3D Textured Model Encryption via 3D Lu Chaotic Mapping

In the coming Virtual/Augmented Reality (VR/AR) era, 3D contents will be popularized just as images and videos today. The security and privacy of these 3D contents should be taken into consideration. 3D contents contain surface models and solid models. The surface models include point clouds, meshes and textured models. Previous work mainly focus on encryption of solid models, point clouds and meshes. This work focuses on the most complicated 3D textured model. We propose a 3D Lu chaotic mapping based encryption method of 3D textured model. We encrypt the vertexes, the polygons and the textures of 3D models separately using the 3D Lu chaotic mapping. Then the encrypted vertices, edges and texture maps are composited together to form the final encrypted 3D textured model. The experimental results reveal that our method can encrypt and decrypt 3D textured models correctly. In addition, our method can resistant several attacks such as brute-force attack and statistic attack.Comment: 13 pages, 7 figures, under review of SCI

STRENGTHEN OF DPNS FEATURES FOR THERANOSTIC APPLICATIONS AND MECHANICAL-CONTROL OF CHEMOTHERAPEUTIC EFFICACY THROUGH MODULATION OF CELL PROLIFERATION.

Solid tumors are complex biological structures which are composed of cellular and matrix components, everything being perfused by blood vessels. During tumor development, modifications of both biochemical and mechanical parameters are observed and can feedback on one another. Cancer cells constantly interact with their mechanical environment and the whole tissue is mostly confined by its surrounding. Compressive mechanical stress develops in part from cell proliferation and could eventually result in the clamping of blood vessels leading to increased interstitial fluid pressure (hydrostatic pressure). The consequent hypoperfusion poses important obstacles to drug delivery and nanomedicines. In fact, the tortuous tumor microvasculature has blood velocities up to one order of magnitude lower compared to healthy capillary networks. Moreover, the fast angiogenesis during tumor progression leads to high vascular density in solid tumors, large gaps exist between endothelial cells in tumor blood vessels, and tumor tissues show selective extravasation and retention of macromolecular drugs (Enhanced Permeation Retention \u2013 EPR \u2013 effect). These effects have served as a basis for the development of drug delivery systems which are aimed at enhancing tumor tissue targeting and drug therapeutic effectiveness. Over the last 15 years, a plethora of materials and different formulations have been proposed for the realization of nanomedicines. Yet, drug-loading efficiency, sequestration by phagocytic cells, and tumor accumulation of nanoparticle-loaded agents - nanomedicines - are sub-optimal. Starting from these considerations, during my PhD, I studied two complementary approaches: in the first two years my work was focused on implementing the characteristics of Discoidal Polymeric Nanoconstructs designed with controlled geometries and mechanical properties. In the last year, I investigated the role of mechanical stress on chemotherapeutic efficacy. More precisely, this work first reviews the use of deformable discoidal nanoconstructs (DPNs) as a novel delivery strategy for therapeutic and imaging agents. Inspired by blood cell behavior, these nanoconstructs are designed to efficiently navigate the circulatory system, minimize sequestration by phagocytic cells, and recognize the tortuous angiogenic microvasculature of neoplastic masses. In this work, the synthesis, drug loading and release, and physico-chemical characterization of DPNs were enhanced with particular emphasis on the ability to independently control size, shape, surface properties, and mechanical stiffness. Two different loading strategies were tested, namely the straightforward \u201cdirect loading\u201d and the \u201cabsorbance loading\u201d. In the former case, the agent was directly mixed with the polymeric paste to realize DPNs whereas, in the latter case, DPNs were first lyophilized and then rehydrated upon exposure to a concentrated aqueous solution of the agent. Under these two loading conditions, the encapsulation efficiencies and release profiles of three different molecules and their corresponding prodrugs were systematically assessed (1,2-Distearoyl-sn-glycero-3-phosphorylethanolamine lipid chains or 1 kDa PEG chains were directly conjugated with Cy5.5 or methotrexate and Doxorubicin). Moderately hydrophobic compounds with low molecular weight showed encapsulation efficiencies of 80%, with absorption loading (direct loading has efficiencies around 1%). The DOX-DPN showed on triple negative breast cancer cells a toxicity comparable to free DOX. Preliminary in vivo preliminary studies conducted with directly loaded Cy5-DPN demonstrated a fairly solid integration of the imaging compound with the polymer matrix of the particles. The second part of the work dissect what happens to free drugs or to drugs carried by nanovectors once they reach the tumor site. As we mention above, the elevated mechanical stress derived from tumor progression could result in blood vessels clamping with consequent reduction of drug efficacy. It is quite obvious to imagine that if the drug fails to reach the tumor it cannot act on it. Indeed, mechanical stress within the tumor site is present from the early stages of the disease. Our goal was to understand what happens when mechanical stress is not yet so large enough to fully collapse the blood vessels. Are there mechanical alterations that can affect the efficacy of a chemotherapeutic? We studied how mechanical perturbations of the tumor microenvironment could contribute to the mechanical-form of Gemcitabine drug resistance. Specifically, we developed a new in vitro strategy to mimic the mechanical compression stress induced by the stroma during tumor progression. We embedded pancreatic tumor spheroids into agarose polymeric matrix in order to demonstrate the effect of mechanical compressive stress on tumor proliferation. Then, we validated our results with other types of mechanical stresses. Finally, we investigated the therapeutic efficacy of a proliferation-based chemotherapy: Gemcitabine. Collectively, having the physical cues of cancer in mind, it can be important to cross-fertilize the fields of physical oncology and nanomedicine

Design and development of auxiliary components for a new two-stroke, stratified-charge, lean-burn gasoline engine

A unique stepped-piston engine was developed by a group of research engineers at Universiti Teknologi Malaysia (UTM), from 2003 to 2005. The development work undertaken by them engulfs design, prototyping and evaluation over a predetermined period of time which was iterative and challenging in nature. The main objective of the program is to demonstrate local R&D capabilities on small engine work that is able to produce mobile powerhouse of comparable output, having low-fuel consumption and acceptable emission than its crankcase counterpart of similar displacement. A two-stroke engine work was selected as it posses a number of technological challenges, increase in its thermal efficiency, which upon successful undertakings will be useful in assisting the group in future powertrain undertakings in UTM. In its carbureted version, the single-cylinder aircooled engine incorporates a three-port transfer system and a dedicated crankcase breather. These features will enable the prototype to have high induction efficiency and to behave very much a two-stroke engine but equipped with a four-stroke crankcase lubrication system. After a series of analytical work the engine was subjected to a series of laboratory trials. It was also tested on a small watercraft platform with promising indication of its flexibility of use as a prime mover in mobile platform. In an effort to further enhance its technology features, the researchers have also embarked on the development of an add-on auxiliary system. The system comprises of an engine control unit (ECU), a directinjector unit, a dedicated lubricant dispenser unit and an embedded common rail fuel unit. This support system was incorporated onto the engine to demonstrate the finer points of environmental-friendly and fuel economy features. The outcome of this complete package is described in the report, covering the methodology and the final characteristics of the mobile power plant

Review on bibliography related to antimicrobials

In this report, a bibliographic research has been done in the field of antimicrobials.In this report, a bibliographic research has been done in the field of antimicrobials. Not all antimicrobials have been included, but those that are being subject of matter in the group GBMI in Terrassa, and others of interest. It includes chitosan and other biopolymers. The effect of nanoparticles is of great interest, and in this sense, the effect of Ag nanoparticles and antibiotic nanoparticles (nanobiotics) has been revised. The report focuses on new publications and the antimicrobial effect of peptides has been considered. In particular, the influence of antimicrobials on membranes has deserved much attention and its study using the Langmuir technique, which is of great utility on biomimetic studies. The building up of antimicrobials systems with new techniques (bottom-up approach), as the Layer-by-Layer technique, can also be found in between the bibliography. It has also been considered the antibiofilm effect, and the new ideas on quorem sensing and quorum quenching.Preprin

Deep Learning Techniques for Music Generation -- A Survey

This paper is a survey and an analysis of different ways of using deep learning (deep artificial neural networks) to generate musical content. We propose a methodology based on five dimensions for our analysis: Objective - What musical content is to be generated? Examples are: melody, polyphony, accompaniment or counterpoint. - For what destination and for what use? To be performed by a human(s) (in the case of a musical score), or by a machine (in the case of an audio file). Representation - What are the concepts to be manipulated? Examples are: waveform, spectrogram, note, chord, meter and beat. - What format is to be used? Examples are: MIDI, piano roll or text. - How will the representation be encoded? Examples are: scalar, one-hot or many-hot. Architecture - What type(s) of deep neural network is (are) to be used? Examples are: feedforward network, recurrent network, autoencoder or generative adversarial networks. Challenge - What are the limitations and open challenges? Examples are: variability, interactivity and creativity. Strategy - How do we model and control the process of generation? Examples are: single-step feedforward, iterative feedforward, sampling or input manipulation. For each dimension, we conduct a comparative analysis of various models and techniques and we propose some tentative multidimensional typology. This typology is bottom-up, based on the analysis of many existing deep-learning based systems for music generation selected from the relevant literature. These systems are described and are used to exemplify the various choices of objective, representation, architecture, challenge and strategy. The last section includes some discussion and some prospects.Comment: 209 pages. This paper is a simplified version of the book: J.-P. Briot, G. Hadjeres and F.-D. Pachet, Deep Learning Techniques for Music Generation, Computational Synthesis and Creative Systems, Springer, 201

Sparse Modeling for Image and Vision Processing

In recent years, a large amount of multi-disciplinary research has been conducted on sparse models and their applications. In statistics and machine learning, the sparsity principle is used to perform model selection---that is, automatically selecting a simple model among a large collection of them. In signal processing, sparse coding consists of representing data with linear combinations of a few dictionary elements. Subsequently, the corresponding tools have been widely adopted by several scientific communities such as neuroscience, bioinformatics, or computer vision. The goal of this monograph is to offer a self-contained view of sparse modeling for visual recognition and image processing. More specifically, we focus on applications where the dictionary is learned and adapted to data, yielding a compact representation that has been successful in various contexts.Comment: 205 pages, to appear in Foundations and Trends in Computer Graphics and Visio