An iconic programming language for sensor-based robots

In this paper we describe an iconic programming language called Onika for sensor-based robotic systems. Onika is both modular and reconfigurable and can be used with any system architecture and real-time operating system. Onika is also a multi-level programming environment wherein tasks are built by connecting a series of icons which, in turn, can be defined in terms of other icons at the lower levels. Expert users are also allowed to use control block form to define servo tasks. The icons in Onika are both shape and color coded, like the pieces of a jigsaw puzzle, thus providing a form of error control in the development of high level applications

Towards learning free naive bayes nearest neighbor-based domain adaptation

As of today, object categorization algorithms are not able to achieve the level of robustness and generality necessary to work reliably in the real world. Even the most powerful convolutional neural network we can train fails to perform satisfactorily when trained and tested on data from different databases. This issue, known as domain adaptation and/or dataset bias in the literature, is due to a distribution mismatch between data collections. Methods addressing it go from max-margin classifiers to learning how to modify the features and obtain a more robust representation. Recent work showed that by casting the problem into the image-to-class recognition framework, the domain adaptation problem is significantly alleviated [23]. Here we follow this approach, and show how a very simple, learning free Naive Bayes Nearest Neighbor (NBNN)-based domain adaptation algorithm can significantly alleviate the distribution mismatch among source and target data, especially when the number of classes and the number of sources grow. Experiments on standard benchmarks used in the literature show that our approach (a) is competitive with the current state of the art on small scale problems, and (b) achieves the current state of the art as the number of classes and sources grows, with minimal computational requirements. © Springer International Publishing Switzerland 2015

Analysis of MAGSAT data of the Indian region

Progress in the development of software for reading MAGSAT data tapes and for the reduction of anomaly data, and in the preparation of data for magnetic anomaly maps is reported

Are You Tampering With My Data?

We propose a novel approach towards adversarial attacks on neural networks (NN), focusing on tampering the data used for training instead of generating attacks on trained models. Our network-agnostic method creates a backdoor during training which can be exploited at test time to force a neural network to exhibit abnormal behaviour. We demonstrate on two widely used datasets (CIFAR-10 and SVHN) that a universal modification of just one pixel per image for all the images of a class in the training set is enough to corrupt the training procedure of several state-of-the-art deep neural networks causing the networks to misclassify any images to which the modification is applied. Our aim is to bring to the attention of the machine learning community, the possibility that even learning-based methods that are personally trained on public datasets can be subject to attacks by a skillful adversary.Comment: 18 page

Characterization and its implication on beneficiation of low grade iron ore by gravity separation

Studies were undertaken on low grade iron ore sample from Noamundi iron ore mines. The objective of this study was to examine the possibility of the physical beneficiation of low grade iron ore sample by physical methods for the blast furnace route of iron production. The present investigation relies on petrography and ore mineralogical characterization, ore textures (primary, secondary, metamorphic), liberation characters and its impact on the mineral beneficiation methods to produce quality concentrate. The geological characters, alteration mineralogy, morphometric variation, ore microscopy (using model microscope with transmitted and reflected light) and thereby understanding the genesis has given proper insight into the occurrence of various minerals. In addition to this, representative samples were employed for detailed investigation by using XRD, SEM-EDS and cathodoluminescence (CL) studies for confirmation of major as well as minor ore minerals and associated gangue minerals. Investigations suggest that lateritic iron ore samples obtained from the study area are composed of hematite (two generations), goethite (two generations) and limonitic material (younger generation) in association with major gangue minerals such as clay minerals (kaolinite, illite), bauxitic minerals(gibbsite, boehmite and diaspore), cryptocrystalline silica(japer, chert) and crystalline quartz as well as apatite and collophane. Fair liberation obtained below 74 micron size. It was interesting to find that inspite of the complex mineralogy of iron ore, beneficiation results using gravity separation like multi gravity separator (MGS), particularly in finer size ranges was encouraging. The result of ore-gangue mineralogical studies were found quite useful in evaluating the separation efficacy of gravity separation process. The process mineralogical data corroborated well with beneficiation results

Yield sensing technologies for perennial and annual horticultural crops: a review

Yield maps provide a detailed account of crop production and potential revenue of a farm. This level of details enables a range of possibilities from improving input management, conducting on-farm experimentation, or generating profitability map, thus creating value for farmers. While this technology is widely available for field crops such as maize, soybean and grain, few yield sensing systems exist for horticultural crops such as berries, field vegetable or orchards. Nevertheless, a wide range of techniques and technologies have been investigated as potential means of sensing crop yield for horticultural crops. This paper reviews yield monitoring approaches that can be divided into proximal, either direct or indirect, and remote measurement principles. It reviews remote sensing as a way to estimate and forecast yield prior to harvest. For each approach, basic principles are explained as well as examples of application in horticultural crops and success rate. The different approaches provide whether a deterministic (direct measurement of weight for instance) or an empirical (capacitance measurements correlated to weight for instance) result, which may impact transferability. The discussion also covers the level of precision required for different tasks and the trend and future perspectives. This review demonstrated the need for more commercial solutions to map yield of horticultural crops. It also showed that several approaches have demonstrated high success rate and that combining technologies may be the best way to provide enough accuracy and robustness for future commercial systems

Identifying the Independent Inertial Parameter Space of Robot Manipulators

This paper presents a new approach to the problem of finding the minimum number of inertial parameters of robot manipulator dynamic equations of motion. Based upon the energy difference equation, it is equally applica ble to serial link manipulators as well as graph structured manipulators. The method is conceptually simple, compu tationally efficient, and easy to implement. In particular, the manipulator kinematics and the joint positions and velocities are the only inputs to the algorithm. Applica tions to a serial link and a graph structured manipulator are illustrated.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/67982/2/10.1177_027836499101000606.pd

ImageNet Large Scale Visual Recognition Challenge

The ImageNet Large Scale Visual Recognition Challenge is a benchmark in object category classification and detection on hundreds of object categories and millions of images. The challenge has been run annually from 2010 to present, attracting participation from more than fifty institutions. This paper describes the creation of this benchmark dataset and the advances in object recognition that have been possible as a result. We discuss the challenges of collecting large-scale ground truth annotation, highlight key breakthroughs in categorical object recognition, provide a detailed analysis of the current state of the field of large-scale image classification and object detection, and compare the state-of-the-art computer vision accuracy with human accuracy. We conclude with lessons learned in the five years of the challenge, and propose future directions and improvements.Comment: 43 pages, 16 figures. v3 includes additional comparisons with PASCAL VOC (per-category comparisons in Table 3, distribution of localization difficulty in Fig 16), a list of queries used for obtaining object detection images (Appendix C), and some additional reference