Universal quantum modifications to general relativistic time dilation in delocalised clocks

The theory of relativity associates a proper time with each moving object via its world line. In quantum theory however, such well-defined trajectories are forbidden. After introducing a general characterisation of quantum clocks, we demonstrate that, in the weak-field, low-velocity limit, all "good" quantum clocks experience time dilation as dictated by general relativity when their state of motion is classical (i.e. Gaussian). For nonclassical states of motion, on the other hand, we find that quantum interference effects may give rise to a significant discrepancy between the proper time and the time measured by the clock. The universality of this discrepancy implies that it is not simply a systematic error, but rather a quantum modification to the proper time itself. We also show how the clock's delocalisation leads to a larger uncertainty in the time it measures -- a consequence of the unavoidable entanglement between the clock time and its center-of-mass degrees of freedom. We demonstrate how this lost precision can be recovered by performing a measurement of the clock's state of motion alongside its time reading.Comment: 7 + 10 pages. V3: accepted versio

Why my photos look sideways or upside down? Detecting Canonical Orientation of Images using Convolutional Neural Networks

Image orientation detection requires high-level scene understanding. Humans use object recognition and contextual scene information to correctly orient images. In literature, the problem of image orientation detection is mostly confronted by using low-level vision features, while some approaches incorporate few easily detectable semantic cues to gain minor improvements. The vast amount of semantic content in images makes orientation detection challenging, and therefore there is a large semantic gap between existing methods and human behavior. Also, existing methods in literature report highly discrepant detection rates, which is mainly due to large differences in datasets and limited variety of test images used for evaluation. In this work, for the first time, we leverage the power of deep learning and adapt pre-trained convolutional neural networks using largest training dataset to-date for the image orientation detection task. An extensive evaluation of our model on different public datasets shows that it remarkably generalizes to correctly orient a large set of unconstrained images; it also significantly outperforms the state-of-the-art and achieves accuracy very close to that of humans

Scene Detection Classification and Tracking for Self-Driven Vehicle

A number of traffic-related issues, including crashes, jams, and pollution, could be resolved by self-driving vehicles (SDVs). Several challenges still need to be overcome, particularly in the areas of precise environmental perception, observed detection, and its classification, to allow the safe navigation of autonomous vehicles (AVs) in crowded urban situations. This article offers a comprehensive examination of the application of deep learning techniques in self-driving cars for scene perception and observed detection. The theoretical foundations of self-driving cars are examined in depth in this research using a deep learning methodology. It explores the current applications of deep learning in this area and provides critical evaluations of their efficacy. This essay begins with an introduction to the ideas of computer vision, deep learning, and self-driving automobiles. It also gives a brief review of artificial general intelligence, highlighting its applicability to the subject at hand. The paper then concentrates on categorising current, robust deep learning libraries and considers their critical contribution to the development of deep learning techniques. The dataset used as label for scene detection for self-driven vehicle. The discussion of several strategies that explicitly handle picture perception issues faced in real-time driving scenarios takes up a sizeable amount of the work. These methods include methods for item detection, recognition, and scene comprehension. In this study, self-driving automobile implementations and tests are critically assessed

Ultraslow Electron Spin Dynamics in GaAs Quantum Wells Probed by Optically Pumped NMR

Optically pumped nuclear magnetic resonance (OPNMR) measurements were performed in two different electron-doped multiple quantum well samples near the fractional quantum Hall effect ground state nu=1/3. Below 0.5K, the spectra provide evidence that spin-reversed charged excitations of the nu=1/3 ground state are localized over the NMR time scale of ~40 microseconds. Furthermore, by varying NMR pulse parameters, the electron spin temperature (as measured by the Knight shift) could be driven above the lattice temperature, which shows that the value of the electron spin-lattice relaxation time lies between 100 microseconds and 500 milliseconds at nu=1/3.Comment: 6 pages (REVTEX), 6 eps figures embedded in text; published version; minor changes to match published versio

Planning in answer set programming while learning action costs for mobile robots

For mobile robots to perform complex missions, it may be necessary for them to plan with incomplete information and reason about the indirect effects of their actions. Answer Set Programming (ASP) provides an elegant way of formalizing domains which involve indirect effects of an action and recursively defined fluents. In this paper, we present an approach that uses ASP for robotic task planning, and demonstrate how ASP can be used to generate plans that acquire missing information necessary to achieve the goal. Action costs are also incorporated with planning to produce optimal plans, and we show how these costs can be estimated from experience making planning adaptive. We evaluate our approach using a realistic simulation of an indoor environment where a robot learns to complete its objective in the shortest time

Conservation of energy by utilisation of waste heat direct power reduction in ferro alloy industry

Ferro Alloy Industry is a power intensive industry. Ferro alloys, such as Ferro Manganese, Ferro Silicon, Silico Manganese are manufactured in electric smelting furnace. Due to thermodynamic considerations, production of these Ferro alloys consume large amount of power. With the development of steel industry, requirement of Ferro Alloys as well as power is increasing day-by-day. Hence due consideration can be given in minimising electric power. The object of this paper is to give various directions in which work can be done to achieve saving in power. The paper deals with various factors affecting power consumpt-ion & work areas for improving technology to reduce energy consumption

Utilisation of ore fines by agglomeration with special reference to sintering of manganese ore fines

Agglomeration is required to coagulate the ore fines to the desired size to make them suitable for metallurgical operations. The well known methods of agglomeration are briquetting, pelletisation and Sintering. About 20-30 % of fines are generated during mining operation, handling and screening of ores before they are sized for the smelting furnace. This paper deals with the various process parameters which are determined for the manufacture of Manganese Ore Sinter on a continuous sinter plant of 20 Tons per day capacity

Planning in action language BC while learning action costs for mobile robots

The action language BC provides an elegant way of formalizing dynamic domains which involve indirect effects of actions and recursively defined fluents. In complex robot task planning domains, it may be necessary for robots to plan with incomplete information, and reason about indirect or recursive action effects. In this paper, we demonstrate how BC can be used for robot task planning to solve these issues. Additionally, action costs are incorporated with planning to produce optimal plans, and we estimate these costs from experience making planning adaptive. This paper presents the first application of BC on a real robot in a realistic domain, which involves human-robot interaction for knowledge acquisition, optimal plan generation to minimize navigation time, and learning for adaptive planning

Detection of Features to Track Objects and Segmentation Using GrabCut for Application in Marker-less Augmented Reality

AbstractAugmented Reality applications have hovered itself over various platforms such as desktop and most recently to handheld devices such as mobile phones and tablets. Augmented Reality (AR) systems have mostly been limited to Head Worn Displays with start-ups such as Magic Leap and Occulus Rift making tremendous advancement in such AR and VR research applications facing a stiff competition with Software giant Microsoft which has recently introduced Holo Lens. AR refers to the augmentation or the conglomeration of virtual objects in the real world scenario which has a distinct but close resemblance to Virtual Reality (VR) systems which are computer simulated environments which render physical presence in imaginary world. Developers and hackers round the globe have directed their research interests in the development of AR and VR based applications especially in the domain of advertisement and gaming. Many open source libraries, SDKs and proprietary software are available worldwide for developers to make such systems. This paper describes an algorithm for an AR prototype which uses a marker less approach to track and segment out real world objects and then overlay the same on another real world scene. The algorithm was tested on Desktop. The results are comparable with other existing algorithms and outperform some of them in terms of robustness, speed, and accuracy, precision and timing analysis