Design and Implementation of a Beam Forming Network for a Phased Array Antenna

This dissertation presents a beam forming network (BFN) for phased array antenna-based on coherently radiating periodic structure (CORPS). The elements of CORPS are selected in such a way to obtain broad band characteristics, good return loss and good isolation between the radiating elements. These elements were arranged in such a way that the BFN naturally produces Gaussian amplitude. This methodology reduces the complexity of the conventional phased array design making it more flexible and minimizing the loss of energy inside the structure. A phase shifter design is proposed for the CORPS. The entire BFN’s sub-blocks have been designed for the frequency band of 5.925 GHz to 6.425 GHz, which find applications in communication satellite, fixed wireless systems.Defence Science Journal, Vol. 65, No. 1, January 2015, pp.46-52, DOI:http://dx.doi.org/10.14429/dsj.65.694

A novel ingress node design for video streaming over optical burst switching networks

This paper introduces a novel ingress node design which takes advantage of video data partitioning in order to deliver enhanced video streaming quality when using H.264/AVC codec over optical burst switching networks. Ns2 simulations show that the proposed scheme delivers improved video traffic quality without affecting other traffic, such as best effort traffic. Although the extra network load is comparatively small, the average gain in video PSNR was 5 dB over existing burst cloning schemes, with a maximum end-to-end delay of 17 ms, and jitter of less than 0.35 ms

Agents in adversarial domains - modelling environments in parallel

We present a model of an environment to evaluate the behavior of an agent trying to hide from a pursuer is presented. The model computes the direction and the amount of protection provided by the environment. The computational complexity of this problem is improved by using a parallel implementation of this algorithm.<br /

Negotiating agents that learn about others’ preferences

In multiagent systems, an agent does not usually have complete information about the preferences and decision making processes of other agents. This might prevent the agents from making coordinated choices, purely due to their ignorance of what others want. This paper describes the integration of a learning module into a communication-intensive negotiating agent architecture. The learning module gives the agents the ability to learn about other agents&rsquo; preferences via past interactions. Over time, the agents can incrementally update their models of other agents&rsquo; preferences and use them to make better coordinated decisions. Combining both communication and learning, as two complement knowledge acquisition methods, helps to reduce the amount of communication needed on average, and is justified in situation where communication is computationally costly or simply not desirable (e.g. to preserve the individual privacy).<br /

Scalable network-wide anomaly detection using compressed data

Detecting network traffic volume anomalies in real time is a key problem as it enables measures to be taken to prevent network congestion which severely affects the end users. Several techniques based on principal component analysis (PCA) have been outlined in the past which detect volume anomalies as outliers in the residual subspace. However, these methods are not scalable to networks with a large number of links. We address this scalability issue with a new approach inspired from the recently developed compressed sensing (CS) theory. This theory induces a universal information sampling sheme right at the network sensory level to reduce the data overhead. Specifically, we address exploit the compressibility characteristics of the network data and describe a framework for anomaly detection in the compressed domain. Our main theoretical contribution is a detailed theoretical analysis of the new approach which obtains the probabilistic bounds on the principal eigenvalues of the compressed data. Subsequently, we prove that volume anomaly detection using compressed data can achieve equivalent performance as it does using the original uncompressed and reduces the computational cost significantly. The experimental results on both the Abiliene and synthetic datasets support our theoretical findings and demonstrate the advantages of the new approach over the existing methods

Analysis of circadian rhythms from online communities of individuals with affective disorders

The circadian system regulates 24 hour rhythms in biological creatures. It impacts mood regulation. The disruptions of circadian rhythms cause destabilization in individuals with affective disorders, such as depression and bipolar disorders. Previous work has examined the role of the circadian system on effects of light interactions on mood-related systems, the effects of light manipulation on brain, the impact of chronic stress on rhythms. However, such studies have been conducted in small, preselected populations. The deluge of data is now changing the landscape of research practice. The unprecedented growth of social media data allows one to study individual behavior across large and diverse populations. In particular, individuals with affective disorders from online communities have not been examined rigorously. In this paper, we aim to use social media as a sensor to identify circadian patterns for individuals with affective disorders in online communities.We use a large scale study cohort of data collecting from online affective disorder communities. We analyze changes in hourly, daily, weekly and seasonal affect of these clinical groups in contrast with control groups of general communities. By comparing the behaviors between the clinical groups and the control groups, our findings show that individuals with affective disorders show a significant distinction in their circadian rhythms across the online activity. The results shed light on the potential of using social media for identifying diurnal individual variation in affective state, providing key indicators and risk factors for noninvasive wellbeing monitoring and prediction

Shifted Legendre polynomial solutions of nonlinear stochastic Itô - Volterra integral equations

In this article, we propose the shifted Legendre polynomial-based solution for solving a stochastic integral equation. The properties of shifted Legendre polynomials are discussed. Also, the stochastic operational matrix required for our proposed methodology is derived. This operational matrix is capable of reducing the given stochastic integral equation into simultaneous equations with N+1 coefficients, where N is the number of terms in the truncated series of function approximation. These unknowns can be found by using any well-known numerical method. In addition to the capability of the operational matrices, an essential advantage of the proposed technique is that it does not require any integration to compute the constant coefficients. This approach may also be used to solve stochastic differential equations, both linear and nonlinear, as well as stochastic partial differential equations. We also prove the convergence of the solution obtained through the proposed method in terms of the expectation of the error function. The upper bound of the error in L² norm between exact and approximate solutions is also elaborately discussed. The applicability of this methodology is tested with a few numerical examples, and the quality of the solution is validated by comparing it with other methods with the help of tables and figures.Publisher's Versio

SUSTAINED RELEASE MICROBEADS OF RITONAVIR: IN VITRO AND IN VIVO EVALUATION

Objective: The main aim of the present investigation was to develop sustained release microbeads of ritonavir that has a shorter half-life (3-5 h) and requires twice a day administration. These formulations exhibit a sustained release of ritonavir that would expect to improve the therapy, better drug utilization, and patient compliance. Methods: Gellan-chitosan and calcium chloride reinforced beads of ritonavir were prepared by ionotropic gelation method employing different concentrations of gellan, chitosan, calcium chloride and drug. The prepared beads were evaluated for various physicochemical parameters such as particle size determination, drug entrapment efficiency, swelling studies, infrared spectroscopy study, differential scanning calorimetry, x-ray diffraction analysis, scanning electron microscopy, in vitro drug release study and in vivo bioavailability studies. Results: From the results, formulation GC-II exhibited higher drug entrapment efficiency (79.65±0.012), higher swelling index, sustained drug release over a period of 24 h, increased oral bioavailability (2.07 times higher than that of pure drug) and decreased elimination rate (2.15 times lesser for ritonavir microbeads) with prolonged elimination half-life (2.15 times more than pure drug) as compared to pure drug. Conclusion: Ritonavir microbeads have demonstrated as a better delivery system for the sustained release of the drug; which may in turn circumvent the drawbacks associated with the conventional therapy

How to assess the acceptance of an electronic health record system?

Being able to access a patient’s clinical data in due time is critical to any medical setting. Clinical data is very diverse both in content and in terms of which system produces it. The Electronic Health Record (EHR) aggregates a patient’s clinical data and makes it available across different systems. Considering that user’s resistance is a critical factor in system implementation failure, the understanding of user behavior remains a relevant object of investigation. The purpose of this paper is to outline how we can assess the technology acceptance of an EHR using the Technology Acceptance Model 3 (TAM3) and the Delphi methodology. An assessment model is proposed in which findings are based on the results of a questionnaire answered by health professionals whose activities are supported by the EHR technology. In the case study simulated in this paper, the results obtained showed an average of 3 points and modes of 4 and 5, which translates to a good level of acceptance.The work has been supported by FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2019.The work has been supported by FCT – Fundação para a Ciência e Tecnologia within the Project Scope DSAIPA/DS/0084/2018

Optimized Broadcast for Deep Learning Workloads on Dense-GPU InfiniBand Clusters: MPI or NCCL?

Dense Multi-GPU systems have recently gained a lot of attention in the HPC arena. Traditionally, MPI runtimes have been primarily designed for clusters with a large number of nodes. However, with the advent of MPI+CUDA applications and CUDA-Aware MPI runtimes like MVAPICH2 and OpenMPI, it has become important to address efficient communication schemes for such dense Multi-GPU nodes. This coupled with new application workloads brought forward by Deep Learning frameworks like Caffe and Microsoft CNTK pose additional design constraints due to very large message communication of GPU buffers during the training phase. In this context, special-purpose libraries like NVIDIA NCCL have been proposed for GPU-based collective communication on dense GPU systems. In this paper, we propose a pipelined chain (ring) design for the MPI_Bcast collective operation along with an enhanced collective tuning framework in MVAPICH2-GDR that enables efficient intra-/inter-node multi-GPU communication. We present an in-depth performance landscape for the proposed MPI_Bcast schemes along with a comparative analysis of NVIDIA NCCL Broadcast and NCCL-based MPI_Bcast. The proposed designs for MVAPICH2-GDR enable up to 14X and 16.6X improvement, compared to NCCL-based solutions, for intra- and inter-node broadcast latency, respectively. In addition, the proposed designs provide up to 7% improvement over NCCL-based solutions for data parallel training of the VGG network on 128 GPUs using Microsoft CNTK.Comment: 8 pages, 3 figure