(1) Background. Neurofeedback has been used in sports since the 1990s, frequently showing positive outcomes in enhancing athletic performance. This systematic review provides an updated analysis of neurofeedback training in sports, evaluating reaction time, cognitive performance, and emotional regulation to address literature gaps and suggest future research directions. (2) Methods. A systematic search was conducted using PubMed, Scopus, Science Direct, and Web of Science databases for articles published from January 2016 to April 2023. The search included only original articles written in English, resulting in 24 studies meeting the inclusion criteria. (3) Results. The reviewed studies cover a wide range of sports, including golf, basketball, swimming, rifle shooting, football, volleyball, athletics, judo, ice hockey, triathlon, handball, fencing, taekwondo, and darts. They involved athletes of varying experience levels (beginners, professionals, and experts) and utilized neurofeedback training targeting different frequency bands (alpha, beta, theta, and SMR), either individually or in mixed protocols. Findings show improvements in sports and cognitive performance, emotional regulation, and anxiety management. (4) Conclusions. This systematic review supports the effectiveness of neurofeedback in enhancing sports and cognitive performance across various disciplines and experience levels. Notable improvements were observed in technical skills, physical performance parameters, scoring, attention, concentration, reaction time, short-term and working memory, self-regulation, and cognitive anxiety. Future research should standardize protocols, include more diverse samples, and explore long-term effects to further validate these findings.

In the framework of indoor air monitoring, this paper proposes an Internet of Things ready solution to detect and classify contaminants. It is based on a compact and low-power integrated system including both sensing and processing capabilities. The sensing is composed of a sensor array on which electrical impedance measurements are performed through a microchip, named SENSIPLUS, while the processing phase is mainly based on Machine Learning techniques, embedded in a low power and low resources micro controller unit, for classification purposes. An extensive experimental campaign on different contaminants has been carried out and raw sensor data have been processed through a lightweight Multi Layer Perceptron for embedded implementation. More complex and computationally costly Deep Learning techniques, as Convolutional Neural Network and Long Short Term Memory, have been adopted as a reference for the validation of Multi Layer Perceptron performance. Results prove good classification capabilities, obtaining an accuracy greater than 75% in average. The obtained results, jointly with the reduced computational costs of the solution, highlight that this proposal is a proof of concept for a pervasive IoT air monitoring system.

Localizing objects in indoor environments with anchor-based systems poses several challenges to be faced. The main one is the design phase. What is the minimum number of anchors to be adopted? What is the optimal placement of the anchors in the operating domain? These are typical issues that need to be solved in this stage. Such issue is generally addressed by several experimental tests, imposing high costs both in the required large amounts of time and in the use of devices. In addition, sometimes the experiments will not warrant to identify the optimal design solution. In this work, a black-box design tool is proposed, able to manage different environments, in terms of domain size, maximum number of available anchors, device metrological features, and target performance. Such an approach, to be effective, needs a preliminary validation versus experimental results to make the future performance predictions reliable and avoid unexpected localization accuracy degradation. For this reason, the presented work adopts a side-by-side development, by evaluating results’ compatibility, in static and dynamic contexts, before addressing further and more complex analyses relying on the design tool only. Such a comparison proves the goodness of the developed design tool, and the outcomes allow an accurate localization system design by finding anchors’ placement solutions with the minimum computational burden.

(1) Background: Neurofeedback training (NFT) has emerged as a promising approach for enhancing cognitive functions and reducing anxiety, yet its specific impact on university student populations requires further investigation. This study aims to examine the effects of NFT on working memory improvement and anxiety reduction within this demographic. (2) Methods: A total of forty healthy university student volunteers were randomized into two groups: an experimental group that received NFT and a control group. The NFT protocol was administered using a 14-channel Emotiv Epoc X headset (EMOTIV, Inc., San Francisco, CA 94102, USA) and BrainViz software version Brain Visualizer 1.1 (EMOTIV, Inc., San Francisco, CA 94102, USA), focusing on the alpha frequency band to target improvements in working memory and reductions in anxiety. Assessment tools, including the Corsi Block and Memory Span tests for working memory and the State-Trait Anxiety Inventory-2 (STAI-2) for anxiety, were applied pre- and post-intervention. (3) Results: The findings indicated an increase in alpha wave amplitude in the experimental group from the second day of NFT, with statistically significant differences observed on days 2 (p < 0.05) and 8 (p < 0.01). Contrary to expectations based on the previous literature, the study did not observe a concurrent positive impact on working memory. Nonetheless, a significant reduction in state anxiety levels was recorded in the experimental group (p < 0.001), corroborating NFT’s potential for anxiety management. (4) Conclusions: While these results suggest some potential of the technique in enhancing neural efficiency, the variability across different days highlights the need for further investigation to fully ascertain its effectiveness. The study confirms the beneficial impact of NFT on reducing state anxiety among university students, underscoring its value in psychological and cognitive performance enhancement. Despite the lack of observed improvements in working memory, these results highlight the need for continued exploration of NFT applications across different populations and settings, emphasizing its potential utility in educational and therapeutic contexts.

Cyber security is one of the most crucial aspects of the Internet of Things (IoT). Among the possible threats, great interest is today paid toward the possible capturing of information caused by external attacks on both client and server sides. Whatever the IoT application, the involved nodes are exposed to cyberattacks mainly through the vulnerability of either the sensor nodes themselves (if they have the capabilities for networking operativity) or the IoT gateways, which are the devices able to create the link between the local nodes of the IoT network, and the wide area networks. Due to the low-cost constraints typical of many IoT applications, the IoT sensor nodes and IoT gateways are often developed on low-performance processing units, in many cases customized for the specific application, and thus not easy to update against new cyber threats that are continuously identified. In the framework of cyberattacks aimed at capturing sensitive information, one of the most known was the heartbleed, which, has allowed attackers to remotely read protected memory from an estimated 24–55 percent of popular HTTPS sites. To overcome such a problem, which was due to a bug of the OpenSSL, a suitable patch was quickly released, thus allowing to avoid the problem in most of the cases. However, IoT devices may require more advanced mitigation techniques, because they are sometimes unable to be patched for several practical reasons. In this scenario, the paper proposes a novel measurement method for inline detecting intrusions due to heartbleed and heartbleed-like attacks. The proposed solution is based on an effective rule which does not require decoding the payload and that can be implemented on a low-performance general-purpose processing unit. Therefore, it can be straightforwardly implemented and included in either IoT sensor nodes or IoT gateways. The realized system has been tested and validated on a number of experiments carried out on a real network, showing performance comparable (in some cases better) with the heavier machine learning-based methods.