Improving Planetary Rover Attitude Estimation via MEMS Sensor Characterization

Micro Electro-Mechanical Systems (MEMS) are currently being considered in the space sector due to its suitable level of performance for spacecrafts in terms of mechanical robustness with low power consumption, small mass and size, and significant advantage in system design and accommodation. However, there is still a lack of understanding regarding the performance and testing of these new sensors, especially in planetary robotics. This paper presents what is missing in the field: a complete methodology regarding the characterization and modeling of MEMS sensors with direct application. A reproducible and complete approach including all the intermediate steps, tools and laboratory equipment is described. The process of sensor error characterization and modeling through to the final integration in the sensor fusion scheme is explained with detail. Although the concept of fusion is relatively easy to comprehend, carefully characterizing and filtering sensor information is not an easy task and is essential for good performance. The strength of the approach has been verified with representative tests of novel high-grade MEMS inertia sensors and exemplary planetary rover platforms with promising results

A phonocardiographic-based fiber-optic sensor and adaptive filtering system for noninvasive continuous fetal heart rate monitoring

This paper focuses on the design, realization, and verification of a novel phonocardiographic-based fiber-optic sensor and adaptive signal processing system for noninvasive continuous fetal heart rate (fHR) monitoring. Our proposed system utilizes two Mach-Zehnder interferometeric sensors. Based on the analysis of real measurement data, we developed a simplified dynamic model for the generation and distribution of heart sounds throughout the human body. Building on this signal model, we then designed, implemented, and verified our adaptive signal processing system by implementing two stochastic gradient-based algorithms: the Least Mean Square Algorithm (LMS), and the Normalized Least Mean Square (NLMS) Algorithm. With this system we were able to extract the fHR information from high quality fetal phonocardiograms (fPCGs), filtered from abdominal maternal phonocardiograms (mPCGs) by performing fPCG signal peak detection. Common signal processing methods such as linear filtering, signal subtraction, and others could not be used for this purpose as fPCG and mPCG signals share overlapping frequency spectra. The performance of the adaptive system was evaluated by using both qualitative (gynecological studies) and quantitative measures such as: Signal-to-Noise Ratio-SNR, Root Mean Square Error-RMSE, Sensitivity-S+, and Positive Predictive Value-PPV.Web of Science174art. no. 89

Implementasi Algoritma New Heuristic Similarity Model (NHSM) Pada Web Based Recommender System

Dalam website e-commerce banyak produk atau jasa yang ditawarkan kepada user dan cukup membuat user kebingungan untuk memilih produk atau jasa apa yang akan mereka gunakan. Tetapi seiring berkembangnya pengetahuan dan teknologi, maka ditemukan suatu cara untuk membantu user mempersempit information overloads ini, yaitu dengan menggunakan recommender system. Tujuan penelitian adalah mengimplementasikan algoritma New Heuristic Similarity Model (NHSM) pada web based recommender system berbasis memory based collaborative filtering dan mengukur keakuratan prediksi menggunakan Mean Absolute Error. Metode pengujian menggunakan empat jenis skenario yaitu skenario perhitungan prediction score, perhitungan similarity, pengujian sparse dataset dan dense dataset. Keempat skenario tersebut diuji dengan menggunakan tiga dataset yaitu MovieLens, Jester Joke dan Yahoo Movie. Hasil penelitian menunjukkan bahwa algoritma NHSM dapat diterapkan pada web based recommender system dan keakuratan prediksi semakin baik jika dataset terisi rating penuh (dense dataset) serta hasil similarity mendekati satu. Kata Kunci: Recommender System, New Heuristic Similarity Model (NHSM), Memory Based Collaborative Filtering, Mean Absolute Error. There are many products or services offered to users in the e-commerce website. Those create users\u27 confusion to choose what products or services they will use. Along with science and technology development, then found a way to help users to narrow down the information overloads by using a recommender system. The research objectives are to implement New Heuristic Similarity Model (NHSM) algorithm in web-based recommender system on memory-based collaborative filtering and measuring prediction accuracy using Mean Absolute Error. The testing method uses four scenarios: calculation of prediction score, calculation of similarity, sparse datasets testing and dense datasets testing. The fourth scenario was tested by using three datasets which are MovieLens, Jester Joke and Yahoo Movie. The results showed that NHSM algorithm can be applied to a web-based recommender system. Prediction accuracy will be better if datasets are filled with full rating (dense dataset) and its value of similarity approaching 1. Keywords: Recommender System, New Heuristic Similarity Model (NHSM), Memory Based Collaborative Filtering, Mean Absolute Error. DAFTAR PUSTAKA Adomavicius, G., & Tuzhilin, A. (2005). Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions. IEEE Transactions on Knowledge and Data Engineering Vol.17, 734-749. Ahn, H. J. (2007). A Hybrid Collaborative Filtering Recommender System Using a New Similarity Measure. Proceedings of the 6th WSEAS International Conference on Applied Computer Science, 494-498. Bhunje, S. (2014, Mei 29). Retrieved Desember 3, 2014, from The Geek: http://theegeek.com/do-you-know-about-collaborative-filtering/ Cacheda, F., Carneiro, V., Fernandez, D., & Formoso, V. (2011). Comparison of Collaborative Filtering Algorithms: Limitations of Current Techniques and Proposals for Scalable, High- Performance Recommender Systems. ACM Transactions on the Web Vol.5. Dennis, A., Wixom, B. H., & Tegarden, D. (2010). Systems Analysis and Design with UML. New Jersey: Wiley. Ekstrand, M. D., Riedl, J. T., & Konstan, J. A. (2010). Collaborative Filtering Recommender System. The Essence of Knowledge: Human-Computer Interaction Vol.4, 81-173. Hafid, Z., Maharani, W., & Firdaus A., Y. (2010). Similarity Measure menggunakan Algoritma Weighted Difference Entropy (WDE) berbasis Memory-based Collaborative Filtering. Bandung: Telkom University. Lee, J., Sun, M., & Lebanon, G. (2012). A Comparative Study of Collaborative Filtering Algorithms. arXiv preprint arXiv:1205.3193. Liu, H., Hu, Z., Mian, A., Tian, H., & Zhu, X. (2014). A New User Similarity Model to Improve the Accuracy of Collaborative Filtering. Knowledge-Based System, 156-166. Melville, P., & Sindhwani, V. (2010). Recommender Systems. Encyclopedia of Machine Learning (pp. 829-838). Springer US. Navidi, W. (2011). Statistics for Engineers and Scientists. New York: McGraw-Hill. Nugroho, D. S. (2010). Analsis dan Implementasi Perbandingan Metode Cosine Similarity dan Correlation Based Similarity Pada Recommender System Berbasis Item-Based Collaborative Filtering. Bandung: Telkom University. Ricci, F., Rokach, L., Shapira, B., & Kantor, P. B. (2011). Recommender Systems Handbook. New York: Springer. Rodriguez, D. (2011). Recommender Systems. In J. Leskovec, A. Rajaraman, & J. D. Ullman, Mining of Massive Datasets. United Kingdom: Cambridge University Press. Sania, R., Maharani, W., & K, A. P. (2010). Analisis Perbandingan Metode Pearson dan Sperman Correlation pada Recommender System. Konferensi Nasional Sistem dan Informatika, 99-105. Shapira, B., & Rokach, L. (2010). Retrieved Desember 24, 2014, from Ben-Gurion University: medlib.tau.ac.il/teldan-2010/bracha.ppt Su, X., & Khoshgoftaar, T. M. (2009). A Survey of Collaborative Filtering Techniques. Hindawi Publishing Corporation: Advance in Artificial Intelligence. Sugiyono. (2010). Metode Penelitian Pendidikan. Bandung: ALFABETA. Willmott, C. J., & Matsuura, K. (2005). Advantages of the Mean Absolute Error (MAE) the Root Mean Square Error (RMSE) in Assessing Average Model Performance. Climate Research Vol.30, 79-82

Information filtering in high velocity text streams using limited memory - An event-driven approach to text stream analysis

This dissertation is concerned with the processing of high velocity text streams using event processing means. It comprises a scientific approach for combining the area of information filtering and event processing. In order to be able to process text streams within event driven means, an event reference model was developed that allows for the conversion of unstructured or semi-structured text streams into discrete event types on which event processing engines can operate. Additionally, a set of essential reference processes in the domain of information filtering and text stream analysis were described using event-driven concepts. In a second step, a reference architecture was designed that described essential architectural components required for the design of information ltering and text stream analysis systems in an event-driven manner. Further to this, a set of architectural patterns for building event driven text analysis systems was derived that support the design and implementation of such systems. Subsequently, a prototype was built using the theoretic foundations. This system was initially used to study the effect of sliding window sizes on the properties of dynamic sub-corpora. It could be shown that small sliding window based corpora are similar to larger sliding windows and thus can be used as a resource-saving alternative. Next, a study of several linguistic aspects of text streams was undertaken that showed that event stream summary statistics can provide interesting insights into the characteristics of high velocity text streams. Finally, four essential information filtering and text stream analysis components were studied, viz. filter policies, term weighting, thresholds and query expansion. These were studied using three temporal search profile types and were evaluated using standard information retrieval performance measures. The goal was to study the efficiency of traditional as well as new algorithms within the given context of high velocity text stream data, in order to provide advise which methods work best. The results of this dissertation are intended to provide software architects and developers with valuable information for the design and implementation of event-driven text stream analysis systems

Recent advances on recursive filtering and sliding mode design for networked nonlinear stochastic systems: A survey

Copyright © 2013 Jun Hu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Some recent advances on the recursive filtering and sliding mode design problems for nonlinear stochastic systems with network-induced phenomena are surveyed. The network-induced phenomena under consideration mainly include missing measurements, fading measurements, signal quantization, probabilistic sensor delays, sensor saturations, randomly occurring nonlinearities, and randomly occurring uncertainties. With respect to these network-induced phenomena, the developments on filtering and sliding mode design problems are systematically reviewed. In particular, concerning the network-induced phenomena, some recent results on the recursive filtering for time-varying nonlinear stochastic systems and sliding mode design for time-invariant nonlinear stochastic systems are given, respectively. Finally, conclusions are proposed and some potential future research works are pointed out.This work was supported in part by the National Natural Science Foundation of China under Grant nos. 61134009, 61329301, 61333012, 61374127 and 11301118, the Engineering and Physical Sciences Research Council (EPSRC) of the UK under Grant no. GR/S27658/01, the Royal Society of the UK, and the Alexander von Humboldt Foundation of Germany

Recent advances on filtering and control for nonlinear stochastic complex systems with incomplete information: A survey

This Article is provided by the Brunel Open Access Publishing Fund - Copyright @ 2012 Hindawi PublishingSome recent advances on the filtering and control problems for nonlinear stochastic complex systems with incomplete information are surveyed. The incomplete information under consideration mainly includes missing measurements, randomly varying sensor delays, signal quantization, sensor saturations, and signal sampling. With such incomplete information, the developments on various filtering and control issues are reviewed in great detail. In particular, the addressed nonlinear stochastic complex systems are so comprehensive that they include conventional nonlinear stochastic systems, different kinds of complex networks, and a large class of sensor networks. The corresponding filtering and control technologies for such nonlinear stochastic complex systems are then discussed. Subsequently, some latest results on the filtering and control problems for the complex systems with incomplete information are given. Finally, conclusions are drawn and several possible future research directions are pointed out.This work was supported in part by the National Natural Science Foundation of China under Grant nos. 61134009, 61104125, 61028008, 61174136, 60974030, and 61074129, the Qing Lan Project of Jiangsu Province of China, the Project sponsored by SRF for ROCS of SEM of China, the Engineering and Physical Sciences Research Council EPSRC of the UK under Grant GR/S27658/01, the Royal Society of the UK, and the Alexander von Humboldt Foundation of Germany