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Balanced binary search tree structures such as AVL, red-black, and splay trees store exactly one element per node. We propose supernode versions of these structures in which each node may have a large number of elements. Some properties of supernode binary search tree structures are established. Experiments oonducted by us show that the supernode structures proposed by us use less space than do the corresponding one-element-per-node versions and also take less time for the standard dictionary operations: search, insert and delete.

When searching for an item x in a dictionary, let t be the number of distinct items referenced since the previous reference to x. The move-to-front-list is a widely known dictionary data structure that supports searches on x in O(t) time. We present a new selforganizing data structure, called the Historical Search Tree, which supports the search in O(log t) time, and thus exploits the temporal locality more efficiently. The Historical Search Tree also handles unsuccessful searches, insertions, and deletions efficiently. We further show how the data structure can be modified to take advantage of spatial locality.

The automation in the diagnosis of medical images is currently a challenging task. The use of Computer Aided Diagnosis (CAD) systems can be a powerful tool for clinicians, especially in situations when hospitals are overflowed. These tools are usually based on artificial intelligence (AI), a field that has been recently revolutionized by deep learning approaches. These alternatives usually obtain a large performance based on complex solutions, leading to a high computational cost and the need of having large databases. In this work, we propose a classification framework based on sparse coding. Images are first partitioned into different tiles, and a dictionary is built after applying PCA to these tiles. The original signals are then transformed as a linear combination of the elements of the dictionary. Then, they are reconstructed by iteratively deactivating the elements associated with each component. Classification is finally performed employing as features the subsequent reconstruction errors. Performance is evaluated in a real context where distinguishing between four different pathologies: control versus bacterial pneumonia versus viral pneumonia versus COVID-19. Our system differentiates between pneumonia patients and controls with an accuracy of 97.74%, whereas in the 4-class context the accuracy is 86.73%. The excellent results and the pioneering use of sparse coding in this scenario evidence that our proposal can assist clinicians when their workload is high.

An algorithmic architecture for a high-performance optical character recognition (OCR) system for hand-printed and handwritten addresses is proposed. The architecture integrates syntactic and contextual post-processing with character recognition to optimise postcode recognition performance, and verifies the postcode against simple features extracted from the remainder of the address to ensure a low error rate.

An enhanced version of the characteristic loci character recognition algorithm was chosen for the system to make it tolerant of variations in writing style. Feature selection for the classifier is performed automatically using the B/W algorithm.

Syntactic and contextual information for hand-printed British postcodes have been integrated into the system by combining low-level postcode syntax information with a dictionary trie structure. A full implementation of the postcode dictionary trie is described. Features which define the town name effectively, and can easily be extracted from a handwritten or hand-printed town name are used for postcode verification.

A database totalling 3473 postcode/address image has used to evaluate the performance of the complete postcode recognition process. The basic character recognition rate for the full unconstrained alphanumeric character set is 63.1%, compared with an expected maximum attainable 75–80%. The addition of the syntactic and contextual knowledge stages produces an overall postcode recognition rate which is equivalent to an alphanumeric character recognition rate of 86–90%. Separate verification experiments on a subset of 820 address images show that, with the first-order features chosen, an overall correct address feature code extraction rate of around 35% is achieved.

The Dirac-$\delta$ distribution may be realized through sequences of convolutions, the latter being also regarded as approximation to the identity. This paper proposes the real form of pre-orthogonal adaptive Fourier decomposition (POAFD) method to realize fast approximation to the identity. It belongs to sparse representation of signals having potential applications in signal and image analysis.

This is a continuation of the study of adaptive Fourier decomposition (AFD).¹⁵ Under a mild condition not in terms of smoothness, a convergence rate is provided. We prove that the selection of the parameters corresponding to Fourier series in the average sense is optimal. We also present the transformation matrices between the adaptive rational orthogonal system and the related sequence of the shifted Cauchy kernels and their derivatives.

Semi-supervised boosting strategy aims at improving the performance of a given classifier with a multitude of unlabeled data. In semi-supervised boosting strategy, a similarity is needed to select unlabeled samples and then a pseudo label will be assigned to the unlabeled sample. A good similarity is helpful to assign a more proper pseudo label to unlabeled samples. Those selected samples with their pseudo labels will serve as labeled samples to train the new component classifier. So, similarity is important in semi-supervised boosting. Gaussian kernel similarity $S_{ij}=exp(-\frac{\parallel x_i-x_j{\parallel }^2}{2{\sigma }^2})$ is used in semi-supervised boosting strategy. There are two drawbacks, first, the Euclidean distance $\parallel x_i-x_j{\parallel }^2$ cannot characterize the complicated relationship between the data samples; second, the parameter $\sigma$ needs to set carefully. So, this paper proposes a novel adaptive similarity based on sparse representation for semi-supervised boosting. Our sparse representation is learned from a “clean” dictionary, which is a low rank matrix obtained from the sample matrix. We evaluate the proposed method on COIL20 databases. Experimental results show that: the semi-supervised boosting algorithm with sparse representation similarity outperforms the algorithm with Gaussian kernel similarity.

The automatic editing with the improvement of content and efficiency becomes urgent, especially for the dictionaries and thesauruses, as they possess regular formats and immense size. However, Chinese characters and words do not have definite alphabetic orders as English words are, it’s always difficult and time consuming to establish the index in the process of compilation. We propose an approach to establish automatically index of entries through Chinese phonetic alphabets and radicals, and then present the new model of index system based on the definition of basic index. Our method can facilitate structuring an automatic editing system and greatly raise the efficiency of compilation.

An algorithmic architecture for a high-performance optical character recognition (OCR) system for hand-printed and handwritten addresses is proposed. The architecture integrates syntactic and contextual post-processing with character recognition to optimise postcode recognition performance, and verifies the postcode against simple features extracted from the remainder of the address to ensure a low error rate.

An enhanced version of the characteristic loci character recognition algorithm was chosen for the system to make it tolerant of variations in writing style. Feature selection for the classifier is performed automatically using the B/W algorithm.

Syntactic and contextual information for hand-printed British postcodes have been integrated into the system by combining low-level postcode syntax information with a dictionary trie structure. A full implementation of the postcode dictionary trie is described. Features which define the town name effectively, and can easily be extracted from a handwritten or hand-printed town name are used for postcode verification.

A database totalling 3473 postcode/address images has been used to evaluate the performance of the complete postcode recognition process. The basic character recognition rate for the full unconstrained alphanumeric character set is 63.1%, compared with an expected maximum attainable 75–80%. The addition of the syntactic and contextual knowledge stages produces an overall postcode recognition rate which is equivalent to an alphanumeric character recognition rate of 86–90%. Separate verification experiments on a subset of 820 address images show that, with the first-order features chosen, an overall correct address feature code extraction rate of around 35% is achieved.