Aimé Lay-Ekuakille’s research while affiliated with University of Salento and other places

The paper introduces a novel inertial trigger sensor featuring a multi-layer stacked single-arm brass beam pendulum, aimed at applications in automotive and aerospace fields, etc. It employs the Lagrangian equation for dynamic modeling and finite element analysis to achieve precise inertia force measurements. The design incorporates advanced fabrication techniques and a high-precision latching mechanism that combines circuit self-locking with mechanical triggering. Validated through simulations and testing, The results of the live ammunition tests indicate that, apart from failures of the Inertia Switch due to errors in the manufacturing process, all other inertial switches functioned normally. This demonstrates that the inertial sensor provides an adjustable trigger time of 0–5 ms and reliably withstands extreme overloads of up to 15000 g. This work significantly enhances the performance of inertial sensors in high-stress environments.

This article presents a proposal for fault-tolerant task mapping on many-core processors to enhance system performance and reduce communication energy. The proposed algorithm maps tasks onto a 2-D mesh network-on-chip (NoC) and a modified NoC (MNoC) platform. The focus of this article is primarily on addressing permanent faults. In the scenario of a permanent fault within the mapped core, the algorithm also proposes a spare core placement strategy. This involves allocating the spare core based on considerations related to communication energy. The proposed task mapping algorithm underwent evaluation using various benchmarks, including multimedia and synthetic benchmarks. The results were then compared to those obtained from a 2-D mesh NoC and three related algorithms, all under the same task graph and NoC size. The simulation results showed that the proposed mapping algorithm on the modified NoC platform leads to improved performance and communication energy reductions when compared to the 2-D mesh NoC and the other three algorithms. To validate the proposed fault-tolerant task mapping algorithm on the modified NoC platform, A Field Programmable Gate Array (FPGA) was used to measure performance metrics such as application runtime, area, and on-chip power consumption in both faulty and non-faulty conditions. The hardware results indicated significant improvements when comparing the proposed FTTM on MNoC and 2-D NoC with existing approaches.

With regard to the development of semiconductor technology, SFET becomes an instrument aimed at satisfying the needs in equipment with steep switching characteristics. SFETs utilize the spin degree of freedom for electrons and spin currents unlike in case of FETs that comprise of traditional. In this section, we outline the design principles of the SFET with particular emphasis on its special features that are related to voltage and current waveforms that lead to steep switchings. Instead, leveraging on the same concept of spin injection and manipulation, the SFET switches rapidly between ON and OFF modes. The high intrinsic spin functionality leads to reduced electron scattering resulting in enhanced electron mobility as well as rapid switching speeds. Using the concept of spin property as a basis for electron opens up new way leading to best performances ever witnessed.

Feedback Field-Effect Transistors (FBFETs) function through a positive feedback loop, where electrons and holes in the channel region impact the energy states of the potential barrier and wall. This positive feedback mechanism results in notable characteristics for FBFETs, including an impressive subthreshold swing of approximately 0 mV/decade at 300 K, a high on-/off-current ratio of around 10^10, and a well-defined saturation region. The power consumption in both the turn-on and turn-off states remains remarkably low until the device is actively operating. Furthermore, the hysteresis induced by carriers accumulated in the potential wall allows FBFETs to serve as memory devices. Additionally, FBFETs exhibit the potential to significantly reduce power consumption in neuromorphic devices, by approximately 100 times. This study delves into the analysis of the FBFET's device structure and operational principles, providing a comprehensive overview of its applications.

IOTA is a Distributed Ledger Technology (DLT) designed for the Internet of Things (IoT), featuring feeless transactions and data-only blocks for information sharing between IoT devices. Its ledger, the Tangle, adopts a Directed Acyclic Graph structure (DAG) that enables it to scale while keeping secure, fitting with the needs of IoT growth. Most of literature work address the behavior and assess the performance of the Tangle by using simulated data, only few of them are based on empirical dataset. Moreover, to our best knowledge, no research has been previously carried out on the evolution of the Tangle at its current Stardust stage using empirical data. Our study conducts a comprehensive analysis of the Tangle at the Stardust stage using real data provided by IOTA Foundation. Our key findings are that the confirmation of new blocks is two times faster in the Stardust stage, and the Tangle is currently being used far below its capacity to carry and secure data from IoT devices. Our study is the first to shed light on the characteristics of the real Tangle in the Stardust stage, which are particularly useful to understand the evolution of the ledger and contribute to its improvements in future upgrades.

The Dielectrically Modulated Full Gate Tunnel Field Effect Transistor (FET) with dual nanocavities, as described in the paper, is a novel device designed as a label-free biosensor for detecting cancer cell biomolecules. This biosensor utilizes the principles of field-effect transistors and incorporates nanocavities to enhance the detection sensitivity. The simulations are conducted using the Silvaco Atlas model, which allowed for the analysis of the device’s electrical characteristics in the presence of various cancer cell biomolecules. The performance of the proposed device is evaluated using several sensing metrics, including current, threshold voltage, and subthreshold slope. These metrics are examined to assess their sensitivity to the presence of different cancer cell biomolecules. By analyzing these electrical characteristics, we can able to determine the device’s ability to detect and differentiate between specific biomolecules associated with cancer cells. One important aspect discussed in the paper is the incorporation of nanocavities in the device design. These nanocavities have a significant impact on enhancing the sensing capabilities of the biosensor. The paper also introduces the concept of the filling factor parameter, which describes the fraction of the nanocavity volume occupied by the cancer cell biomolecules. This parameter plays a crucial role in achieving optimal sensing performance. Overall, the paper presents a comprehensive analysis of the proposed Dielectrically Modulated Full gate Tunnel FET embedded with dual nanocavities as a label-free biosensor for cancer cell biomolecules. The simulations conducted using the Silvaco Atlas model provide valuable insights into the device’s electrical characteristics and its sensitivity to different biomolecules. The study emphasizes the significance of nanocavities and their filling factor parameter in achieving enhanced sensing performance for cancer cell detection.

Nanomedicine, in particular the use nanoparticles for drug delivery (DD), is an increasing field of development and advancement. The influence and the interference of magnetic field about delivery action is an important field of investigation. Vaccines and specific drugs for known and rare pathologies can get benefits from this approach. The main goal of the work is to provide a modelling that can support the know-how of the impact of a magnetic field about the delivery of drug allocated on Nanoparticles (NPs), thus defining guidelines to measure the delivery function for generic biocompatible NPs. In particular, by means of Finite Element Method (FEM) approach, the impact magnetic field, on gold nanoparticle (GN), used in nanomedicine for DD process, is analysed to facilitate the knowledge of the delivery mechanisms. The study is addressed to the estimation of the magnetic field distribution on the GNs interface, supporting the efficacy of the magnetic field stimulation. The paper is concluded by discussing measurement approaches useful to define guidelines. The main goal of the paper is to provide a methodology to understand DD phenomena in nanotechnology using a magnetic field as stimuli, by defining at the same time tools and approaches tailoring a measurement protocol in nanotechnology.

Robotic beds are an advanced version of automatic beds, often used for physical rehabilitation with minimal impact on neurophysiology. These devices play a crucial role in aiding bedridden individuals by helping them regain upright positions, particularly through verticalization, which improves vascularization by increasing oxygen levels in the blood. This enhanced oxygenation is key for detecting pathologies associated with biomechanical stress. By adjusting the bed to various angular orientations, the sound of breathing can be analyzed to indirectly quantify pulmonary activity. This paper presents new findings on feature extraction from breathing sounds by leveraging the verticalization angles of medical robotic beds. We compare traditional methods with artificial intelligence-based techniques, showing that both approaches yield improved results. For clarity, we focus on data collected at a 60° angle, which facilitates accurate diagnosis for individual patients. Notably, robotic beds enable the detection of breathing abnormalities, offering a new avenue for bypassing conventional rehabilitation methods.

Machine learning (ML) is a well-known subfield of artificial intelligence (AI) that aims at developing algorithms and statistical models able to empower computer systems to automatically adapt to a specific task through experience or learning from data [...]