Performance Trade-offs Research Articles

Computation offloading for ground robotic systems communicating over WiFi – an empirical exploration on performance and energy trade-offs

ContextRobotic systems are known to perform computation-intensive tasks with limited computational resources and battery life. Such systems might benefit from offloading heavy workloads to the Cloud; however, in some cases, this implies high network traffic that degrades performance and energy consumption.GoalIn this study, we aim at evaluating the impact of different computation offloading strategies on performance and energy consumption in the context of autonomous robots.MethodWe conduct two controlled experiments involving a robotic mission based on the Turtlebot3 robot and ROS 1. The mission consists of three tasks that are recurrent in robotics and good candidates for computation offloading in research, namely, SLAM mapping, navigation stack, and object recognition. Each of the tasks is either executed on board or offloaded in a full-factorial experiment design. The obtained measures are then statistically analyzed.ResultsThe results show that offloading the object recognition task causes a more significant decrease in resource utilization and energy consumption than both SLAM mapping and navigation. However, object recognition affects the volume of network traffic significantly to the extent that it can easily cause network congestion.ConclusionsIn the context of our experiments (i.e., those involving small-scale ground ROS-based mobile robots operating under WiFi networks), offloading object recognition is beneficial in terms of performance and energy consumption. Nevertheless, large network bandwidth needs to be available for object recognition offloading. While the image resolution and frame rate have a significant impact on not only the network traffic but also energy consumption and performance, these parameters need to be carefully set so that the results of this task can be always received in time, which is particularly crucial in real-time systems.

Performance trade-offs of auto scaling schemes for NFV with reliability requirements

A major concern for future mobile networks is to meet extremely stringent reliability requirements without incurring very high energy consumption. To this aim, we need auto-scaling techniques to switch on/off servers on demand, ensuring that they are active when required. One key challenge is to keep the right number of active servers while accounting for both the fallibility of servers and their non-zero boot up time: if this number is too small we risk service disruption, but if it is too large resources will be wasted. In this paper, we analyze this challenge by assessing the performance of different strategies to support an energy-efficient highly-reliable service, which range from deploying few blade (i.e., reliable) servers to deploying many nano (i.e., unreliable) servers. Our main take-away message is two-fold: (1) a server farm of nano servers can provide a more energy efficient operation than a farm of blade servers, but (2) it involves a very dynamic operation both in terms of task migrations and (de)activations, which challenges the endurance of the hardware.

Early-stage analysis of a novel insulation material based on MPCM-doped cementitious foam: Modelling of properties, identification of production process hotspots and exploration of performance trade-offs

This study presents an early-stage design exploration of NRG-Foam, an innovative insulation material composed of cementitious foam doped with microencapsulated phase change materials (MPCMs). The study comprises the static part that utilizes life cycle assessment and life cycle costing assessment for getting insight into the impacts of the NRG-Foam production process and the dynamic part that identifies the trade-offs between performance characteristics of NRG-Foam using multi-objective optimization. The production of MPCMs was found to be a major contributor to environmental impacts while the addition of small amounts of reduced graphene oxide amplifies the impacts even further. The hot spot analysis pinpointed high electricity consumption as the main driver of environmental impacts. A multi-objective optimization analysis revealed trade-offs between performance characteristics, emphasizing the necessity of compromises during material development. The selection of the MPCM type was shown to be determinative of the final properties of NRG-Foam.

Micropositioning and microvibration isolation of a novel hybrid active–passive platform with two-axis actuator for optical payloads

Space missions face developmental limitations in the endeavor to attenuate microvibration significantly while aiming to achieve high micropositioning accuracy. To overcome this issue, various passive, active, and hybrid approaches have been proposed. Regarding the tradeoff performance, the hybrid method may be the most suitable among these approaches. However, the significantly small mass ratio between the platform and service payload cannot be adapted to the recent and future space missions. Therefore, a novel modular-based three-leg supporting hybrid active–passive platform is proposed, which aims to improve the microvibration isolation and micropositioning performance of ultraprecision equipment in a dynamic environment. A Newton–Euler-based method was adopted to describe the system accurately. To achieve motion decoupling of the system, we synthesized a suitable location layout for the three modular units and modal decoupling control method. In addition, an innovative custom-made two-axis actuator with a large stroke and high thrust was employed to strengthen the operational accuracy and dynamic performance. To simultaneously determine the microvibration isolation and micropositioning performances, hybrid micropositioning and microvibration isolation controllers, which synthesize the velocity feedforward and feedback, are proposed. Experiments and actual application demonstrated the effectiveness of the proposed scheme in terms of microvibration attenuation and micropositioning.

Thermotolerant coral-algal mutualisms maintain high rates of nutrient transfer while exposed to heat stress.

Symbiotic mutualisms are essential to ecosystems and numerous species across the tree of life. For reef-building corals, the benefits of their association with endosymbiotic dinoflagellates differ within and across taxa, and nutrient exchange between these partners is influenced by environmental conditions. Furthermore, it is widely assumed that corals associated with symbionts in the genus Durusdinium tolerate high thermal stress at the expense of lower nutrient exchange to support coral growth. We traced both inorganic carbon (H13CO3-) and nitrate (15NO3-) uptake by divergent symbiont species and quantified nutrient transfer to the host coral under normal temperatures as well as in colonies exposed to high thermal stress. Colonies representative of diverse coral taxa associated with Durusdinium trenchii or Cladocopium spp. exhibited similar nutrient exchange under ambient conditions. By contrast, heat-exposed colonies with D. trenchii experienced less physiological stress than conspecifics with Cladocopium spp. while high carbon assimilation and nutrient transfer to the host was maintained. This discovery differs from the prevailing notion that these mutualisms inevitably suffer trade-offs in physiological performance. These findings emphasize that many host-symbiont combinations adapted to high-temperature equatorial environments are high-functioning mutualisms; and why their increased prevalence is likely to be important to the future productivity and stability of coral reef ecosystems.

Controlling jumps through latches in small jumping robots

Small jumping robots can use springs to maximize jump performance, but they are typically not able to control the height of each jump owing to design constraints. This study explores the use of the jumper’s latch, the component that mediates the release of energy stored in the spring, as a tool for controlling jumps. A reduced-order model that considers the dynamics of the actuator pulling the latch and the effect of spring force on the latch is presented. This model is then validated using high speed video and ground reaction force measurements from a 4g jumper. Both the model and experimental results demonstrate that jump performance in small insect-inspired resource-constrained robots can be tuned to a range of outputs using latch mediation, despite starting with a fixed spring potential energy. For a fixed set of input voltages to the latch actuator, the results also show that a jumper with a larger latch radius has greater tunability. However, this greater tunability comes with a trade-off in maximum performance. Finally, we define a new metric, ‘Tunability Range,’ to capture the range of controllable jump behaviors that a jumper with a fixed spring compression can attain given a set of control inputs (i.e. latch actuation voltage) to choose from.

Simplified Self-Coherent FSO Transmission Boosted by Digital Resolution Enhancer

The joint application of free-space optics (FSO) transmission with coherent detection has recently appeared to enable unprecedented optical wireless capacity. Nevertheless, due to the associated cost and power consumption, simplified self-coherent FSO links are currently being explored to optimize the performance vs. complexity tradeoff. In particular, the utilization of low-complexity virtual carrier self-coherent transmission provides an appealing cost-effective alternative for high-capacity FSO links. However, this transmission paradigm is associated with quantization noise problems due to limited digital-to-analog converter (DAC) resolution. Following all the stated requirements, this work presents a simplified virtual carrier-assisted self-coherent FSO link supported by a low-resolution DAC and Nyquist rate phase reconstruction using the DC-Value algorithm. The utilization of such a low-resolution DAC is aided by a digital resolution enhancement (DRE) method. Employing this simplified self-coherent approach, we experimentally demonstrated 100 Gbps FSO transmission over an outdoor 42 m link using a DAC with resolution as low as 3 bits.

Visual-RRT: Integrating IBVS as a steering method in an RRT planner

In this paper, we present a new approach to robot motion planning that anticipates the use of vision-based feedback control during task execution. We accomplish this by incorporating an image-based visual servo (IBVS) controller directly into the steering function used by a Rapidly Exploring Random Tree (RRT) planner. Our approach requires a number of extensions to traditional RRT-style planning. First, we derive a new sampling strategy that augments the usual state information by including image features that will be used by the IBVS control law. These augmented samples are then used by our new IBVS steering function, which simulates an IBVS control law to generate local trajectories that extend the current tree. These trajectories must be validated to ensure that they are collision-free and that all image features remain unoccluded and within the camera field of view throughout the local trajectory. We also provide a formal proof showing that the proposed approach is probabilistically complete. We have applied our approach to the problem of planning trajectories for three different systems: a robotic arm, an unmanned aerial vehicle (UAV) and a car-like robot, which are equipped with an IBVS control law. We explore performance trade-offs in the control design via simulation studies and demonstrate real-world effectiveness via experiments in which a small-scale car-like robot uses IBVS to navigate a track that includes a number of obstacles and potential occlusions. By exploring performance trade-offs, we mean that several elements, such as the metric used to identify nearest neighbors in the RRT and the steering method used to generate nodes, are tested and compared.

MaGNAS: A Mapping-Aware Graph Neural Architecture Search Framework for Heterogeneous MPSoC Deployment

Graph Neural Networks (GNNs) are becoming increasingly popular for vision-based applications due to their intrinsic capacity in modeling structural and contextual relations between various parts of an image frame. On another front, the rising popularity of deep vision-based applications at the edge has been facilitated by the recent advancements in heterogeneous multi-processor Systems on Chips (MPSoCs) that enable inference under real-time, stringent execution requirements. By extension, GNNs employed for vision-based applications must adhere to the same execution requirements. Yet contrary to typical deep neural networks, the irregular flow of graph learning operations poses a challenge to running GNNs on such heterogeneous MPSoC platforms. In this paper, we propose a novel unified design-mapping approach for efficient processing of vision GNN workloads on heterogeneous MPSoC platforms. Particularly, we develop MaGNAS, a mapping-aware Graph Neural Architecture Search framework. MaGNAS proposes a GNN architectural design space coupled with prospective mapping options on a heterogeneous SoC to identify model architectures that maximize on-device resource efficiency. To achieve this, MaGNAS employs a two-tier evolutionary search to identify optimal GNNs and mapping pairings that yield the best performance trade-offs. Through designing a supernet derived from the recent Vision GNN (ViG) architecture, we conducted experiments on four (04) state-of-the-art vision datasets using both ( i ) a real hardware SoC platform (NVIDIA Xavier AGX) and ( ii ) a performance/cost model simulator for DNN accelerators. Our experimental results demonstrate that MaGNAS is able to provide 1.57 × latency speedup and is 3.38 × more energy-efficient for several vision datasets executed on the Xavier MPSoC vs. the GPU-only deployment while sustaining an average 0.11% accuracy reduction from the baseline.

Robust Multiplexing Waveform Design for Integrated OFDM Radar and Communication via Complex Weight Optimization

Compared to traditional single orthogonal frequency division multiplexing (OFDM) radar and OFDM communication systems, integrated OFDM radar and communication systems have the advantages of improved sharing of scarce spectrum resources, a simple hardware structure and a reduced interference between signals. In this paper, a constraint relaxation-based robust OFDM multiplexing waveform (ROW-CR) design method for integrated radar and communication systems is proposed. Considering the influence of correlated clutter and jamming signals, transmission and reception models of different system platforms are established by allocating subcarrier complex weights of different antenna signals so as to determine radar conditional mutual information and communication channel capacity parameters. Meanwhile, a restricted closed model is introduced through the limit range of the target frequency response. Then, a robust OFDM waveform optimization problem for integrated radar and communication systems is constructed by the minimax criterion, and the closed-form solution is obtained by adopting an improved method based on trace function properties and constraint relaxation, resulting in a better radar and communication performance trade-off. In addition, a parameter hierarchical optimization-based robust OFDM waveform (ROW-PHO) design method is further explored to reduce the computational complexity, and this method can also ensure a low system performance loss. Finally, the numerical simulation results verify the effectiveness of the proposed methods.

MCA: Multidimensional collaborative attention in deep convolutional neural networks for image recognition

A broad range of prior research has demonstrated that attention mechanisms offer great potential in advancing the performance of deep convolutional neural networks (CNNs). However, most existing approaches either ignore modeling attention in both channel and spatial dimensions or introduce higher model complexity and heavier computational burden. To alleviate this dilemma, in this paper, we propose a lightweight and efficient multidimensional collaborative attention, MCA, a novel method for simultaneously inferring attention in channel, height, and width dimensions with almost free additional overhead by using a three-branch architecture. For the essential components of MCA, we not only develop an adaptive combination mechanism for merging dual cross-dimension feature responses in squeeze transformation, enhancing the informativeness and discriminability of feature descriptors but also design a gating mechanism in excitation transformation that adaptively determines the coverage of interaction to capture local feature interactions, overcoming the paradox of performance and computational overhead trade-off. Our MCA is simple yet general and can be easily plugged into various classic CNNs as a plug-and-play module and trained along with the vanilla networks in an end-to-end manner. Extensive experimental results for image recognition on CIFAR and ImageNet-1K datasets demonstrate the superiority of our method over other state-of-the-art (SOTA) counterparts. In addition, we also provide insight into the practical benefits of MCA by visually inspecting the GradCAM++ visualization results. The code is available at https://github.com/ndsclark/MCANet.

Driveline oscillation attenuation through Clutch Micro-Slip and Model Predictive Control

This paper considers a Model Predictive Control (MPC) approach to micro-slip clutch control for driveline shuffle attenuation and longitudinal acceleration oscillation reduction in vehicles with an Automated Manual Transmission system. We introduce an original MPC formulation to consider the performance tradeoff between the clutch slip regulation, the torsional oscillation attenuation and the jerking reduction. A particular problem to be considered is handling saturation constraints on the transmitted torque. We employ a piecewise-affine linear model of the actuator-driveline system to take care of special manoeuvres that cause inversions in the transmission motion. On the methodological side, we present a switching MPC approach where the underlying optimization problem is a quadratic programme for a fast online controller implementation on real-world transmission control unit platforms. To show the effectiveness of the proposed approach, we present extensive simulation results and experimental tests performed on a prototype vehicle.

A superconducting quantum processor architecture design method for improving performance and reducing frequency collisions

More physical qubits and qubit connections integrated on a superconducting quantum processor can improve the ability to execute quantum programs, but on the other hand, they might increase the probability of frequency collisions. Considering a performance and frequency collisions trade-off, it is a feasible method to optimize processor architecture aiming at running specific quantum programs. To this end, we propose a method for designing superconducting quantum processor architectures for the purpose of running specific quantum programs. Different from existing methods, our method is mainly based on graph theory to optimize processor architecture design. First, we convert the trade-off problem into the optimization of the distance between two points as well as the maximum degree in the processor architecture graph. Second, we consider the actual physical routing constraints and build a mathematical model for the optimization problem. Finally, we propose an automatic processor architecture design flow based on the mathematical model, which is implemented with an improved genetic algorithm. To show the effectiveness of our method, we selected sixteen quantum programs with different qubit numbers and different functions for comparison. Simulation results show that the architecture schemes of our method outperform IBM’s general-purpose square lattice architecture schemes. Compared to the general-purpose architecture schemes, the architecture schemes of our method have an average performance improvement of 15.61% and a minimum reduction of 21.33% in the probability of frequency collisions. Furthermore, in most of the selected quantum programs, our architecture schemes perform better than the eff-5-freq’s architecture schemes, with an average 6.58% improvement in performance and a minimum 6.45% reduction in the probability of frequency collisions. Therefore, our method can provide superconducting quantum processor architecture design with better performance and lower probability of frequency collisions for quantum programs.

Rostering of air traffic controllers – current practices in Europe, key performance trade-offs and future opportunities

The process of staff planning and rostering is one of the key factors affecting the performance of an Air Navigation Service Provider in terms of capacity and cost-efficiency delivered to airspace users. This paper gives a summary of previous works and current rostering practices applied at Area Control Centres (ACCs) in Europe. Furthermore, it identifies key trade-offs between rostering flexibility, capacity and productivity by establishing a relationship between ACC demand, rostering flexibility in place and the number of Air Traffic Control Officers (ATCOs) needed, as well as by exploring how varying rostering practices applied in ACCs affect spare capacity in the system. To that end, an initial mathematical model for optimizing ATCO staffing requirements has been proposed and tested on a case study involving three representative ACCs, reflecting different hourly variability in demand. The results suggest that - for different combinations of shift patterns and capacity profiles - substantial variation exists in both the minimum number of ATCOs and the associated spare ACC capacity. It has also been shown that a certain amount of spare capacity is inevitably present in the system due to inability to perfectly match ATCO hours with demand. Different options and enablers for exploiting such capacity are also discussed in the paper.

Seismic Performance of Multistory Chevron‐braced Steel Structures with Yielding Beams

AbstractLateral loads are resisted by braces in concentrically braced frames. Therefore, braces meet the demand for the load combination based on the seismic provisions. The rest of the system including the connections are determined according to the capacity‐based design, where the brace members yield under tension and buckle under compression. Chevron or inverted V‐braced frames are generally preferred because they allow large openings for doors and windows. However, the seismic‐design requirements in current building codes lead to deep, heavy chevron beams to resist the unbalanced load at beam members due to pre‐ and post‐buckling compressive strength deterioration and full tensile yielding of the braces. This results in uneconomical design. Recent large‐scale experiments of chevron‐braced frames have demonstrated that limited beam yielding is not detrimental to chevron‐braced frame behavior and on the contrary, it utilizes a second yielding mechanism and thereby increases the story drift capacity at which the braces fracture. Also, they propose a new design method for chevron braced frame with yielded beam. This paper expands the previous findings by investigating the seismic performance of 9‐story structures which are designed according to provisions and proposed method. Structural analysis program SAP2000 is utilized for linear design by modelling the full‐scale 9‐story building. Nonlinear finite element analysis software Perform 3D is utilized for the nonlinear time history analysis in which only a single chevron braced frame is modelled. Results show that the proposed building design has larger interstory drift ratios. However, the adjusted collapse margin ratio and the collapse probability for maximum considered earthquake satisfy the required limitations. In other words, the proposed chevron frame design offers a more economical alternative with no significant tradeoff in seismic performance.

Collaborative 3D Object Detection for Autonomous Vehicles via Learnable Communications

3D object detection from LiDAR point cloud is a challenging task in autonomous driving systems. Collaborative perception can incorporate information from spatially diverse sensors and provide significant benefits for accurate 3D object detection from point clouds. In this work, we consider that the autonomous vehicle uses local point cloud data and combines information from neighboring infrastructures through wireless links for cooperative 3D object detection. However, information sharing among vehicles and infrastructures in predefined communication schemes may result in communication congestion and/or bring limited performance improvement. To this end, we propose a novel collaborative 3D object detection framework using an encoder-decoder network architecture and an attention-based learnable communications scheme. It consists of three components: a feature encoder network that maps point clouds into feature maps; an attention-based communication module that propagates compact and fine-grained query feature maps from the vehicle to support infrastructures, and optimizes attention weights between query and key to refine support feature maps; a region proposal network that fuses local feature maps and weighted support feature maps for 3D object detection. We evaluate the performance of the proposed framework on CARLA-3D, a new dataset that we synthesized using CARLA for 3D cooperative object detection. Experimental results and bandwidth consumption analysis show that the proposed collaborative 3D object detection framework achieves a better detection performance and communication bandwidth trade-off than five baseline 3D object detection models under different detection difficulties.

Can Decentralized Control Outperform Centralized? The Role of Communication Latency

In this paper, we examine the influence of communication latency on performance of networked control systems. Even though distributed control architectures offer advantages in terms of communication, maintenance costs, and scalability, it is an open question how communication latency that varies with network topology influences closed-loop performance. For networks in which delays increase with the number of links, we establish the existence of a fundamental performance trade-off that arises from control architecture. In particular, we utilize consensus dynamics with single- and double-integrator agents to show that, if delays increase fast enough, a sparse controller with nearest neighbor interactions can outperform the centralized one with all-to-all communication topology.

Information Access Costs with a Wide-Angle Desktop Display

In choosing where to present information on large displays, multiple monitors, and head-mounted displays, the potential arises to create access costs associated with placing information outside of the immediate field of view. The current experiment examined performance tradeoffs associated with increasing information access effort ranging from 16 to 128 degrees of lateral separation on a wide-angle desktop display. During a spatial integration task, participants indicated whether grid coordinate numbers shown in one location on the screen were within a designated zone on a map displayed in another location. Results showed a significant non-linear trend of head movements, a linear trend of response time, and no effect on error, when the two pieces of information were displayed at greater separation distances. These results have implications for designing visual displays, suggesting where information access effort needs to be considered when scanning for information outside of the immediate field of view.

Time-to-Market and Product Performance Tradeoff Revisited

The profitability, and even the survival, of organizations depends on the number and quality of new products introduced into the market. However, the process of developing new products (and estimating the proper timing for their introduction into the market) remains a rich topic for research and investigation within the product development (PD) community as it lies at the intersection of the engineering and marketing disciplines. A unifying model that brings both engineering and marketing concerns together is necessary to bridge the gap between both disciplines. In this article, a mathematical model is introduced to study the tradeoff between product performance and time-to-market for different PD scenarios. These scenarios vary in three main aspects related to product characteristics (i.e., the product complexity and the product newness) and supply chain configuration (i.e., the degree of supplier involvement). The model aims to maximize the revenue of the firm over a limited marketing window. The optimal solution reveals interesting managerial insights regarding the time that must be spent on “system design” and on “detailed design” phases for each PD scenario.

Toward the future of firefighter gear: Assessing fluorinated and non-fluorinated outer shells following simulated on-the-job exposures.

In 2022, the occupation of firefighting was categorized as a "Group 1" carcinogen, meaning it is known to be carcinogenic to humans. The personal protective equipment that structural firefighters wear is designed to safeguard them from thermal, physical, and chemical hazards while maintaining thermo-physiological comfort. Typically, the outer layer of structural turnout gear is finished with a durable water and oil-repellent (DWR) based on per- and polyfluoroalkyl substances (PFAS) that helps limit exposure to water and hazardous liquids. The PFAS-based aqueous emulsion typically used in DWR finishes is highly persistent and can cause various health problems if absorbed into the body through ingestion, inhalation, and/or dermal absorption. In response, the U.S. Fire Service has begun using non-PFAS water repellants in firefighter turnout gear. This study aims to evaluate the performance of both traditional PFAS-based and alternative non-PFAS outer shell materials. The study involved exposing both PFAS-based and non-PFAS DWR outer shell materials in turnout composites to simulated job exposures (i.e., weathering, thermal exposure, and laundering) that artificially aged the materials. After exposures, samples were evaluated for repellency, durability, thermal protection, and surface chemistry analysis to determine any potential performance trade-offs that may exist. Non-PFAS outer shell fabrics were found not to be diesel/oil-repellent, posing a potential flammability hazard if exposed to diesel and subsequent flame on an emergency response. Both PFAS-based and non-PFAS sets of fabrics performed similarly in terms of thermal protective performance, tearing strength, and water repellency. The surface analysis suggests that both PFAS and non-PFAS chemistries can degrade and shed from fabrics during the aging process. The study indicates that firefighters should be educated and trained regarding the potential performance trade-offs, such as oil absorption and flammability concerns when transitioning to non-PFAS outer shell materials.