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This study presents a novel solution for ambient assisted living (AAL) applications that utilizes spiking neural networks (SNNs) and reconfigurable neuromorphic processors. As demographic shifts result in an increased need for eldercare, due to a large elderly population that favors independence, there is a pressing need for efficient solutions. Traditional deep neural networks (DNNs) are typically energy-intensive and computationally demanding. In contrast, this study turns to SNNs, which are more energy-efficient and mimic biological neural processes, offering a viable alternative to DNNs. We propose asynchronous cellular automaton-based neurons (ACANs), which stand out for their hardware-efficient design and ability to reproduce complex neural behaviors. By utilizing the remote supervised method ( R e S u M e ), this study improves spike train learning efficiency in SNNs. We apply this to movement recognition in an elderly population, using motion capture data. Our results highlight a high classification accuracy of 83.4 % , demonstrating the approach's efficacy in precise movement activity classification. This method's significant advantage lies in its potential for real-time, energy-efficient processing in AAL environments. Our findings not only demonstrate SNNs' superiority over conventional DNNs in computational efficiency but also pave the way for practical neuromorphic computing applications in eldercare. [ABSTRACT FROM AUTHOR]

This study validated muscle activation estimations generated by OpenSim during the gait of elderly fallers. Ten healthy elderly participants walked on an instrumented treadmill, monitored by motion capture, force platforms, and 12 surface EMG sensors. Static optimization was used to calculate muscle activations, evaluated through cosine similarity, comparing them with EMG signals from 12 muscles of the right leg. Findings revealed varied similarity levels across muscles and gait phases. During stance phase, tibialis anterior (TIBA), peroneus longus (PERL), soleus (SOL), gastrocnemius lateralis (GASL), semitendinosus (SEMI), tensor fasciae latae (TFL), and rectus femoris (RECF) demonstrated poor similarity (cosim < 0.6), while gluteus medius (GMED), biceps femoris long head (BFLH), and vastus lateralis (VL) exhibited moderate similarity (0.6 ≤ cosim ≤ 0.8), and gluteus maximus (GMAX) and vastus medialis (VASM) displayed high similarity (cosim > 0.8). During the swing phase, only SOL demonstrated inadequate similarity, while GASL, GMAX, GMED, BFLH, SEMI, TFL, RECF, and VASL exhibited moderate similarity, and TIBA, PERL, and VASM showed high similarity. Comparing the different 10% intervals of the gait cycle generally produced more favorable similarity results. For most of the muscles and intervals, good agreement was found. Moderate agreement was estimated in the cases of TIBA (0–10%), PERL (60–70%), GASL (60–70%), TFL (10–20%), RECF (0–10%, 80–100%), and GMED (50–60%). Bad agreement was found in the cases of SOL (60–70%), GASL (0–10%), and TFL (0–10%). In conclusion, the study's validation outcomes were acceptable in most cases, underlining the potential for user-friendly musculoskeletal modeling routines to study muscle output during elderly gait. [ABSTRACT FROM AUTHOR]

An estimation of the electric sources in the heart was conducted using a novel method, based on Huygens' Principle, aiming at a direct estimation of equivalent bioelectric sources over the heart's surface in real time. The main scope of this work was to establish a new, fast approach to the solution of the inverse electrocardiography problem. The study was based on recorded electrocardiograms (ECGs). Based on Huygens' Principle, measurements obtained from the surfaceof a patient's thorax were interpolated over the surface of the employed volume conductor model and considered as secondary Huygens' sources. These sources, being non-zero only over the surface under study, were employed to determine the weighting factors of the eigenfunctions' expansion, describing the generated voltage distribution over the whole conductor volume. With the availability of the potential distribution stemming from measurements, the electromagnetics reciprocity theorem is applied once again to yield the equivalent sources over the pericardium. The methodology is self-validated, since the surface potentials calculated from these equivalent sources are in very good agreement with ECG measurements. The ultimate aim of this effort is to create a tool providing the equivalent epicardial voltage or current sources in real time, i.e., during the ECG measurements with multiple electrodes. [ABSTRACT FROM AUTHOR]

The development of novel hardware computing systems and methods has been a topic of increased interest for researchers worldwide. New materials, devices, and architectures are being explored as a means to deliver more efficient solutions to contemporary issues. Along with the advancement of technology, there is a continuous increase in methods available to address significant challenges. However, the increased needs to be fulfilled have also led to problems of increasing complexity that require better and faster computing and processing capabilities. Moreover, there is a wide range of problems in several applications that cannot be addressed using the currently available methods and tools. As a consequence, the need for emerging and more efficient computing methods is of utmost importance and constitutes a topic of active research. Among several proposed solutions, we distinguish the development of a novel nanoelectronic device, called a "memristor", that can be utilized both for storing and processing, and thus it has emerged as a promising circuit element for the design of compact and energy-efficient circuits and systems. The memristor has been proposed for a wide range of applications. However, in this work, we focus on its use in computing architectures based on the concept of Cellular Automata. The combination of the memristor's performance characteristics with Cellular Automata has boosted further the concept of processing and storing information on the same physical units of a system, which has been extensively studied in the literature as it provides a very good candidate for the implementation of Cellular Automata computing with increased potential and improved characteristics, compared to traditional hardware implementations. In this context, this paper reviews the most recent advancements toward the development of Cellular-Automata-based computing coupled with memristor devices. Several approaches for the design of such novel architectures, called "Memristive Cellular Automata", exist in the literature. This extensive review provides a thorough insight into the most important developments so far, helping the reader to grasp all the necessary information, which is here presented in an organized and structured manner. Thus, this article aims to pave the way for further development in the field and to bring attention to technological aspects that require further investigation. [ABSTRACT FROM AUTHOR]

In this article, taking into consideration the exceptional technological properties of a unique 2-D material, namely Graphene, we are envisioning its usage as the structure material of a non-back-gated re-programmable switching device. The proposed topology is analyzed in depth, not only by verifying its operation and re-programmability as a 2-input XOR, 3-input XOR and 3-input Majority gate, but also by examining its computing performance in terms of area, delay and power dissipation. More specifically, we are utilizing L-shaped Graphene Nanoribbons (GNRs) to develop comb-shaped Graphene based switching devices. These devices are in position with effective programming through biasing to design any combinatorial circuit as resulting from the aforementioned universal set of Boolean gates. The resulting figures of merit regarding the area with a universal footprint of $\text{2.53 nm}^{2}$ for every gate independently of the number of inputs, the propagation delay with $2.05\times {10^{-2}}\;\text{ps}$ and, last but not least, the power dissipation with only $\text{10.204 nW}$ for the gates with greater number of inputs, are quite encouraging and promising. Moreover, the ability of the proposed topology to pave the way towards the implementation of basic circuits has been further investigated, by demonstrating an example of a 1-bit full adder cell and its sufficient operation arriving from the corresponding successful SPICE simulation results. [ABSTRACT FROM AUTHOR]

Nowadays, the huge power consumption and the inability of the conventional circuits to deal with real-time classification tasks have necessitated the devising of new electronic devices with inherent neuromorphic functionalities. Resistive switching memories arise as an ideal candidate due to their low footprint and small leakage current dissipation, while their intrinsic randomness is smoothly leveraged for implementing neuromorphic functionalities. In this review, valence change memories or conductive bridge memories for emulating neuromorphic characteristics are demonstrated. Moreover, the impact of the device structure and the incorporation of Pt nanoparticles is thoroughly investigated. Interestingly, our devices possess the ability to emulate various artificial synaptic functionalities, including paired-pulsed facilitation and paired-pulse depression, long-term plasticity and four different types of spike-dependent plasticity. Our approach provides valuable insights from a material design point of view towards the development of multifunctional synaptic elements that operate with low power consumption and exhibit biological-like behavior. [ABSTRACT FROM AUTHOR]

Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication. [ABSTRACT FROM AUTHOR]

This study presents an application that estimates all possible paths within an urban environment and proposes the optimal one according to objective criteria and individualized options. The application grounds its functionality on a Cellular Automaton (CA) that models the area of interest and provides the rule that activates the processing of input data. The model incorporates the term walkability as a discrete magnitude that integrates qualitative features of pedestrian mobility. The proposed application fosters sustainable forms of movement at a local level that undoubtedly improves the human physical status and upgrades civilian living conditions. In the context of this paper, we provide a thorough description of the operational principles of the model, and we test its validity by applying it in Xanthi, a medium size city located in northeastern Greece. Finally, we describe variants of the initial scenario to demonstrate the sensibility of the model in external assessments as well as its resilience in biasing manipulations. [ABSTRACT FROM AUTHOR]

Computational acceleration for image processing tasks on the edge is becoming increasingly important for many applications. This work presents a new neuro-inspired architecture which incorporates inmemory computing properties for image processing complex computational tasks in addition to analog memory. The proposed architecture was based on segmented crossbar topology, which, as it turns out, reduces many phenomena that affect the performance on such systems. The extended architectural capabilities of this structure were also tested in a systematic analysis that was performed on a novel depth map extraction application from a single defocused image. All results were validated through Spice simulations using a novel moving barrier memristor model. [ABSTRACT FROM AUTHOR]

Unconventional and, specifically, wave computing has been repeatedly studied in laboratory based experiments by utilizing chemical systems like a thin film of Belousov–Zhabotinsky (BZ) reactions. Nonetheless, the principles demonstrated by this chemical computer were mimicked by mathematical models to enhance the understanding of these systems and enable a more detailed investigation of their capacity. As expected, the computerized counterparts of the laboratory based experiments are faster and less expensive. A further step of acceleration in wave-based computing is the development of electrical circuits that imitate the dynamics of chemical computers. A key component of the electrical circuits is the memristor which facilitates the non-linear behavior of the chemical systems. As part of this concept, the road-map of the inspiration from wave-based computing on chemical media towards the implementation of equivalent systems on oscillating memristive circuits was studied here. For illustration reasons, the most straightforward example was demonstrated, namely the approximation of Boolean gates. [ABSTRACT FROM AUTHOR]

One of the most famous problems in computer science and cellular automata (CAs) is the Firing Squad Synchronization Problem (FSSP). In the FSSP, a one-dimensional CA with just a single active cell (initially called "General") eventually reaches a state in which all the CA cells (called "soldiers" in analogy to real-world firing squad) are simultaneously active. Since the introduction of the FSSP as a well known problem in the 60's, and during the last decades, many FSSP solution algorithms have been proposed in the literature. Nevertheless, the inherited error correction capabilities of these algorithms have not been extensively studied. In this paper, an error detection and correction algorithm that utilizes the patterns appearing in 1-D Mazoyer's 8-state solution is analyzed and presented. Furthermore, the patterns of 2-D Umeo's FSSP algorithm are also thoroughly investigated. In such a case, extra patterns emerge from CA cells grouping, during the application of 1-D FSSP solution to 2-D CA grid, enhancing the error tolerance of the proposed algorithm, i.e. almost every possible error can be detected. Finally, another version, even more efficient, of error detection and correction algorithm with memory effect considering previous CA states usage, is also introduced. Although the usage of previous CA states results to increased utilization of computational resources, at the same time it enables the proposed algorithm to detect every possible error state in every possible CA cell, advancing significantly the algorithm's performance. [ABSTRACT FROM AUTHOR]

Resistive Random Access Memory (ReRAM) is a promising novel memory technology for non-volatile storing, with low-power operation and ultra-high area density. However, ReRAM memories still face issues through commercialization, mainly owing to the fact that the high fabrication variations and the stochastic switching of the manufactured ReRAM devices cause high Bit Error Rate (BER). Given that ReRAM devices are nonlinear elements, the nonlinear phenomenon of Stochastic Resonance (SR), which defines that an input disturbance with specific characteristics can improve the total performance of the nonlinear system, is used to reduce the ReRAM cell’s BER. Thus, in this brief, the BER of a single ReRAM cell is explored, using the Stanford PKU model, and is improved after the application of specific additive input noise. The power dissipation of the proposed approach is also evaluated and compared with the consideration of higher amplitude writing pulses in the lack of noise, showing that the proposed noise-induced technique can decrease the BER without the excessive increase of the power dissipation. As a first step, towards the experimental verification of the proposed method, noise-induced measurements on a single fabricated ReRAM device are also performed. Overall, the presented results of the BER reduction with low power dissipation, reaching up to 3.26 × less power consumption considering 100 ns writing pulses, are encouraging for ReRAM designers, delivering a circuit-level solution against the device-level problem. [ABSTRACT FROM AUTHOR]