An essential characteristic that an exploration robot must possess is to be autonomous. This is necessary because it will usually do its task in remote or hard-to-reach places. One of the primary elements of a navigation system is the information that can be acquired by the sensors of the environment in which it will operate. For this reason, an algorithm based on convolutional neural networks is proposed for the detection of rocks in environments similar to Mars. The methodology proposed here is based on the use of a Single-Shot-Detector (SSD) network architecture, which has been modified to evaluate the performance. The main contribution of this study is to provide an alternative methodology to detect rocks in planetary images because most of the previous works only focus on classification problems and used handmade feature vectors.

Affective computing aims to develop systems capable of effectively interacting with people through emotion recognition. Neuroscience and psychology have established models that classify universal human emotions, providing a foundational framework for developing emotion recognition systems. Brain activity related to emotional states can be captured through electroencephalography (EEG), enabling the creation of models that classify emotions even in uncontrolled environments. In this study, we propose an emotion recognition model based on EEG signals using deep learning techniques on a proprietary database. To improve the separability of emotions, we explored various data transformation techniques, including Fourier Neural Networks and quantum rotations. The convolutional neural network model, combined with quantum rotations, achieved a 95% accuracy in emotion classification, particularly in distinguishing sad emotions. The integration of these transformations can further enhance overall emotion recognition performance.

A Kalman filter can be used to fill space–state reconstruction dynamics based on knowledge of a system and partial measurements. However, its performance relies on accurate modeling of the system dynamics and a proper characterization of the uncertainties, which can be hard to obtain in real-life scenarios. In this work, we explore how the values of a Kalman gain matrix can be estimated by using spiking neural networks through a combination of biologically plausible neuron models with spike-time-dependent plasticity learning algorithms. The performance of proposed neural architecture is verified with simulations of some representative nonlinear systems, which show promising results. This approach traces a path for its implementation in neuromorphic analog hardware that can learn and reconstruct partial and changing dynamics of a system without the massive power consumption that is typically needed in a Von Neumann-based computer architecture.

Robot navigation allows mobile robots to navigate among obstacles without hitting them and reaching the specified goal point. In addition to preventing collisions, it is also essential for mobile robots to sense and maintain an appropriate battery power level at all times to avoid failures and non-fulfillment with their scheduled tasks. Therefore, selecting the proper time to recharge the batteries is crucial to address the navigation algorithm design for the robot’s prolonged autonomous operation. In this paper, a machine learning algorithm is used to ensure the extended robot autonomy based on a reinforcement learning method combined with a fuzzy inference system. The proposal enables a mobile robot to learn whether to continue through its path toward the destination or modify its course on the fly, if necessary, to proceed toward the battery charging station, based on its current state. The proposal performs a flexible behavior to choose an action that allows a robot to move from a starting to a destination point, guaranteeing battery charge availability. This paper shows the obtained results using an approach with thirty-six states and its reduction with twenty states. The conducted simulations show that the robot requires fewer training epochs to achieve ten consecutive successes in the fifteen proposed scenarios than traditional reinforcement learning methods exhibit. Moreover, in four scenarios, the robot ends up with a battery level above 80%, that value is higher than the obtained results with two deterministic methods.

In computer vision, human action recognition is a hot topic, popularized by the development of deep learning. Deep learning models typically accept video input without prior processing and train them to achieve recognition. However, conducting preliminary motion analysis can be beneficial in directing the model training to prioritize the motion of individuals with less priority for the environment in which the action occurs. This paper puts forth a novel methodology for human action recognition based on motion information that employs transfer-learning techniques. The proposed method comprises four stages: (1) human detection and tracking, (2) motion estimation, (3) feature extraction, and (4) action recognition using a two-stream model. In order to develop this work, a customized dataset was utilized, comprising videos of diverse actions (e.g., walking, running, cycling, drinking, and falling) extracted from multiple public sources and websites, including Pexels and MixKit. This realistic and diverse dataset allowed for a comprehensive evaluation of the proposed method, demonstrating its effectiveness in different scenarios and conditions. Furthermore, the performance of seven pre-trained models for feature extraction was evaluated. The models analyzed were Inception-v3, MobileNet-v2, MobileNet-v3-L, VGG-16, VGG-19, Xception, and ConvNeXt-L. The results demonstrated that the ConvNeXt-L model yielded the most optimal outcomes. Furthermore, using pre-trained models for feature extraction facilitated the training process on a personal computer with a single graphics processing unit, achieving an accuracy of 94.9%. The experimental findings and outcomes suggest that integrating motion information enhances action recognition performance.

Analog neuromorphic prototyping is essential for designing and testing spiking neuron models that use memristive devices as synapses. These prototypes can have various circuit configurations, implying different response behaviors that custom silicon designs lack. The prototype’s behavior results can be optimized for a specific foundry node, which can be used to produce a customized on-chip parallel deep neural network. Spiking neurons mimic how the biological neurons in the brain communicate through electrical potentials. Doing so enables more powerful and efficient functionality than traditional artificial neural networks that run on von Neumann computers or graphic processing unit-based platforms. Therefore, on-chip parallel deep neural network technology can accelerate deep learning processing, aiming to exploit the brain’s unique features of asynchronous and event-driven processing by leveraging the neuromorphic hardware’s inherent parallelism and analog computation capabilities. This paper presents the design and implementation of a leaky integrate-and-fire (LIF) neuron prototype implemented with commercially available components on a PCB board. The simulations conducted in LTSpice agree well with the electrical test measurements. The results demonstrate that this design can be used to interconnect many boards to build layers of physical spiking neurons, with spike-timing-dependent plasticity as the primary learning algorithm, contributing to the realization of experiments in the early stage of adopting analog neuromorphic computing.