity term. In particular, when φ is convex, homogeneous of degree one, and with linear growth (for instance the total variation of u), we recover the (BV,BV ^*) decomposition of the data f, as suggested by Y. Meyer (Oscillating Patterns in Image Processing and Nonlinear Evolution Equations, University Lecture Series, vol. 22, Am. Math. Soc., Providence, 2001). Practical minimization methods are presented, together with theoretical, experimental results and comparisons to illustrate the validity of the proposed models. Moreover, we also show that by a slight modification of the associated Euler-Lagrange equations, we obtain well-behaved approximations and improved results.

Mobility of Air-Stable p -type Polythiophene Field-Effect Transistors Fabricated Using Oxidative Chemical Vapor Deposition

Air-stable organic field-effect transistors (FETs) based on an unsubstituted polythiophene (PT) channel, processed using oxidative chemical vapor deposition (oCVD), have been investigated. The intrinsic properties of PT, including its rigid backbone structure and resistance to reactions with water and oxygen, lead to excellent air stability of oCVD PT-based FET devices. The effect of the channel/metalization contact resistance on the field-effect mobility (μFE) of PT-based FETs has also been investigated. Due to the channel/metallization contact resistance, the actual voltages applied to the channel are found to be significantly lower than the intended drain bias because of the voltage drops that occur at the source/drain contacts. Transmission-line measurements reveal that more than 30% of the intended drain bias is lost at all gate voltages applied to the channel. Reconstructed output characteristics excluding the contact effect allow the extraction of a corrected μFE, which is approximately 40% higher than that with contact resistance.     

Adaptive infinite dropout for noisy and sparse data streams

Machine Learning - The ability to analyze data streams, which arrive sequentially and possibly infinitely, is increasingly vital in various online applications. However, data streams pose various...     

Max–min fairness in multi-commodity flows

In this paper, we provide a study of Max–Min Fair (MMF) multi-commodity flows and focus on some of their applications to multi-commodity networks. We first present the theoretical background for the problem of MMF and recall its relations with lexicographic optimization as well as a polynomial approach for achieving leximin maximization. We next describe two applications to telecommunication networks, one on routing and the second on load-balancing. We provide some deeper theoretical analysis of MMF multi-commodity flows, show how to solve the lexicographically minimum load network problem for the link load functions most frequently used in telecommunication networks. Some computational results illustrate the behavior of the obtained solutions and the required CPU time for a range of random and well-dimensioned networks.     

Combining skeleton and accelerometer data for human fine-grained activity recognition and abnormal behaviour detection with deep temporal convolutional networks

Multimedia Tools and Applications - Single sensing modality is widely adopted for human activity recognition (HAR) for decades and it has made a significant stride. However, it often suffers from...     

Computational analysis of contractility in engineered heart tissue

Engineered heart tissue (EHT) is a potential therapy for heart failure and the basis of functional in vitro assays of novel cardiovascular treatments. Self-organizing EHT can be generated in fiber form, which makes the assessment of contractile function convenient with a force transducer. Contractile function is a key parameter of EHT performance. Analysis of EHT force data is often performed manually; however, this approach is time consuming, incomplete and subjective. Therefore, the purpose of this study was to develop a computer algorithm to efficiently and objectively analyze EHT force data. This algorithm incorporates data filtering, individual contraction detection and validation, inter/intracontractile analysis and intersample analysis. We found the algorithm to be accurate in contraction detection, validation and magnitude measurement as compared to human operators. The algorithm was efficient in processing hundreds of data acquisitions and was able to determine force-length curves, force-frequency relationships and compare various contractile parameters such as peak systolic force generation. We conclude that this computer algorithm is a key adjunct to the objective and efficient assessment of EHT contractile function.     

Antibacterial performance of TiO2 ultrafine nanopowder synthesized by a chemical vapor condensation method: Effect of synthesis temperature and precursor vapor concentration

TiO₂ nanoparticles were synthesized using a chemical vapor condensation (CVC) method, and their physicochemical properties were characterized to optimize the synthesis conditions for antibacterial activity. The antibacterial activities of CVC-TiO₂ nanoparticles and commercialized TiO₂ nanoparticles (P25, Deggusa) were investigated according to UV exposure time and amount of photocatalyst. We found that the specific surface area and the crystallinity of CVC-TiO₂ nanoparticles were varied depending on synthesis temperature and precursor vapor concentration. As a result, the CVC-TiO₂ nanoparticles showed a higher specific surface area and better crystallinity than that of P25TiO₂. More importantly, CVC-TiO₂ nanoparticles generated a larger amount of hydroxyl radicals than P25TiO₂. Consequently CVC-TiO₂ nanoparticles were more effective as an antibacterial photocatalyst than P25TiO₂ under irradiation with UV light. Based-on these results, the optimum synthetic conditions of CVC-TiO₂ nanoparticles for bactericidal effect were found.     

Biodiesel production via peanut oil extraction using diesel-based reverse-micellar microemulsions

Vegetable oils have been studied as a feasible substitute for diesel fuel, and short term tests using neat vegetable oils have shown results comparable to those of diesel fuel. However, engine problems arise due to the high oil viscosity after long-term usage. Vegetable oil/diesel blending as biodiesel fuel has been shown to be one technique to reduce vegetable oil viscosity. The goal of this research is to demonstrate the feasibility of producing this biodiesel fuel via vegetable oil extraction using diesel-based reverse-micellar microemulsions as an extraction solvent. In this extraction technique, peanut oil is directly extracted into the oil phase of the microemulsion based on the “likes dissolve likes” principle and the product of the extraction process is peanut oil/diesel blend. The results show that diesel-based reverse micellar extract oil from peanuts more effectively than both diesel and hexane alone under the same extraction condition. An extraction efficiency of 95% was achieved at room temperature and short extraction time of 10 min in just a single extraction step. The extracted peanut oil/diesel blend was tested for peanut oil fraction, viscosity, cloud point and pour point, which all meet the requirements for biodiesel fuel.