Data-Reserved Periodic Diffusion LMS With Low Communication Cost Over Networks

In this paper, we analyze diffusion strategies in which all nodes attempt to estimate a common vector parameter for achieving distributed estimation in adaptive networks. Under diffusion strategies, each node essentially needs to share processed data with predefined neighbors. Although the use of internode communication has contributed significantly to improving convergence performance based on diffusion, such communications consume a huge quantity of power in data transmission. In developing low-power consumption diffusion strategies, it is very important to reduce the communication cost without significant degradation of convergence performance. For that purpose, we propose a data-reserved periodic diffusion least-mean-squares (LMS) algorithm in which each node updates and transmits an estimate periodically while reserving its measurement data even during non-update time. By applying these reserved data in an adaptation step at update time, the proposed algorithm mitigates the decline in convergence speed incurred by most conventional periodic schemes. For a period p, the total cost of communication is reduced to a factor of 1/p relative to the conventional adapt-then-combine (ATC) diffusion LMS algorithm. The loss of combination steps in this process leads naturally to a slight increase in the steady-state error as the period p increases, as is theoretically confirmed through mathematical analysis. We also prove an interesting property of the proposed algorithm, namely, that it suffers less degradation of the steady-state error than the conventional diffusion in a noisy communication environment. Experimental results show that the proposed algorithm outperforms related conventional algorithms and, in particular, outperforms ATC diffusion LMS over a network with noisy links.11Ysciescopu

Robust Distributed Clustering Algorithm Over Multitask Networks

We propose a new adaptive clustering algorithm that is robust to various multitask environments. Positional relationships among optimal vectors and a reference signal are determined by using the mean-square deviation relation derived from a one-step least-mean-square update. Clustering is performed by combining determinations on the positional relationships at several iterations. From this geometrical basis, unlike the conventional clustering algorithms using simple thresholding method, the proposed algorithm can perform clustering accurately in various multitask environments. Simulation results show that the proposed algorithm has more accurate estimation accuracy than the conventional algorithms and is insensitive to parameter selection.11Ysciescopu

Uncultured Members of the Oral Microbiome.

Around one-third of oral bacteria cannot be cultured using conventional methods. Some bacteria have specific requirements for nutrients while others may be inhibited by substances in the culture media or produced by other bacteria. Oral bacteria have evolved as part of multispecies biofilms, and many thus require interaction with other bacterial species to grow. In vitro models have been developed that mimic these interactions and have been used to grow previously uncultivated organisms

Efficient adaptive strategies over distributed networks

Distributed wireless sensor networks finds many remote sensing applications like battle field surveillance, target localisation, environmental monitoring, precision agriculture, smart spaces and medical applications. Due to their vast range of applications efficient design and implementation become the current area of research.A distributed network consists of certain number of processing elements called nodes.These nodes are distributed over a geographical area which collects the information for particular phenomena and communicates with other nodes of the network to arrive at estimation the parameter. A network needs effective and efficient designs to function properly with the limited available resources.In this thesis,we review some of the computationally efficient adaptive distributed strategies developed using incremental partial update techniques. The schemes mentioned here solve the problem of linear estimation with less number of computations in a cooperative fashion. In a distributed network each node contains local computing equipment which estimates and shares them with other nodes. The resulting algorithms are less complex in competitions and in communication because of Incremental partial update algorithms and each node communicate with immediate node only. Computational complexity analysis is evaluated and performance characteristics of each algorithm are given with computer simulations. Simulation results show that with a small degradation in performance, a considerable amount of computational complexity is reduced

Educational Technology Integration Among Community College Instructors

Over the last two decades, educational technology (ET) integration has become an increasingly important aspect of higher education, particularly with the growth of online, distance and hybrid courses and degree programs. Furthermore, accrediting agencies such as the Higher Learning Commission (HLC) are paying close attention to online and hybrid courses and degree programs, making effective use of ET even more important to colleges and universities. Even in traditional, on-campus classrooms, some instructors are not using ET effectively to augment teaching and learning. The main purpose of this research study was to examine a holistic view of educational technology integration into teaching and learning among community college instructors. Additionally, the study aimed to identify some positive and negative factors of educational technology integration and the ways in which those factors affect technology integration among faculty. The study concentrated on identifying facilitative conditions that influence ET integration among instructors at five community colleges. Elyâs (1999) Conditions of Educational Technology Implementation (CETI) theory served as a theoretical framework for this research study. Ely\u27s (1999) CETI framework is based on the comprehensive perspective of ET integration and implementation. Elyâs (1999) theoretical framework includes eight conditions of educational technology implementation (CETI): Availability of time, Existence of knowledge and skills, Leadership, Participation, Availability of resources, Commitment, Rewards, Dissatisfaction with the status quo. The research study used and applied quantitative research methods of data collection. The data was collected from 307 instructors who were teaching at five Midwestern state community colleges at the time of survey completion. Data collection was accomplished through the use of an electronic survey. There were two sections in the survey questionnaires. The first was a personal demographic questionnaire to collect demographic information from participants of the study. The second was the educational technology integration questionnaire, which included 60 questions and used six-point Likert-like scale items (1 = strongly disagree, 2 = disagree, 3 = slightly disagree, 4 = slightly agree, 5 = agree and 6 = strongly agree) for data collection purposes. An open-ended question was also included at the end of the survey to collect additional comments about instructorsâ self-perceptions of educational technology integration and facilitative factors that influence them to integrate educational technology. The research study specifically investigated the effects of these predictor variables (degree program, gender, academic rank, education level and facilitative conditions) by addressing the following research questions through null hypothesis: 1. Are there differences in instructorsâ beliefs about educational technology integration into teaching and learning based on discipline (degree program)? There was a statistically significant difference between English, Education, and Humanities disciplines and Engineering, Technology, and Energy disciplines. The ANOVA showed statistical significance with the following F (9,297) = 1.93, p =.047) values. Therefore, H-null:1 was rejected due to the differences in between disciplines. 2. Are there differences in the factors related to educational technology integration into teaching and learning between male and female instructors? There was no statistically significant difference in means and standard deviation scores between male and female instructors based, on the sample t-test analysis. The t-test examination revealed the following results: (t 305 =1.074; p=.284 \u3e0.05). Therefore, H-null: 2 was retained due to no statistical differences between male and female instructors in terms of educational technology integration. 3. Are there differences in competencies in educational technology integration among instructors based on academic ranks (professor, associate professor, assistant professor, instructor, lecturer, and other)? Overall, there were small differences in mean scores between instructor ranks in terms of educational technology (ET) integration. However, the ANOVA test showed no statistically significant differences between faculty ranks. The one-way ANOVA was equal to F (5,301) = .793, p =.555). Therefore, H-null: 3 was retained, due to no statistical differences between instructors based on faculty ranks. 4. Are there differences in technology integration into teaching and learning based on the facilitative conditions (time, skills, leadership, participation, resources, commitment, rewards, and dissatisfaction with the status quo)? Based on ANOVA results, there were statistically significant differences between community colleges in terms of facilitative factors. The one-way ANOVA had a F value of (4,302) = 3.817, p =.005). Therefore, H-null: 4 was rejected due to statistical difference between community colleges in terms of facilitative conditions. 5. Are there differences in educational technology training needs of instructors based on educational level (trade/technical/vocational training, associate degree, bachelorâs degree, masterâs degree, professional degree, or doctorate degree)? Based on the ANOVA result, there was a statistically significant difference between groups in terms of technology training needs. The ANOVA test had an F value of (2,304) = 5.929, p =.003). Therefore, H-null: 5 was rejected due to statistical differences between instructors based on the educational level

Partial diffusion Kalman filtering for distributed state estimation in multiagent networks

Many problems in multiagent networks can be solved through distributed learning (state estimation) of linear dynamical systems. In this paper, we develop a partial-diffusion Kalman filtering (PDKF) algorithm, as a fully distributed solution for state estimation in the multiagent networks with limited communication resources. In the PDKF algorithm, every agent (node) is allowed to share only a subset of its intermediate estimate vectors with its neighbors at each iteration, reducing the amount of internode communications. We analyze the stability of the PDKF algorithm and show that the algorithm is stable and convergent in both mean and mean-square senses. We also derive a closed-form expression for the steady-state mean-square deviation criterion. Furthermore, we show theoretically and by numerical examples that the PDKF algorithm provides a trade-off between the estimation performance and the communication cost that is extremely profitable

Energy-efficient diffusion Kalman filtering for multi-agent networks in IoT

Increasing the energy efficiency of an Internet of Things (IoT) system is a major challenge for its successful implementation. To reduce the computation and storage burden and enhance the efficiency of traditional IoT, an energy-efficient diffusion-based algorithm for state estimation in multi-agent networks is proposed in this paper. In the proposed algorithm (referred to as reduced-link diffusion Kalman filter (RL-diffKF)) the nodes (agents) can communicate only with a fraction of their neighbors and each node runs a local Kalman filter to estimate the state of a linear dynamic system. This algorithm results in a significant reduction in communication cost during both adaptation and aggregation processes albeit at the expense of possible degradation in the network performance. To justify the stability and convergence of the RL-diffKF algorithm, an in-depth analysis of the performance is reported. We also consider the problem of optimal selection of combination weights and use the idea of minimum variance estimation to analytically derive the adaptive combiners. The theoretical findings are verified through numerical simulations