Security threats are crucial challenges that deter Mixed reality (MR) communication in medical telepresence. This research aims to improve the security by reducing the chances of types of various attacks occurring during the real-time data transmission in surgical telepresence as well as reduce the time of the cryptographic algorithm and keep the quality of the media used. The proposed model consists of an enhanced RC6 algorithm in combination. Dynamic keys are generated from the RC6 algorithm mixed with RC4 to create dynamic S-box and permutation table, preventing various known attacks during the real-time data transmission. For every next session, a new key is created, avoiding possible reuse of the same key from the attacker. The results obtained from our proposed system are showing better performance compared to the state of art. The resistance to the tested attacks is measured throughout the entropy, Pick to Signal Noise Ratio (PSNR) is decreased for the encrypted image than the state of art, structural similarity index (SSIM) closer to zero. The execution time of the algorithm is decreased for an average of 20%. The proposed system is focusing on preventing the brute force attack occurred during the surgical telepresence data transmission. The paper proposes a framework that enhances the security related to data transmission during surgeries with acceptable performance.
Localized swelling has been observed in 24Cr-24Ni-Nb steel transportation rollers used in the normalizing furnace of a plate
mill after prolonged service at high temperature. Due to high localized thermal and mechanical stresses, the chromia layer
formed on the roller surface ruptures, exposing the roller substrate to furnace oxygen. Oxidation of second-phase carbides
results in the formation of carbon monoxide at very high partial pressure. This leads to formation of voids, leading in turn
to localized swelling of the roller material. 相似文献
Hindalco’s aluminum electrolysis cells were initially installed in 1962, and the technology was based on 1950s-generation pots. Although Hindalco expanded its aluminums melting capacity from 20,000 tonnes per year to 175,000 tonnes per year, the basic design of the pots remained unchanged. In view of energy price increases, and to keep pace with the latest developments in aluminum smelting technology, Hindalco undertook efforts to modernize its facilities. In spite of numerous constraints, the Hindalco smelter has been able to achieve performance nearly equivalent to that of 1980s-generation pots by retrofitting new technologies. This has resulted in considerable savings in electrical energy consumption and raw materials usage. 相似文献
In a recent paper [D.Chavan et al., Rev. Sci. Instrum. 81, 123702 (2010)] we have demonstrated that ferrule-top cantilevers, obtained by carving the end of a ferruled fiber, can be used for contact mode atomic force microscopy in ambient conditions. Here we show that those probes can provide tapping mode images at both room and cryogenic temperatures (12 K). 相似文献
Abstract: Pedestrian detection techniques are important and challenging especially for complex real world scenes. They can be used for ensuring pedestrian safety, ADASs (advance driver assistance systems) and safety surveillance systems. In this paper, we propose a novel approach for multi-person tracking-by-detection using deformable part models in Kalman filtering framework. The Kalman filter is used to keep track of each person and a unique label is assigned to each tracked individual. Based on this approach, people can enter and leave the scene randomly. We test and demonstrate our results on Caltech Pedestrian benchmark, which is two orders of magnitude larger than any other existing datasets and consists of pedestrians varying widely in appearance, pose and scale. Complex situations such as people occluded by each other are handled gracefully and individual persons can be tracked correctly after a group of people split. Experiments confirm the real-time performance and robustness of our system, working in complex scenes. Our tracking model gives a tracking accuracy of 72.8% and a tracking precision of 82.3%. We can further reduce false positives by 2.8%, using Kalman filtering. 相似文献
Transfer learning seeks to leverage previously learned tasks to achieve faster learning in a new task. In this paper, we consider
transfer learning in the context of related but distinct Reinforcement Learning (RL) problems. In particular, our RL problems are derived from Semi-Markov Decision Processes (SMDPs) that share the same
transition dynamics but have different reward functions that are linear in a set of reward features. We formally define the
transfer learning problem in the context of RL as learning an efficient algorithm to solve any SMDP drawn from a fixed distribution
after experiencing a finite number of them. Furthermore, we introduce an online algorithm to solve this problem, Variable-Reward
Reinforcement Learning (VRRL), that compactly stores the optimal value functions for several SMDPs, and uses them to optimally
initialize the value function for a new SMDP. We generalize our method to a hierarchical RL setting where the different SMDPs
share the same task hierarchy. Our experimental results in a simplified real-time strategy domain show that significant transfer
learning occurs in both flat and hierarchical settings. Transfer is especially effective in the hierarchical setting where
the overall value functions are decomposed into subtask value functions which are more widely amenable to transfer across
different SMDPs. 相似文献
In this paper, we present the feed-forward neural network (FFNN) and recurrent neural network (RNN) models for predicting Boolean function complexity (BFC). In order to acquire the training data for the neural networks (NNs), we conducted experiments for a large number of randomly generated single output Boolean functions (BFs) and derived the simulated graphs for number of min-terms against the BFC for different number of variables. For NN model (NNM) development, we looked at three data transformation techniques for pre-processing the NN-training and validation data. The trained NNMs are used for complexity estimation for the Boolean logic expressions with a given number of variables and sum of products (SOP) terms. Both FFNNs and RNNs were evaluated against the ISCAS benchmark results. Our FFNNs and RNNs were able to predict the BFC with correlations of 0.811 and 0.629 with the benchmark results, respectively. 相似文献
An application of topology optimization to design viscoelastic composite materials with elastic moduli that soften with frequency
is presented. The material is a two-phase composite whose first constituent is isotropic and viscoelastic while the other
is an orthotropic material with negative stiffness but stable. A concept for this material based on a lumped parameter model
is used. The performance of the topology optimization approach in this context is illustrated using three examples. 相似文献
The X-ray powder diffraction, dielectric and thermal studies of bismuth vanadate (Bi2VO5.5) ceramic have been carried out as a function of temperature (300–900 K). The hightemperature X-ray studies, supported by differential scanning calorimetry, clearly demonstrate that Bi2VO5.5 undergoes two major phase transitions at 730 and 835 K. It was found that the one at 730 K is associated with both the ferroelectric and the crystallographic transition, while at 835 K, Bi2VO5.5 undergoes only the crystallographic transition. Anomalies in both the dielectric constant and specific heat curves have been observed at 730 and 835 K. The total heat, Q, and entropy, S, associated with the transition at 730 K were found to be higher than those at 835 K. 相似文献
Fine-grained image search is one of the most challenging tasks
in computer vision that aims to retrieve similar images at the fine-grained
level for a given query image. The key objective is to learn discriminative
fine-grained features by training deep models such that similar images are
clustered, and dissimilar images are separated in the low embedding space.
Previous works primarily focused on defining local structure loss functions
like triplet loss, pairwise loss, etc. However, training via these approaches
takes a long training time, and they have poor accuracy. Additionally, representations learned through it tend to tighten up in the embedded space and
lose generalizability to unseen classes. This paper proposes a noise-assisted
representation learning method for fine-grained image retrieval to mitigate
these issues. In the proposed work, class manifold learning is performed
in which positive pairs are created with noise insertion operation instead
of tightening class clusters. And other instances are treated as negatives
within the same cluster. Then a loss function is defined to penalize when
the distance between instances of the same class becomes too small relative
to the noise pair in that class in embedded space. The proposed approach is
validated on CARS-196 and CUB-200 datasets and achieved better retrieval
results (85.38% recall@1 for CARS-196% and 70.13% recall@1 for CUB-200)
compared to other existing methods. 相似文献