Recent Patents on Signal Processing (Discontinued) - Volume 4, Issue 1, 2014

Reliable seismic waves characterization is essential for better understanding wave propagation phenomena, providing new physical insight into soil properties. Many works in this area have been based on detecting special patterns or clusters in seismic data, event detection using parametric models, and time-frequency analysis. In this paper we present an approach making use of the short-term time-frequency Rényi entropy and maximum a posteriori probability (MAP) estimator, operating on Rényi entropy, as a new space of decision; this method enables more robust feature extraction and a more accurate classification. This approach was used in simulation and in the analysis of the earthquake records during the Kocaeli, Arcelik seism, Turkey, August 1999, a strong to moderate ground motion. 87.57.nm, 43.60.Hj, 89.70.Cf, 02.50.-r, 91.30.-f.

In this paper, we design distributed Turbo Product Codes (TPC) for cooperative communications. Specifically, to improve performance by using multiple relays, we propose Cyclic Interleaver (CI), a novel method for TPC to obtain independent Multiple Vertical Parities (MVP) for different relays. With this method, codewords in different relays are all in the same coding space of the product codes and hence can be jointly decoded. At the receiver, we further modify the existing turbo product decoder to iteratively decode the source information from data received from both the source node and the relay nodes. The system performance is demonstrated via simulations.

Biometric systems use automated methods for pattern recognition in determining the authenticity of a specific physiological or behavioral characteristic of an individual to determine or verify identity. In this work, 2D and 3D palmprints are integrated in order to construct an efficient multi-biometric identification system based on matching score level fusion. In this paper, we try to evaluate the usefulness of the 2D and 3D palmprints for improving the palmprint based person identification systems. For that purpose, we propose several systems of exploiting the palmprint modalities. In this study, the Gabor filter response and the wavelet transform are used for representing the palmprint traits. First, the 2D palmprint is filtered by the Gabor filter. The real and imaginary responses of the filtered image are used to create three different vectors. Second, the 2D palmprint is transformed into several sub-bands using wavelet transform. After that, some sub-bands are used to create several vectors. Subsequently, the Radial Basis Function (RBF) is used for modeling and classifying the feature vectors. The results of some classifiers are combined using matching score level fusion strategy. The proposed system was tested and evaluated, for its efficacy on the available PolyU 2D and 3D palmprint database of 300 persons. The obtained experimental results show that the system yields the best performance for identifying palmprint and it is able to provide the highest degree of security.

Intra-coding is the application of spatial prediction coding to video data either on a whole frame basis or on individual macroblocks. This paper outlines the growing sophistication in intra-coding within recent standard codecs, the topic which is primarily relevant to compression for data storage. It then specializes to intra-coding for error resilience and intra-refresh, the topic which is relevant to video streaming applications. Efficient intra coding is becoming important for real-time high-resolution video, due to the computational cost of inter-coding, as the paper will consider for recent codecs. The paper will compare error-resilience performance across a variety of schemes. There are choices, which the paper analyses, as to whether to protect: on a whole frame basis; by intra-coding selected macro-blocks; and whether to use an isolated region; a cyclic intra-refresh line; or randomized insertion of macroblocks. The paper contains over 100 references, including recent patents in this field.

In automobiles, the brake system is an essential part responsible for control of the vehicle. Any failure in the brake system generates subsequent catastrophic effects on the vehicle cum passenger’s safety. Hence condition monitoring of the brake system is indispensable. This study focuses on the condition monitoring of a hydraulic brake system through vibration analysis. A machine learning approach was used for this vibration analysis. A hydraulic brake system test rig was fabricated. Frequently occurring fault conditions were simulated. Under good and faulty conditions of a brake system, the vibration signals were acquired using a piezoelectric transducer. From the vibration signal statistical features were extracted. The best feature set was identified for classification using attribute evaluator. Selected features were then classified using K Star algorithm. The classification accuracy of such artificial intelligence technique was then compared with the decision tree (DT) and Locally Weighted Learning (LWL) algorithm. Comparative results for fault diagnosis of a hydraulic brake system were reported and discussed. For brake fault diagnosis, K Star performs better and it gives the maximum classification accuracy as 98.55%. The model built can be used for condition monitoring of a hydraulic brake system.