John R. Deller

Set membership identification in digital signal processing

Set membership (SM) identification refers to a class of techniques for estimating parameters of linear systems or signal models under a priori information that constrains the solutions to certain sets. When data do not help refine these membership sets, the effort of updating the parameter estimates at those points can be avoided. An intuitive development is given, first in one dimension and then in the general case, of an SM algorithm based on least-squares estimation. Two useful versions of the method are described, one of which can be implemented on a systolic array processor. The relationship of the featured SM method to both historical and current developments is discussed. Application to real speech data illustrates the developments.<<ETX>>

Set-membership identification and filtering for signal processing applications

Optimal bounding ellipsoid (OBE) algorithms comprise a class of novel recursive identification methods for affine-in-parameters system and signal models. OBE algorithms exploit a priori knowledge of bounds on model disturbances to derive a monotonically nonincreasing set of solutions that are feasible in light of the observations and the model structure. In turn, these sets admit criteria for ascertaining the information content of incoming observations, obviating the expense of updating when data are redundant. Relative to classical recursive methods for this task, OBE algorithms are efficient, robust, and exhibit remarkable tracking ability, rendering them particularly attractive for realtime signal processing and control applications. After placing the OBE algorithms in the hierarchy of the broader set-membership identification methods, this article introduces the underlying set-theoretic concepts, compares and contrasts the various published OBE algorithms including the motivation for each development, then concludes with some illustrations of OBE algorithm performance. More recent work on the use of OBE processing infiltering tasks is also included in the discussion. The paper is a survey of a broad and evolving topic, and extensive references to further information are included.

A 'systolic array' formulation of the optimal bounding ellipsoid algorithm

A previously published recursive estimation algorithm (ibid., vol.ASSP-34, p.1331-4, 1986), which updates the parameter vector for a linear system only when incoming data are sufficiently informative, is reformulated to be implementable using well-known systolic array processing schemes. In particular, the optimal bounding ellipsoid algorithm (OBE) for identifying an autoregressive moving-average (ARMA) system is formulated as a conventional weighted recursive least squares (WRLS) estimator with special weights. In this framework, OBE can be implemented using algorithms developed for LS solutions on systolic machines. Adaptation by a sliding window is easily added to this formulation. A simulation example is given to illustrate the results. >

Speech watermarking with objective fidelity and robustness criteria

Speech watermarking strategies inevitably alter original signal content. Fidelity is adversely affected by increased perturbation while watermark robustness to attack is generally improved. Parameter-embedded watermarking is effected through slight perturbations of parametric models of some deeply integrated dynamics of the speech. Within this framework, a specific algorithm is presented in which the fidelity-robustness tradeoff can be objectively assessed and quantifiably adjusted according to specific measures. An overview of the general parameter-embedding strategy is followed by presentation of the featured algorithm, analysis of its properties, and experiments with speech data to assess fidelity, robustness, and other performance properties.

A consistently convergent OBE algorithm with automatic estimation of error bounds

Conventional optimal bounding ellipsoid (OBE) algorithms require a priori knowledge of error bounds which is unknown in most applications. Conservative (overestimated) error bounds used in practice may lead to inconsistent parameter estimation. This paper presents an enhanced OBE algorithm that is proven to be consistently convergent without a priori knowledge of error bounds. Only a lower bound of the tail probability of the disturbance process is required. Simulations comparing conventional and the enhanced OBE algorithm is provided.

Video object matching across multiple non-overlapping camera views based on multi-feature fusion and incremental learning

Abstract Matching objects across multiple cameras with non-overlapping views is a necessary but difficult task in the wide area video surveillance. Owing to the lack of spatio-temporal information, only the visual information can be used in some scenarios, especially when the cameras are widely separated. This paper proposes a novel framework based on multi-feature fusion and incremental learning to match the objects across disjoint views in the absence of space–time cues. We first develop a competitive major feature histogram fusion representation (CMFH 1 ) to formulate the appearance model for characterizing the potentially matching objects. The appearances of the objects can change over time and hence the models should be continuously updated. We then adopt an improved incremental general multicategory support vector machine algorithm (IGMSVM 2 ) to update the appearance models online and match the objects based on a classification method. Only a small amount of samples are needed for building an accurate classification model in our method. Several tests are performed on CAVIAR, ISCAPS and VIPeR databases where the objects change significantly due to variations in the viewpoint, illumination and poses. Experimental results demonstrate the advantages of the proposed methodology in terms of computational efficiency, computation storage, and matching accuracy over that of other state-of-the-art classification-based matching approaches. The system developed in this research can be used in real-time video surveillance applications.

On encryption-compression tradeoff of pre/post-filtered images

Advances in network communications have necessitated secure local-storage and transmission of multimedia content. In particular, military networks need to securely store sensitive imagery which at a later stage may be transmitted over bandwidth-constrained wireless networks. This work investigates compression efficiency of JPEG and JPEG 2000 standards for encrypted images. An encryption technique proposed by Kuo et al. in [4] is employed. The technique scrambles the phase spectrum of an image by addition of the phase of an all-pass pre-filter. The post-filter inverts the encryption process, provided the correct pseudo-random filter coefficients are available at the receiver. Additional benefits of pre/post-filter encryption include the prevention of blocking effects and better robustness to channel noise [4]. Since both JPEG and JPEG 2000 exploit spatial and perceptual redundancies for compression, pre/post-filtered (encrypted) images are susceptible to compression inefficiencies. The PSNR difference between the unencrypted and pre/post-filtered images after decompression is determined for various compression rates. Compression efficiency decreases with an increase in compression rate. For JPEG and JPEG 2000 compression rates between 0.5 to 2.5 bpp, the difference in PSNR is negligible. Partial encryption is proposed wherein a subset of image phase coefficients are scrambled. Due to the phase sensitivity of images, even partial scrambling of the phase information results in unintelligible data. The effect of compression on partially encrypted images is observed for various bit-rates. When 25% of image phase coefficients are scrambled, the JPEG and JPEG 2000 compression performance of encrypted images is almost similar to that of unencrypted images for compression rates in the 0.5 to 3.5 bpp range.

Practical considerations in the use of a new OBE algorithm that blindly estimates error bounds

Optimal bounding ellipsoid (OBE) identification algorithms require precise knowledge of bounds on model disturbance sequences, and such bounds are often difficult to ascertain in practice. The OBE algorithm with automatic bound estimation (OBE-ABE) theoretically obviates the need for precise a priori bound estimates, thereby removing the major obstacle to practical application of these powerful and interesting methods. Performance assessment of OBE-ABE, particularly with regard to its favorable convergence behavior, has involved asymptotic analysis over infinite frames of data. This paper discusses application of OBE-ABE to short-time frames of data, suggesting that the favorable asymptotic results apply to finite processing if care is taken in the choice of certain parameters. Example case studies, one using real speech data, illustrate the theoretical discussions.

An interpretable and converging set-membership algorithm

Set membership (SM)-based techniques, with least square error overlay, suffer from a trade-off between interpretability and proof of convergence. The authors introduce a modified SM algorithm with forgetting covariance updating in conjunction with minimum volume data selecting strategy. The convergence properties of this algorithm and its resemblance to the stochastic approximation method are discussed.<<ETX>>

Breast Cancer Detection Using Haralick Features of Images Reconstructed from Ultra Wideband Microwave Scans

Microwave scanning of the breast would provide a technology for cancer detection and screening that is significantly safer than current methods involving radiation. This research focuses on finding the best way for accurate characterization of cancerous signals and normal signals using clinical data collected from a previously developed ultra wideband (UWB) antenna, BRATUMASS (Breast Tumor Microwave Sensor System). BRATUMASS which detects changes in dielectric constants within the breast. The signals collected from the microwave scanning procedure are reconstructed into a single, informative representation of the breast via diffraction tomography. This representation contains the information of the breast’s conductivity and the change in dielectric constants. We illustrate the feasibility of using Haralick features to make distinctions among breasts with a malignant tumor present and breasts with no malignancy in data collected from Shanghai Sixth People’s Hospital and Shanghai First People’s Hospital.