Search Results (20)

Detecting rotated parabolic shapes in digital images remains a significant challenge in computer vision, especially in embedded environments constrained by computational and memory resources. This study introduces a novel field-programmable gate array (FPGA)-based genetic algorithm (GA) architecture specifically tailored for rotated parabola detection, implemented as an intellectual property (IP) core on a PYNQ-Z1 system-on-chip (SoC) platform. The architecture encodes four parabola parameters into fixed-length chromosomes, assesses their geometric consistency with a 640 × 480 binary edge image using a hardware fitness function, and executes the entire evolutionary process in programmable logic. Image pre-processing is executed on an external CPU, using Canny edge detection for synthetic images and Holistically Nested Edge Detection (HED). For real images, post-processing and result visualization are conducted on the ARM processor using the PYNQ framework. Experimental results on synthetic images demonstrate mean accuracies of 98.47% and 95.23%, with detection success rates of up to 96%. For real images, since manually annotated ground truth is not available, results are presented as qualitative observations of convergence consistency across 100 independent runs. These findings demonstrate the feasibility of detecting rotated parabolas on resource-constrained embedded platforms and indicate promising applications in domains where parabolic patterns are prevalent, such as structural inspection, biomedical imaging, and perception modules for autonomous vehicles and driver-assistance systems. Full article

To address the issues of coarse-grained thread allocation and difficult load balancing in compiler automatic parallelization for processors, this paper proposes a loop-block-level automatic parallelization method for compilers based on an iterative compilation mode, using the SWGCC compiler on the Sunway platform. An automatic parallelization method that independently sets the number of threads for each loop block is designed within the SWGCC, which allocates threads to each parallelizable loop block in the program at a finer granularity. Meanwhile, iterative compilation is combined with a genetic algorithm to iteratively optimize the optimal thread group. Through operations such as the chromosome encoding of thread allocation schemes, weighted mutation operations based on loop execution proportions, and fitness function-guided population evolution, the optimal thread combination is efficiently searched for the loop block thread allocation algorithm. Experiments are validated on the Sunway processor using the SPEC2006 test suite. The results reveal that the loop-block-level compiler automatic parallelization algorithm combined with the evolutionary algorithm achieves a maximum performance score improvement of 19% and an average performance score improvement of 4% compared to the baseline automatic parallelization algorithm in the tests. Full article

Biomimetics has emerged as a transformative interdisciplinary approach that harnesses nature’s evolutionary strategies to develop sustainable solutions across diverse fields. This study explores its integrative role in shaping smart cities, advancing artificial intelligence and robotics, innovating biomedical applications, and enhancing computational design tools. By analysing the evolution of biomimetic principles and their technological impact, this work highlights how nature-inspired solutions contribute to energy efficiency, adaptive urban planning, bioengineered materials, and intelligent systems. Furthermore, this paper discusses future perspectives on biomimetics-driven innovations, emphasising their potential to foster resilience, efficiency, and sustainability in rapidly evolving technological landscapes. Particular attention is given to neuromorphic hardware, a biologically inspired computing paradigm that mimics neural processing through spike-based communication and analogue architectures. Key components such as memristors and neuromorphic processors enable adaptive, low-power, task-specific computation, with wide-ranging applications in robotics, AI, healthcare, and renewable energy systems. Furthermore, this paper analyses how self-organising cities, conceptualised as complex adaptive systems, embody biomimetic traits such as resilience, decentralised optimisation, and autonomous resource management. Full article

The simplest evolutionary model for catching prey by an agent (predator) is considered. The simulation is performed on the basis of a software-emulated Intel i8080 processor. Maximizing the number of catches is chosen as the objective function. This function is associated with energy dissipation and developmental time. It is shown that during Darwinian evolution, agents with an initially a random set of processor commands subsequently acquire a successful catching skill. It is found that in the process of evolution, a logarithmic relationship between astronomical and developmental times arises in agents. This result is important for the ideas available in the literature about the close connection of such concepts as time, Darwinian selection, and the maximization of entropy production. Full article

The paper considers an innovative model of autonomous charging stations where a program implementing a scheduling algorithm and a set of jobs being scheduled are driven by the same common power source. It is assumed that one of the well-known local search metaheuristics—an evolutionary algorithm—is used for the scheduling process. The algorithm is designed to search for a sequence of charging jobs resulting in a schedule of the minimum length. Since processors with variable processing speeds can be used for computations, this has interesting consequences both from a theoretical and practical point of view. It is shown in the paper that the problem of choosing the right processor speed under given constraints and an assumed scheduling criterion is a non-trivial one. We formulate a general problem of determining the computation speed of the evolutionary algorithm based on the proposed model of a computational task and the adopted problem of scheduling charging jobs. The novelty of the paper consists of two aspects: (i) proposing the new model of the autonomous charging station operating according to the basics of edge computing; and (ii) developing the methodology for dynamically changing the computational speed, taking into account power and energy constraints as well as the results of computations obtained in the current iteration of the algorithm. Some approaches for selecting the appropriate speed of computations are proposed and discussed. Conclusions and possible directions for future research are also given. Full article

Multi-objective task graph scheduling is a well-known NP-hard problem that plays a significant role in heterogeneous distributed systems. The solution to the problem is expected to optimize all scheduling objectives. Pretty large state-of-the-art algorithms exist in the literature that mostly apply different metaheuristics for solving the problem. This study proposes a new hybrid algorithm comprising an improved multi-objective differential evolution algorithm (DE) and Pareto-front neighborhood search to solve the problem. The novelty of the proposed hybrid method is achieved by improving DE and hybridizing it with the neighborhood search method. The proposed method improves the performance of differential evolution by applying appropriate solution representation as well as effective selection, crossover, and mutation operators. Likewise, the neighborhood search algorithm is applied to improve the extracted Pareto-front and speed up the evolution process. The effectiveness and performance of the developed method are assessed over well-known test problems collected from the related literature. Meanwhile, the values of spacing and hyper-volume metrics are calculated. Moreover, the Wilcoxon signed method is applied to carry out pairwise statistical tests over the obtained results. The obtained results for the makespan, reliability, and flow-time of 50, 18, and 41, respectively, by the proposed hybrid algorithm in the study confirmed that the developed algorithm outperforms all proposed methods considering the performance and quality of objective values. Full article

This work is devoted to design, implement and validate an active mass damper (AMD) for vibration mitigation in slender structures. The control law, defined by means of genetic algorithm optimization, is deployed on a low-cost processor (NI myRIO-1900), and experimentally validated on a 13.5-m lively timber footbridge. As is known, problems arising from human-induced vibrations in slender, lightweight and low-damped structures usually require the installation of mechanical devices, such as an AMD, in order to be mitigated. This kind of device tends to reduce the movement of the structure, which can be potentially large when it is subjected to dynamic loads whose main components match its natural frequencies. In those conditions, the AMD is sought to improve the comfort and fulfil the serviceability conditions for the pedestrian use according to some design guides. After the dynamic identification of the actuator, the procedure consisted of the experimental characterization and identification of the modal properties of the structure (natural frequencies and damping ratios). Once the equivalent state space system of the structure is obtained, the design of the control law is developed, based on state feedback, which was deployed in the low-cost controller. Finally, experimental adjustments (filters, gains, etc.) were implemented and the validation test was carried out. The system performance has been evaluated using different metrics, both in the frequency and time domain, and under different loads scenarios, including pedestrian transits to demonstrate the feasibility, robustness and good performance of the proposed system. The strengths of the presented work reside in: (1) the use of genetic evolutionary algorithms to optimize both the state estimator gain and the feedback gain that commands the actuator, whose performance is further tested and analyzed using different fitness functions related to both time and frequency domains and (2) the implementation of the active control system in a low-cost processor, which represents a significant advantage when it comes to implement this system in a real structure. Full article

The input layer, hidden layer, and output layer are three models of the neural processors that make up feedforward neural networks (FNNs). Evolutionary algorithms have been extensively employed in training FNNs, which can correctly actualize any finite training sample set. In this paper, an enhanced marine predators algorithm (MPA) based on the ranking-based mutation operator (EMPA) was presented to train FNNs, and the objective was to attain the minimum classification, prediction, and approximation errors by modifying the connection weight and deviation value. The ranking-based mutation operator not only determines the best search agent and elevates the exploitation ability, but it also delays premature convergence and accelerates the optimization process. The EMPA integrates exploration and exploitation to mitigate search stagnation, and it has sufficient stability and flexibility to acquire the finest solution. To assess the significance and stability of the EMPA, a series of experiments on seventeen distinct datasets from the machine learning repository of the University of California Irvine (UCI) were utilized. The experimental results demonstrated that the EMPA has a quicker convergence speed, greater calculation accuracy, higher classification rate, strong stability and robustness, which is productive and reliable for training FNNs. Full article

Modern hardware accelerator cards create an accessible platform for developers to reduce execution times for computationally expensive algorithms. The most widely used systems, however, have dedicated memory spaces, resulting in the processor having to transfer data to the accelerator-card memory space before the computation can be executed. Currently, the performance increase from using an accelerator card for data-intensive algorithms is limited by the data movement. To this end, this work aims to reduce the effect of data movement and improve overall performance by systematically caching data on the accelerator card. We designed a software-controlled split cache where data are cached on the accelerator and assessed its efficacy using a data-intensive Bioinformatics application that infers the evolutionary history of a set of organisms by constructing phylogenetic trees. Our results revealed that software-controlled data caching on a datacenter-grade FPGA accelerator card reduced the overhead of data movement by 90%. This resulted in a reduction of the total execution time between 32% and 40% for the entire application when phylogenetic trees of various sizes were constructed. Full article

There are many factors affecting the quality and safety of agricultural products in the supply chain of agricultural products. In order to ensure the quality and safety of agricultural products, suppliers and processors need to take their own quality measures to ensure the quality of agricultural products. Quality inspection departments need to strictly supervise suppliers and processors to ensure the implementation of quality measures by both parties. Within the supply chain, the decisions of these three stakeholders are affected by the initial intention, the cost of quality measures, and the penalty amount of the quality inspection department. Outside the supply chain, they are affected by government regulation and consumer feedback. This paper takes the stakeholders in the agricultural product supply chain as the object, brings suppliers, processors, and quality inspection departments into the evolutionary game model, brings the factors that affect the decision-making of these three stakeholders into the model as parameters to analyze the stability of the model in different situations, and then analyzes the factors that affect the decision-making of stakeholders through mathematical simulation according to specific examples. The results show that the enthusiasm of stakeholders to ensure the quality of agricultural products is most affected by the initial intention of each other and the cost of quality measures. At the same time, the punishment of the quality inspection department, the feedback of consumers, and the supervision of the government also play a good role in promoting quality. Full article

How can single cells without nervous systems perform complex behaviours such as habituation, associative learning and decision making, which are considered the hallmark of animals with a brain? Are there molecular systems that underlie cognitive properties equivalent to those of the brain? This review follows the development of the idea of molecular brains from Darwin’s “root brain hypothesis”, through bacterial chemotaxis, to the recent discovery of neuron-like r-protein networks in the ribosome. By combining a structural biology view with a Bayesian brain approach, this review explores the evolutionary labyrinth of information processing systems across scales. Ribosomal protein networks open a window into what were probably the earliest signalling systems to emerge before the radiation of the three kingdoms. While ribosomal networks are characterised by long-lasting interactions between their protein nodes, cell signalling networks are essentially based on transient interactions. As a corollary, while signals propagated in persistent networks may be ephemeral, networks whose interactions are transient constrain signals diffusing into the cytoplasm to be durable in time, such as post-translational modifications of proteins or second messenger synthesis. The duration and nature of the signals, in turn, implies different mechanisms for the integration of multiple signals and decision making. Evolution then reinvented networks with persistent interactions with the development of nervous systems in metazoans. Ribosomal protein networks and simple nervous systems display architectural and functional analogies whose comparison could suggest scale invariance in information processing. At the molecular level, the significant complexification of eukaryotic ribosomal protein networks is associated with a burst in the acquisition of new conserved aromatic amino acids. Knowing that aromatic residues play a critical role in allosteric receptors and channels, this observation suggests a general role of π systems and their interactions with charged amino acids in multiple signal integration and information processing. We think that these findings may provide the molecular basis for designing future computers with organic processors. Full article

In this paper, we propose direct simulations between a given network of evolutionary processors with an arbitrary topology of the underlying graph and a network of evolutionary processors with underlying graphs—that is, a complete graph, a star graph and a grid graph, respectively. All of these simulations are time complexity preserving—namely, each computational step in the given network is simulated by a constant number of computational steps in the constructed network. These results might be used to efficiently convert a solution of a problem based on networks of evolutionary processors provided that the underlying graph of the solution is not desired. Full article

This paper compares the latency, accuracy, training time and hardware costs of neural networks compressed with our new multi-objective evolutionary algorithm called NEMOKD, and with quantisation. We evaluate NEMOKD on Intel’s Movidius Myriad X VPU processor, and quantisation on Xilinx’s programmable Z7020 FPGA hardware. Evolving models with NEMOKD increases inference accuracy by up to 82% at the cost of 38% increased latency, with throughput performance of 100–590 image frames-per-second (FPS). Quantisation identifies a sweet spot of 3 bit precision in the trade-off between latency, hardware requirements, training time and accuracy. Parallelising FPGA implementations of 2 and 3 bit quantised neural networks increases throughput from 6 k FPS to 373 k FPS, a 62$\times$ speedup. Full article

Networks of picture processors is a massively distributed and parallel computational model inspired by the evolutionary cellular processes, which offers efficient solutions for NP-complete problems. This bio-inspired model computes two-dimensional strings (pictures) using simple rewriting rules (evolutionary operations). The functioning of this model mimics a community of cells (pictures) that are evolving according to these bio-operations via a selection process that filters valid surviving cells. In this paper, we propose an extension of this model that empowers it with a flexible method that selects the processed pictures based on a quantitative evaluation of its content. In order to show the versatility of this extension, we introduce a solver for a cryptographic proof-of-work based on the hardness of finding a solution to a set of random quadratic equations over the finite field ${\mathbb{F}}_2$. This problem is demonstrated to be NP-hard, even with quadratic polynomials over the field ${\mathbb{F}}_2$, when the number of equations and the number of variables are of roughly the same size. The proposed solution runs in $O\left(n^2\right)$ computational steps for any size $(n,m)$ of the input pictures. In this context, this paper opens up a wide field of research that looks for theoretical and practical solutions of cryptographic problems via software/hardware implementations based on bio-inspired computational models. Full article

Properties such as high resolution, contactless (and thus wear-free) measurement, low power consumption, robustness against temperature and contamination as well as low cost make magnetic position and orientation systems appealing for a large number of industrial applications. Nevertheless, one major practical challenge is their sensitivity to fabrication tolerances. In this work, we propose a novel method for magnetic position system calibration based on the analytical computation of the magnetic field and on the application of an evolutionary optimization algorithm. This scheme enables the calibration of more than 10 degrees of freedom within a few seconds on standard quad-core ×86 processors, and is demonstrated by calibrating a highly cost-efficient 3D-printed 3-axis magnetic joystick. Full article