One of the most frequently used approaches to represent collaborative mapping are probabilistic occupancy grid maps. These maps can be exchanged and integrated among robots to reduce the overall exploration time, which is the main advantage of the collaborative systems. Such map fusion requires solving the unknown initial correspondence problem. This article presents an effective feature-based map fusion approach that includes processing the spatial occupancy probabilities and detecting features based on locally adaptive nonlinear diffusion filtering. We also present a procedure to verify and accept the correct transformation to avoid ambiguous map merging. Further, a global grid fusion strategy based on the Bayesian inference, which is independent of the order of merging, is also provided. It is shown that the presented method is suitable for identifying geometrically consistent features across various mapping conditions, such as low overlapping and different grid resolutions. We also present the results based on hierarchical map fusion to merge six individual maps at once in order to constrict a consistent global map for SLAM.

The mammalian auditory system is able to process sounds over an extraordinarily large dynamic range, which makes it possible to extract information from very small changes both in sound amplitude and frequency. Evidently, response of the cochlea is essentially nonlinear, where it operates within Hopf bifurcation boundaries to maximize tuning and amplification. This paper presents a set of piecewise linear (PWL) and multiplierless piecewise linear (MLPWL1 and MLPWL2) active cochlear models, which mimic a range of behaviors, similar to the biological cochlea. These proposed models show similar dynamical characteristics of the Hopf equation for the active nonlinear artificial cochlea. Accordingly, a compact model structure is proposed upon which a 2-D cochlea is developed. The proposed models are investigated, in terms of their digital realization and hardware cost, targeting large scale implementation. Hardware synthesis and physical implementation on a FPGA show that the proposed models can reproduce precise active cochlea behaviors with higher performance and considerably lower computational costs in comparison with the original model. Results indicate that the MLPWL1 model has a lower computational overhead, precision, and hardware cost, while the PWL model has a higher precision and dynamically tracks the original model. On the other hand, the MLPWL2 model outperforms the others in terms of accuracy, dynamical tracking of the original model and implementation cost. The gain variations of the original, PWL, MLPWL1, and MLPWL2 models are 230, 100, 105, and 230 dB, respectively. The mean normalized root mean square errors (NRMSEs) of the PWL, MLPWL1, and MLPWL2 models are 0.11%, 11.97%, and 0.34%, respectively, as compared to the original cochlear model.

Memristor as an emerging history dependent nanometer scaled element will play an important role in future nanoelectronic computing technologies. Some pure and hybrid memristor-based implementation techniques have been proposed in recent years. Material implication logic is one of the significant areas for memristor-based logic implementation. In this paper a memristor-based linear feedback shift register is implemented based on material implication logic. It is implemented by 8 memristors which is considerably used less area in comparison with conventional CMOS-based peers. Also the proposed memristor-based LFSR circuit needs 55 computational steps for generating a 4-bits number.

The World Health Organization reports approximately 1.35 million fatalities annually due to road traffic accidents, with pedestrians constituting 23% of these deaths. This highlights the critical need to enhance pedestrian safety, especially given the significant role human error plays in road accidents. Autonomous vehicles present a promising solution to mitigate these fatalities by improving road safety through advanced prediction of pedestrian behavior. With the autonomous vehicle market projected to grow substantially and offer various economic benefits, including reduced driving costs and enhanced safety, understanding and predicting pedestrian actions and intentions is essential for integrating autonomous vehicles into traffic systems effectively. Despite significant advancements, replicating human social understanding in autonomous vehicles remains challenging, particularly in predicting the complex and unpredictable behavior of vulnerable road users like pedestrians. Moreover, the inherent uncertainty in pedestrian behavior adds another layer of complexity, requiring robust methods to quantify and manage this uncertainty effectively. This review provides a structured and in-depth analysis of pedestrian intention prediction techniques, with a unique focus on how uncertainty is modeled and managed. We categorize existing approaches based on prediction duration, feature type, and model architecture, and critically examine benchmark datasets and performance metrics. Furthermore, we explore the implications of uncertainty types—epistemic and aleatoric—and discuss their integration into autonomous vehicle systems. By synthesizing recent developments and highlighting the limitations of current methodologies, this paper aims to advance the understanding of Pedestrian intention Prediction and contribute to safer and more reliable autonomous vehicle deployment.

Human–robot interaction is of the utmost importance as it enables seamless collaboration and communication between humans and robots, leading to enhanced productivity and efficiency. It involves gathering data from humans, transmitting the data to a robot for execution, and providing feedback to the human. To perform complex tasks, such as robotic grasping and manipulation, which require both human intelligence and robotic capabilities, effective interaction modes are required. To address this issue, we use a wearable glove to collect relevant data from a human demonstrator for improved human–robot interaction. Accelerometer, pressure, and flexi sensors were embedded in the wearable glove to measure motion and force information for handling objects of different sizes, materials, and conditions. A machine learning algorithm is proposed to recognize grasp orientation and position, based on the multi-sensor fusion method.