Testing real-time multi input-output systems

In formal testing, the assumption of input enabling is typically made. This assumption requires all inputs to be enabled anytime. In addition, the useful concept of quiescence is sometimes applied. Briefly, a system is in a quiescent state when it cannot produce outputs. In this paper, we relax the input enabling assumption, and allow some input sets to be enabled while others remain disabled. Moreover, we also relax the general bound M used in timed systems to detect quiescence, and allow different bounds for different sets of outputs. By considering the tioco-M theory, an enriched theory for timed testing with repetitive quiescence, and allowing the partition of input sets and output sets, we introduce the mtioco^M relation. A test derivation procedure which is nondeterministic and parameterized is further developed, and shown to be sound and complete wrt mtioco^

Testing multi input-output real-time systems (Extended version)

In formal testing, the assumption of input enabling is typically made. This assumption requires all inputs to be enabled anytime. In addition, the useful concept of quiescence is sometimes applied. Briefly, a system is in a quiescent state when it cannot produce outputs. In this paper, we relax the input enabling assumption, and allow some input sets to be enabled while others remain disabled. Moreover, we also relax the general bound M used in timed systems to detect quiescence, and allow different bounds for different sets of outputs. By considering the tiocoM theory, an enriched theory for timed testing with repetitive quiescence, and allowing the partition of input sets and output sets, we introduce the mtiocoM relation. A test derivation procedure which is nondeterministic and parameterized is further developed, and shown to be sound and complete wrt mtiocoM

Monitoring and control of the cardiovascular system during indoor exercise

University of Technology, Sydney. Faculty of Engineering and Information Technology.The increase in obesity and diabetes is of great public health, social and economic concern worldwide. Modern treadmill systems can provide effective, safe and practical indoor exercise for the consumption of extra energy. However, an uncontrolled treadmill can cause excessive exertion on the cardiovascular system. To avoid excessive cardiovascular stress, an efficient way of monitoring and controlling of exercise strength is to regulate treadmill speed and/or gradient to stimulate the exerciser's heart rate following a predefined profile. In this thesis, an automated treadmill system has been developed, which includes wireless portable ECG and tri-axial accelerometer sensors, and a Labview based control module. Based on this automated system, efficient rate detection techniques have been developed by using the pitch estimation method. Different types of multiloop integral control configurations have been proposed and implemented to regulate the heart rate and/or step rate by manipulating treadmill speed and/or gradient. These control structures have been placed under real time testing which includes Single- Input Single-Output (SISO), Multiple-Input Single-Output (MISO) and Multiple- Input Multiple-Output (MIMO) control by using the established Labview module. It has been found that MISO control is the most efficient method, and would be effective in making the treadmill exercise more reliable and safer in rapidly tracking the heart rate profile to achieve desired exercising outcome. For this reason, this thesis also proposes the concept of Multi-loop Integral Controllability (MIC) and proves the existence of multi-loop integral controllers which can obtain unconditional multi-loop stability of the Two-Input Single-Output automated treadmill system. The benefit of our automated control system includes assisting patients in postcardiac attack rehabilitation and therapy to safely control the heart rate to follow a suitable profile. This reduces the need for supervision by medical professionals. Furthermore, in athletics and fitness applications, an automatic control system can allow users to optimize their training intensity

CASPNet++: Joint Multi-Agent Motion Prediction

The prediction of road users' future motion is a critical task in supporting advanced driver-assistance systems (ADAS). It plays an even more crucial role for autonomous driving (AD) in enabling the planning and execution of safe driving maneuvers. Based on our previous work, Context-Aware Scene Prediction Network (CASPNet), an improved system, CASPNet++, is proposed. In this work, we focus on further enhancing the interaction modeling and scene understanding to support the joint prediction of all road users in a scene using spatiotemporal grids to model future occupancy. Moreover, an instance-based output head is introduced to provide multi-modal trajectories for agents of interest. In extensive quantitative and qualitative analysis, we demonstrate the scalability of CASPNet++ in utilizing and fusing diverse environmental input sources such as HD maps, Radar detection, and Lidar segmentation. Tested on the urban-focused prediction dataset nuScenes, CASPNet++ reaches state-of-the-art performance. The model has been deployed in a testing vehicle, running in real-time with moderate computational resources.Comment: 8 pages, 6 figure

A unified structural interpretation of some well-known stability-test procedures for linear systems

A number of well-known stability-test procedures for continuous-and discrete-time systems are re-examined in a unified manner, leading to well-defined network-theoretic interpretations. The representation and network interpretation are based on the fact that the stability of any linear system (scalar or multivariable) is equivalent to the stability of a related all-pass system, which in turn can always be synthesized as a cascade of (scalar or matrix) two-pair all-pass (lossless) networks. The original system of interest is stable if and only if each all-pass two-pair is stable (and hence "lossless bounded real"). As a result of this interpretation, a number of related issues, such as enumeration of unstable poles, prematured terminations, and singularity situations can all be approached in a unified manner, based only on "two-pair extraction formulas." In addition, the network interpretation also leads to direct test procedures for testing relative stability, and the stability of multi-input, multi-output systems

Multi-Robot Transfer Learning: A Dynamical System Perspective

Multi-robot transfer learning allows a robot to use data generated by a second, similar robot to improve its own behavior. The potential advantages are reducing the time of training and the unavoidable risks that exist during the training phase. Transfer learning algorithms aim to find an optimal transfer map between different robots. In this paper, we investigate, through a theoretical study of single-input single-output (SISO) systems, the properties of such optimal transfer maps. We first show that the optimal transfer learning map is, in general, a dynamic system. The main contribution of the paper is to provide an algorithm for determining the properties of this optimal dynamic map including its order and regressors (i.e., the variables it depends on). The proposed algorithm does not require detailed knowledge of the robots' dynamics, but relies on basic system properties easily obtainable through simple experimental tests. We validate the proposed algorithm experimentally through an example of transfer learning between two different quadrotor platforms. Experimental results show that an optimal dynamic map, with correct properties obtained from our proposed algorithm, achieves 60-70% reduction of transfer learning error compared to the cases when the data is directly transferred or transferred using an optimal static map.Comment: 7 pages, 6 figures, accepted at the 2017 IEEE/RSJ International Conference on Intelligent Robots and System

DeepSQLi: Deep Semantic Learning for Testing SQL Injection

Security is unarguably the most serious concern for Web applications, to which SQL injection (SQLi) attack is one of the most devastating attacks. Automatically testing SQLi vulnerabilities is of ultimate importance, yet is unfortunately far from trivial to implement. This is because the existence of a huge, or potentially infinite, number of variants and semantic possibilities of SQL leading to SQLi attacks on various Web applications. In this paper, we propose a deep natural language processing based tool, dubbed DeepSQLi, to generate test cases for detecting SQLi vulnerabilities. Through adopting deep learning based neural language model and sequence of words prediction, DeepSQLi is equipped with the ability to learn the semantic knowledge embedded in SQLi attacks, allowing it to translate user inputs (or a test case) into a new test case, which is semantically related and potentially more sophisticated. Experiments are conducted to compare DeepSQLi with SQLmap, a state-of-the-art SQLi testing automation tool, on six real-world Web applications that are of different scales, characteristics and domains. Empirical results demonstrate the effectiveness and the remarkable superiority of DeepSQLi over SQLmap, such that more SQLi vulnerabilities can be identified by using a less number of test cases, whilst running much faster

DeepGauge: Multi-Granularity Testing Criteria for Deep Learning Systems

Deep learning (DL) defines a new data-driven programming paradigm that constructs the internal system logic of a crafted neuron network through a set of training data. We have seen wide adoption of DL in many safety-critical scenarios. However, a plethora of studies have shown that the state-of-the-art DL systems suffer from various vulnerabilities which can lead to severe consequences when applied to real-world applications. Currently, the testing adequacy of a DL system is usually measured by the accuracy of test data. Considering the limitation of accessible high quality test data, good accuracy performance on test data can hardly provide confidence to the testing adequacy and generality of DL systems. Unlike traditional software systems that have clear and controllable logic and functionality, the lack of interpretability in a DL system makes system analysis and defect detection difficult, which could potentially hinder its real-world deployment. In this paper, we propose DeepGauge, a set of multi-granularity testing criteria for DL systems, which aims at rendering a multi-faceted portrayal of the testbed. The in-depth evaluation of our proposed testing criteria is demonstrated on two well-known datasets, five DL systems, and with four state-of-the-art adversarial attack techniques against DL. The potential usefulness of DeepGauge sheds light on the construction of more generic and robust DL systems.Comment: The 33rd IEEE/ACM International Conference on Automated Software Engineering (ASE 2018