Search

Understanding human emotions and behavior in closed environments is essential for creating more empathetic and humane spaces. Environmental factors, such as temperature, noise, and light, play a crucial role in influencing behavior, but individuals' emotional states are equally important and often go unnoticed. Artificial Intelligence of Behavior (AIoB) offers a novel approach that integrates environmental measurements with human emotions to create spatially adaptive processes that can influence behavior. In this article, we present a new human emotion sensor developed using video cameras and implemented on a System on Chip (SoC) development board. Our approach uses Convolutional Neural Networks (CNNs) to recognize the presence of emotions in enclosed spaces and generate parameters that can influence emotional states and behavior within an AIoB system. The research successfully integrates advanced CNN technology into a System on Chip (SoC) platform, allowing for real-time processing of video data. The versatility of utilizing an energy-efficient SoC extends its application to smart environments aimed at improving mental health. By employing algorithms capable of detecting emotional states across various individuals, the study enhances its effectiveness. Additionally, it identifies the best CNN operations tailored to the technical specifications of the devices involved. Thus, The development involves a three-step process: (i) collecting enough data to build a robust model, (ii) training the model and evaluating its performance using test values, and (iii) applying the model on the development board. Our study demonstrates the feasibility of using AIoB to recognize and respond to human emotions in closed areas. By integrating emotional cues with environmental measurements, our system can create more personalized and empathetic spaces that cater to the needs of individuals. Our approach could have significant implications for designing public spaces to promote well-being and emotional satisfaction.

Precision Agriculture (PA) is gaining new momentum due to its ability to accurately adjust the resources to a crop’s needs while maintaining/enhancing quality levels. However, crop-damaging pests compromise yields, jeopardizing the benefits of PA. Computer vision-based pest detection techniques offer promising avenues to overcome potential losses for farmers. The recent object detection framework, YOLOv8 (You Only Look Once) applied to real-time insect monitoring is an open-source, cutting-edge PA approach based on Convolutional Neural Network (CNN) models that enables precise and quick decision making in agricultural crops. Under this umbrella, traditional pest studies using YOLO or other deep-learning solutions focused on only one or a few insects for specific crops provide an excessively narrow solution. In this paper, we propose a new form of using YOLO for pest detection with a generalist perspective by intensively testing a YOLOv8-based tool implementing a single insect category. The goal is to detect the presence of any type of insect in any type of crop in real time. A comprehensive performance evaluation is carried out using a well-known dataset. The results of the training, validation, and testing phases are then discussed, obtaining an $mAP_{50}$ value of 0.967 for the m model and an $mAP_{50-95}$ value of 0.632 for the l model. Finally, we also identify the premises to elaborate a complete and useful dataset able to unleash the full potential of YOLOv8.

Managing red–green–blue (RGB) lighting conditions within structures may evoke emotions and positively influence behavior. Intelligent RGB lighting systems based on environmental data measurements can substantially enhance the perception of comfort. This study presents a challenge that requires a holistic and integrated approach to implement an automatic RGB artificial lighting control system that can be utilized in various structures and indoor environments. Initially, the challenge spans the identification of environmental variables directly impacting comfort up to the careful selection of suitable sensors. The result is the development of a sophisticated and autonomous system that can adjust RGB lighting in real time, creating environments that are both comfortable and energy-efficient. This automated system fosters the creation of appropriate atmospheres across different contexts. The identification and monitoring of environmental variables are achieved through a neuro-fuzzy control mechanism, where fuzzy rules and membership functions are defined based on late positive potential timings and the influence of artificial lighting on human emotions. The outcomes from this study are an interconnected system capable of performing both online and offline operations to enable the monitoring of environmental variables and the efficient management of artificial lighting based on these metrics. A pilot study, with reference to an EEG wave registry system, yielded significant results. These tests had a statistically relevant result with an average frequency of approximately 9.8 Hz, indicative of a state of comfort among people. Despite a 10% deviation margin, 87% of measurements during the test remained consistent. This research study contributes to human behavior by fostering a relaxing environment and enabling a reduction in energy consumption through the use of efficient lighting. Moreover, the environment intention enables the creation of stimuli in three emotional states: activation, relaxation, and neutral, allowing behavioral adaptation to an intention to occur automatically in fluctuating environmental conditions.

Nonlinear acoustics is a critical area of study with practical applications in fields such as underwater communications, medical imaging, non-destructive testing, and sonar. This paper offers a comprehensive analysis of the Westervelt and Burgers equations, along with their related boundary problems, and investigates the characteristics of parametric generation, thereby making substantial advancements in the theoretical comprehension of nonlinear acoustic waves. Our analysis sheds new light on the dynamics of nonlinear acoustic waves and their behavior in various media, providing valuable insights into the physics of sound propagation. Finally, parametric effects can be intelligently exploited for communication applications. Thus, through the appropriate selection of encodings, it is possible to develop underwater acoustic communication systems with greater directivity and range than classical systems.

We are witnessing the full integration of the Internet of Things (IoT) into many social and economic sectors. Part of this unprecedented growth is due to the emergence of new communication technologies such as Low Power Wide Area Networks (LPWAN), which have been the catalyst for previously unfeasible smart applications. Efforts to optimize energy consumption in these types of networks have been necessary to extend their lifetime. However, not much attention has been paid to the study and optimization of the carbon footprints (CF) of these network deployments. In general, it has always been understood that minimizing energy consumption should also minimize the carbon footprint. In this work, the carbon footprint of a generic IoT network that uses renewable energy sources and communicates via LoRa is explored, and an optimization framework is proposed. We have found that minimizing energy consumption and the carbon footprint are two different things. In fact, we show that it is not possible to minimize the carbon footprint without greater energy consumption, and vice versa. This is due to the placement of gateways in the network. Our findings could be extrapolated to other networks with similar topologies. These results suggest that a fresh perspective on the optimization of IoT networks is needed to seriously consider environmental sustainability criteria that has been ignored up to now.

Background Computer Generated Animations (CGA), when applied to three-dimensional (3D) city models (3DCM), can be used as powerful tools to support urban decision-making. This leads to a new paradigm, based on procedural modeling, that allows the integration of known urban structures. This paper introduces a new workflow for the development of high-quality approximations of urban models in a short time and enables facilities to be imported from other cities into a given city model, following specific generation rules. Thus, this workflow provides a very simple approach to observe, study, and simulate the implementation of models already developed in other cities, in a city where they are not yet adopted. Examples of these models include all types of mobility systems and urban infrastructure. This allows us to perceive the environmental impact of certain decisions in the real world, as well as to carry out simple simulations to determine the changes that can occur in the flows of people, traffic, and other city activities.

Piezoelectric transducers are widely used for generating acoustic energy, and choosing the right radiating element is crucial for efficient energy conversion. In recent decades, numerous studies have been conducted to characterize ceramics based on their elastic, dielectric, and electromechanical properties, which have improved our understanding of their vibrational behavior and aided in the manufacturing of piezoelectric transducers for ultrasonic applications. However, most of these studies have focused on the characterization of ceramics and transducers using electrical impedance to obtain resonance and anti-resonance frequencies. Few studies have explored other important quantities such as acoustic sensitivity using the direct comparison method. In this work, we present a comprehensive study that covers the design, manufacturing, and experimental validation of a small-sized, easy-to-assemble piezoelectric acoustic sensor for low-frequency applications, using a soft ceramic PIC255 from PI Ceramic with a diameter of 10 mm and a thickness of 5 mm. We present two methods, analytical and numerical, for sensor design, followed by experimental validation, allowing for a direct comparison of measurements with simulated results. This work provides a useful evaluation and characterization tool for future applications of ultrasonic measurement systems.

Flow-guided nanocommunication networks comprise a new paradigm that enables among other things, new medical applications for monitoring, information gathering, and data transmission inside the human body. Although promising, this type of network is conceived to operate in a very hostile environment (e.g. blood circulatory system) where nano-node mobility, along with reduced coverage hinder successful transmissions. In addition to these concerns, it is necessary to take into account the scarce amount of energy available in nano-nodes and the important limitations to harvesting energy. Thus, it is essential to carefully select network parameters to tailor network design and improve performance or satisfy specific application requirements and objectives. To this end, this article presents an optimization framework to maximize throughput by selecting the appropriate frame size as a function of the available energy, nano-router location, and transmission rate. This optimization is conducted for different scenarios where the study can reveal whether a general setup that can work in almost all situations is desirable or, depending on the application, a pre-setting customization of network parameters before deployment is more advantageous in terms of performance. Maximizing throughput ensures information transmission and establishes the maximum amount of data that a nano-router can receive. These two questions are critical for the development of reliable medical applications that can operate within the capabilities that flow-guided nano-networks can offer. Thus, the main technical contributions of this paper are: i) the extension of the flow-guided nano-network analytical model, and ii) the derivation of a throughput optimization framework for this type of networks, which provides a suitable frame size as a function of different network parameters such as flow velocity, nano-router location, and transmission power.

Terahertz-based nano-networks are emerging as a groundbreaking technology able to play a decisive role in future medical applications owing to their ability to precisely quantify figures, such as the viral load in a patient or to predict sepsis shock or heart attacks before they occur. Due to the extremely limited size of the devices composing these nano-networks, the use of the Terahertz (THz) band has emerged as the enabling technology for their communication. However, the characteristics of the THz band, which strictly reduce the communication range inside the human body, together with the energy limitations of nano-nodes make the in-body deployment of nano-nodes a challenging task. To overcome these problems, we propose a novel in-body flow-guided nano-network architecture consisting of three different devices: i) nano-node, ii) nano-router, and iii) bio-sensor. As the performance of this type of nano-network has not been previously explored, a theoretical framework capturing all its particularities is derived to properly model its behavior and evaluate its feasibility in real medical applications. Employing this analytical model, a thorough sensitivity study of its key parameters is accomplished. Finally, we analyze the terahertz flow-guided nano-network design to satisfy the requirements of several medical applications of interest.

Deficient air quality in industrial environments creates a number of problems that affect both the staff and the ecosystems of a particular area. To address this, periodic measurements must be taken to monitor the pollutant substances discharged into the atmosphere. However, the deployed system should also be adapted to the specific requirements of the industry. This paper presents a complete air quality monitoring infrastructure based on the IoT paradigm that is fully integrable into current industrial systems. It includes the development of two highly precise compact devices to facilitate real-time monitoring of particulate matter concentrations and polluting gases in the air. These devices are able to collect other information of interest, such as the temperature and humidity of the environment or the Global Positioning System (GPS) location of the device. Furthermore, machine learning techniques have been applied to the Big Data collected by this system. The results identify that the Gaussian Process Regression is the technique with the highest accuracy among the air quality data sets gathered by the devices. This provides our solution with, for instance, the intelligence to predict when safety levels might be surpassed.

In this paper, the automation of the evacuation process of a military ship is studied in real time. For this purpose, a scenario is reconfigured to produce a failure or damage. Then, an optimal network of alternative escape routes is computed. The resulting escape route map can be indicated by lighting the appropriate corridors on the ship. Through these corridors, the members of the embarked population and the entire process is monitored so that the crew can reach their lifeboats in the shortest possible time. To undertake this automated process, the dynamic ship evacuation monitoring system (SIMONE, from its acronym in Spanish: Sistema de Monitorización Dinámica de Evacuación de Buques) has been developed. This system integrates a communication gateway with the integrated platform control system (IPCS) and integrated lighting system that will be installed in new Spanish naval constructions.

Continuous progress of nanocommunications and nano-networking is opening the door to the development of innovative yet unimaginable services, with a special focus on medical applications. Among several nano-network topologies, flow-guided nanocommunication networks have recently emerged as a promising solution to monitoring, gathering information, and data communication inside the human body. In particular, flow-guided nano-networks display a number of specific characteristics, such as the type of nodes comprising the network or the ability of a nano-node to transmit successfully, which significantly differentiates them from other types of networks, both at the nano and larger scales. This paper presents the first analytical study on the behavior of these networks, with the objective of evaluating their metrics mathematically. To this end, a theoretical framework of the flow-guided nano-networks is developed and an analytical model derived. The main results reveal that, due to frame collisions, there is an optimal number of nano-nodes for any flow-guided network, which, as a consequence, limits the maximum achievable throughput. Finally, the analytical results obtained are validated through simulations and are further discussed.

Future wireless communications are heading towards an all-Internet Protocol (all-IP) design, and will rely on the Session Initiation Protocol (SIP) to manage services, such as voice over IP (VoIP). The centralized architecture of traditional SIP has numerous disadvantages for mobile ad hoc services that may be possibly overcome by advanced peer-to-peer (P2P) technologies initially developed for the Internet. In the context of mobile sensing, P2PSIP protocols facilitate decentralized and fast communications with sensor-enabled terminals. Nevertheless, in order to make P2PSIP protocols feasible in mobile sensing networks, it is necessary to minimize overhead transmissions for signaling purposes, which reduces the battery lifetime. In this paper, we present a solution to improve the management of wireless overlay networks by defining an adaptive algorithm for the calculation of refresh time. The main advantage of the proposed algorithm is that it takes into account new parameters, such as the delay between nodes, and provides satisfactory performance and reliability levels at a much lower management overhead than previous approaches. The proposed solution can be applied to many structured P2P overlays or P2PSIP protocols. We evaluate it with Kademlia-based distributed hash tables (DHT) and dSIP.

The extension of the network lifetime of Wireless Sensor Networks (WSN) is an important issue that has not been appropriately solved yet. This paper addresses this concern and proposes some techniques to plan an arbitrary WSN. To this end, we suggest a hierarchical network architecture, similar to realistic scenarios, where nodes with renewable energy sources (denoted as primary nodes) carry out most message delivery tasks, and nodes equipped with conventional chemical batteries (denoted as secondary nodes) are those with less communication demands. The key design issue of this network architecture is the development of a new optimization framework to calculate the optimal assignment of renewable energy supplies (primary node assignment) to maximize network lifetime, obtaining the minimum number of energy supplies and their node assignment. We also conduct a second optimization step to additionally minimize the number of packet hops between the source and the sink. In this work, we present an algorithm that approaches the results of the optimization framework, but with much faster execution speed, which is a good alternative for large-scale WSN networks. Finally, the network model, the optimization process and the designed algorithm are further evaluated and validated by means of computer simulation under realistic conditions. The results obtained are discussed comparatively.