Robotic systems are typically composed of various subsystems, such as localization and navigation, each encompassing numerous configurable components (e.g., selecting different planning algorithms). Once an algorithm ...
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Robotic systems are typically composed of various subsystems, such as localization and navigation, each encompassing numerous configurable components (e.g., selecting different planning algorithms). Once an algorithm has been selected for a component, its associated configuration options must be set to the appropriate values. Configuration options across the system stack interact nontrivially. Finding optimal configurations for highly configurable robots to achieve desired performance poses a significant challenge due to the interactions between configuration options across software and hardware that result in an exponentially large and complex configuration space. These challenges are further compounded by the need for transferability between different environments and robotic platforms. Data efficient optimization algorithms (e.g., Bayesian optimization) have been increasingly employed to automate the tuning of configurable parameters in cyber-physical systems. However, such optimization algorithms converge at later stages, often after exhausting the allocated budget (e.g., optimization steps, allotted time) and lacking transferability. This article proposes causal understanding and remediation for enhancing robot performance (CURE)-a method that identifies causally relevant configuration options, enabling the optimization process to operate in a reduced search space, thereby enabling faster optimization of robot performance. CURE abstracts the causal relationships between various configuration options and the robot performance objectives by learning a causal model in the source (a low-cost environment such as the Gazebo simulator) and applying the learned knowledge to perform optimization in the target (e.g., Turtlebot 3 physical robot). We demonstrate the effectiveness and transferability of CURE by conducting experiments that involve varying degrees of deployment changes in both physical robots and simulation.
A novel thin, optically transparent microwave metamaterial absorber (TMMA) is presented. The proposed structure utilizes a 3-mm-thick polycarbonate (PC) main substrate coated on both sides with 8-nm-thick gold thin fi...
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A novel thin, optically transparent microwave metamaterial absorber (TMMA) is presented. The proposed structure utilizes a 3-mm-thick polycarbonate (PC) main substrate coated on both sides with 8-nm-thick gold thin films deposited via thermal evaporation. To achieve maximum absorption bandwidth, polarization insensitivity, and angular stability, the unit cell of the TMMA is optimized through a pixelation and binary optimization technique. This process results in an optimal pattern of gold nanolayer on the upper surface of the substrate. The resulting periodic pattern is transferred using optical lithography. The bottom gold film acts as the electrical ground plane. An additional 3-mm-thick PC superstrate is then incorporated to improve impedance matching and protect the coating of the main substrate. Comprehensive full-wave electromagnetic simulations are employed to assess the absorption performance. The design is subsequently validated through sample fabrication and experimental measurements. The proposed TMMA exhibits over 90% absorption within an ultrawideband (UWB) range of 5.1-25.2 GHz while maintaining an average optical transparency of approximately 70%. The symmetrical design additionally offers wide angular stability and minimal polarization sensitivity.
Aerial swarms can substantially improve the effectiveness of drones in applications such as inspection, monitoring, and search for rescue. This is especially true when those swarms are made of several individual drone...
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Aerial swarms can substantially improve the effectiveness of drones in applications such as inspection, monitoring, and search for rescue. This is especially true when those swarms are made of several individual drones that use local sensing and coordination rules to achieve collective motion. Despite recent progress in swarm autonomy, human control and decision-making are still critical for missions where lives are at risk or human cognitive skills are required. However, first-person-view (FPV) teleoperation systems require one or more human operators per drone, limiting the scalability of these systems to swarms. This work investigates the performance, preference, and behaviour of pilots using different FPV interfaces for teleoperation of aerial swarms. Interfaces with single and multiple perspectives were experimentally studied with humans piloting a simulated aerial swarm through an obstacle course. Participants were found to prefer and perform better with views from the back of the swarm, while views from the front caused users to fly faster but resulted in more crashes. Presenting users with multiple views at once resulted in a slower completion time, and users were found to focus on the largest view, regardless of its perspective within the swarm.
The increased instantaneous luminosity of the Large Hadron Collider (LHC) in Run 3 brings the need for the upgrade of the A Toroidal LHC Apparatus (ATLAS) trigger system. The newly commissioned Phase-I L1Topo system, ...
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The increased instantaneous luminosity of the Large Hadron Collider (LHC) in Run 3 brings the need for the upgrade of the A Toroidal LHC Apparatus (ATLAS) trigger system. The newly commissioned Phase-I L1Topo system, which replaces its Phase-0 predecessor, processes data from the feature extractors (FEXes) and the upgraded muon to central trigger processor interface (MUCTPI) to perform topological and multiplicity triggers. The L1Topo system consists of three ATCA modules, each hosting two processor field programmable gate arrays (FPGAs) (Xilinx Ultrascale+9P). The L1Topo firmware is composed of a large number of sort/select, decision, and multiplicity algorithms, that are automatically assembled and configured based on the provided trigger menu. For the high-luminosity LHC (HL-LHC), the Phase-I L1Topo system will be replaced by a Global Trigger, a time-multiplexed system, which concentrates the data of a full event into a single FPGA. In order to match the new operational environment, the fully synchronous, very low latency (new data arriving every 25 ns), parallel implementation [similar to 2.5M look-up tables (LUTs)] of the Phase-I topological firmware is being adapted to a significantly higher latency budget (new data arriving every 1.2 mu s) and a substantially tighter resource budget (similar to 100k LUTs). The main challenge is to allow for multiple working points of the utilized resources and latency for each algorithm. A detailed overview of the Phase-I L1Topo hardware and firmware is provided. Preliminary performance results achieved by the Phase-I L1Topo together with a description of the challenges found during the commissioning process are included. Phase-II-related firmware adaptations are also discussed.
In this article, we aim to search for new optimal and suboptimal odd binary Z-complementary pairs (OBZCPs) for lengths up to 49. As an alternative to the celebrated binary Golay complementary pairs, optimal OBZCPs are...
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In this article, we aim to search for new optimal and suboptimal odd binary Z-complementary pairs (OBZCPs) for lengths up to 49. As an alternative to the celebrated binary Golay complementary pairs, optimal OBZCPs are the best almost-complementary sequence pairs having odd lengths. We introduce a computer search algorithm with time complexity O(2(N)), where N denotes the sequence length and then show optimal results for all 27 <= N <= 33 and N = 37,41,49. For those sequence lengths (i.e., N = 35,39,43,45,47) with no optimal pairs, we show OBZCPs with largest zero-correlation zone widths (i.e., Z-optimal). Finally, based on the Pursley-Sarwate criterion, we present a table of OBZCPs with smallest combined auto-correlation and cross-correlation.
Novel telecommunication systems build on a cloudified architecture running softwarized network services as disaggregated virtual network functions (VNFs) on commercial off-the-shelf (COTS) hardware to improve costs an...
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Novel telecommunication systems build on a cloudified architecture running softwarized network services as disaggregated virtual network functions (VNFs) on commercial off-the-shelf (COTS) hardware to improve costs and flexibility. Given the stringent processing deadlines of modern applications, these systems are critically dependent on a closed-loop control algorithm to orchestrate the execution of the disaggregated components. At the moment, however, the formal model for implementing such real-time control loops is mostly missing. In this paper, we introduce a new real-time VNF execution environment that runs entirely on COTS hardware. First, we define a comprehensive formal model that enables us to reason about packet processing delays across disaggregated VNF processing chains analytically. Then we integrate the model into a gradient-optimization control algorithm to provide optimal scheduling for real-time infocommunication services in a programmable way. We present experimental evidence that our model gives a proper delay estimation on a real software switch. We evaluate our control algorithm on multiple representative use cases using a software switch simulator. Our results show the algorithm drives the system to a real-time capable state in just a few control periods even in case of complex services.
The realized performance (error-cost tradeoff) of three computational electromagnetic (CEM) methods, which use parallel algorithms on a supercomputer to predict the radar cross section (RCS) of complex targets, are qu...
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The realized performance (error-cost tradeoff) of three computational electromagnetic (CEM) methods, which use parallel algorithms on a supercomputer to predict the radar cross section (RCS) of complex targets, are quantified using the Austin RCS Benchmark Suite. The article demonstrates how modern benchmark suites can be used to evaluate CEM methods empirically and compare their performances objectively. The Austin RCS Benchmark Suite [1], [2] has recently been populated with 20 carefully selected problem sets that span a wide range in six dimensions of computational difficulty [3].
This article discusses the integration challenges and strategies for designing mobile robots, by focusing on the task-driven, optimal selection of hardware and software to balance safety, efficiency, and minimal usage...
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This article discusses the integration challenges and strategies for designing mobile robots, by focusing on the task-driven, optimal selection of hardware and software to balance safety, efficiency, and minimal usage of resources such as costs, energy, computational requirements, and weight. We emphasize the interplay between perception and motion planning in decision-making by introducing the concept of occupancy queries to quantify the perception requirements for sampling-based motion planners. Sensor and algorithm performance are evaluated using false negative rate and false positive rate across various factors such as geometric relationships, object properties, sensor resolution, and environmental conditions. By integrating perception requirements with perception performance, an integer linear programming approach is proposed for efficient sensor and algorithm selection and placement. This forms the basis for a codesign optimization that includes the robot body, motion planner, perception pipeline, and computing unit. We refer to this framework for solving the codesign problem of mobile robots as CODEI, short for codesign of embodied intelligence. A case study on developing an autonomous vehicle for urban scenarios provides actionable information for designers, and shows that complex tasks escalate resource demands, with task performance affecting choices of the autonomy stack. The study demonstrates that resource prioritization influences sensor choice: cameras are preferred for cost-effective and lightweight designs, while lidar sensors are chosen for better energy and computational efficiency.
As an important technology in high-speed systems, equalizer (EQ) is used to mitigate inter-symbol interference (ISI) caused by inconsistent attenuation of high and low frequencies. The difficulty of signal integrity i...
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As an important technology in high-speed systems, equalizer (EQ) is used to mitigate inter-symbol interference (ISI) caused by inconsistent attenuation of high and low frequencies. The difficulty of signal integrity improvement increases the complexity of EQ design, making the existing algorithms inefficient in high-dimensional searching and constraint processing. In this article, a local multi-constraint modeling-Bayesian optimization (BO) with region partitioning is proposed, aiming to provide a general optimization solution for high-dimensional multi-constraint EQs and improve convergence accuracy and efficiency. The constraint filtering mechanism is used to exclude areas that violate simulation-independent constraints. Local modeling and region partitioning techniques complement each other, taking into account both the local accuracy of the model and the global search performance of the algorithm. The multi-constraint modeling strategy allows simulation-dependent constraints to be pre-judged through the surrogate model, overcoming the shortcomings of the traditional solution of adding the penalty term to the target value, which makes it difficult to balance the weights and can only judge the constraints after simulation, thereby reducing the waste of computing resources caused by simulating data that violates the constraints. The proposed algorithm is applied to EQ optimization in a 16 Gbps high-bandwidth memory channel and a 64 Gbps differential peripheral component interconnect express channel, respectively. The algorithm is developed based on PyTorch, and the eye diagrams are obtained using Keysight ADS software. Two applications are conducted on computer with Intel Core i5-13500 processor and 32 GB RAM. By utilizing the region partitioning and constraint filtering techniques, the actual number of simulations in the optimization can be significantly reduced. The experimental results demonstrate that the proposed algorithm has significant shorter computing tim
Augmented reality (AR) is emerging as the next ubiquitous wearable technology and is expected to significantly transform various industries in the near future. There has been tremendous investment in developing AR eye...
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Augmented reality (AR) is emerging as the next ubiquitous wearable technology and is expected to significantly transform various industries in the near future. There has been tremendous investment in developing AR eyeglasses in recent years, including about $45 billion investment by Meta since 2021. Despite such efforts, the existing displays are very bulky in form factor and there has not yet been a socially acceptable eyeglasses-style AR display. Such wearable display eyeglasses promise to unlock enormous potential in diverse applications such as medicine, education, navigation, and many more;but until eyeglass-style AR glasses are realized, those possibilities remain only a dream. My research addresses this problem and makes progress "towards everyday-use augmented reality eyeglasses" through computational imaging, displays, and perception. My dissertation (Chakravarthula, 2021) made advances in three key and seemingly distinct areas: first, digital holography and advanced algorithms for compact, high-quality, true 3-D holographic displays;second, hardware and software for robust and comprehensive 3-D eye tracking via Purkinje Images;and third, automatic focus adjusting AR display eyeglasses for well-focused virtual and real imagery, toward potentially achieving 20/20 vision for users of all ages.
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