14544468
Using Exact Locality Sensitive Mapping to Group and Detect Audio-Based Cover Songs
Cover song detection is becoming a very hot research topic when plentiful personal music recordings or performance are released on the Internet. A nice cover song recognizer helps us group and detect cover songs to improve the searching experience. The traditional detection is to match two musical audio sequences by exhaustive pairwise comparisons. Different from the existing work, our aim is to generate a group of concatenated feature sets based on regression modeling and arrange them by indexing-based approximate techniques to avoid complicated audio sequence comparisons. We mainly focus on using exact locality sensitive mapping (ELSM) to join the concatenated feature sets and soft hash values. Similarity-invariance among audio sequence comparison is applied to define an optimal combination of several audio features. Soft hash values are pre-calculated to help locate searching range more accurately. Furthermore, we implement our algorithms in analyzing the real audio cover songs and grouping and detecting a batch of relevant cover songs embedded in large audio datasets.
9576587
Non-Threshold based Event Detection for 3D Environment Monitoring in Sensor Networks
Event detection is a crucial task for wireless sensor network applications, especially environment monitoring. Existing approaches for event detection are mainly based on some predefined threshold values, and thus are often inaccurate and incapable of capturing complex events. For example, in coal mine monitoring scenarios, gas leakage or water osmosis can hardly be described by the overrun of specified attribute thresholds, but some complex pattern in the full-scale view of the environmental data. To address this issue, we propose a non-threshold based approach for the real 3D sensor monitoring environment. We employ energy-efficient methods to collect a time series of data maps from the sensor network and detect complex events through matching the gathered data to spatio-temporal data patterns. Finally, we conduct trace driven simulations to prove the efficacy and efficiency of this approach on detecting events of complex phenomena from real-life records.
7517195
Spotting Significant Changing Subgraphs in Evolving Graphs
Graphs are popularly used to model structural relationships between objects. In many application domains such as social networks, sensor networks and telecommunication, graphs evolve over time. In this paper, we study a new problem of discovering the subgraphs that exhibit significant changes in evolving graphs. This problem is challenging since it is hard to define changing regions that are closely related to the actual changes (i.e., additions/deletions of edges/nodes) in graphs. We formalize the problem, and design an efficient algorithm that is able to identify the changing subgraphs incrementally. Our experimental results on real datasets show that our solution is very efficient and the resultant subgraphs are of high quality.
119545784
The EDAM project: Mining atmospheric aerosol datasets: Research Articles
There is a great need to better understand the sources, dynamics, and compositions of atmospheric aerosols. The traditional approach for particle measurement, collecting bulk samples of particulates on filters, is not adequate for studying particle dynamics and real-time correlations. This has led to the development of a new generation of real-time instruments that provide continuous or semicontinuous streams of data about certain aerosol properties. However, these instruments have added a significant level of complexity to atmospheric aerosol data and dramatically increased the amounts of data to be collected, managed, and analyzed. Our ability to integrate the data from all of these new and complex instruments now lags far behind our data-collection capabilities, and severely limits our ability to understand the data and act upon it in a timely manner. In this article, we present an overview of EDAM (Exploratory Data Analysis and Management), a joint project between researchers in Atmospheric Chemistry and Computer Science. Important objectives include environmental monitoring and data quality assurance, and real-time data mining offers great potential. While atmospheric aerosol analysis is an important and challenging domain, our objective is to develop techniques that have broader applicability. © 2005 Wiley Periodicals, Inc. Int J Int Syst 20: 759–787, 2005.
12758296
A Concise Representation of Range Queries
With the advance of wireless communication technology, it is quite common for people to view maps or get related services from the handheld devices, such as mobile phones and PDAs. Range queries, as one of the most commonly used tools, are often posed by the users to retrieve needful information from a spatial database. However, due to the limits of communication bandwidth and hardware power of handheld devices, displaying all the results of a range query on a handheld device is neither communication efficient nor informative to the users. This is simply because that there are often too many results returned from a range query. In view of this problem, we present a novel idea that a concise representation of a specified size for the range query results, while incurring minimal information loss, shall be computed and returned to the user. Such a concise range query not only reduces communication costs, but also offers better usability to the users, providing an opportunity for interactive exploration. The usefulness of the concise range queries is confirmed by comparing it with other possible alternatives, such as sampling and clustering. Then we propose algorithms to find a good concise representation.
8704395
Summarization Graph Indexing: Beyond Frequent Structure-Based Approach
Graph is an important data structure to model complex structural data, such as chemical compounds, proteins, and XML documents. Among many graph data-based applications, sub-graph search is a key problem, which is defined as given a query Q, retrieving all graphs containing Q as a sub-graph in the graph database. Most existing sub-graph search methods try to filter out false positives (graphs that are not possible in the results) as many as possible by indexing some frequent sub-structures in graph database, such as [20, 22, 4, 23]. However, due to ignoring the relationships between sub-structures, these methods still admit a high percentage of false positives. In this paper, we propose a novel concept, Summarization Graph, which is a complete graph and captures most topology information of the original graph, such as sub-structures and their relationships. Based on Summarization Graphs, we convert the filtering problem into retrieving objects with set-valued attributes. Moreover, we build an efficient signature file-based index to improve the filtering process. We prove theoretically that the pruning power of our method is larger than existing structure-based approaches. Finally, we show by extensive experimental study on real and synthetic data sets that the size of candidate set generated by Summarization Graph-based approach is only about 50% of that left by existing graph indexing methods, and the total response time of our method is reduced 2-10 times.
24767232
The Research and Application of Human Detection Based on Support Vector Machine Using in Intelligent Video Surveillance System
This paper presented a special design and implementation of human detection based on SVM (support vector machine) and this method is used in intelligent video surveillance system. In order to simplify the design of the SVM classifier and improve efficiency of machine learning, both a grid vector representation and a center radiating vector representation are proposed to abstract features of the object. The sample data is obtained through processing and analysis including human and no-human which forms the training input to SVM. Finally, we used the trained recognizer to identify whether there is somebody broken into the object region. If there is, the automatic warning device gives the alarm, which guarantees a real-time surveillance.
17691520
Design of an Intelligent Controller for a Model Helicopter Using Neuro-Predictive Method with Fuzzy Compensation
In this paper, a Neuro-Predictive (NP) controller is designed and implemented on a highly non-linear system, a model helicopter in a constrained situation. It is observed that the closed loop system with the NP controller has a significant overshoot and a long settling time in comparison to the same system with an existing fuzzy controller. In order to improve the undesired system performance, a Sugeno-type fuzzy compensator, having only two rules, is added to the control loop to adjust control input. The newly designed Neuro-Predictive control with Fuzzy Compensator (NPFC) improves the system performance in both overshoot and settling time. Furthermore, it is shown that the NPFC controlled system is robust to disturbance and parameter changes.
616754
Automated Planning of CPICH Power for Enhancing HSDPA Performance at Cell Edges with Preserved Control of R99 Soft Handover
We present and demonstrate a novel approach for automated planning of common pilot channel (CPICH) power in mobile networks with co-existing HSDPA and R99 services. CPICH power allocation greatly influences the cell coverage pattern. A conventional strategy is to uniformly allocate a constant proportion of the total power to CPICH. We study non-uniform CPICH and optimize its allocation for enhancing HSDPA performance. We focus on HSDPA performance at cell edges, where user throughput is typically very low. Our approach is based on a linear-integer mathematical model; solving the model results in a power allocation that both ensures CPICH coverage and optimizes HSDPA performance at cell edges. Moreover, the model allows for precise control of soft handover (SHO) regions for R99 service. Experimental results show that our approach yields significant enhancement of HSDPA performance at cell edges with preserved R99 SHO control.
2677048
Convergence analysis of convex incremental neural networks
Recently, a convex incremental algorithm (CI-ELM) has been proposed in Huang and Chen (Neurocomputing 70:3056–3062, 2007), which randomly chooses hidden neurons and then analytically determines the output weights connecting with the hidden layer and the output layer. Though hidden neurons are generated randomly, the network constructed by CI-ELM is still based on the principle of universal approximation. The random approximation theory breaks through the limitation of most conventional theories, eliminating the need for tuning hidden neurons. However, due to the random characteristic, some of the neurons contribute little to decrease the residual error, which eventually increase the complexity and computation of neural networks. Thus, CI-ELM cannot precisely give out its convergence rate. Based on Lee’s results (Lee et al., IEEE Trans Inf Theory 42(6):2118–2132, 1996), we first show the convergence rate of a maximum CI-ELM, and then systematically analyze the convergence rate of an enhanced CI-ELM. Different from CI-ELM, the hidden neurons of the two algorithms are chosen by following the maximum or optimality principle under the same structure as CI-ELM. Further, the proof process also demonstrates that our algorithms achieve smaller residual errors than CI-ELM. Since the proposed neural networks remove these “useless” neurons, they improve the efficiency of neural networks. The experimental results on benchmark regression problems will support our conclusions.
183594
Efficient Type Checking for a Subclass of Regular Expression Types
Type checking is an important problem in statically typed XML processing languages. Most type systems are implemented based on tree automata for the tree structure of XML documents and schemas. It is known that the complexity of inclusion problem for tree automata is in EXPTIME. In fact, most definitions in XML Schema are actually DTDs. The inclusion problem of DTDs can be reduced to inclusion of one-unambiguous regular expressions in DTDs. We have developed a method for type checking that works for regular expression types which are DTDs. The method is based onone-unambiguous regular expressions together with using the information of labels in DTDs, which can work in top-down or bottom-up manner. Although our method supports DTDs only now, it is general and can support more general schemas by integrating with the conventional method of type checking. The experiments show that our method is more efficient in practice.