Interfacing TuLiP with the JPL Statechart Autocoder: Initial progress toward synthesis of flight software from formal specifications

This paper describes the implementation of an interface connecting the two tools : the JPL SCA (Statechart Autocoder) and TuLiP (Temporal Logic Planning Toolbox) to enable the automatic synthesis of low level implementation code directly from formal specifications. With system dynamics, bounds on uncertainty and formal specifications as inputs, TuLiP synthesizes Mealy machines that are correct-by-construction. An interface is built that automatically translates these Mealy machines into UML statecharts. The SCA accepts the UML statecharts (as XML files) to synthesize flight-certified implementation code. The functionality of the interface is demonstrated through three example systems of varying complexity a) a simple thermostat b) a simple speed controller for an autonomous vehicle and c) a more complex speed controller for an autonomous vehicle with a map-element. In the thermostat controller, there is a specification regarding the desired temperature range that has to be met despite disturbance from the environment. Similarly, in the speed-controllers there are specifications about safe driving speeds depending on sensor health (sensors fail unpredictably) and the map-location. The significance of these demonstrations is the potential circumventing of some of the manual design of statecharts for flight software/controllers. As a result, we expect that less testing and validation will be necessary. In applications where the products of synthesis are used alongside manually designed components, extensive testing or new certificates of correctness of the composition may still be required

Depth and IMU aided image deblurring based on deep learning

Abstract. With the wide usage and spread of camera phones, it becomes necessary to tackle the problem of the image blur. Embedding a camera in those small devices implies obviously small sensor size compared to sensors in professional cameras such as full-frame Digital Single-Lens Reflex (DSLR) cameras. As a result, this can dramatically affect the collected amount of photons on the image sensor. To overcome this, a long exposure time is needed, but with slight motions that often happen in handheld devices, experiencing image blur is inevitable. Our interest in this thesis is the motion blur that can be caused by the camera motion, scene (objects in the scene) motion, or generally the relative motion between the camera and scene. We use deep neural network (DNN) models in contrary to conventional (non DNN-based) methods which are computationally expensive and time-consuming. The process of deblurring an image is guided by utilizing the scene depth and camera’s inertial measurement unit (IMU) records. One of the challenges of adopting DNN solutions is that a relatively huge amount of data is needed to train the neural network. Moreover, several hyperparameters need to be tuned including the network architecture itself. To train our network, a novel and promising method of synthesizing spatially-variant motion blur is proposed that considers the depth variations in the scene, which showed improvement of results against other methods. In addition to the synthetic dataset generation algorithm, a real blurry and sharp dataset collection setup is designed. This setup can provide thousands of real blurry and sharp images which can be of paramount benefit in DNN training or fine-tuning