9319216
Distance-Based Sparse Associative Memory Neural Network Algorithm for Pattern Recognition
A sparse two-Dimension distance weighted approach for improving the performance of exponential correlation associative memory (ECAM) and modified exponential correlation associative memory (MECAM) is presented in this paper. The approach is inspired by biological visual perception mechanism and extensively existing sparse small-world network phenomenon. By means of the approach, the two new associative memory neural networks, i.e., distance-based sparse ECAM (DBS-ECAM) and distance-based sparse MECAM (DBS-MECAM), are induced by introducing both the decaying two-Dimension distance factor and small-world architecture into ECAM and MECAM’s evolution rule for image processing application. Such a new configuration can reduce the connection complexity of conventional fully connected associative memories so that makes AM’ VLSI implementation easier. More importantly, the experiments performed on the binary visual images show DBS-ECAM and DBS-MECAM can learn and recognize patterns more effectively than ECAM and MECAM, respectively.
17212464
A unified SWSI-KAMs framework and performance evaluation on face recognition
The Kernel method is an effective and popular trick in machine learning. In this paper, by introducing it into conventional auto-associative memory models (AMs), we construct a unified framework of kernel auto-associative memory models (KAMs), which makes the existing exponential and polynomial AMs become its special cases. Further, in order to reduce KAM's connect complexity, inspired by ''small-world network'' recently described by Watts and Strogatz, we propose another unified framework of small-world structure (SWS) inspired kernel auto-associative memory models (SWSI-KAMs), which, in principle, makes KAMs simpler in structure. Simulation results on the FERET face database show that, the SWSI-KAMs adopting kernels such as Exponential and Hyperbolic tangent kernels have advantages of configuration simplicity while their recognition performance is almost as good as or even better than corresponding KAMs with full connectivity. In the end, the SWSI-KAM adopting Exponential kernel with different connectivities was emphatically investigated for robustness based on those face images which were added random noises and/or partially occluded in a mosaic way, and the experiments demonstrate that the SWSI-KAM with Exponential kernel is more robust in all cases of network connectivity of 20%, 40% and 60% than both PCA and recently proposed (PC)^2A algorithms for face recognition. cognition.
33955420
Continuously Maintaining Sliding Window Skylines in a Sensor Network
Currently, wireless sensor network has been widely used in environment monitoring. The skyline query, as an important operator for multiple criteria decision making and data mining, plays an important role in many sensing applications. Though skyline queries have been well-studied in traditional database system, the existing solutions designed for data stored in a centralized site are not directly applicable to sensor environment due to the unique characteristics of wireless sensor network. In this paper, we propose an energy-efficient algorithm, called Sliding Window Skyline Monitoring Algorithm (SWSMA), to continuously maintain sliding window skylines over a wireless sensor network. Specifically, SWSMA employs two types of filters within each sensor to reduce the amount of data transferred and save the energy consumption as a consequence. In addition to SWSMA, a set of optimization mechanisms are also discussed to improve the performance of SWSMA. Our extensive simulation studies show that SWSMA together with the optimization techniques performs effectively on reducing communication cost and saving the energy on monitoring sliding window skylines.
14090386
In-network Outlier Cleaning for Data Collection in Sensor Networks
Outliers are very common in the environmental data monitored by a sensor network consisting of many inexpensive, low fidelity, and frequently failed sensors. The limited battery power and costly data transmission have introduced a new challenge for outlier cleaning in sensor networks: it must be done innetwork to avoid spending energy on transmitting outliers. In this paper, we propose an in-network outlier cleaning approach, including wavelet based outlier correction and neighboring DTW(Dynamic Time Warping) distance-based outlier removal. The cleaning process is accomplished during multi-hop data forwarding process, and makes use of the neighboring relation in the hop-count based routing algorithm. Our approach guarantees that most of the outliers can be either corrected, or removed from further transmission within 2 hops. We have simulated a spatialtemporal correlated environmental area, and evaluated the outlier cleaning approach in it. The results show that our approach can effectively clean the sensing data and reduce outlier traffic.
27024485
A new design of multiple classifier system and its application to the classification of time series data
In this paper, we propose the scheme of multiple input representation-adaptive ensemble generation and aggregation(MIR-AEGA) for the classification of time series data. MIR-AEGA employs a set of heterogeneous classifiers, each of which takes a different representation of time series data as the input. MIR-AEGA adopts an "overfitting and selection" strategy. In the training phase, different ensembles of classifiers are adaptively generated by fitting the validation data ' globally in different degrees. The test data are then classified by each of the generated ensembles. The final decision is made by taking consideration into both the ability of each ensemble to fit the validation data locally and the possible overfitting effects. We claim that MIR-AEGA has two advantages (1) By using multiple representations, it exploits the temporal information of time series data as much as possible, thus could improve the overall performance (2) By tweaking the trade-off between the ability to fit the validation data and the overfitting effects, we expect the performance of this method is reliable in different situations. In this paper, the performance of MIR- AEGA is also assessed experimentally in comparison with other benchmark techniques. The experimental results demonstrate the good performance and the reliability of MIR-AEGA for the classification of time series data.
1926702
Nonthreshold-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.
22672181
Inclusion Test Algorithms for One-Unambiguous Regular Expressions
One-unambiguous regular expressions are used in DTD. It is known that inclusion for one-unambiguous regular expressions is in PTIME. However, there has been no study on algorithms for the inclusion. In this paper we present algorithms for checking inclusion of one-unambiguous regular expressions. A classical way is based on automata, following which one algorithm is provided and improvements are given. The other algorithm is based on derivatives, utilizing a property introduced here that the number of derivatives of a one-unambiguous regular expression is finite. We conducted preliminary experiments by implementing typechecking of XML using the algorithms. The results show that typechecking using the new algorithms is more efficient than the typechecking used for XDuce.