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Smartphones penetration-rate continues expanding, from 44% in 2017 up to 59% expected increase in 2022 as reported by Strategy Analytics. At present, smartphones dominate the global mobile traffic, which in turn is dominated by video communications. For mobile systems with limited power capabilities, the processing of real-time multimedia information with affixed security represents a real challenge. In this work, we propose a high-performance encryption scheme capable of running on low-power smartphones without holding back video coding operation. We are aimed at destroying the meaning of the entropy coded bitstream by inserting random bit errors to induce error propagation and impede natural self-resynchronization process. The scheme consists of three main process: 1) A new integer chaos-based Coupled-Map Lattice-CML for creating secure pseudo-random trajectories; 2) Random bit flipping of the bitstream based on a Dynamic Reference Point (DRP) not exposed to attackers; and 3) Random selection of CML byte-trajectories for both DRP and bit flipping processes for increased security. Implementation of the scheme on smartphones with different CPU-power capabilities shows excellent performance to handle high-bandwidth real-time video smoothly (one-to-one and group calls). The scheme provides high scalable security with encrypted data volume fluctuating between 0.7%-3% of the total compressed data (video and still images).

Microfossils, tiny fossils whose study requires the use of a microscope, have been widely applied in many fields of earth, life, and environmental sciences. The abundance and high diversity of microfossils, as well as the need for rapid identification, call for automated methods to classify microfossils. In this study, we constructed an open dataset of three-dimensional (3D) microfossils and proposed a deep learning-based approach for microfossil classification. The dataset, named `Archives of Digital Morphology' (ADMorph), currently contains more than ten thousand 3D models from five classes of 410 million-year-old fishes. The deep learning-based method includes data preprocessing, feature extraction, and 3D microfossil model classification. To assess the method performance and dataset representability, we performed extensive experiments. Compared with multiview convolutional neural networks (MVCNN) (91.54%), PointNet (64.13%), and VoxNet (78.15%), the method proposed herein had higher accuracy (97.60%) on the experimental dataset. We also verified data preprocessing (92.36%) and feature extraction (97.10%). We combined them to obtain the macroaveraging accuracy of 97.60%, the highest accuracy of 100%, and the lowest accuracy of 88.78%. We suggest that the proposed method can be applied to other 3D fossils and biomorphological research fields. The fast-accumulating 3D fossil models might become a source of information-rich datasets for deep learning.

In recent years, the growth of the Internet along with multimedia data delivery, have caused the misuse and even alteration of the original digital content. As a security mechanism, digital watermarking has been used to protect digital content, traditionally in copyright protection applications, and more recently digital watermarking has been used in content enhancement applications. Regardless of the application, digital watermarking schemes always cause some level of signal degradation. There are however, certain applications as in the military or medical fields, where loss of information cannot be tolerated during the reconstruction of host signals, therefore conventional watermarking schemes do not satisfy the quality requirements. In order to reconstruct signals without loss of data, reversible watermarking is employed. Recent research in reversible watermarking has focused on either, the improvement of embedding capacity or the reduction of perceptual impact of the schemes, but scarcely exploring the robustness of reversible schemes. In this survey, we provide a deep review of reversible watermarking schemes and propose a new classification outline based on the robustness point of view. Finally, some future directions for reversible watermarking research are presented and open problems in robust reversible watermarking are outlined.

We propose a generalized 3D shape descriptor for the efficient classification of 3D archaeological artifacts. Our descriptor is based on a multi-view approach of curvature features, consisting of the following steps: pose normalization of 3D models, local curvature descriptor calculation, construction of 3D shape descriptor using the multi-view approach and curvature maps, and dimensionality reduction by random projections. We generate two descriptors from two different paradigms: 1) handcrafted, wherein the descriptor is manually designed for object feature extraction, and directly passed on to the classifier and 2) machine learnt, in which the descriptor automatically learns the object features through a pretrained deep neural network model (VGG-16) for transfer learning and passed on to the classifier. These descriptors are applied to two different archaeological datasets: 1) non-public Mexican dataset, represented by a collection of 963 3D archaeological objects from the Templo Mayor Museum in México City that includes anthropomorphic sculptures, figurines, masks, ceramic vessels, and musical instruments; and 2) 3D pottery content-based retrieval benchmark dataset, consisting of 411 objects. Once the multi-view descriptors are obtained, we evaluate their effectiveness by using the following object classification schemes: $K$ -nearest neighbor, support vector machine, and structured support vector machine. Our object descriptors classification results are compared against five popular 3D descriptors in the literature, namely, rotation invariant spherical harmonic, histogram of spherical orientations, signature of histograms of orientations, symmetry descriptor, and reflective symmetry descriptor. Experimentally, we were able to verify that our machine learnt and handcrafted descriptors offer the best classification accuracy (20% better on average than comparative descriptors), independently of the classification methods. Our proposed descriptors are able to capture sufficient information to discern among different classes, concluding that it adequately characterizes the datasets.

In this paper is described the implementation of the frequency-dependent line model (FD-Line) in a real-time digital power system simulator. The main goal with such development is to describe a general procedure to incorporate new realistic models of power system components in modern real-time simulators based on the Electromagnetic Transients Program (EMTP). In this procedure are described, firstly, the steps to obtain the time domain solution of the differential equations that models the electromagnetic behavior in multi-phase transmission lines with frequency dependent parameters. After, the algorithmic solution of the FD-Line model is implemented in Simulink environment, through an S-function programmed in C language, for running off-line simulations of electromagnetic transients. This implementation allows the free assembling of the FD Line model with any element of the Power System Blockset library and also, it can be used to build any network topology. The main advantage of having a power network built in Simulink is that can be executed in real-time by means of the commercial eMEGAsim simulator. Finally, several simulation cases are presented to validate the accuracy and the real-time performance of the FD-Line model.

Due to pervasive communication infrastructures, a plethora of enabling technologies is being developed over mobile and wired networks. Among these, video streaming services over IP are the most challenging in terms of quality, real-time requirements and security. In this paper, we propose a novel scheme to efficiently secure variable length coded (VLC) multimedia bit streams, such as H.264. It is based on code word error diffusion and variable size segment shuffling. The codeword diffusion and the shuffling mechanisms are based on random operations from a secure and computationally efficient chaos-based pseudo-random number generator. The proposed scheme is ubiquitous to the end users and can be deployed at any node in the network. It provides different levels of security, with encrypted data volume fluctuating between 5.5–17%. It works on the compressed bit stream without requiring any decoding. It provides excellent encryption speeds on different platforms, including mobile devices. It is 200% faster and 150% more power efficient when compared with AES software-based full encryption schemes. Regarding security, the scheme is robust to well-known attacks in the literature, such as brute force and known/chosen plain text attacks.