Head tracking at large angles from the straight ahead position

One of the big advantages of a helmet sight in a high performance aircraft is its off-boresight capability in aiming a fire control system. However, tracking data using a target that is moving rapidly and randomly for an extended period of time is missing. This study is intended to provide data in this area that will be of value to engineers in designing head control systems

Gait Design for a Snake Robot by Connecting Curve Segments and Experimental Demonstration

This paper presents a method for designing the gait of a snake robot that moves in a complicated environment. We propose a method for expressing the target form of a snake robot by connecting curve segments whose curvature and torsion are already known. Because the characteristics of each combined shape are clear, we can design the target form intuitively and approximate a snake robot configuration to this form with low computational cost. In addition, we propose two novel gaits for the snake robot as a design example of the proposed method. The first gait is aimed at moving over a flange on a pipe, while the other is the crawler gait aimed at moving over rough terrain. We demonstrated the effectiveness of the two gaits on a pipe and rough terrain in experiments

Development and initial validation of the determinants of physical activity questionnaire

Background: Physical activity interventions are more likely to be effective if they target causal determinants of behaviour change. Targeting requires accurate identification of specific theoretical determinants of physical activity. Two studies were undertaken to develop and validate the Determinants of Physical Activity Questionnaire. Methods In Study 1, 832 male and female university staff and students were recruited from 49 universities across the UK and completed the 66-item measure, which is based on the Theoretical Domains Framework. Confirmatory factor analysis was undertaken on a calibration sample to generate the model, which resulted in a loss of 31 items. A validation sample was used to cross-validate the model. 20 new items were added and Study 2 tested the revised model in a sample of 466 male and female university students together with a physical activity measure. Results: The final model consisted of 11 factors and 34 items, and CFA produced a reasonable fit χ2 (472) = 852.3, p < .001, CFI = .933, SRMR = .105, RMSEA = .042 (CI = .037-.046), as well as generally acceptable levels of discriminant validity, internal consistency, and test-retest reliability. Eight subscales significantly differentiated between high and low exercisers, indicating that those who exercise less report more barriers for physical activity. Conclusions: A theoretically underpinned measure of determinants of physical activity has been developed with reasonable reliability and validity. Further work is required to test the measure amongst a more representative sample. This study provides an innovative approach to identifying potential barriers to physical activity. This approach illustrates a method for moving from diagnosing implementation difficulties to designing and evaluating interventions

MTDeep: Boosting the Security of Deep Neural Nets Against Adversarial Attacks with Moving Target Defense

Present attack methods can make state-of-the-art classification systems based on deep neural networks misclassify every adversarially modified test example. The design of general defense strategies against a wide range of such attacks still remains a challenging problem. In this paper, we draw inspiration from the fields of cybersecurity and multi-agent systems and propose to leverage the concept of Moving Target Defense (MTD) in designing a meta-defense for 'boosting' the robustness of an ensemble of deep neural networks (DNNs) for visual classification tasks against such adversarial attacks. To classify an input image, a trained network is picked randomly from this set of networks by formulating the interaction between a Defender (who hosts the classification networks) and their (Legitimate and Malicious) users as a Bayesian Stackelberg Game (BSG). We empirically show that this approach, MTDeep, reduces misclassification on perturbed images in various datasets such as MNIST, FashionMNIST, and ImageNet while maintaining high classification accuracy on legitimate test images. We then demonstrate that our framework, being the first meta-defense technique, can be used in conjunction with any existing defense mechanism to provide more resilience against adversarial attacks that can be afforded by these defense mechanisms. Lastly, to quantify the increase in robustness of an ensemble-based classification system when we use MTDeep, we analyze the properties of a set of DNNs and introduce the concept of differential immunity that formalizes the notion of attack transferability.Comment: Accepted to the Conference on Decision and Game Theory for Security (GameSec), 201