Application-specific Constraints Research Articles

Graph learning from incomplete graph signals: From batch to online methods

Inferring graph topologies from data is crucial in many graph-related applications. Existing works typically assume that signals are observed at all nodes, which may not hold due to application-specific constraints. The problem becomes more challenging when data are sequentially available and no delay is tolerated. To address these issues, we propose an approach for learning graphs from incomplete data. First, the problem of learning graphs with missing data is formulated as maximizing the posterior distribution with hidden variables from a Bayesian perspective. Then, we propose an expectation maximization (EM) algorithm to solve the induced problem, in which graph learning and graph signal recovery are jointly performed. Furthermore, we extend the proposed EM algorithm to an online version to accommodate the delay-sensitive situations of sequential data. Theoretically, we analyze the dynamic regret of the proposed online algorithm, illustrating the effectiveness of our algorithm in tracking graphs from partial observations in an online manner. Finally, extensive experiments on synthetic and real data are conducted, and the results corroborate that our approach can learn graphs effectively from incomplete data in both batch and online situations.

Time-varying graph learning from smooth and stationary graph signals with hidden nodes

Learning graph structure from observed signals over graph is a crucial task in many graph signal processing (GSP) applications. Existing approaches focus on inferring static graph, typically assuming that all nodes are available. However, these approaches ignore the situation where only a subset of nodes are available from spatiotemporal measurements, and the remaining nodes are never observed due to application-specific constraints, resulting in time-varying graph estimation accuracy declines dramatically. To handle this problem, we propose a framework that consider the presence of hidden nodes to identify time-varying graph. Specifically, we assume that the graph signals are smooth and stationary on the graphs and only a small number of edges are allowed to change between two consecutive graphs. With these assumptions, we present a challenging time-varying graph inference problem, which models the influence of hidden nodes in terms of estimating the graph-shift operator matrices that have a form of graph Laplacian. Moreover, we emphasize similar edge pattern (column-sparsity) between different graphs. Finally, our method is evaluated on both synthetic and real-world data. The experimental results demonstrate the advantage of our method when compared to existing benchmarking methods.

Pragmatic Evaluation of Health Monitoring & Analysis Models from an Empirical Perspective

Implementing and deploying several linked modules that can conduct real-time analysis and recommendation of patient datasets is necessary for designing health monitoring and analysis models. These databases include, but are not limited to, blood test results, computer tomography (CT) scans, MRI scans, PET scans, and other imaging tests. A combination of signal processing and image processing methods are used to process them. These methods include data collection, pre-processing, feature extraction and selection, classification, and context-specific post-processing. Researchers have put forward a variety of machine learning (ML) and deep learning (DL) techniques to carry out these tasks, which help with the high-accuracy categorization of these datasets. However, the internal operational features and the quantitative and qualitative performance indicators of each of these models differ. These models also demonstrate various functional subtleties, contextual benefits, application-specific constraints, and deployment-specific future research directions. It is difficult for researchers to pinpoint models that perform well for their application-specific use cases because of the vast range of performance. In order to reduce this uncertainty, this paper discusses a review of several Health Monitoring & Analysis Models in terms of their internal operational features & performance measurements. Readers will be able to recognise models that are appropriate for their application-specific use cases based on this discussion. When compared to other models, it was shown that Convolutional Neural Networks (CNNs), Masked Region CNN (MRCNN), Recurrent NN (RNN), Q-Learning, and Reinforcement learning models had greater analytical performance. They are hence suitable for clinical use cases. These models' worse scaling performance is a result of their increased complexity and higher implementation costs. This paper compares evaluated models in terms of accuracy, computational latency, deployment complexity, scalability, and deployment cost metrics to analyse such scenarios. This comparison will help users choose the best models for their performance-specific use cases. In this article, a new Health Monitoring Metric (HMM), which integrates many performance indicators to identify the best-performing models under various real-time patient settings, is reviewed to make the process of model selection even easier for real-time scenarios.

An Algorithm to Mine Therapeutic Motifs for Cancer From Networks of Genetic Interactions

Study of pairwise genetic interactions, such as mutually exclusive mutations, has led to understanding of underlying mechanisms in cancer. Investigation of various combinatorial motifs within networks of such interactions can lead to deeper insights into its mutational landscape and inform therapy development. One such motif called the Between-Pathway Model (BPM) represents redundant or compensatory pathways that can be therapeutically exploited. Finding such BPM motifs is challenging since most formulations require solving variants of the NP-complete maximum weight bipartite subgraph problem. In this paper we design an algorithm based on Integer Linear Programming (ILP) to solve this problem. In our experiments, our approach outperforms the best previous method to mine BPM motifs. Further, our ILP-based approach allows us to easily model additional application-specific constraints. We illustrate this advantage through a new application of BPM motifs that can potentially aid in finding combination therapies to combat cancer.

Design synthesis and optimization of a 4-SPS intrinsically compliant parallel wrist rehabilitation robotic orthosis

Abstract Neuroplasticity allows the human nervous system to adapt and relearn motor control following stroke. Rehabilitation therapy, which enhances neuroplasticity, can be made more effective if assisted by robotic tools. In this paper, a novel 4-SPS parallel robot has been developed to provide recovery of wrist movements post-stroke. The novel mechanism presented here was inspired by the forearm anatomy and can provide the rotational degrees of freedom required for all wrist movements. The robot design has been discussed in detail along with the necessary constructional, kinematic, and static analyses. The spatial workspace of the robot is estimated considering various dimensional and application-specific constraints besides checking for singular configurations. The wrist robot has been further evaluated using important performance indices such as condition number, actuator forces, and stiffness. The pneumatic artificial muscles exhibit varying stiffness, and therefore, workspace points are reached with different overall stiffness of the robot. It is essential to assess robot workspace points that can be reached with positive forces in actuators while maintaining a positive definite overall stiffness matrix. After the above analysis, design optimization has been carried out using an evolutionary algorithm whereby three critical criteria are optimized simultaneously for optimal wrist robot design.

Design of a Reconfigurable Parallel Continuum Robot With Tendon-Actuated Kinematic Chains

In this letter, a novel spatial parallel continuum robot is proposed. It is composed of three tendon-actuated continuum robots as kinematic chains that are coupled at a common end effector platform by spherical joints. A modular design approach is used, allowing the reconfiguration of each continuum robot's base position and orientation to adapt the overall structure to application specific constraints and environments. The parallel continuum robot is evaluated in terms of its position and orientation repeatability. Furthermore, comparisons are made to the performance of the single continuum kinematic chains. Overall, average position and orientation repeatabilities of 3.3 mm and 1.2° can be achieved for the parallel continuum robot, with a single individual continuum segment showing repeatabilities of 5.5 mm and 3.2°. The proposed parallel continuum robot exhibits improved repeatabilities than each of the employed individual continuum segments, improving the average repeatability by 67% for position and 167% for orientation, respectively.

Network Topology Inference From Heterogeneous Incomplete Graph Signals

Inferring network topologies from observed graph-structured data (also known as graph signals) is a crucial task in many applications of network science. Existing papers on network topology inference typically assume that the observations at all nodes are available. However, there are many situations where only partial observations can be collected due to application-specific constraints. To handle the missing data problem, we propose a framework that relies on heterogeneous incomplete data from a collection of related networks to identify multiple network topologies simultaneously. This work advocates the Gaussian graphical model (GGM) and casts the topology inference problem in terms of estimating the precision matrix that has a form of graph Laplacian. Firstly, an unbiased estimator for the covariance matrix of incomplete data is established and then algorithms based on the alternating direction method of multipliers (ADMM) are developed to jointly estimate graph topologies, rather than estimate each graph topology separately. So that we can borrow information across the multiple related networks to eliminate the impact of missing data. Moreover, non-asymptotic statistical analysis is provided, which proves the consistency of the graph estimator and enables us to investigate the effect of several key factors on the graph estimation error bound. Furthermore, based on the consistent graph estimator, an adaptive algorithm that utilizes the reweighting scheme is proposed to improve the estimation accuracy of the graph-edge structure. Finally, we evaluate our method on both real and synthetic datasets, and the experimental results demonstrate the advantage of our method in comparison with benchmarking algorithms.

Effects of the Target on the Performance of an Ultra-Low Power Eddy Current Displacement Sensor for Industrial Applications

The demand for smart, low-power, and low-cost sensors is rapidly increasing with the proliferation of industry automation. In this context, an Ultra-Low Power Eddy Current Displacement Sensor (ULP-ECDS) targeting common industrial applications and designed to be embedded in wireless Industrial Internet of Things (IIoT) devices is presented. A complete characterization of the realized ULP-ECDS operating with different metallic targets was carried out. The choice of the considered targets in terms of material and thickness was inspired by typical industrial scenarios. The experimental results show that the realized prototype works properly with extremely low supply voltages, allowing for obtaining an ultra-low power consumption, significantly lower than other state-of-the-art solutions. In particular, the proposed sensor reached the best resolution of 2 µm in case of a carbon steel target when operated with a supply voltage of 200 mV and with a power consumption of 150 µW. By accepting a resolution of 12 µm, it is possible to further reduce the power consumption of the sensor to less than 10 µW. The obtained results also demonstrate how the performances of the sensor are strongly dependent on both the target and the demodulation technique used to extract the displacement information. This allowed for defining some practical guidelines that can help the design of effective solutions considering application-specific constraints.

Simultaneous Facility Location and Path Optimization in Static and Dynamic Networks

We present a framework for solving simultaneously the problems of facility location and path optimization in static and dynamic spatial networks. In the static setting, the objective is to determine facility locations and transportation paths from each node to the destination via the network of facilities such that the total cost of commodity transportation is minimized. This is an NP-hard problem. We propose a novel stage-wise viewpoint of the paths which is instrumental in designing the decision variable space in our framework. We use the maximum entropy principle to solve the resulting optimization problem. In the dynamic setting, nodes and destinations are dynamic. We design an appropriate control Lyapunov function to determine the time evolution of facilities and paths such that the transportation cost at each time instant is minimized. Our framework enables quantifying attributes of the facilities and transportation links in terms of the decision variables. Consequently, it becomes possible to incorporate application specific constraints on individual facilities, links, and network topology. We demonstrate the efficacy of our proposed framework through extensive simulations.

Design Space Exploration of Hardware Spiking Neurons for Embedded Artificial Intelligence

Machine learning is yielding unprecedented interest in research and industry, due to recent success in many applied contexts such as image classification and object recognition. However, the deployment of these systems requires huge computing capabilities, thus making them unsuitable for embedded systems. To deal with this limitation, many researchers are investigating brain-inspired computing, which would be a perfect alternative to the conventional Von Neumann architecture based computers (CPU/GPU) that meet the requirements for computing performance, but not for energy-efficiency. Therefore, neuromorphic hardware circuits that are adaptable for both parallel and distributed computations need to be designed. In this paper, we focus on Spiking Neural Networks (SNNs) with a comprehensive study of neural coding methods and hardware exploration. In this context, we propose a framework for neuromorphic hardware design space exploration, which allows to define a suitable architecture based on application-specific constraints and starting from a wide variety of possible architectural choices. For this framework, we have developed a behavioral level simulator for neuromorphic hardware architectural exploration named NAXT. Moreover, we propose modified versions of the standard Rate Coding technique to make trade-offs with the Time Coding paradigm, which is characterized by the low number of spikes propagating in the network. Thus, we are able to reduce the number of spikes while keeping the same neuron’s model, which results in an SNN with fewer events to process. By doing so, we seek to reduce the amount of power consumed by the hardware. Furthermore, we present three neuromorphic hardware architectures in order to quantitatively study the implementation of SNNs. One of these architectures integrates a novel hybrid structure: a highly-parallel computation core for most solicited layers, and time-multiplexed computation units for deeper layers. These architectures are derived from a novel funnel-like Design Space Exploration framework for neuromorphic hardware.

Combined economic and technological evaluation of battery energy storage for grid applications

Batteries will play critical roles in modernizing energy grids, as they will allow a greater penetration of renewable energy and perform applications that better match supply with demand. Applying storage technology is a business decision that requires potential revenues to be accurately estimated to determine the economic viability, which requires models that consider market rules and prices, along with battery and application-specific constraints. Here we use models of storage connected to the California energy grid and show how the application-governed duty cycles (power profiles) of different applications affect different battery chemistries. We reveal critical trade-offs between battery chemistries and the applicability of energy content in the battery and show that accurate revenue measurement can only be achieved if a realistic battery operation in each application is considered. The findings in this work could call for a paradigm shift in how the true economic values of energy storage devices could be assessed. Large variations exist in the revenue prediction of grid-scale storage due to uncertainties in operations of storage technologies. Here the authors integrate the economic evaluation of energy storage with key battery parameters for a realistic measure of revenues.

On the optimal placement of cameras for surveillance and the underlying set cover problem

Given a delimited surveillance area, represented in either 2D or 3D, and a set of feasible camera locations and orientations, the optimal camera placement problem (OCP) is that of identifying camera configurations which optimise a predefined objective under a set of application-specific constraints. While this problem appears in many modern applications such as area surveillance, target tracking, photogrammetry or traffic and crowd analysis, it finds its roots in one of Karp’s 21 original NP-complete problems: set cover (SCP). If the two problems are often structurally identical, many papers dealing with optimal camera placement use this relationship implicitly, and a lot of the work done on the SCP has yet to find its way into the literature of the applied problem. In this survey, we review both in terms of problem modelling, preprocessing and solving approaches, and attempt to bring them together by suggesting open lines of research for future works.

Semi-Blind Inference of Topologies and Dynamical Processes Over Dynamic Graphs

Network science provides valuable insights across numerous disciplines including sociology, biology, neuroscience and engineering. A task of major practical importance in these application domains is inferring the network structure from noisy observations at a subset of nodes. Available methods for topology inference typically assume that the process over the network is observed at all nodes. However, application-specific constraints may prevent acquiring network-wide observations. Alleviating the limited flexibility of existing approaches, this work advocates structural models for graph processes and develops novel algorithms for joint inference of the network topology and processes from partial nodal observations. Structural equation models (SEMs) and structural vector autoregressive models (SVARMs) have well-documented merits in identifying even directed topologies of complex graphs; while SEMs capture contemporaneous causal dependencies among nodes, SVARMs further account for time-lagged influences. This paper develops algorithms that iterate between inferring directed graphs that "best" fit the data, and estimating the network processes at reduced computational complexity by leveraging tools related to Kalman smoothing. To further accommodate delay-sensitive applications, an online joint inference approach is put forth that even tracks time-evolving topologies. Furthermore, conditions for identifying the network topology given partial observations are specified. It is proved that the required number of observations for unique identification reduces significantly when the network structure is sparse. Numerical tests with synthetic as well as real datasets corroborate the effectiveness of the novel approach.

Toward Massive Machine Type Cellular Communications

Cellular networks have been engineered and optimized to carrying ever-increasing amounts of mobile data, but over the last few years, a new class of applications based on machine-centric communications has begun to emerge. Automated devices such as sensors, tracking devices, and meters, often referred to as machine-to-machine (M2M) or machine-type communications (MTC), introduce an attractive revenue stream for mobile network operators, if a massive number of them can be efficiently supported. The novel technical challenges posed by MTC applications include increased overhead and control signaling as well as diverse application-specific constraints such as ultra-low complexity, extreme energy efficiency, critical timing, and continuous data intensive uploading. This article explains the new requirements and challenges that large-scale MTC applications introduce, and provides a survey of key techniques for overcoming them. We focus on the potential of 4.5G and 5G networks to serve both the high data rate needs of conventional human-type communication (HTC) subscribers and the forecasted billions of new MTC devices. We also opine on attractive economic models that will enable this new class of cellular subscribers to grow to its full potential.

The First Interlaced Continuum Robot, Devised to Intrinsically Follow the Leader.

Flexible probes that are safely deployed to hard-to-reach targets while avoiding critical structures are strategic in several high-impact application fields, including the biomedical sector and the sector of inspections at large. A critical problem for these tools is the best approach for deploying an entire tool body, not only its tip, on a sought trajectory. A probe that achieves this deployment is considered to follow the leader (or to achieve follow-the-leader deployment) because its body sections follow the track traced by its tip. Follow-the-leader deployment through cavities is complicated due to a lack of external supports. Currently, no definitive implementation for a probe that is intrinsically able to follow the leader, i.e., without relying on external supports, has been achieved. In this paper, we present a completely new device, namely the first interlaced continuum robot, devised to intrinsically follow the leader. We developed the interlaced configuration by pursuing a conceptual approach irrespective of application-specific constraints and assuming two flexible tools with controllable stiffness. We questioned the possibility of solving the previously mentioned deployment problem by harnessing probe symmetry during the design process. This study examines the entire development of the novel interlaced probe: model-based conceptual design, detailed design and prototyping, and preliminary experimental assessment. Our probe can build a track with a radius of curvature that is as small as twice the probe diameter, which enables it to outperform state-of-the-art tools that are aimed at follow-the-leader deployment. Despite the limitations that are inherently associated with its original character, this study provides a prototypical approach to the design of interlaced continuum systems and demonstrates the first interlaced continuum probe, which is intrinsically able to follow the leader.

Colored-Edge Graph Approach for the Modeling of Multimodal Transportation Systems

Many networked systems involve multiple modes of transport. Such systems are called multimodal, and examples include logistic networks, biomedical phenomena and telecommunication networks. Existing techniques for determining minimal paths in multimodal networks have either required heuristics or else application-specific constraints to obtain tractable problems, removing the multimodal traits of the network during analysis. In this paper weighted colored-edge graphs are introduced for modeling multimodal networks, where colors represent the modes of transportation. Minimal paths are selected using a partial order that compares the weights in each color, resulting in a Pareto set of minimal paths. Although the computation of minimal paths is theoretically intractable and [Formula: see text]-complete, the approach is shown to be tractable through experimental analyses without the need to apply heuristics or constraints.

Optimization model for web based multimodal interactive simulations

This paper presents a technique for optimizing the performance of web based multimodal interactive simulations. For such applications where visual quality and the performance of simulations directly influence user experience, overloading of hardware resources may result in unsatisfactory reduction in the quality of the simulation and user satisfaction. However, optimization of simulation performance on individual hardware platforms is not practical. Hence, we present a mixed integer programming model to optimize the performance of graphical rendering and simulation performance while satisfying application specific constraints. Our approach includes three distinct phases: identification, optimization and update. In the identification phase, the computing and rendering capabilities of the client device are evaluated using an exploratory proxy code. This data is utilized in conjunction with user specified design requirements in the optimization phase to ensure best possible computational resource allocation. The optimum solution is used for rendering (e.g. texture size, canvas resolution) and simulation parameters (e.g. simulation domain) in the update phase. Test results are presented on multiple hardware platforms with diverse computing and graphics capabilities to demonstrate the effectiveness of our approach.

QoS-Aware Cross-Layer Configuration for Industrial Wireless Sensor Networks

In many applications of industrial sensor networks, stringent reliability and maximum delay constraints paired with priority demands on a sensor-basis are present. These quality of service (QoS) requirements pose tough challenges for industrial wireless sensor networks that are deployed to an ever larger extent due to their flexibility and extendibility. In this paper, we introduce an integrated cross-layer framework, SchedEx-GA, spanning medium access control (MAC) layer and network layer. SchedEx-GA attempts to identify a network configuration that fulfills all application-specific process requirements over a topology including the sensor publish rates, maximum acceptable delay, service differentiation, and event transport reliabilities. The network configuration comprises the decision for routing, as well as scheduling. For many of the evaluated topologies it is not possible to find a valid configuration due to the physical conditions of the environment. We therefore introduce a converging algorithm on top of the framework which configures a given topology by additional sink positioning in order to build a backbone with the gateway that guarantees the application specific constraints. The results show that, in order to guarantee a high end-to-end reliability of $99.999\%$ for all flows in a network containing emergency, control loop, and monitoring traffic, a backbone with multiple sinks is often required for the tested topologies. Additional features, such as multichannel utilization and aggregation, though, can substantially reduce the demand for required sinks. In its present version, the framework is used for centralized control, but with the potential to be extended for decentralized control in future work.

Database support for unstructured meshes

Despite ubiquitous usage of unstructured mesh in many application domains (e.g., computer aided design, scientific simulation, climate modeling, etc.), there is no specialized mesh database which supports storing and querying such data structures. Existing mesh libraries use file-based APIs which do not support declarative querying and are difficult to maintain. A mesh database can benefit these domains in several ways such as: declarative query language, ease of maintenance, query optimization, etc. In this thesis work, the core idea is to have a very general model which can represent objects from different domains and specialize it to smaller object classes using combinatorial constraints. We propose the Incidence multi-Graph Complex (ImG-Complex) data model for storing combinatorial aspect of meshes in a database. We extend incidence graph (IG) representation with multi-incidence information (ImG) to represent a class of objects which we call ImG-Complexes. ImG-Complex can support a wide range of application domains. We introduce optional and application-specific constraints to restrain the general ImG model to specific object classes or specific geometric representations. The constraints check validity of meshes based on the properties of the modeled object class. Finally, we show how graph databases can be utilized and reused to query some combinatorial mesh queries based on the (possibly constrained) ImG model. In particular, we show the strengths and limitations of a graph-only query language in expressing combinatorial mesh queries.

Deconstraining DSLs

Strongly-typed functional languages provide a powerful framework for embedding Domain-Specific Languages (DSLs). However, building type-safe functions defined over an embedded DSL can introduce application-specific type constraints that end up being imposed on the DSL data types themselves. At best, these constraints are unwieldy and at worst they can limit the range of DSL expressions that can be built. We present a simple solution to this problem that allows application-specific constraints to be specified at the point of use of a DSL expression rather than when the DSL's embedding types are defined. Our solution applies equally to both tagged and tagless representations and, importantly, also works in the presence of higher-rank types.