Search Results (35)

Block-based hybrid video coders typically use inter-prediction and bidirectionally coded (B) frames to improve compression efficiency. For this purpose, they employ look-ahead buffers, perform out-of-sequence frame coding, and implement similarity search-based general-purpose algorithms for motion estimation. While effective, these methods increase computational complexity and may not suit delay-sensitive practical applications such as real-time drone video transmission. If future motion can be predicted from external metadata, encoding can be optimized with lower complexity. In this study, a mathematical model for predicting motion vectors in drone video using only flight parameters is proposed. A remote-controlled drone with a fixed downward-facing camera recorded 4K video at 50 fps during autonomous flights over a marked terrain. Four flight parameters were varied independently, altitude, horizontal speed, vertical speed, and rotational rate. OpenCV was used to detect ground markers and compute motion vectors for temporal distances of 5 and 25 frames. Polynomial surface fitting was applied to derive motion models for translational, rotational, and elevational motion, which were later combined. The model was validated using complex motion scenarios (e.g., circular, ramp, helix), yielding worst-case prediction errors of approximately −1 ± 3 and −6 ± 14 pixels at 5 and 25 frames, respectively. The results suggest that flight-aware modeling enables accurate and low-complexity motion vector prediction for drone video coding. Full article

The accurate positioning and state estimation of surface vessels are prerequisites to autonomous navigation. Recently, the rapid development of 3D LiDARs has promoted the autonomy of both land and aerial vehicles, which has attracted the interest of researchers in the maritime community. However, in traditional maritime surface multi-scenario applications, LiDAR scan matching has low point cloud scanning and matching efficiency and insufficient positional accuracy when dealing with large-scale point clouds, so it has difficulty meeting the real-time demand of low-computing-power platforms. In this paper, we use ICP-SVD for point cloud alignment in the Stanford dataset and outdoor dock scenarios and propose an optimization scheme (iVox + ICP-SVD) that incorporates the voxel structure iVox. Experiments show that the average search time of iVox is 72.23% and 96.8% higher than that of ikd-tree and kd-tree, respectively. Executed on an NVIDIA Jetson Nano (four ARM Cortex-A57 cores @ 1.43 GHz) the algorithm processes 18 k downsampled points in 56 ms on average and 65 ms in the worst case—i.e., ≤15 Hz—so every scan is completed before the next 10–20 Hz LiDAR sweep arrives. During a 73 min continuous harbor trial the CPU temperature stabilized at 68 °C without thermal throttling, confirming that the reported latency is a sustainable, field-proven upper bound rather than a laboratory best case. This dramatically improves the retrieval efficiency while effectively maintaining the matching accuracy. As a result, the overall alignment process is significantly accelerated, providing an efficient and reliable solution for real-time point cloud processing. Full article

Stroke is a neurological condition that often results in long-term motor deficits. Given the high prevalence of motor impairments worldwide, there is a critical need to explore innovative neurorehabilitation strategies that aim to enhance the quality of life of patients. One promising approach involves brain–computer interface (BCI) systems controlled by electroencephalographic (EEG) signals elicited when a subject performs motor imagery (MI), which is the mental simulation of movement without actual execution. Such systems have shown potential for facilitating motor recovery by promoting neuroplastic mechanisms. Controlling BCI systems based on MI-EEG signals involves the following sequential stages: recording the raw signal, preprocessing, feature extraction and selection, and classification. Each of these stages can be executed using several techniques and numerous parameter combinations. In this study, we searched for the combination of feature extraction technique, time window, frequency range, and classifier that could provide the best classification accuracy for the BCI Competition 2008 IV 2a benchmark dataset (BCI-C), characterized by EEG-MI data of different limbs (four classes, of which three were used in this work), and the NeuroSCP EEG-MI dataset, a custom experimental protocol developed in our laboratory, consisting of EEG recordings of different movements with the same limb (three classes—right dominant arm). The mean classification accuracy for BCI-C was 76%. When the subjects were evaluated individually, the best-case classification accuracy was 94% and the worst case was 54%. For the NeuroSCP dataset, the average classification result was 53%. The individual subject’s evaluation best-case was 71% and the worst case was 35%, which is close to the chance level (33%). These results indicate that techniques commonly applied to classify different limb MI based on EEG features cannot perform well when classifying different MI tasks with the same limb. Therefore, we propose other techniques, such as EEG functional connectivity, as a feature that could be tested in future works to classify different MI tasks of the same limb. Full article

In this study, we propose a novel metaheuristic algorithm named Kangaroo Escape optimization Technique (KET), inspired by the survival-driven escape strategies of kangaroos in unpredictable environments. The algorithm integrates a chaotic logistic energy adaptation strategy to balance a two-phase exploration process—zigzag motion and long-jump escape—and an adaptive exploitation phase with local search guided by either nearby elite solutions or random peers. A unique decoy drop mechanism is introduced to prevent premature convergence and ensure dynamic diversity. KET is applied to optimize the parameters of a fractional-order Proportional Integral Derivative (PID) controller for Load Frequency Control (LFC) in interconnected power systems. The designed fractional-order PID controller-based KET optimization extends the conventional PID by introducing fractional calculus into the integral and derivative terms, allowing for more flexible and precise control dynamics. This added flexibility enables enhanced robustness and tuning capability, particularly useful in complex and uncertain systems such as modern power systems. Comparative results with existing state-of-the-art algorithms demonstrate the superior robustness, convergence speed, and control accuracy of the proposed approach under dynamic scenarios. The proposed KET-fractional order PID controller offers 29.6% greater robustness under worst-case conditions and 36% higher consistency across multiple runs compared to existing techniques. It achieves optimal performance faster than the Neural Network Algorithm (NNA), achieving its best Integral of Time Absolute Error (ITAE) value within the first 20 iterations, demonstrating its superior learning rate and early-stage search efficiency. In addition to LFC, the robustness and generality of the proposed KET were validated on a standard speed reducer design problem, demonstrating superior optimization performance and consistent convergence when compared to several recent metaheuristics. Full article

This work presents a novel fully automated computational framework for optimizing profile extrusion dies, aiming to achieve balanced flow at the die flow channel outlet while minimizing total pressure drop. The framework integrates non-isothermal, non-Newtonian flow modeling in OpenFOAM with a geometry parameterization routine in FreeCAD and a Bayesian optimization algorithm from Scikit-Optimize. A custom solver was developed to account for temperature-dependent viscosity using the Bird–Carreau–Arrhenius model, incorporating viscous dissipation and a novel boundary condition to replicate the thermal regulation used in the experimental process. For optimization, the die flow channel outlet cross-section is discretized into elemental sections, enabling localized flow analysis and establishing a convergence criterion based on the total objective function value. A case study on a tire tread die demonstrates the framework’s ability to iteratively refine internal geometry by adjusting key design parameters, resulting in significant improvements in outlet velocity uniformity and reduced pressure drop. Within the searching space, the results showed an optimal objective function of 0.2001 for the best configuration, compared to 0.7333 for the worst configuration, representing an enhancement of 72.7%. The results validate the effectiveness of the proposed framework in navigating complex design spaces with minimal manual input, offering a robust and generalizable approach to extrusion die optimization. This methodology enhances process efficiency, reduces development time, and improves final product quality, particularly for complex and asymmetric die geometries commonly found in the automotive and tire manufacturing industries. Full article

This article presents a method for solving the inverse problem of computed tomography using an incomplete dataset. The problem focuses on reconstructing spatial objects based on the data collected from transmitters and receivers (referred to as projection vectors). The novelty of the proposed approach lies in combining two types of algorithms, namely heuristic and deterministic. Specifically, Artificial Bee Colony (ABC) and Jellyfish Search (JS) algorithms were utilized and compared as heuristic methods, while the deterministic methods were based on the Hooke–Jeeves (HJ) and Nelder–Mead (NM) approaches. By merging these techniques, a hybrid algorithm was developed, integrating the strengths of both heuristic and deterministic algorithms. The proposed hybrid algorithm proved to be approximately five to six times faster than an approach relying solely on metaheuristics while also providing more accurate results. In the worst-case test, the fitness function value for the hybrid algorithm was approximately 22% lower than that of the purely metaheuristic-based approach. Experimental tests further demonstrated that the hybrid algorithm, whether based on Hooke–Jeeves or Nelder–Mead, was stable and well suited for solving the considered problem. The article includes experimental results that confirm the effectiveness, accuracy, and efficiency of the proposed method. Full article

In this research, considering the air methodology, an axisymmetric model was developed, validated, and calibrated for the numerical simulation of an Oscillating Water Column (OWC) converter subjected to a realistic sea state, representative of the Cassino beach, in the south of Brazil. To do so, the Finite Volume Method (FVM) was used, through the Fluent software (Version 18.1), for the airflow inside the hydro-pneumatic chamber and turbine duct of the OWC. Furthermore, the influence of geometric parameters on the available power of the OWC converter was evaluated through Constructal Design combined with Exhaustive Search. For this, a search space with 100 geometric configurations for the hydro-pneumatic chamber was defined by means of the variation in two degrees of freedom: the ratio between the height and diameter of the hydro-pneumatic chamber (H₁/L₁) and the ratio between the height and diameter of the smallest base of the connection, whose surface of revolution has a trapezoidal shape, between the hydro-pneumatic chamber and the turbine duct (H₂/L₂). The ratio between the height and diameter of the turbine duct (H₃/L₃) was kept constant. The results indicated that the highest available power of the converter was achieved by the lowest values of H₁/L₁ and highest values of H₂/L₂, with the optimal case being obtained by H₁/L₁ = 0.1 and H₂/L₂ = 0.81, achieving a power 839 times greater than the worst case. The values found are impractical in real devices, making it necessary to limit the power of the converters to 500 kW to make this assessment closer to reality; thus, the highest power obtained was 15.5 times greater than that found in the worst case, these values being consistent with other studies developed. As a theoretical recommendation for practical purposes, one can infer that the ratio H₁/L₁ has a greater influence over the OWC’s available power than the ratio H₂/L₂. Full article

We aimed to improve the available information on morphology and stage for cutaneous melanoma in the population-based cancer registry of the Bucaramanga Metropolitan Area in Colombia. The incidence and survival rates and the distribution of melanoma patients by age, gender, anatomical subsite, and histological subtype were calculated. All 113 melanoma patients (median age 61) were followed up (median time 7.4 years). This exercise (filling in missing information in the registry by manual search of patient clinical record and other available information) yielded more identified invasive melanomas and cases with complete information on anatomical localization and stage. Age-standardized incidence and mortality rates were 1.86 and 1.08, being slightly higher for males. Most melanomas were localized on the lower limbs, followed by the trunk. For 35% of all melanomas, the morphological subtype remained unknown. Most of the remaining melanomas were nodular and acral lentiginous melanomas. Overall global and relative 5-year survival was 61.6% and 71.3%, respectively, with poorer survival for males than females. Melanomas on the head and neck and unspecified anatomical sites had the worst survival. Patients without stage information in their medical files had excellent survival, unlike patients for whom medical files were no longer available. This study shows the possibility of improving data availability and the importance of good quality population-based data. Full article

Linear probing continues to be one of the best practical hashing algorithms due to its good average performance, efficiency, and simplicity of implementation. However, the worst-case performance of linear probing seems to degrade with high load factors due to a primary-clustering tendency of one collision to cause more nearby collisions. It is known that the maximum cluster size produced by linear probing, and hence the length of the longest probe sequence needed to insert or search for a key in a hash table of size n, is $\Omega \left(\log n\right)$, in probability. In this article, we introduce linear probing hashing schemes that employ two linear probe sequences to find empty cells for the keys. Our results show that two-way linear probing is a promising alternative to linear probing for hash tables. We show that two-way linear probing has an asymptotically almost surely $O\left(\log \log n\right)$ maximum cluster size when the load factor is constant. Matching lower bounds on the maximum cluster size produced by any two-way linear probing algorithm are obtained as well. Our analysis is based on a novel approach that uses the multiple-choice paradigm and witness trees. Full article

Given the increasing global energy demand, the present study aimed to analyze the influence of bathymetry on the generation and propagation of realistic irregular waves and to geometrically optimize a wave energy converter (WEC) device of the oscillating water column (OWC) type. In essence, the OWC WEC can be defined as a partially submerged structure that is open to the sea below the free water surface (hydropneumatic chamber) and connected to a duct that is open to the atmosphere (in which the turbine is installed); its operational principle is based on the compression and decompression of air inside the hydropneumatic chamber due to incident waves, which causes an alternating air flow that drives the turbine and enables electricity generation. The computational fluid dynamics software package Fluent was used to numerically reproduce the OWC WEC according to its operational principles, with a simplification that allowed its available power to be determined, i.e., without considering the turbine. The volume of fluid (VOF) multiphase model was employed to treat the interface between the phases. The WaveMIMO methodology was used to generate realistic irregular waves mimicking those that occur on the coast of Tramandaí, Rio Grande do Sul, Brazil. The constructal design method, along with an exhaustive search technique, was employed. The degree of freedom $H_1/L$ (the ratio between the height and length of the hydropneumatic chamber of the OWC) was varied to maximize the available power in the device. The results showed that realistic irregular waves were adequately generated within both wave channels, with and without bathymetry, and that wave propagation in both computational domains was not significantly influenced by the wave channel bathymetry. Regarding the geometric evaluation, the optimal geometry found, ${\left(H_1/L\right)}_o$ = 0.1985, which maximized the available hydropneumatic power, i.e., the one that yielded a power of 25.44 W, was 2.28 times more efficient than the worst case found, which had $H_1/L$ = 2.2789. Full article

With the progress of society and the diversification of transportation modes, people are faced with more and more complicated travel choices, and thus, multi-criteria route choosing optimization problems have drawn increased attention in recent years. A number of multi-criteria traffic network equilibrium problems have been proposed, but most of them do not involve data uncertainty nor computational methods. This paper focuses on the methods for solving robust multi-criteria traffic network equilibrium problems with path capacity constraints. The concepts of the robust vector equilibrium and the robust vector equilibrium with respect to the worst case are introduced, respectively. For the robust vector equilibrium, an equivalent min–max optimization problem is constructed. A direct search algorithm, in which the step size without derivatives and redundant parameters, is proposed for solving this min–max problem. In addition, we construct a smoothing optimization problem based on a variant version of ReLU activation function to compute the robust weak vector equilibrium flows with respect to the worst case and then find robust vector equilibrium flows with respect to the worst case by using the heaviside step function. Finally, extensive numerical examples are given to illustrate the excellence of our algorithms compared with existing algorithms. It is shown that the proposed min–max algorithm may take less time to find the robust vector equilibrium flows and the smoothing method can more effectively generate a subset of the robust vector equilibrium with respect to the worst case. Full article

This perspective is a qualitative meta-analysis study using a critical interpretive synthesis that narrates three future and equally plausible scenarios of social and economic development in the State of Kuwait over the next 15 years. The first scenario follows what we call the ‘Sustainable Growth’ model as defined by the United Nations Development Goals and the Kuwait Vision 2035 presented by the Amir Sheikh Sabah Al-Ahmad Al-Jaber Al-Sabah. As a polar opposite, the next scenario is what we call the ‘Mismanaged Resourced-Based Autocracy’ model, a negative reflection of the worst-case scenario. The third scenario is in between these two, and we call it the ‘Equality of Outcome Between Societal Groups’ model. So as not to lay blame for past actions or point fingers, which could prove counterproductive to a consensus-building process for needed actions, we chose to use the pasts of other countries for future projections for the State of Kuwait. Our search through recent socio-economic pasts revealed that Singapore was the best fit for the first scenario, Venezuela for the second, and Lebanon for the third. All these countries became fully independent at approximately the same time as the State of Kuwait and share many other similarities. The three future projections were used as input variables to the outcome, which was a bottom-up and top-down consensus-making process regarding utilitarian action for Kuwait to be used by Non-Government Organizations (NGOs), Think-Tanks, Development Agencies, the government and the parliament. Full article

Reaching a diagnosis and its communication are two of the most meaningful events in the physician–patient relationship. When facing a disease, most of the patients’ expectations rely on the hope that their clinicians would be able to understand the cause of their illness and eventually end it. Rare diseases are a peculiar subset of conditions in which the search for a diagnosis might reveal a long and painful journey scattered by doubts and requiring, in most cases, a long waiting time. For many individuals affected by a rare disease, turning to research might represent their last chance to obtain an answer to their questions. Time is the worst enemy, threatening to disrupt the fragile balance among affected individuals, their referring physicians, and researchers. It is consuming at all levels, draining economic, emotional, and social resources, and triggering unpredictable reactions in each stakeholder group. Managing waiting time is one of the most burdensome tasks for all the parties playing a role in the search for a diagnosis: the patients and their referring physicians urge to obtain a diagnosis in order to know the condition they are dealing with and establish proper management, respectively. On the other hand, researchers need to be objective and scientifically act to give a rigorous answer to their demands. While moving towards the same goal, patients, clinicians, and researchers might have different expectations and perceive the same waiting time as differently hard or tolerable. The lack of information on mutual needs and the absence of effective communication among the parties are the most common mechanisms of the failure of the therapeutic alliance that risk compromising the common goal of a proper diagnosis. In the landscape of modern medicine that goes faster and claims high standards of cure, rare diseases represent an exception where physicians and researchers should learn to cope with time in order to care for patients. Full article

An effective energy oversight represents a major concern throughout the world, and the problem has become even more stringent recently. The prediction of energy load and consumption depends on various factors such as temperature, plugged load, etc. The machine learning and deep learning (DL) approaches developed in the last decade provide a very high level of accuracy for various types of applications, including time-series forecasting. Accordingly, the number of prediction models for this task is continuously growing. The current study does not only overview the most recent and relevant DL for energy supply and demand, but it also emphasizes the fact that not many recent methods use parameter tuning for enhancing the results. To fill the abovementioned gap, in the research conducted for the purpose of this manuscript, a canonical and straightforward long short-term memory (LSTM) DL model for electricity load is developed and tuned for multivariate time-series forecasting. One open dataset from Europe is used as a benchmark, and the performance of LSTM models for a one-step-ahead prediction is evaluated. Reported results can be used as a benchmark for hybrid LSTM-optimization approaches for multivariate energy time-series forecasting in power systems. The current work highlights that parameter tuning leads to better results when using metaheuristics for this purpose in all cases: while grid search achieves a coefficient of determination ($R^2$) of 0.9136, the metaheuristic that led to the worst result is still notably better with the corresponding score of 0.9515. Full article

Accurate electricity price forecasting (EPF) can provide a necessary basis for market decision making by power market participants to reduce the operating cost of the power system and ensure the system’s stable operation. To address the characteristics of high frequency, strong nonlinearity, and high volatility of electricity prices, this paper proposes a short-term electricity price forecasting model based on a two-layer variational modal decomposition (VMD) technique, using the sparrow search algorithm (SSA) to optimize the long and short-term memory network (LSTM). The original electricity price sequence is decomposed into multiple modal components using VMD. Then, each piece is predicted separately using an SSA-optimized LSTM. For the element with the worst prediction accuracy, IMF-worst is decomposed for a second time using VMD to explore the price characteristics further. Finally, the prediction results of each modal component are reconstructed to obtain the final prediction results. To verify the validity and accuracy of the proposed model, this paper uses data from three electricity markets, Australia, Spain, and France, for validation analysis. The experimental results show that the proposed model has MAPE of 0.39%, 1.58%, and 0.95%, RMSE of 0.25, 0.9, and 0.3, and MAE of 0.19, 0.68, and 0.31 in three different cases, indicating that the proposed model can well handle the nonlinear and non-stationarity characteristics of the electricity price series and has superior forecasting performance. Full article