Physics-Informed Computer Vision: A Review and Perspectives

Incorporation of physical information in machine learning frameworks are opening and transforming many application domains. Here the learning process is augmented through the induction of fundamental knowledge and governing physical laws. In this work we explore their utility for computer vision tasks in interpreting and understanding visual data. We present a systematic literature review of formulation and approaches to computer vision tasks guided by physical laws. We begin by decomposing the popular computer vision pipeline into a taxonomy of stages and investigate approaches to incorporate governing physical equations in each stage. Existing approaches in each task are analyzed with regard to what governing physical processes are modeled, formulated and how they are incorporated, i.e. modify data (observation bias), modify networks (inductive bias), and modify losses (learning bias). The taxonomy offers a unified view of the application of the physics-informed capability, highlighting where physics-informed learning has been conducted and where the gaps and opportunities are. Finally, we highlight open problems and challenges to inform future research. While still in its early days, the study of physics-informed computer vision has the promise to develop better computer vision models that can improve physical plausibility, accuracy, data efficiency and generalization in increasingly realistic applications

A Survey on Physics Informed Reinforcement Learning: Review and Open Problems

The inclusion of physical information in machine learning frameworks has revolutionized many application areas. This involves enhancing the learning process by incorporating physical constraints and adhering to physical laws. In this work we explore their utility for reinforcement learning applications. We present a thorough review of the literature on incorporating physics information, as known as physics priors, in reinforcement learning approaches, commonly referred to as physics-informed reinforcement learning (PIRL). We introduce a novel taxonomy with the reinforcement learning pipeline as the backbone to classify existing works, compare and contrast them, and derive crucial insights. Existing works are analyzed with regard to the representation/ form of the governing physics modeled for integration, their specific contribution to the typical reinforcement learning architecture, and their connection to the underlying reinforcement learning pipeline stages. We also identify core learning architectures and physics incorporation biases (i.e., observational, inductive and learning) of existing PIRL approaches and use them to further categorize the works for better understanding and adaptation. By providing a comprehensive perspective on the implementation of the physics-informed capability, the taxonomy presents a cohesive approach to PIRL. It identifies the areas where this approach has been applied, as well as the gaps and opportunities that exist. Additionally, the taxonomy sheds light on unresolved issues and challenges, which can guide future research. This nascent field holds great potential for enhancing reinforcement learning algorithms by increasing their physical plausibility, precision, data efficiency, and applicability in real-world scenarios

Safe Human Robot-Interaction using Switched Model Reference Admittance Control

Physical Human-Robot Interaction (pHRI) task involves tight coupling between safety constraints and compliance with human intentions. In this paper, a novel switched model reference admittance controller is developed to maintain compliance with the external force while upholding safety constraints in the workspace for an n-link manipulator involved in pHRI. A switched reference model is designed for the admittance controller to generate the reference trajectory within the safe workspace. The stability analysis of the switched reference model is carried out by an appropriate selection of the Common Quadratic Lyapunov Function (CQLF) so that asymptotic convergence of the trajectory tracking error is ensured. The efficacy of the proposed controller is validated in simulation on a two-link robot manipulator

Multiple generation of Bengali static signatures

Handwritten signature datasets are really necessary for the purpose of developing and training automatic signature verification systems. It is desired that all samples in a signature dataset should exhibit both inter-personal and intra-personal variability. A possibility to model this reality seems to be obtained through the synthesis of signatures. In this paper we propose a method based on motor equivalence model theory to generate static Bengali signatures. This theory divides the human action to write mainly into cognitive and motor levels. Due to difference between scripts, we have redesigned our previous synthesizer [1,2], which generates static Western signatures. The experiments assess whether this method can approach the intra and inter-personal variability of the Bengali-100 Static Signature DB from a performance-based validation. The similarities reported in the experimental results proof the ability of the synthesizer to generate signature images in this script

Energy conservation in wireless sensor network using irregular cellular automata

Wireless sensor nodes are severely constrained by energy consumption as they are battery powered. Most topology control techniques that are used for energy conservation assume that the sensor nodes are uniformly deployed, mostly in form of grids. In a real world scenario a grid based deployment presents its own set of problems and limitations. The current paper proposes improvement over a block cellular automata based energy conservation approach. In place of uniform grid, an irregular cellular automata approach has been proposed in this paper. Voronoi tessellations are used for clustering and a new neighborhood rule is proposed for non-uniform neighborhood of irregular cellular automata.</p

Sybil node detection in peer-to-peer networks using indirect validation

Peer- to- peer networks are used extensively today. Due to this wide use P2P networks is a target of malicious attacks. The most mentionable of them is the Sybil attack. Existing approaches for detection and mitigation of Sybil nodes are either computationally costly or are dependent on belief models found in social networks. It has been found that these belief models are themselves vulnerable to other attacks. In this paper, we propose a new type of indirect validation where we have a two stage validation in place to check that if a suspected node is Sybil or not. We crosscheck belief data from local monitor nodes and community detection data from randomly selected global monitor nodes, and then validate a suspected node. The proposed approach is found to be with less computation overheads and less vulnerable to malicious attacks.</p

Multiagent coalition formation for distributed area coverage and exploration

In area coverage problems with multiple agents, team formation turns out quite beneficial. In this paper we propose a team formation algorithm using coalition game theory. We also implement elements from weighted graph games and weighted synergy graphs for making the approach simpler in representation and application.</p

Energy conservation in wireless sensor network using block cellular automata

Energy conservation is a very critical issue in WSN. A lot of work has been done on the techniques of topology control so that sensor nodes which are not in direct use can be put to a low power consuming state, thus saving energy. Recently improvements have been done in this field by considering individual sensors as individual cell in a grid and applying cellular automata principles to it. Our work uses Block cellular automata, thus considering a number of sensor nodes as a group or block. The grouping or clustering is performed by Grid clustering technique. The member nodes of a block or cluster will select a Cluster Head (CH), which will represent the block as a whole. The CA principles are applied here, by taking a whole block as a single unit and it will decide on the power mode of the CH. Other power conservation algorithms are applied within the cluster so as to increase the overall network lifetime.</p

Beaconless cooperative localization in wireless sensor network: Implementing Cellular Automata Von Neumann neighborhood

Localization in WSN is a field under constant research. Localization is a critical parameter in certain applications. Our survey shows that most of these existing methods are either 'Beacon' dependent or depends on complex probabilistic calculations and assumptions. We propose a simple algorithm using the concept of minimalistic neighborhood, of Von Neumann type Cellular Automata. The proposed algorithm stores a relative neighborhood based location data and forms a connected graph. Such graphs at each node (with self at the root) help with proper localization, with very little computational and storage overheads.</p

Consensus achievement in multi-agent swarms using weighted median

Achievement of consensus in multi-agent system is a topic of much research lately. Most of the consensus achievement algorithms are iteration based and are not able to address additional objectives and constraints. We propose an algorithm that is simple, distributed and able to achieve consensus after considering multiple objectives. In a group of decision making agents, to find a consensus, we calculate the median of the group. Ultimately the median is pulled to a lower or higher value depending on the summation of constraint based parameters or weights of the agents. Analysis showed the efficacy of the algorithm.</p