Email Security: The Challenges of Network Security

Now a day’s, network security has become very important. For those securities Simple Mail Transport Protocol is the most widely used protocol for e-mail delivery. But, it lackssecurity features like privacy, authentication and integrity of e-mail message. To make e-mail communication secure and private,e-mail servers adopted one or more security features. The security protocolsprovide a most security but it also has several limitations. This paper discusseslimitations of e-mail security protocols, analyses and evaluates theireffectiveness in e-mail servers, as well as outlines the various attack methods which are used, and various defencemechanisms against them. DOI: 10.17762/ijritcc2321-8169.15060

Multi-modal filtering for non-linear estimation

Multi-modal densities appear frequently in time series and practical applications. However, they are not well represented by common state estimators, such as the Extended Kalman Filter and the Unscented Kalman Filter, which additionally suffer from the fact that uncertainty is often not captured sufficiently well. This can result in incoherent and divergent tracking performance. In this paper, we address these issues by devising a non-linear filtering algorithm where densities are represented by Gaussian mixture models, whose parameters are estimated in closed form. The resulting method exhibits a superior performance on nonlinear benchmarks. © 2014 IEEE

Multi-modal filtering for non-linear estimation

Multi-modal densities appear frequently in time series and practical applications. However, they are not well represented by common state estimators, such as the Extended Kalman Filter and the Unscented Kalman Filter, which additionally suffer from the fact that uncertainty is often not captured sufficiently well. This can result in incoherent and divergent tracking performance. In this paper, we address these issues by devising a non-linear filtering algorithm where densities are represented by Gaussian mixture models, whose parameters are estimated in closed form. The resulting method exhibits a superior performance on nonlinear benchmarks

Performance Analysis of Transmit Antenna Selection with MRC in MIMO for Image Transmission in Multipath Fading Channels Using Simulink

Multiple antenna configurations can be used to increase the data throughput reducing the effects of multipath fading and interference when channel bandwidth is limited. Orthogonal Space Time Block Codes along with Transmit antenna selection can improve the performance of multiple input multiple output systems. In this paper, we present the Transmit Antenna Selection (TAS) technique based on the Maximal Ratio Combining (MRC) scheme with single antenna selection for image transmission. The performance analysis of the system was carried out under different fading channels i.e. Rayleigh and Rician channel for image input. We design end to end TAS/MRC system in Simulink with advancements in the channel designs and receive diversity techniques along with the feedback models. The Bit Error Rate (BER) analysis was performed for the combinations of number of transmit and receive antennas for TAS/MRC system for various fading environments

Interaction primitives for human-robot cooperation tasks

To engage in cooperative activities with human partners, robots have to possess basic interactive abilities and skills. However, programming such interactive skills is a challenging task, as each interaction partner can have different timing or an alternative way of executing movements. In this paper, we propose to learn interaction skills by observing how two humans engage in a similar task. To this end, we introduce a new representation called Interaction Primitives. Interaction primitives build on the framework of dynamic motor primitives (DMPs) by maintaining a distribution over the parameters of the DMP. With this distribution, we can learn the inherent correlations of cooperative activities which allow us to infer the behavior of the partner and to participate in the cooperation. We will provide algorithms for synchronizing and adapting the behavior of humans and robots during joint physical activities

Data-Efficient Reinforcement Learning with Probabilistic Model Predictive Control

Trial-and-error based reinforcement learning (RL) has seen rapid advancements in recent times, especially with the advent of deep neural networks. However, the majority of autonomous RL algorithms either rely on engineered features or a large number of interactions with the environment. Such a large number of interactions may be impractical in many real-world applications. For example, robots are subject to wear and tear and, hence, millions of interactions may change or damage the system. Moreover, practical systems have limitations in the form of the maximum torque that can be safely applied. To reduce the number of system interactions while naturally handling constraints, we propose a model-based RL framework based on Model Predictive Control (MPC). In particular, we propose to learn a probabilistic transition model using Gaussian Processes (GPs) to incorporate model uncertainties into long-term predictions, thereby, reducing the impact of model errors. We then use MPC to find a control sequence that minimises the expected long-term cost. We provide theoretical guarantees for the first-order optimality in the GP-based transition models with deterministic approximate inference for long-term planning. The proposed framework demonstrates superior data efficiency and learning rates compared to the current state of the art