Decomposing GR(1) Games with Singleton Liveness Guarantees for Efficient Synthesis

Temporal logic based synthesis approaches are often used to find trajectories that are correct-by-construction for tasks in systems with complex behavior. Some examples of such tasks include synchronization for multi-agent hybrid systems, reactive motion planning for robots. However, the scalability of such approaches is of concern and at times a bottleneck when transitioning from theory to practice. In this paper, we identify a class of problems in the GR(1) fragment of linear-time temporal logic (LTL) where the synthesis problem allows for a decomposition that enables easy parallelization. This decomposition also reduces the alternation depth, resulting in more efficient synthesis. A multi-agent robot gridworld example with coordination tasks is presented to demonstrate the application of the developed ideas and also to perform empirical analysis for benchmarking the decomposition-based synthesis approach

Computation of Eclipse Time for Low-Earth Orbiting Small Satellites

This paper addresses a method for computing eclipse and daylight time for satellites in LEO. Continuous health-monitoring of the satellite is required for the success the mission. For this, the satellites are primarily powered by solar energy but during the eclipse time, the satellite’s functioning is affected as it cannot generate power during this period and also, its temperature drops rapidly. To tackle these issues, computing eclipse time for satellites becomes essential so as to plan for sizing its secondary power source for continuous functioning. In this work, eclipse time is being computed and it is shown that eclipse time is a function of altitude, size of earth and the orbital beta angle

Bluetooth based home automation by using arduino

The world is rapidly becoming more automated. People had limited time to do any work, therefore automation became an easy approach to control any device that worked according to our wishes. This study is primarily concerned with the development and design of a home automation system using an Arduino and a Bluetooth module. With an Android application, a home automation system provides simple and current technology. The Uno Arduino with Bluetooth module is used to control home appliances such as fans, bulbs, air conditioning, and refrigerators. When individuals are not at home, our main goal is to monitor and operate houses using an Android phone and to provide a security-based smart home. Our goal is to control household appliances in a smart home in a user friendly, low-cost, and easy-to-install manner

Fast Automatic Verification of Large-Scale Systems with Lookup Tables

Modern safety-critical systems are difficult to formally verify, largely due to their large scale. In particular, the widespread use of lookup tables in embedded systems across diverse industries, such as aeronautics and automotive systems, create a critical obstacle to the scalability of formal verification. This paper presents a novel approach for the formal verification of large-scale systems with lookup tables. We use a learning-based technique to automatically learn abstractions of the lookup tables and use the abstractions to then prove the desired property. If the verification fails, we propose a falsification heuristic to search for a violation of the specification. In contrast with previous work on lookup table verification, our technique is completely automatic, making it ideal for deployment in a production environment. To our knowledge, our approach is the only technique that can automatically verify large-scale systems lookup with tables. We illustrate the effectiveness of our technique on a benchmark which cannot be handled by the commercial tool SLDV, and we demonstrate the performance improvement provided by our technique

Adaptation of Different Compomers to Primary Teeth Cavities

Background: Compomers remain the material of choice for restoration of primary teeth as they combine the best of GIC and composites. However, as it is a resinous material, attentionis focused on polymerization shrinkage causing gaps at restoration cavity interface. Gaps represent decreased efficacy of adaptation. Aim: To evaluate the marginal adaptation of compomers (Dyract, Compoglass, and F-2000) in class I and V cavities in primary molars. Materials and methods: Sixty noncarious primary molars were divided for three compomers (20), which were subdivided to two groups. Standard class I and V cavities (10 each) were prepared and restored. The cavity interface was examined and observations analyzed. The cavities were etched prior to restoration and margins were exposed. The cavosurface margins were inspected under stereomicroscope for surface gaps. Then buccolingually sectioned, they were examined for marginal gaps. Two specimens each were selected for SEM. Chi-square test was used to determine statistical significance (p < 0.05). Results: All compomers showed good adaptation at cavosurface, with class I better than V. Compoglass and Dyract were better adapted to cavity walls than F-2000. SEM revealed close interlaced adaptation of filling material to etched cavity. Conclusion: This study has shown that compomers provide good adaptation at cavity margins with compules (Compoglass and Dyract) being a better mode of dispensing than syringe tubes (F-2000). SEM showed gaps and pooling of adhesive and air in few samples.&nbsp

Inverse Abstraction of Neural Networks Using Symbolic Interpolation

Neural networks in real-world applications have to satisfy critical properties such as safety and reliability. The analysis of such properties typically requires extracting information through computing pre-images of the network transformations, but it is well-known that explicit computation of pre-images is intractable. We introduce new methods for computing compact symbolic abstractions of pre-images by computing their overapproximations and underapproximations through all layers. The abstraction of pre-images enables formal analysis and knowledge extraction without affecting standard learning algorithms. We use inverse abstractions to automatically extract simple control laws and compact representations for pre-images corresponding to unsafe outputs. We illustrate that the extracted abstractions are interpretable and can be used for analyzing complex properties

Inverse Abstraction of Neural Networks Using Symbolic Interpolation

Neural networks in real-world applications have to satisfy critical properties such as safety and reliability. The analysis of such properties typically requires extracting information through computing pre-images of the network transformations, but it is well-known that explicit computation of pre-images is intractable. We introduce new methods for computing compact symbolic abstractions of pre-images by computing their overapproximations and underapproximations through all layers. The abstraction of pre-images enables formal analysis and knowledge extraction without affecting standard learning algorithms. We use inverse abstractions to automatically extract simple control laws and compact representations for pre-images corresponding to unsafe outputs. We illustrate that the extracted abstractions are interpretable and can be used for analyzing complex properties

Enhancing tolerance to unexpected jumps in GR(1) games

When used as part of a hybrid controller, finite-memory strategies synthesized from linear-time temporal logic (LTL) specifications rely on an accurate dynamics model in order to ensure correctness of trajectories. In the presence of uncertainty about the underlying model, there may exist unexpected trajectories that manifest as unexpected transitions under control of the strategy. While some disturbances can be captured by augmenting the dynamics model, such approaches may be conservative in that bisimulations may fail to exist for which strategies can be synthesized. In this paper, we consider games of the GR(1) fragment of LTL, and we characterize the tolerance of hybrid controllers to perturbations that appear as unexpected jumps (transitions) to states in the discrete strategy part of the controller. As a first step, we show robustness to certain unexpected transitions that occur in a finite manner, i.e., despite a certain number of unexpected jumps, the sequence of states obtained will still meet a stricter specification and hence the original specification. Additionally, we propose algorithms to improve robustness by increasing tolerance to additional disturbances. A robot gridworld example is presented to demonstrate the application of the developed ideas and also to perform empirical analysis

Identifying and exploiting tolerance to unexpected jumps in synthesized strategies for GR(1) specifications

When used as part of a hybrid controller, finite-memory strategies synthesized from LTL specifications rely on an accurate dynamics model in order to ensure correctness of trajectories. In the presence of uncertainty about this underlying model, there may exist unexpected trajectories that manifest as unexpected transitions under control of the strategy. While some disturbances can be captured by augmenting the dynamics model, such approaches may be conservative in that bisimulations may fail to exist for which strategies can be synthesized. In this paper, we characterize the tolerance of such hybrid controllers - synthesized for generalized reactivity(1) specifications- to disturbances that appear as unexpected jumps (transitions) to states in the discrete strategy part of the controller. As a first step, we show robustness to certain unexpected transitions that occur in a finite-manner, i.e., despite a certain number of unexpected jumps, the sequence of states obtained will still meet a stricter specification and hence the original specification. Additionally, we propose algorithms to improve robustness by increasing tolerance to additional disturbances. A robot gridworld example is presented to demonstrate the application of the developed ideas and also to obtain empirical computational and memory cost estimates