Adaptive split spectrum processing for ultrasonic signal in the pulse echo test

In this paper, an Adaptive Split Spectrum Processing technique (A-SSP) is proposed, to improve ultrasonic echoes detection. It is an arrangement of conventional Split Spectrum Processing (SSP) with an empirical method of analyzing nonlinear and non-stationary signals, called Empirical Mode Decomposition (EMD). This proposed technique allows breaking up the signal into several bands of frequencies in an adaptive way and intrinsic to the treated signal using EMD. It enables us to know the internal contents and the local changes of the ultrasonic signal and makes the detection of any desired targets more flexible for the coherent noise problem. In the combination phase of A-SSP, a linear operation for selected intrinsic mode functions and a non linear one for non selected intrinsic mode functions are used to reconstruct the signal with separated echoes.To evaluate the proposed techniques (A-SSP with different combination operations), firstly a mortar specimen with artificial defect is used to resolve the defects detection and localization problem. Secondly a paste cement specimen is also used to resolve the materials characterization problem. The signals were obtained using a technique applied in pulse-echo mode, known as the prism technique. Numerical and experimental tests were performed to verify the effectiveness and reliability of the proposed technique and to show its excellent performances

Face–Iris Multimodal Biometric Identification System

Multimodal biometrics technology has recently gained interest due to its capacity to overcome certain inherent limitations of the single biometric modalities and to improve the overall recognition rate. A common biometric recognition system consists of sensing, feature extraction, and matching modules. The robustness of the system depends much more on the reliability to extract relevant information from the single biometric traits. This paper proposes a new feature extraction technique for a multimodal biometric system using face–iris traits. The iris feature extraction is carried out using an efficient multi-resolution 2D Log-Gabor filter to capture textural information in different scales and orientations. On the other hand, the facial features are computed using the powerful method of singular spectrum analysis (SSA) in conjunction with the wavelet transform. SSA aims at expanding signals or images into interpretable and physically meaningful components. In this study, SSA is applied and combined with the normal inverse Gaussian (NIG) statistical features derived from wavelet transform. The fusion process of relevant features from the two modalities are combined at a hybrid fusion level. The evaluation process is performed on a chimeric database and consists of Olivetti research laboratory (ORL) and face recognition technology (FERET) for face and Chinese academy of science institute of automation (CASIA) v3.0 iris image database (CASIA V3) interval for iris. Experimental results show the robustness

A bi-dimensional empirical mode decomposition based watermarking scheme

An invisible robust, non blind watermarking scheme for digital images is presented. The proposed algorithmcombines the Discrete Wavelet Transform (DWT) and the Bi-dimensional Empirical Mode Decomposition (BEMD). Unlikeprevious works where the watermark bits are embedded directly on the wavelet coefficients, the proposed scheme suggests rather the embedding of the wavelet coefficients of the mean trend results by performing the BEMD on the host image, using Singular Value Decomposition (SVD). The watermarked image has a very good perceptual transparency. The extraction algorithm is a non-blind process, which uses the original image as a reference for retrieving the watermark. The proposed algorithm is robust against rotation, translation, compression and noise addition. It has also a superior Peak Signal to Noise Ratio (PSNR) for the watermarked image. The obtained results, tested on different images by various attacks, are satisfactory in terms of imperceptibility and robustness

A new spectral index for the extraction of built-up land features from Landsat 8 satellite imagery

Extracting built-up areas from remote sensing data like Landsat 8 satellite is a challenge. We have investigated it by proposing a new index referred as Built-up Land Features Extraction Index (BLFEI). The BLFEI index takes advantage of its simplicity and good separability between the four major component of urban system, namely built-up, barren, vegetation and water. The histogram overlap method and the Spectral Discrimination Index (SDI) are used to study separability. BLFEI index uses the two bands of infrared shortwaves, the red and green bands of the visible spectrum. OLI imagery of Algiers, Algeria, was used to extract built-up areas through BLFEI and some new previously developed built-up indices used for comparison. The water areas are masked out leading to Otsu’s thresholding algorithm to automatically find the optimal value for extracting built-up land from waterless regions. BLFEI, the new index improved the separability by 25% and the accuracy by 5%.open access</p