The US HDTV standard

This standard for digital high-definition television has been submitted to the Federal Communications Commission for its approval. Based as it is on the MPEG-2 video compression and transport protocol, multiple transmission formats, Dolby AC-3 digital audio, and vestigial sideband digital modulation, this HDTV system will supply the home with superb images and spectacular sound while also, as part of the National Information Infrastructure (NII), relaying digital data. An HDTV system employing multiple transmission formats, digital compression, packetization, and modulation techniques is a marked advance over current world television standards such as NTSC, PAL, or SECAM     

Fast Hartley transforms for image processing

The fast Hartley transform (FHT) is used to transform two-dimensional image data. Because the Hartley transform is real-valued, it does not require complex operations. Both spectra and autocorrelations of two-dimensional ultrasound images of normal and abnormal livers were computed     

Effect of chemical inhomogeneity on domains and ferroelectric properties of Fe‐modified 0.77Bi0.5Na0.5TiO3–0.23SrTiO3

Lead‐free 0.77(Bi_0.5Na_0.5)TiO₃–0.23Sr(Ti_1?xFe_x)O₃ (x = 0, 0.04) (BNT–23STF_x) was prepared using a conventional solid‐state reaction route. The effects of Fe‐modification on the chemical homogeneity from a μm scale perspective, the core‐shell domains structures, and the ferroelectric properties were investigated. The chemical homogeneity was analyzed using energy dispersive X‐ray mapping in scanning transmission electron microscopy mode, and the field‐dependent behaviors of strain and polarization were obtained to determine the ferroelectric properties. Substituting Fe³⁺ for Ti⁴⁺ resulted in completely different electrical behavior and properties, despite similar XRD patterns and microstructures. The Fe‐substitution promoted the mobility of Sr²⁺ ions in the BNT phase and, as a consequence, the chemical homogeneity increased and the core‐domains collapsed. Extending the ceramic processing, such as milling time and sintering time, affected domain distribution and compositional inhomogeneity, which led to a gradual transformation from ferroelectric to relaxor.     

Ultimate strength of dented steel plates under axial compressive loads

In this paper the ultimate strength characteristics of dented steel plates under axial compressive loads are investigated using the ANSYS nonlinear finite element code. The effects of shape, size (depth, diameter), and location of the dent on the ultimate strength behavior of simply supported steel plates under axial thrust are studied. A closed-form formula for predicting the ultimate compressive strength of dented steel plates are empirically derived by curve fitting based on the computed results. The results and insights developed in the present study will be useful for damage tolerant design of steel plated structures with local denting.     

Byte Frequency Based Indicators for Crypto-Ransomware Detection from Empirical Analysis

File entropy is one of the major indicators of crypto-ransomware because the encryption by ransomware increases the randomness of file contents. However, entropy-based ransomware detection has certain limitations; for example, when distinguishing ransomware-encrypted files from normal files with inherently high-level entropy, misclassification is very possible. In addition, the entropy evaluation cost for an entire file renders entropy-based detection impractical for large files. In this paper, we propose two indicators based on byte frequency for use in ransomware detection; these are termed EntropySA and DistSA, and both consider the interesting characteristics of certain file subareas termed "sample areas' (SAs). For an encrypted file, both the sampled area and the whole file exhibit high-level randomness, but for a plain file, the sampled area embeds informative structures such as a file header and thus exhibits relatively low-level randomness even though the entire file exhibits high-level randomness. EntropySA and DistSA use "byte frequency" and a variation of byte frequency, respectively, derived from sampled areas. Both indicators cause less overhead than other entropy-based detection methods, as experimentally proven using realistic ransomware samples. To evaluate the effectiveness and feasibility of our indicators, we also employ three expensive but elaborate classification models (neural network, support vector machine and threshold-based approaches). Using these models, our experimental indicators yielded an average F1-measure of 0.994 and an average detection rate of 99.46% for file encryption attacks by realistic ransomware samples.     

Evaluating a range of learning schedules: hybrid training schedules may be as good as or better than distributed practice for some tasks

We investigated theoretically and empirically a range of training schedules on tasks with three knowledge types: declarative, procedural, and perceptual-motor. We predicted performance for 6435 potential eight-block training schedules with ACT-R's declarative memory equations. Hybrid training schedules (schedules consisting of distributed and massed practice) were predicted to produce better performance than purely distributed or massed training schedules. The results of an empirical study (N = 40) testing four exemplar schedules indicated a more complex picture. There were no statistical differences among the groups in the declarative and procedural tasks. We also found that participants in the hybrid practice groups produced reliably better performance than ones in the distributed practice group for the perceptual-motor task – the results indicate training schedules with some spacing and some intensiveness may lead to better performance, particularly for perceptual-motor tasks, and that tasks with mixed types of knowledge might be better taught with a hybrid schedule.     

A Dynamic k-Nearest Neighbor Method for WLAN-Based Positioning Systems

The static k-Nearest Neighbor (k-NN) method for localization has limitations in accuracy due to the fixed k value in the algorithm. To address this problem, and achieve better accuracy, we propose a new dynamic k-Nearest Neighbor (Dk-NN) method in which the optimal k value changes based on the topologies and distances of its nearest neighbors. The proposed method has been validated using the WLAN-fingerprint data sets collected at COEX, one of the largest convention centers in Seoul, Korea. The proposed method significantly reduced both the mean error distances and the standard deviations of location estimations, leading to a significant improvement in accuracy by ~ 23% compared to the cluster filtered k-NN (CFK) method, and ~ 17% compared to the k-NN (k = 1) method.