Factor Graphs for Heterogeneous Bayesian Decentralized Data Fusion

This paper explores the use of factor graphs as an inference and analysis tool for Bayesian peer-to-peer decentralized data fusion. We propose a framework by which agents can each use local factor graphs to represent relevant partitions of a complex global joint probability distribution, thus allowing them to avoid reasoning over the entirety of a more complex model and saving communication as well as computation cost. This allows heterogeneous multi-robot systems to cooperate on a variety of real world, task oriented missions, where scalability and modularity are key. To develop the initial theory and analyze the limits of this approach, we focus our attention on static linear Gaussian systems in tree-structured networks and use Channel Filters (also represented by factor graphs) to explicitly track common information. We discuss how this representation can be used to describe various multi-robot applications and to design and analyze new heterogeneous data fusion algorithms. We validate our method in simulations of a multi-agent multi-target tracking and cooperative multi-agent mapping problems, and discuss the computation and communication gains of this approach.Comment: 8 pages, 6 figures, 1 table, submitted to the 24th International Conference on Information Fusio

Heterogeneous Bayesian Decentralized Data Fusion: An Empirical Study

In multi-robot applications, inference over large state spaces can often be divided into smaller overlapping sub-problems that can then be collaboratively solved in parallel over `separate' subsets of states. To this end, the factor graph decentralized data fusion (FG-DDF) framework was developed to analyze and exploit conditional independence in heterogeneous Bayesian decentralized fusion problems, in which robots update and fuse pdfs over different locally overlapping random states. This allows robots to efficiently use smaller probabilistic models and sparse message passing to accurately and scalably fuse relevant local parts of a larger global joint state pdf, while accounting for data dependencies between robots. Whereas prior work required limiting assumptions about network connectivity and model linearity, this paper relaxes these to empirically explore the applicability and robustness of FG-DDF in more general settings. We develop a new heterogeneous fusion rule which generalizes the homogeneous covariance intersection algorithm, and test it in multi-robot tracking and localization scenarios with non-linear motion/observation models under communication dropout. Simulation and linear hardware experiments show that, in practice, the FG-DDF continues to provide consistent filtered estimates under these more practical operating conditions, while reducing computation and communication costs by more than 95%, thus enabling the design of scalable real-world multi-robot systems.Comment: 7 pages, 2 figures, submitted to IEEE Conference on Robotics and Automation (ICRA 2023

Exploiting Structure for Optimal Multi-Agent Bayesian Decentralized Estimation

A key challenge in Bayesian decentralized data fusion is the `rumor propagation' or `double counting' phenomenon, where previously sent data circulates back to its sender. It is often addressed by approximate methods like covariance intersection (CI) which takes a weighted average of the estimates to compute the bound. The problem is that this bound is not tight, i.e. the estimate is often over-conservative. In this paper, we show that by exploiting the probabilistic independence structure in multi-agent decentralized fusion problems a tighter bound can be found using (i) an expansion to the CI algorithm that uses multiple (non-monolithic) weighting factors instead of one (monolithic) factor in the original CI and (ii) a general optimization scheme that is able to compute optimal bounds and fully exploit an arbitrary dependency structure. We compare our methods and show that on a simple problem, they converge to the same solution. We then test our new non-monolithic CI algorithm on a large-scale target tracking simulation and show that it achieves a tighter bound and a more accurate estimate compared to the original monolithic CI.Comment: 4 pages, 4 figures. presented at the Inference and Decision Making for Autonomous Vehicles (IDMAV) RSS 2023 worksho

Electrifying green synthesis: recent advances in electrochemical annulation reactions

Electricity originating from renewable resources can be used for highly sustainable and economically attractive applications. With electrons as the mass-free reagent, the use of a stoichiometric amount of oxidants in annulation reactions can be avoided, thereby eliminating the production of waste. Considered as a modern reaction configuration, the availability of electrochemical methods is expanding synthetic applications in the field of organic chemistry. Electrochemical transformations possess many benefits over traditional reagent-based methodologies, such as high functional group tolerance, mild conditions, easy scale up setup, high yields and selective transformations. In this review, we targeted electrochemical annulation reactions involving mediators and mediator-free conditions with generation of new C–C, C–heteroatom and heteroatom–heteroatom bonds, their mechanistic insights, as well as the reactivity of substrates. We also explain the recent use of sacrificial electrodes in annulation reactions

Modeling orientation perception adaptation to altered gravity environments with memory of past sensorimotor states

Transitioning between gravitational environments results in a central reinterpretation of sensory information, producing an adapted sensorimotor state suitable for motor actions and perceptions in the new environment. Critically, this central adaptation is not instantaneous, and complete adaptation may require weeks of prolonged exposure to novel environments. To mitigate risks associated with the lagging time course of adaptation (e.g., spatial orientation misperceptions, alterations in locomotor and postural control, and motion sickness), it is critical that we better understand sensorimotor states during adaptation. Recently, efforts have emerged to model human perception of orientation and self-motion during sensorimotor adaptation to new gravity stimuli. While these nascent computational frameworks are well suited for modeling exposure to novel gravitational stimuli, they have yet to distinguish how the central nervous system (CNS) reinterprets sensory information from familiar environmental stimuli (i.e., readaptation). Here, we present a theoretical framework and resulting computational model of vestibular adaptation to gravity transitions which captures the role of implicit memory. This advancement enables faster readaptation to familiar gravitational stimuli, which has been observed in repeat flyers, by considering vestibular signals dependent on the new gravity environment, through Bayesian inference. The evolution and weighting of hypotheses considered by the CNS is modeled via a Rao-Blackwellized particle filter algorithm. Sensorimotor adaptation learning is facilitated by retaining a memory of past harmonious states, represented by a conditional state transition probability density function, which allows the model to consider previously experienced gravity levels (while also dynamically learning new states) when formulating new alternative hypotheses of gravity. In order to demonstrate our theoretical framework and motivate future experiments, we perform a variety of simulations. These simulations demonstrate the effectiveness of this model and its potential to advance our understanding of transitory states during which central reinterpretation occurs, ultimately mitigating the risks associated with the lagging time course of adaptation to gravitational environments

Mode division multiplexing using an orbital angular momentum mode sorter and MIMO-DSP over a graded-index few-mode optical fibre

Mode division multiplexing (MDM)– using a multimode optical fiber’s N spatial modes as data channels to transmit N independent data streams – has received interest as it can potentially increase optical fiber data transmission capacity N-times with respect to single mode optical fibers. Two challenges of MDM are (1) designing mode (de)multiplexers with high mode selectivity (2) designing mode (de)multiplexers without cascaded beam splitting’s 1/N insertion loss. One spatial mode basis that has received interest is that of orbital angular momentum (OAM) modes. In this paper, using a device referred to as an OAM mode sorter, we show that OAM modes can be (de)multiplexed over a multimode optical fiber with higher than −15 dB mode selectivity and without cascaded beam splitting’s 1/N insertion loss. As a proof of concept, the OAM modes of the LP11 mode group (OAM−1,0 and OAM+1,0), each carrying 20-Gbit/s polarization division multiplexed and quadrature phase shift keyed data streams, are transmitted 5km over a graded-index, few-mode optical fibre. Channel crosstalk is mitigated using 4 × 4 multiple-input-multiple-output digital-signal-processing with <1.5 dB power penalties at a bit-error-rate of 2 × 10−3

Assessment of radiographic morphology of mandibular condyles: a radiographic study

Background: Panoramic radiographs are the most common radiographic tool used by the dental clinicians to evaluate teeth, mandible and other related structures of the jaws. Mandibular condyle is an important anatomical landmark for facial growth, expressed in an upward and backward direction. The presentation of mandibular condyle differs widely among different group of ages and individuals. Materials and methods: The retrospective cross-sectional study was conducted from Nov 2018 to March 2019 at Dow International Dental College (DIDC) Karachi that includes radiographic evaluation of 500 mandibular condyles. All retrievable OPGs were obtained and data were extracted regarding age, gender and condylar morphology. Results: The morphological appearances of mandibular condyle have great variation among different age groups and subjects. Normally we recognize five basic shapes i.e. Oval, Bird beak, crooked finger, diamond and mixed. Out of 250 pair of condylar heads that were evaluated, 50% were oval, 40% bird beak, 4.8% crooked finger and diamond 4.8%. Conclusions: All four morphological types of mandibular condyles were observed and the oval shape condyles were most prevalent among both genders and all age groups. In future studies, the inclusion of other parameters and large sample size may provide unique information

Preparation and characterization of layer-diffusion processed InBi2Se4 thin films for photovoltaics application

In this research work, optoelectronic properties of Indium bismuth selenide (InBi2Se4) thin films are studied for their potentials for photovoltaic applications. The InBi2Se4 films are prepared via a thermal co-evaporation technique on glass substrate using Bi2S3 powders and indium granules. The as-deposited films are then annealed at different temperatures to convert into InBi2Se4 thin films. Results show that the obtained InBi2Se4 films possess excellent optoelectronic properties as an optimum bandgap of 1.2 eV was obtained for the film annealed at 350oC. Based on characterisation results of current and voltage realiationships, both as-deposited and annealed InBi2Se4 thin films show a linear relationship between current and annealing temperature. It was also noted that with increasing grain-size of the film, the current is also increased at a fixed applied voltage