Unbounded Predicate Inner Product Functional Encryption from Pairings

Predicate inner product functional encryption (P-IPFE) is essentially attribute-based IPFE (AB-IPFE) which additionally hides attributes associated to ciphertexts. In a P-IPFE, a message x is encrypted under an attribute w and a secret key is generated for a pair (y, v) such that recovery of ⟨ x, y⟩ requires the vectors w, v to satisfy a linear relation. We call a P-IPFE unbounded if it can encrypt unbounded length attributes and message vectors. ∙ zero predicate IPFE. We construct the first unbounded zero predicate IPFE (UZP-IPFE) which recovers ⟨ x, y⟩ if ⟨ w, v⟩ = 0 . This construction is inspired by the unbounded IPFE of Tomida and Takashima (ASIACRYPT 2018) and the unbounded zero inner product encryption of Okamoto and Takashima (ASIACRYPT 2012). The UZP-IPFE stands secure against general attackers capable of decrypting the challenge ciphertext. Concretely, it provides full attribute-hiding security in the indistinguishability-based semi-adaptive model under the standard symmetric external Diffie–Hellman assumption. ∙ non-zero predicate IPFE. We present the first unbounded non-zero predicate IPFE (UNP-IPFE) that successfully recovers ⟨ x, y⟩ if ⟨ w, v⟩ ≠ 0 . We generically transform an unbounded quadratic FE (UQFE) scheme to weak attribute-hiding UNP-IPFE in both public and secret key setting. Interestingly, our secret key simulation secure UNP-IPFE has succinct secret keys and is constructed from a novel succinct UQFE that we build in the random oracle model. We leave the problem of constructing a succinct public key UNP-IPFE or UQFE in the standard model as an important open problem

A comparative study of intrathecal levobupivacaine-clonidine and bupivacaine in the quality of anesthesia for patients undergoing hernioplasty

Background: Bupivacaine is most commonly used amino-amide drug for subarachnoid block in hernioplasty. Levobupivacaine has similar pharmacological activity to that of bupivacaine with minimal cardiotoxicity. Clonidine, an α2 adrenergic agonist, potentiates the action of local anesthetics when used intrathecally and enhances post-operative analgesia. Aims and Objectives: This prospective, comparative, observational study was aimed to compare the effects of 0.5% levobupivacaine with clonidine and 0.5% hyperbaric bupivacaine in patients undergoing hernioplasty for the quality of surgical anesthesia and hemodynamic changes with any significant intraoperative complications. Materials and Methods: After receiving approval from the institutional ethics committee and written informed consent, 80 male patients aged between 18 and 60 years, BMI 150 cm, and American society of anesthesiologists physical status1 and 2 posted for elective hernioplasty were enrolled into two equal groups of 40 patients, group LC and group B. Patients in group LC received 15 mg 0.5% isobaric levobupivacaine with 30 μg clonidine and patients in group B received 15 mg hyperbaric bupivacaine intrathecally. SPSS version 20 was used for analysis, and P<0.05 was considered statistically significant. Results: In group LC, onsets of both sensory and motor blocks were delayed, whereas durations of motor and sensory block with analgesia were longer. Tachycardia, hypotension, nausea, vomiting, and shivering were observed greater in numbers in group B, whereas incidence of bradycardia was more in group LC. Conclusion: Prolonged duration of sensory and motor block, prolonged analgesic effect, and hemodynamic stability without any significant adverse effects may make this combination a better alternative to hyperbaric bupivacaine for hernioplasty

Unbounded Predicate Inner Product Functional Encryption from Pairings

Predicate inner product functional encryption (P-IPFE) is essentially attribute-based IPFE (AB-IPFE) which additionally hides attributes associated to ciphertexts. In a P-IPFE, a message x is encrypted under an attribute w and a secret key is generated for a pair (y, v) such that recovery of ⟨x, y⟩ requires the vectors w, v to satisfy a linear relation. We call a P-IPFE unbounded if it can encrypt unbounded length attributes and message vectors. • zero predicate IPFE. We construct the first unbounded zero predicate IPFE (UZP-IPFE) which recovers ⟨x,y⟩ if ⟨w,v⟩ = 0. This construction is inspired by the unbounded IPFE of Tomida and Takashima (ASIACRYPT 2018) and the unbounded zero inner product encryption of Okamoto and Takashima (ASIACRYPT 2012). The UZP-IPFE stands secure against general attackers capable of decrypting the challenge ciphertext. Concretely, it provides full attribute-hiding security in the indistinguishability-based semi-adaptive model under the standard symmetric external Diffie-Hellman assumption. • non-zero predicate IPFE. We present the first unbounded non-zero predicate IPFE (UNP-IPFE) that successfully recovers ⟨x, y⟩ if ⟨w, v⟩ ≠ 0. We generically transform an unbounded quadratic FE (UQFE) scheme to weak attribute-hiding UNP-IPFE in both public and secret key settings. Interestingly, our secret key simulation secure UNP-IPFE has succinct secret keys and is constructed from a novel succinct UQFE that we build in the random oracle model. We leave the problem of constructing a succinct public key UNP-IPFE or UQFE in the standard model as an important open problem

Sistema domótico para adultos mayores con dependencia funcional

Trabajo de investigaciónEn este trabajo de grado, se implementa un sistema domótico que se controla por medio de un control manual inalámbrico que establece la comunicación por medio de radio frecuencia de tipo punto a punto, también incorpora un sistema de reconocimiento de voz, donde ejecuta comandos básicos para activar y desactivar un interruptor, un actuador o un sensor. Al integrar este sistema de comunicación por RF, según las pruebas realizadas, se garantiza un buen alcance en la comunicación entre dispositivos de forma instantánea. Este proyecto se desarrolla, ya que, según las estadísticas a nivel nacional y encuestas realizadas, hay un cierto porcentaje de personas adultas mayores con limitaciones físicas, entre la que más se destaca, se conoce como dependencia funcional. Este sistema les brindará una forma de mitigar este tipo de dificultades diarias en el hogar al usuario. El sistema domótico se implementó en las dependencias principales de una vivienda, donde se pone prueba su funcionamiento según la orden o el comando ejecutado.INTRODUCCIÓN 1. PLANTEAMIENTO Y FORMULACIÓN DEL PROBLEMA 2. OBJETIVOS 3. JUSTIFICACION 4. ANTECEDENTES 5. MARCO TEORICO 6. METODOLOGÍA 7. RECOPILACION DE INFORMACIÓN 8. RECOPILACIÓN DE INFORMACIÓN DE LA POBLACIÓN OBJETIVO 9. DISEÑO 10. DESCRIPCIÓN DEL FUNCIONAMIENTO 11. IMPLEMENTACIÓN 12. DESCRIPCIÓN FINANCIERA DEL SISTEMA DOMÓTICO 13. CONCLUSIONES BIBLIOGRAFÍA ANEXOSPregradoIngeniero Electrónic

Biometric Pulse Counting System using Microcontroller 8051

ABSTRACT: This paper gives a novel idea about the design and implementation of a microcontroller based pulse counter. Intel 8051 is used as the main microcontroller unit and the system has been fabricated to count the total number of pulse in a minute. IR sensors are connected as the input sensors which read the blood flow through the fingers. The microcontroller counts the number of input signals and thus generates an output on a display window build using three 7 segment IC. This system is highly efficient, accurate and economical and thus can be effectively used as an instrument to measure pulse on medical grounds

Standardised Sonneratia apetala Buch.-Ham. fruit extract inhibits human neutrophil elastase and attenuates elastase-induced lung injury in mice

Chronic obstructive pulmonary disease (COPD) along with asthma is a major and increasing global health problem. Smoking contributes to about 80%–90% of total COPD cases in the world. COPD leads to the narrowing of small airways and destruction of lung tissue leading to emphysema primarily caused by neutrophil elastase. Neutrophil elastase plays an important role in disease progression in COPD patients and has emerged as an important target for drug discovery. Sonneratia apetala Buch.-Ham. is a mangrove plant belonging to family Sonneratiaceae. It is widely found in the Sundarban regions of India. While the fruits of this plant have antibacterial, antifungal, antioxidant and astringent activities, fruit and leaf extracts have been shown to reduce the symptoms of asthma and cough. The aim of this study is to find whether hydro alcoholic fruit extracts of S. apetala inhibit neutrophil elastase and thus prevent the progression of neutrophil elastase-driven lung emphysema. The hydroalcoholic extract, ethanol: water (90:10), of the S. apetala Buch.-Ham. fresh fruits (SAM) were used for neutrophil elastase enzyme kinetic assay and IC50 of the extract was determined. The novel HPLC method has been developed and the extract was standardized with gallic acid and ellagic acid as standards. The extract was further subjected to LC-MS2 profiling to identify key phytochemicals. The standardized SAM extract contains 53 μg/mg of gallic acid and 95 μg/mg of ellagic acid, based on the HPLC calibration curve. SAM also reversed the elastase-induced morphological change of human epithelial cells and prevented the release of ICAM-1 in vitro and an MTT assay was conducted to assess the viability. Further, 10 mg/kg SAM had reduced alveolar collapse induced by neutrophil elastase in the mice model. Thus, in this study, we reported for the first time that S. apetala fruit extract has the potential to inhibit human neutrophil elastase in vitro and in vivo

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Nouvelles approches de restauration d'images inspirées des concepts de la mécanique quantique

Decomposition of digital images into other basis or dictionaries than time or space domains is a very common and effective approach in image processing and analysis. Such a decomposition is commonly obtained using fixed transformations (e.g., Fourier or wavelet) or dictionaries learned from example databases or from the signal or image itself. In recent years, with the growth of computing power, data-driven strategies exploiting the redundancy within patches extracted from one or several images to increase sparsity have become more prominent. They have demonstrated very promising image restoration results. The question to pursue in this thesis is how to design such an adaptive transformation based on principles of quantum mechanics. In this thesis, we explore new possibilities of constructing such image-dependent bases inspired by quantum mechanics. First, we construct an image-dependent basis using the wave solutions of the Schrödinger equation, in particular, by considering the image as a potential in the discretized Schrödinger equation. The efficiency of the proposed decomposition is illustrated through denoising results in the case of Gaussian, Poisson, and speckle noises and compared to the state-of-the-art algorithms. We further generalize our proposed adaptive basis by exploiting the data-driven strategy inspired by quantum many-body theory. Based on patch analysis, the similarity measures in a local image neighborhood are formalized through a term akin to interaction in quantum mechanics that can efficiently preserve the local structures of real images. The versatile nature of this adaptive basis extends the scope of its application to image-independent or image-dependent noise scenarios without any adjustment. We carry out a rigorous comparison with contemporary methods to demonstrate the denoising capability of the proposed algorithm regardless of the image characteristics, noise statistics and intensity. We show the ability of our approaches to deal with real-medical data such as clinical dental computed tomography image denoising and medical ultrasound image despeckling applications. We further extend our work to image deconvolution and super-resolution tasks exploiting our proposed quantum adaptive denoisers. In particular, following recent developments, we impose these external denoisers as a prior functions within the Plug-and-Play and Regularization by Denoising approaches. Lastly, we present a deep neural network architecture unfolding our proposed baseline adaptive denoising algorithm, relying on the theory of quantum many-body physics. The key ingredients of the proposed method are on one hand, its ability to handle non-local image structures through the patch-interaction term and the quantum-based Hamiltonian operator, and, on the other hand, its flexibility to adapt the hyperparameters patch wisely, due to the training process. Furthermore, it is shown that with very slight modifications, this network can be enhanced to solve more challenging image restoration tasks such as image deblurring, super-resolution and inpainting. Despite a compact and interpretable (from a physical perspective) architecture, the proposed deep learning network outperforms several recent benchmark algorithms from the literature, designed specifically for each task. Finally, we address the problem of clinical cardiac ultrasound image enhancement to demonstrate the potential of our proposed deep unfolded network in real-world medical applications.La décomposition d'images numériques en d'autres bases ou dictionnaires que les domaines temporel ou spatial est une approche très courante et efficace dans le traitement et l'analyse d'images. Une telle décomposition est couramment obtenue à l'aide de transformations fixes ou de dictionnaires appris à partir de bases de données d'exemple ou à partir du signal ou de l'image eux-mêmes. Ces dernières années, avec la croissance de la puissance de calcul, les stratégies exploitant la redondance des patchs extraits d'une ou de plusieurs images pour faciliter leur décomposition parcimonieuse sont devenues très populaire, notamment grâce à leur efficacité à restaurer des images. Un des objectifs de cette thèse est de savoir comment concevoir une telle transformation adaptative à l’aide de principes de la mécanique quantique. Cette thèse explore de nouvelles approches de construction de telles bases dépendantes de l'image inspirées de la mécanique quantique. Tout d'abord, nous construisons une base dépendante de l'image en utilisant les solutions d'onde de l'équation de Schrödinger. En particulier, en considérant l'image comme un potentiel dans l'équation de Schrödinger discrétisée, nous obtenons les solutions d'onde qui constitue une base et qui joue le rôle de transformée. L'efficacité de la décomposition proposée est illustrée par des résultats de débruitage dans le cas des bruits Gaussiens, de Poisson et de speckle et par comparaison aux algorithmes de l'état de l'art. Cette décomposition adaptative est ensuite généralisée en s’inspirant de la théorie quantique à plusieurs corps. Sur la base de l'analyse par patchs, les mesures de similarité dans un voisinage d'image local sont formalisées par un terme apparenté à l'interaction en mécanique quantique qui peut efficacement préserver les structures locales des images. La nature polyvalente de cette base adaptative étend la portée de son application à des scénarios de bruit indépendants ou dépendants de l'image sans aucun ajustement. Nous effectuons une comparaison rigoureuse avec les méthodes existantes pour démontrer la capacité de débruitage de l'algorithme proposé, quelles que soient les caractéristiques de l'image, les statistiques de bruit et l'intensité. Nous montrons la capacité de nos approches à traiter des données médicales réelles telles que le débruitage d'images de tomodensitométrie dentaire clinique et les applications de despeckling d'images d'échographie médicale. Nous étendons encore notre travail aux tâches de déconvolution d'image et de super-résolution en exploitant nos algorithmes de debruitage adaptatifs quantiques proposés. En particulier, suite à des développements récents, nous imposons ces débruiteurs externes comme fonction préalable au sein des approches de type Plug-and-Play et Régularisation par Débruitage. Enfin, nous présentons une architecture de réseau neuronal profond dépliant notre proposition d'algorithme de débruitage adaptatif, reposant sur la théorie de la physique quantique à plusieurs corps. Les ingrédients clés de la méthode proposée sont d'une part, sa capacité à gérer des structures d'image non locales à travers le terme d'interaction patch et l'opérateur Hamiltonien quantique, et, d'autre part, sa flexibilité pour adapter les hyperparamètres aux caractéristiques de chaque patch. De plus, il est démontré qu'avec de très légères modifications, ce réseau peut être amélioré pour résoudre des tâches de restauration d'image plus difficiles telles que le défloutage d'image, la super-résolution et l'inpainting. Malgré une architecture compacte et interprétable (d'un point de vue physique), le réseau d'apprentissage profond proposé améliore plusieurs algorithmes de référence récents de la littérature, conçus spécifiquement pour chaque tâche. Enfin, nous abordons le problème de l'amélioration des image échocardiographiques clinique pour démontrer le potentiel de notre réseau profond dans des applications médicales réelles

Data-driven Computational Models of Fruit Fly Embryogenesis

In the process of development, a single cell divides into multiple cells, differentiates into tissues and ultimately the tissues go through mechanical deformations to give rise to functional organs. The processes have been studied with increasingly sophisticated tools of genetics, molecular biology, and microscopy in the early fruit fly (Drosophila melanogaster) embryo. In this thesis, we show how computational analysis of the experimental data coupled with biophysical models can lead to mechanistic understanding and predictive power about the processes of development. In the first part of the thesis (chapter 2), we focus on the process of nuclear divisions in the embryo. We extracted point patterns formed by centers of the nuclei in successive nuclear cycles analyzing the live imaging data. Descriptors from statistical mechanics revealed that while the point pattern doesn’t have any long-range order, the characteristic distance between them scales with inverse square-root of density as the number density of the nuclei doubles with mitotic divisions. We showed that a particle-based model with adaptive inter-particle force-field reproduces this feature and used that model to construct a virtual embryo on the surface of a prolate spheroid with nuclear positions similar to the real embryo. In Chapter 3, we focus on gene regulation by signaling network, the primary mechanism used by the embryo for cellular differentiation. We used experimental data from different levels in the signaling pathway for the terminal patterning system of fruit fly controlled by RAS/ERK signaling. We proposed an integrated model that takes into multiple events such as nuclear division, shuttling of transcription factor into nucleus, binding of transcription factor to DNA, and transcription and learned the parameters of the model from the experimental data that makes quantitative prediction and gives us mechanistic understanding. Next, we utilized similar models on the virtual embryo generated from chapter 2 to model gene expression patterns with a single nucleus resolution. In the final part of the thesis (chapter 4), we focus on embryo-scale movements of the cells that lead to morphogenetic deformation. We integrated live imaging, tissue cartography, and particle image velocimetry to construct the time dependent velocity field on the surface of the virtual embryo. Finally, we used dimensionality reduction techniques to show that wild type embryos are characterized by stereotype modes in space and time, whereas, embryos which are metabolically perturbed show self-organized oscillatory instability. Altogether, this thesis tries to integrate a diverse set of data analysis and modeling techniques to integrate biological understanding obtained over decades to a quantitative form with predictive power. Furthermore, this thesis is also a first step towards integrated self-consistent models of development that integrate multiple stages and events