Cost Function Research Articles

A Collision Avoidance Strategy Based on Entropy-Increasing Risk Perception in a Vehicle–Pedestrian-Integrated Reaction Space

Ensuring pedestrian safety is one of the most significant challenges for autonomous driving systems in urban scenarios due to the non-cooperative and unpredictable nature of pedestrian movements. To tackle this problem, firstly, we propose a collision avoidance strategy based on entropy-increasing risk perception in a vehicle–pedestrian reaction space. Our approach combines a limited range of reaction space regions with entropy to quantify the risk of pedestrian–vehicle collision. Then, multi-vehicle candidate trajectories are generated using the path and speed sequence method, and the uncertain states of pedestrians are predicted based on the social force model and Markov model accordingly. Finally, to determine the optimal collision avoidance trajectory, we use quantitative reaction-space entropy as a new “cost function” to measure potential risk and perform multi-objective trajectory optimization based on the elitist non-dominated-sorting genetic algorithm region-focused (NSGA-RF) approach. Simulation results show that our proposed strategy can enhance the safety of the planned trajectory interaction between vehicles and pedestrians for autonomous driving under normal and emergency conditions.

Simultaneous reduction of number of spots and energy layers in intensity modulated proton therapy for rapid spot scanning delivery.

Reducing proton treatment time improves patient comfort and decreases the risk of error from intrafractional motion, but must be balanced against clinical goals and treatment planquality. To improve the delivery efficiency of spot scanning proton therapy by simultaneously reducing the number of spots and energy layers using the reweighted regularizationmethod. We formulated the proton treatment planning problem as a convex optimization problem with a cost function consisting of a dosimetric plan quality term plus a weighted regularization term. We iteratively solved this problem and adaptively updated the regularization weights to promote the sparsity of both the spots and energy layers. The proposed algorithm was tested on four head-and-neck cancer patients, and its performance, in terms of reducing the number of spots and energy layers, was compared with existing standard and group regularization methods. We also compared the effectiveness of the three methods ( , group , and reweighted ) at improving plan delivery efficiency without compromising dosimetric plan quality by constructing each of their Pareto surfaces charting the trade-off between plan delivery and planquality. The reweighted regularization method reduced the number of spots and energy layers by an average over all patients of and , respectively, with an insignificant cost to dosimetric plan quality. From the Pareto surfaces, it is clear that reweighted provided a better trade-off between plan delivery efficiency and dosimetric plan quality than standard or group regularization, requiring the lowest cost to quality to achieve any given level of deliveryefficiency. Reweighted regularization is a powerful method for simultaneously promoting the sparsity of spots and energy layers at a small cost to dosimetric plan quality. This sparsity reduces the time required for spot scanning and energy layer switching, thereby improving the delivery efficiency of protonplans.

Knowledge-based self-tuning of PID control gains for continuum soft robots

In recent years, a class of soft (deformable) robots has attracted the attention of researchers due to their continuum features being advantageous over rigid robots for dexterous and safe compliant motions. These distinct advantages are obtained from the elastomer hyperelastic material properties because continuum robots are manufactured and actuated through embedded pneumatic energy fields. In this way, the continuum deformation raises novel problems in modeling and control due to its highly nonlinear behavior. That is, the deformable shape of its components varies continuously, unlike rigid robots, suggesting that the controller should compensate for its varying continuum model. Motivated by the deformable soft robot subject to parametric uncertainties and unmodeled dynamics, this paper proposes an adaptive, instead of constant, tuning of feedback gains of a model-free controller. The tuning mechanism is based on a knowledge-based scheme using a discrete wavelet neural network (DWN) by an efficient input–output identification updating its parameters and weights online, with a gradient descent algorithm driven by the identification error. At the same time, the control feedback gains are self-tuned, minimizing a convex cost function of tracking errors. Dynamic simulations show the performance of the proposed approach considering the parameters of a real soft robot mimicking a gelatin-like behavior. Finally, some discussions on the proposed scheme’s computational cost, feasibility, and viability are briefly addressed.

An adapted model predictive control MPPT for validation of optimum GMPP tracking under partial shading conditions

The energy generation efficiency of photovoltaic (PV) systems is compromised by partial shading conditions (PSCs) of solar irradiance with many maximum power points (MPPs) while tracking output power. Addressing this challenge in the PV system, this article proposes an adapted hybrid control algorithm that tracks the global maximum power point (GMPP) by preventing it from settling at different local maximum power points (LMPPs). The proposed scheme involves the deployment of a 3 × 3 multi-string PV array with a single modified boost converter model and an adapted perturb and observe-based model predictive control (APO-MPC) algorithm. In contrast to traditional strategies, this technique effectively extracts and stabilizes the output power by predicting upcoming future states through the computation of reference current. The boost converter regulates voltage and current levels of the whole PV array, while the proposed algorithm dynamically adjusts the converter's operation to track the GMPP by minimizing the cost function of MPC. Additionally, it reduces hardware costs by eliminating the need for an output current sensor, all while ensuring effective tracking across a variety of climatic profiles. The research illustrates the efficient validation of the proposed method with accurate and stable convergence towards the GMPP with minimal sensors, consequently reducing overall hardware expenses. Simulation and hardware-based outcomes reveal that this approach outperforms classical techniques in terms of both cost-effectiveness and power extraction efficiency, even under PSCs of constant, rapidly changing, and linearly changing irradiances.

Constrained alternating minimization for parameter mapping (CAMP)

ObjectiveTo improve image quality in highly accelerated parameter mapping by incorporating a linear constraint that relates consecutive images. ApproachIn multi-echo T1 or T2 mapping, scan time is often shortened by acquiring undersampled but complementary measures of k-space at each TE or TI. However, residual undersampling artifacts from the individual images can then degrade the quality of the final parameter maps.In this work, a new reconstruction method, dubbed Constrained Alternating Minimization for Parameter mapping (CAMP), is introduced. This method simultaneously extracts T2 or T1* maps in addition to an image for each TE or TI from accelerated datasets, leveraging the constraints of the decay to improve the reconstructed image quality. The model enforces exponential decay through a linear constraint, resulting in a biconvex objective function that lends itself to alternating minimization. The method was tested in four in vivo volunteer experiments and validated in phantom studies and healthy subjects, using T2 and T1 mapping, with accelerations of up to 12. Main resultsCAMP is demonstrated for accelerated radial and Cartesian acquisitions in T2 and T1 mapping. The method is even applied to generate an entire T2 weighted image series from a single TSE dataset, despite the blockwise k-space sampling at each echo time. Experimental undersampled phantom and in vivo results processed with CAMP exhibit reduced artifacts without introducing bias. SignificanceFor a wide array of applications, CAMP linearizes the model cost function without sacrificing model accuracy so that the well-conditioned and highly efficient reconstruction algorithm improves the image quality of accelerated parameter maps.

A new predictive intelligent controller and path planning for mobile robots

This paper studies the synchronization and control of chaotic systems, and a new chaotic-based path is designed for Mobile robots (MRs). The chaotic systems are used to design an unpredictable path for a class of patrol MRs. To enhance security, multiple chaotic systems with a chaotic switching mechanism are introduced for path planning. The main challenge is that the dynamics of MRs are entirely unknown. The physical dynamics are not reliable in practice, because many parameters in MR dynamics are changed due to unknown environmental conditions such as noisy sensors, time delays, friction, and non-ideal actuators. Also, the chaotic switching of reference signals between chaotic signals imposes a high dynamic perturbation. So, a powerful fractional-order predictive controller on the basis of type-3 (T3) fuzzy-logic systems (FLSs) is developed. The estimation and prediction errors of T3-FLSs are compensated by a designed parallel compensator. T3-FLSs are online tuned such that stability is ensured, and the defined cost function for prediction controller is minimized. The suggested scheme is implemented on a real-world MR, and the results demonstrate the febrility and the accuracy. Also, in several simulations, the efficacy of the designed controller is examined. The simulation and experimental results show that the accuracy is improved more than 70% in comparison with conventional predictive controllers under noisy signals and uncertain dynamics.

Motion planning for off‐road autonomous driving based on human‐like cognition and weight adaptation

AbstractDriving in an off‐road environment is challenging for autonomous vehicles due to the complex and varied terrain. To ensure stable and efficient travel, the vehicle requires consideration and balancing of environmental factors, such as undulations, roughness, and obstacles, to generate optimal trajectories that can adapt to changing scenarios. However, traditional motion planners often utilize a fixed cost function for trajectory optimization, making it difficult to adapt to different driving strategies in challenging irregular terrains and uncommon scenarios. To address these issues, we propose an adaptive motion planner based on human‐like cognition and cost evaluation for off‐road driving. First, we construct a multilayer map describing different features of off‐road terrains, including terrain elevation, roughness, obstacle, and artificial potential field map. Subsequently, we employ a convolutional neural network‐long short‐term memory network to learn the trajectories planned by human drivers in various off‐road scenarios. Then, based on human‐like generated trajectories in different environments, we design a primitive‐based trajectory planner that aims to mimic human trajectories and cost weight selection, generating trajectories that are consistent with the dynamics of off‐road vehicles. Finally, we compute optimal cost weights and select and extend behavioral primitives to generate highly adaptive, stable, and efficient trajectories. We validate the effectiveness of the proposed method through experiments in a desert off‐road environment with complex terrain and varying road conditions. The experimental results show that the proposed human‐like motion planner has excellent adaptability to different off‐road conditions. It shows real‐time operation, greater stability, and more human‐like planning ability in diverse and challenging scenarios.

DQN based coverage control for multi‐agent system in line intersection region

AbstractGenerally, the coverage control is studied in a convex region, in which the agent kinematics and the coverage environment both have strong limitations. It is difficult to directly apply these results to practical scenarios, such as the road environment or indoor environment. In this study, the multi‐agent coverage control problems in a line intersection region is investigated, where the agents can only move along the given lines. To present the agents motion in this line intersection region, the moving directions and velocities of the agents are analyzed in the first part. Then, the coverage control model for the multi‐agent system in line intersection region is presented, in which the cost function is provided based on the agent's minimum moving distance and the agent motions are used as the constraints. To solve this constrained coverage problem, the deep Q‐learning network (DQN) is employed to find the optimal positions for each agent in the line intersection region. In final, numerical simulations are presented to validate the feasibility and effectiveness of proposed approaches.

Adaptive Cruise Control Based on Safe Deep Reinforcement Learning.

Adaptive cruise control (ACC) enables efficient, safe, and intelligent vehicle control by autonomously adjusting speed and ensuring a safe following distance from the vehicle in front. This paper proposes a novel adaptive cruise system, namely the Safety-First Reinforcement Learning Adaptive Cruise Control (SFRL-ACC). This system aims to leverage the model-free nature and high real-time inference efficiency of Deep Reinforcement Learning (DRL) to overcome the challenges of modeling difficulties and lower computational efficiency faced by current optimization control-based ACC methods while simultaneously maintaining safety advantages and optimizing ride comfort. Firstly, we transform the ACC problem into a safe DRL formulation Constrained Markov Decision Process (CMDP) by carefully designing state, action, reward, and cost functions. Subsequently, we propose the Projected Constrained Policy Optimization (PCPO)-based ACC Algorithm SFRL-ACC, which is specifically tailored to solve the CMDP problem. PCPO incorporates safety constraints that further restrict the trust region formed by the Kullback-Leibler (KL) divergence, facilitating DRL policy updates that maximize performance while keeping safety costs within their limit bounds. Finally, we train an SFRL-ACC policy and compare its computation time, traffic efficiency, ride comfort, and safety with state-of-the-art MPC-based ACC control methods. The experimental results prove the superiority of the proposed method in the aforementioned performance aspects.

Assessment of an auto-routing method for topology generation of aircraft power supply systems

AbstractDuring early phases of aircraft development, overall systems design is performed to estimate relevant system design parameters such as mass, power consumption, and compliance with architectural requirements (redundancy). The GeneSys software framework has been developed for parametric modeling of aircraft on-board systems for overall systems design. To this end, model-based architecture definition, topology generation (components positioning and connections routing), and system sizing are conducted. The topology generation is currently performed using design templates containing knowledge-based heuristics derived from systems topologies of existing aircraft. However, using such design templates limits their generic application to novel aircraft models because exceptions for geometric differences between these models need to be implemented. This especially applies to the topology generation of power supply systems (electric, hydraulic) due to their high number of interfaces to consumer systems. To overcome these limitations, an automated routing method is presented in this paper as a generic approach for topology generation. With this method, the system topology is generated based on a defined routing network, which comprises areas of the aircraft for allowed components positioning and connections routing. Using Dijkstra’s algorithm, the shortest path connections are found within the established routing network. Furthermore, boundary conditions for routing, for instance adding a cost function for power segregation, can be defined. An assessment of the automated routing method applied on the electric power supply system is performed using qualitative parameters such as the capability to adapt the routing network. Quantitative parameters like cable length and system mass are assessed as well.

An improved model predictive control for linear induction machine drive-based split-source inverters

The Split Source Inverter (SSI) is a single-stage DC-AC converter topology widely recognized for its beneficial features compared to the Z-source inverter. These advantages include a reduced number of components, a constant input current, and minimized losses. This study aims to extensively investigate and analyze the operation of a 3 kW Linear Induction Motor (LIM) using SSI through comprehensive simulations. The suggested control approach, employing finite control-model predictive thrust control (FC-MPTC), exhibits remarkable dynamic behavior and achieves a fast transient response without necessitating modifications to the control loop. Moreover, the proposed method allows for the manipulation of multiple variables by utilizing a single cost function, eliminating the need for lookup tables. The efficacy of the proposed control method is validated through comprehensive simulations and experimental tests, which exhibit accurate reference tracking speed and superior dynamic performance in regulating the thrust and primary flux. The results also showed that the proposed method had lower undulations of 5.8% than the traditional method. The proposed approach outperforms the DTC-SVM method under varying load and speed conditions by delivering remarkable accuracy and improved control characteristics.

Acoustic 2-D Full-Waveform Inversion with Non-Balanced Finite differences and Adaptive Weight Decay Methods

Full waveform inversion (FWI) is a powerful seismic imaging technique that aims to achieve high-resolution subsurface models by iteratively comparing recorded seismic waveforms with modeled waveforms. The success of FWI relies on the precision of forward modeling algorithms and the effectiveness of optimization strategies. In this study, we propose a FWI methodology that leverages non-balanced staggered-grid finite differences schemes (NBFD) in the forward modeling process. By employing operators with spatial differencing of varying lengths, these schemes enhance the efficiency of forward modeling.For the optimization process, we employ adaptive gradient-based optimization with weight decay, a widely-used approach in machine learning and deep learning algorithms for model training. These methods demonstrate efficient optimization processing for large data volumes with high precision. The objective of adaptive gradient-based optimization is to dynamically adjust the update rate model parameters during the iterative process. This facilitates improved convergence speed, enhances stability, and overcomes challenges associated with traditional fixed-rate adjustment methods.To ensure accurate inversion results and avoid local minima in the cost function minimization, we implement a Multiscaling inversion strategy within the workflow. The obtained results are compared with those achieved through conventional FWI using standard staggered finite differences and various optimization methods. Our findings demonstrate that employing non-balanced staggered-grid finite differences scheme leads to significant improvements compared to the previously reported results in the literature.

Mechanism design for public projects via three machine learning based approaches

We study mechanism design for nonexcludable and excludable binary public project problems. Our aim is to maximize the expected number of consumers and the expected agents’ welfare. We first show that for the nonexcludable public project model, there is no need for machine learning based mechanism design. We identify a sufficient condition on the prior distribution for the existing conservative equal costs mechanism to be the optimal strategy-proof and individually rational mechanism. For general distributions, we propose a dynamic program that solves for the optimal mechanism. For the excludable public project model, we identify a similar sufficient condition for the existing serial cost sharing mechanism to be optimal for 2 and 3 agents. We derive a numerical upper bound and use it to show that for several common distributions, the serial cost sharing mechanism is close to optimality. The serial cost sharing mechanism is not optimal in general. We propose three machine learning based approaches for designing better performing mechanisms. We focus on the family of largest unanimous mechanisms, which characterizes all strategy-proof and individually rational mechanisms for the excludable public project model. A largest unanimous mechanism describes an iterative mechanism, which is defined by an exponential number of mechanism parameters. Our first approach describes the largest unanimous mechanism family using a neural network and training is carried out by minimizing a cost function that combines the mechanism design objective and the constraint violation penalty. We interpret the largest unanimous mechanisms as price-oriented rationing-free (PORF) mechanisms, which enables us to move the mechanisms’ iterative decision making off the neural network, to a separate simulation process, therefore avoiding the vanishing gradient problem. We also feed the prior distribution’s analytical form into the cost function to achieve high-quality gradients for efficient training. Our second approach treats the mechanism design task as a Markov Decision Process with an exponential number of states. During the Markov decision process, the non-consumers are gradually removed from the system. We train multiple neural networks, each for a different number of remaining agents, to learn the optimal value function on the states. Training is carried out by supervised learning toward a set of manually prepared base cases and the Bellman equation. Our third approach is based on reinforcement learning for a Partially Observable Markov Decision Process. Each RL episode randomly draws a type profile, which is hidden from the RL agent (mechanism designer). The RL agent only observes which cost share offers have been accepted under the largest unanimous mechanism under discussion. We use a continuous action space reinforcement learning approach to adjust the offer policy (i.e., adjust mechanism parameters). Lastly, our first two approaches use “supervision to manual mechanisms” as a systematic way for network initialization, which is potentially valuable for machine learning based mechanism design in general.

Reliability-based design optimization for a vertical-type breakwater with multiple limit-state equations under Korean marine environments varying from sea to sea

In this study, as part of basic research aimed at enhancing the accuracy of load and resistance coefficients to ensure their suitability for practical design and promoting the application of underutilized reliability-based design in Korea, the author conducts optimal design based on the reliability analysis of a vertical-type breakwater in the seas off of Haeundae, Yeosu, Mokpo, Gunsan, and Incheon—representative ports in Korea. In doing so, the author utilized the double-loop approach, which simultaneously addresses a reliability problem nested within an optimization process, employing the Polak–He optimization algorithm. To mitigate the substantial numerical effort required by the double-loop approach based on the Polak–He optimization algorithm, which necessitates the gradients of both cost and constraint functions, the subset simulation method was employed. In this process, the author deliberately refrained from using design waves of a specific return period and linear probabilistic models such as the Gaussian distribution, especially concerning wave and lift forces, often viewed as barriers to the widespread application of reliability-based design in Korea. Instead, the author focused on characterizing the uncertainties associated with the wave force, lift force, and overturning moment—variables that significantly impact the integrity of vertical-type breakwaters—by developing probabilistic models for these random variables directly from long-term in situ wave data. These models capture the varied characteristics of the Korean marine environment from sea to sea. In this way, the need for additional assumptions concerning the interrelationship between significant wave and maximum wave heights, along with the wave period, can be eliminated. Following Occam's razor principle, which suggests that explanations constructed with the smallest possible set of assumptions are superior, the reliability-based design optimization of a vertical-type breakwater presented in this study demonstrates promise in terms of simplicity and practicality. The limit state of the vertical-type breakwater was defined to encompass sliding, overturning, and collapse failures, and the strong interrelations between the wave force, lift force, and overturning moment were described using the Nataf joint distribution. As anticipated, simulation results show that solely considering sliding failure, as in the current reliability-based design platform in Korea, leads to an underestimated failure probability. Furthermore, ensuring a consistent failure probability for vertical-type breakwaters using design waves with a specific return period, as in past studies, is not feasible. In contrast, this study demonstrated that breakwaters optimally designed to meet the reliability index requirement of β = 3.5–4 consistently maintain a target failure probability in all sea areas.

Hemodynamics of ventricular-arterial coupling under enhanced external counterpulsation: An optimized dual-source lumped parameter model

Background and objectiveEnhanced external counterpulsation (EECP) is a mechanically assisted circulation technique widely used in the rehabilitation and management of ischemic cardiovascular diseases. It contributes to cardiovascular functions by regulating the afterload of ventricle to improve hemodynamic effects, including increased diastolic blood pressure at aortic root, increased cardiac output and enhanced blood perfusion to multiple organs including coronary circulation. However, the effects of EECP on the coupling of the ventricle and the arterial system, termed ventricular-arterial coupling (VAC), remain elusive. We aimed to investigate the acute effect of EECP on the dynamic interaction between the left ventricle and its afterload of the arterial system from the perspective of ventricular output work. MethodsA neural network assisted optimization algorithm was proposed to identify the ordinary differential equation (ODE) relation between aortic root blood pressure and flow rate. Based on the optimized order of ODE, a lumped parameter model (LPM) under EECP was developed taking into consideration of the simultaneous action of cardiac and EECP pressure sources. The ventricular output work, in terms of aortic pressure and flow rate cooperated with the LPM, was used to characterize the VAC of ventricle and its afterload. The VAC subjected to the principle of minimal ventricular output work was validated by solving the Euler-Poisson equation of cost function, ultimately determining the waveforms of aortic pressure and flow rate. ResultsA third-order ODE can precisely describe the hemodynamic relationship between aortic pressure and flow rate. An optimized dual-source LPM with three energy-storage elements has been constructed, showing the potential in probing VAC under EECP. The LPM simulation results demonstrated that the VAC in terms of aortic pressure and flow rate yielded to the minimal ventricular output work under different EECP pressures. ConclusionsThe ventricular-arterial coupling under EECP is subjected to the minimal ventricular output work, which can serve as a criterion for determining aortic pressure and flow rate. This study provides insight for the understanding of VAC and has the potential in characterizing the performance of the ventricular and arterial system under EECP.

Fast Newton-like extremum seeking with asymptotic convergence guarantees

Extremum seeking denotes control approaches to steer the input of a dynamical system towards the optimizer of an associated cost function. The main strength of those schemes is to achieve their objective without any prior knowledge of the mathematical expression of the cost, nor the value of its gradient. However, those algorithms typically suffer from a slow convergence speed. In this paper, we propose a novel class of Newton-like extremum seeking systems separating the gradient and Hessian estimation from the update of the cost input towards the optimizer. The stability properties of the proposed schemes, including the ability of some of them to enforce the asymptotic convergence of the cost inputs to the optimizer, are proved. Their performances are also examined via some numerical examples. The latter will demonstrate the ability of the presented schemes to significantly reduce the convergence time compared with existing Newton-like schemes.

Whole-Body Dynamics for Humanoid Robot Fall Protection Trajectory Generation with Wall Support.

When humanoid robots work in human environments, they are prone to falling. However, when there are objects around that can be utilized, humanoid robots can leverage them to achieve balance. To address this issue, this paper established the state equation of a robot using a variable height-inverted pendulum model and implemented online trajectory optimization using model predictive control. For the arms' optimal joint angles during movement, this paper took the distributed polygon method to calculate the arm postures. To ensure that the robot reached the target position smoothly and rapidly during its motion, this paper adopts a whole-body motion control approach, establishing a cost function for multi-objective constraints on the robot's movement. These constraints include whole-body dynamics, center of mass constraints, arm's end effector constraints, friction constraints, and center of pressure constraints. In the simulation, four sets of methods were compared, and the experimental results indicate that compared to free fall motion, adopting the method proposed in this paper reduces the maximum acceleration of the robot when it touches the wall to 69.1 m/s2, effectively reducing the impact force upon landing. Finally, in the actual experiment, we positioned the robot 0.85 m away from the wall and applied a forward pushing force. We observed that the robot could stably land on the wall, and the impact force was within the range acceptable to the robot, confirming the practical effectiveness of the proposed method.

Fixed-time cluster optimization of multi-agent systems on directed detail-balanced networks

This paper investigates the fixed-time cluster optimization problem of first-order multi-agent systems (MASs) under directed detail-balanced networks, in which the optimization objective is a linear combination of local objective functions. Two piecewise distributed control protocols are proposed to solve the optimization problem with time-invariant and time-varying objective functions, respectively. Under the two proposed piecewise control protocols, the fixed-time cluster optimization problem can be solved on the basis of maintaining the cluster consensus unchanged. Furthermore, the initial states of all the agents need not be restricted to the minimum of their local cost functions. Finally, we provide two examples to illustrate the effectiveness of the proposed control protocols.

Convolutional neural network for improved event-based Shack-Hartmann wavefront reconstruction

Shack-Hartmann wavefront sensing is a technique for measuring wavefront aberrations, whose use in adaptive optics relies on fast position tracking of an array of spots. These sensors conventionally use frame-based cameras operating at a fixed sampling rate to report pixel intensities, even though only a fraction of the pixels have signal. Prior in-lab experiments have shown feasibility of event-based cameras for Shack-Hartmann wavefront sensing (SHWFS), asynchronously reporting the spot locations as log intensity changes at a microsecond time scale. In our work, we propose a convolutional neural network (CNN) called event-based wavefront network (EBWFNet) that achieves highly accurate estimation of the spot centroid position in real time. We developed a custom Shack-Hartmann wavefront sensing hardware with a common aperture for the synchronized frame- and event-based cameras so that spot centroid locations computed from the frame-based camera may be used to train/test the event-CNN-based centroid position estimation method in an unsupervised manner. Field testing with this hardware allows us to conclude that the proposed EBWFNet achieves sub-pixel accuracy in real-world scenarios with substantial improvement over the state-of-the-art event-based SHWFS. An ablation study reveals the impact of data processing, CNN components, and training cost function; and an unoptimized MATLAB implementation is shown to run faster than 800 Hz on a single GPU.

Toffoli-depth reduction method preserving in-place quantum circuits and its application to SHA3-256

When implementing a quantum circuit for a desired reversible function, an attempt is made to design an accurate quantum circuit. Then, the quantum circuit is optimized based on a specific cost function, such as design cost, width (the number of qubits), depth, etc. In particular, if an in-place subcircuit itself can be optimized while maintaining its in-place property, it will be a very useful way to increase efficiency without changing the initial architecture of the entire quantum circuit. Furthermore, since its (clean) work qubits can easily be utilized in subsequent subroutines of the quantum circuit, it has an additional important advantage in terms of width. In this paper, for the first time to the best of our knowledge, we present a global Toffoli-depth reduction methodology for an in-place version reversible circuit in the case that the given input circuit is optimized with Toffoli-count. We mainly introduce a process to optimize the χ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\chi $$\\end{document} internal function block in SHA3-256 to explain our Toffoli-depth reduction approach preserving the in-place property, and hence, well-balanced five χ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\chi $$\\end{document} quantum circuits are induced in terms of its width and T-depth. And then, we apply these five χ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\chi $$\\end{document} circuits to design the entire SHA3-256 cryptosystem. One of the proposed SHA3-256 quantum circuits has a width of 1600 and a T-depth of 264, and this shows a result of 50% and 38.9% reduction compared to the previous circuit in terms of width and T-depth, respectively. Other versions of our SHA3-256 circuits just required 10–33 qubits per one T-depth compared to the previous results which require over 1800 qubits per T-depth, and so this means that our SHA3-256 circuits are well-balanced. Finally, we constructed Grover’s algorithm circuit using each version that realized SHA3-256. When output circuits for the presented method were used, quantum volume values of Grover’s algorithm circuits became 33 and 50%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\%$$\\end{document} of the value when the input circuit was used.