Search Results (593)

The inherent bottleneck of the von Neumann architecture and the limited power budget of edge devices necessitate energy-efficient hardware solutions for artificial intelligence. Memristor-based In-Memory Computing (IMC) has emerged as a promising candidate; however, the high-power consumption of peripheral circuits, particularly Analog-to-Digital Converters (ADCs), and the reliability issues of memristive devices remain significant challenges. In this paper, we propose a hybrid Convolutional Spiking Neural Network (CSNN) architecture designed for resource-constrained edge computing. Our approach integrates digital Non-Leaky Integrate-and-Fire (NLIF) neurons with Knowm Self-Directed Channel (SDC) memristor-based synapses in a 1T1R crossbar array. To maximize power efficiency, we replace conventional high-resolution ADCs with a streamlined readout circuit utilizing a Current Sense Amplifier (CSA) and a 1-bit comparator. Furthermore, we employ an intensity-to-latency temporal coding scheme to minimize spike activity and mitigate device endurance degradation. We validated the proposed system using the MNIST dataset, achieving a classification accuracy of 97.8%, which is comparable to state-of-the-art floating-point SNNs using supervised learning methods. Power analysis confirms that our 1-bit readout method consumes only 18.4% of the energy required by an 8-bit ADC-based approach while maintaining negligible accuracy loss. Additionally, the deterministic single-spike nature of our temporal coding significantly reduces write stress on memristors compared to rate coding. These results demonstrate that the proposed hybrid CSNN offers a robust and energy-efficient solution for neuromorphic edge intelligence. Full article

Efficient energy harvesting from open-channel flows offers a sustainable solution for powering distributed sensing systems in water infrastructure. This study investigates a piezoelectric wake-excited membrane vortex-induced vibration (VIV) energy harvester through a combined numerical and mechanical approach. The device features an upstream cylindrical bluff body that generates a periodic vortex street, exciting a downstream flexible membrane equipped with surface-mounted piezoelectric patches. A one-way coupled CFD–FEM framework implemented in ANSYS was employed to assess the effects of membrane length, material stiffness, and flow conditions on hydrodynamic loading, structural deformation, and deformation power. Results show that membrane length mainly affects oscillation amplitude and force levels, whereas material stiffness has a stronger influence on membrane deformation and RMS mechanical power. Among the investigated materials, low-stiffness polyethylene yields the highest deformation power, while none of the analysed configurations reaches a full lock-in condition within the explored parameter range. Complementary mechanical analysis revealed that the stiffness of commercial piezoelectric patches significantly reduces local strain, thereby constraining the practically harvestable energy in the present baseline configuration. Spectral power density analysis identified the dominant shedding frequency and its harmonics, confirming that the flow response is governed by a coherent periodic excitation. These findings highlight key design trade-offs in wake-excited membrane harvesters and provide useful guidance for the future optimisation of self-powered hydraulic monitoring systems. Full article

Beyond supporting ultra-high-capacity data transmission, metropolitan and access networks are expected to enable real-time infrastructure monitoring, driving the emergence of integrated sensing and communication (ISAC). Distributed acoustic sensing (DAS) has proven to be well-suited to urban sensing application requirements, yet its seamless integration into ISAC remains challenging—conventional high-peak-power sensing pulses in DAS induce nonlinear crosstalk in communication channels. DAS inherently suffers from interference fading due to single-frequency laser sources, which limits sensitivity. Here, we propose an ISAC architecture based on an electro-optic (EO) comb and a 7-core fiber, achieving nonlinearity-suppressed self-homodyne transmission and fading-suppressed DAS. Unmodulated comb lines and sensing pulses are polarization-multiplexed into orthogonal polarization states within the central core to minimize nonlinear crosstalk while delivering local oscillators (LOs) for wavelength division multiplexing (WDM) coherent transmission within six outer cores—achieving 10.56 Tbit/s capacity. In addition to supporting WDM transmission, the EO comb’s wavelength diversity is also exploited to enhance DAS performance. Specifically, a dual-pulse probe loaded onto four comb lines yields a 6 dB signal-to-noise ratio gain and a 64% reduction in fading occurrences, achieving a sensitivity of 1.72 $p\epsilon /\sqrt{\mathrm{Hz}}$ with 8 m spatial resolution. Moreover, our system supports simultaneous multi-wavelength backscatter detection in sensing and simplified digital signal processing in self-homodyne communication, reducing receiver complexity and cost. Our work presents a scalable, energy-efficient ISAC framework that unifies high-capacity communication with high-sensitivity sensing, providing a blueprint for future intelligent optical networks. Full article

Frequency-Modulated Continuous-Wave (FMCW) millimeter-wave (mmWave) radars, originally developed for automotive applications, can be also explored for environmental sensing due to their compact size, low cost, and robustness under adverse environmental conditions. However, measurements obtained from commercial automotive radars are often affected by environmental noise and intrinsic self-interference caused by coupling between transmitting and receiving patch antennas, which can degrade the reliability of relative power-based range profiles. In this paper, the performance of the AWR1843BOOST FMCW mmWave radar from Texas Instruments is investigated, with particular emphasis on noise due to antenna coupling. A sub-optimal post-processing technique based on Noise Environmental Measurement (NEM) removal is proposed to remove both deterministic noise, associated with antenna coupling, and stochastic noise, related to environmental contributions. The proposed approach is validated through controlled laboratory experiments involving different targets characterized by distinct dielectric properties, including a metallic object, an absorbing object, and a target with varying degrees of wetness. The experimental results demonstrate that the NEM removal technique significantly enhances the clarity of the backscattered target’s relative power, preserving differences between target values, and improves the radar’s sensitivity to material properties and water content. Measurements accomplished at the electromagnetic and remote sensing laboratory of the Università degli Studi di Napoli Parthenope confirmed the soundness of the proposed NEM removal technique and the sensitivity of the AWR radar to the dielectric properties of targets. Full article

For communities on the margins of hostile or indifferent power structures, the political order can be experienced as a force whose acts are not motivated by reasons in accord with recognizable norms. Power, then, as a social phenomenon, is naturalized in the sense that it is dehumanized. Derrida explored some of this territory in his final seminar, the Beast and the Sovereign. Power becomes a latent animality, structuring social life as it removes itself from mechanisms of accountability. At the same time, the Black church ritual, in the United States and elsewhere, provides an experience of a self-sustaining power, whose invisibility is taken as coextensive with its omnipresence. The act of worship becomes a project of counter-habituation whereby power can be constituted as just and life-affirming. Simone Weil’s spiritual writings on the necessity of God’s love can be of some assistance here, but her concern with “decreation” is on its face a self-erasing theological enterprise, the sociopolitical implications of which would seem to put it at odds with a movement, among marginalized people, toward increased recognition. A look at the relation between Weil’s writing method—which I analyze as a kind of endophrasis—and Edmund Husserl’s transcendental understanding of the self provides a way to reorganize our understanding of the sociocultural project supported by the ritual. To grasp the counter-habituating project of the ritual, we must see it as founded in non-linguistic thinking and post-linguistic acts. These acts are, in part, improvisational, which is a key to habituating the recognition of higher-order necessity through free activity. They bring the worshiper “through” culturally determined linguistic acts to another kind of experience, in which the freedom to worship an invisible God is manifest. Full article

Chronic and infected wounds remain a major clinical challenge due to their dynamic microenvironments and the lack of real-time diagnostic feedback in conventional dressings. Recent advances in stimuli-responsive nanomaterial-based biosensors have enabled the development of smart wound-care systems capable of continuous monitoring and on-demand therapeutic intervention. This review systematically summarizes progress in nanomaterial-enabled wound biosensing strategies, with a focus on pH, reactive oxygen species, and temperature nanosensors, which serve as key indicators of infection, inflammation, and healing status. We discuss the sensing mechanisms and functional roles of diverse nanomaterials. A particular focus is placed on emerging multimodal and theranostic platforms which integrate biochemical and physical sensing with controlled drug release, photothermal or photodynamic therapy, and redox regulation. These systems represent a shift from passive wound monitoring toward closed-loop, adaptive wound management. Also, future perspectives are outlined, highlighting the convergence of nanomaterials, self-powered electronics, and intelligent data processing as a pathway toward personalized and precision wound care. Full article

This study presents a sustainable, battery-free UV (ultraviolet) dose sensor designed for intelligent food packaging applications. The device integrates laser-induced graphene (LIG) electrodes, a ZnO-CNT (carbon nanotube) UV-active composite, and a bio-derived ionochromic cell composed of blueberry anthocyanins and a NaCl electrolyte. This work advances the platform by introducing a quantitative and predictive dose–color mapping framework for cumulative UV detection under zero-bias operation. A controlled charge-injection protocol was employed to emulate UV-generated photocurrent, enabling systematic investigation of charge-transfer-driven ionochromic kinetics across five current levels (0.2–3 mA). HSB (hue–saturation–brightness)-based colorimetric analysis was performed to quantify the time-dependent chromatic evolution, and a numerical fitting model was developed to map charge accumulation to color shifts. Using this calibration, the color response at microampere-level photocurrents—corresponding to real zero-bias UV operation—can be predicted. The resulting model enables estimation of the cumulative time required for the ionochromic cell to transition from red to purple under realistic UV intensities. By combining self-powered sensing with predictive colorimetric modeling, this work significantly enhances the functionality of battery-free UV indicators, enabling quantitative dose measurement without external electronics for safer food-supply-chain monitoring. Full article

This work presents a compact, self-powered wireless CO₂ sensing node for autonomous environmental monitoring. The system integrates a high-efficiency multijunction photovoltaic (PV) module, a 4000 F hybrid supercapacitor operating at 3.6–4.2 V, and a custom power management system in a LiPo-sized form factor. The PV module, composed of nine parallel triple-junction solar cells, achieves an average efficiency of 27% and delivers peak power at 4.26 V under 600 W/m² irradiance. The sensing unit includes miniaturized CO₂, humidity, and temperature sensors with LoRa-based wireless communication. The low-power NDIR CO₂ sensor provides a resolution of 15–20 ppm and a response time of ~45 s. Week-long tests demonstrated fully autonomous operation with reliable 5 min data transmission, capturing diurnal CO₂ variations associated with plant activity even under low irradiance. Energy storage occurs for irradiance levels ≥65 W/m², and long-term simulations confirm stable supercapacitor voltage over yearly cycles. This work demonstrates a compact multijunction solar–hybrid supercapacitor platform capable of sustaining WSN for long-term, maintenance-free CO₂ monitoring under real-world and low-irradiance conditions. Our results demonstrate that the sensing node can reliably monitor plant-driven CO₂ dynamics, clearly resolving the expected photosynthesis–respiration cycles and their dependence on incident solar radiation, while simultaneously sustaining its energy budget under highly challenging illumination and transmission conditions. Full article

Unmanned aerial vehicle (UAV) swarms can form sparse virtual antenna arrays (VAAs) for airborne sensing and communications, but their beamforming performance is highly vulnerable to quasi-static formation drift and the limited number of snapshots available within each coherent processing interval. This paper proposes a beacon-aided self-calibration and robust beamforming framework for narrowband UAV-swarm uplinks in strong-interference, low-snapshot regimes. We consider one signal of interest (SOI) and multiple co-channel interferers characterized by their coarse direction-of-arrival (DOA) information. The key idea is to exploit a single dominant non-SOI emitter as a strong calibration source (beacon) to learn the quasi-static geometry drift from data. First, the beacon spatial signature is extracted from the sample covariance matrix via eigenvector–steering-vector alignment, and a correlation-based gate is used to decide whether geometry calibration is reliable. When the gate is passed, the inter-UAV position drift is estimated from element-wise steering ratios to build a calibrated array manifold. Second, using the calibrated steering vectors and coarse DOA information, the interference-plus-noise covariance matrix (INCM) is reconstructed through a low-dimensional non-negative power fitting with mild diagonal loading. Finally, a geometry-aware minimum-variance distortionless response (MVDR) beamformer is designed based on the reconstructed INCM. Simulations on coprime-inspired UAV formations with a single dominant interferer show that the proposed scheme recovers most of the SINR loss caused by geometry mismatch and consistently outperforms baseline MVDR, worst-case MVDR, a recent covariance-reconstruction baseline, and URGLQ in the low-snapshot regime. For example, in a representative setting with $N_{\mathrm{uav}}=7$, ${\sigma }_p=0.10$, ${\mathrm{INR}}_c=30$ dB, and $L=10$, the proposed method achieves approximately 14 dB output SINR at ${\mathrm{SNR}}_{\mathrm{in}}=10$ dB, outperforming nominal SCM-MVDR by about 13 dB and approaching a genie-aided MVDR bound within a few dB, while retaining a computational complexity comparable to standard MVDR. Full article

Low-frequency acoustic fields—common in ventilation ducts, building façades, and industrial infrastructure—remain an underutilized source for ambient energy harvesting, particularly in humid environments where conventional contact-based or mechanically resonant harvesters may degrade over time. This study introduces a theoretical framework for converting acoustic pressure oscillations into electrical power through acoustic–electrokinetic coupling and proposes the Acoustic Baroionic Harvester (ABH) as a solid-state concept combining a Helmholtz resonator with a charged nanoporous membrane. The model is derived from coupled electrokinetic and fluid-mechanical governing relations, leading to closed-form expressions for the open-circuit voltage, internal electrokinetic resistance, and maximum deliverable power as functions of membrane surface charge, electrolyte properties, pore geometry, and resonance-induced pressure amplification. Numerical simulations are performed to validate the analytical scaling laws and to determine operating regimes that maximize power transfer to an external load. Under representative low-frequency acoustic excitation, the ABH predicts open-circuit voltages on the order of tens of millivolts and maximum power densities in the sub-microwatt-per-square-centimeter range. A compact CAD conceptual design tuned to approximately 120 Hz with a moderate resonance quality factor supports the feasibility of practical integration. The proposed approach enables micro-power generation from persistent low-frequency acoustic sources and provides a physically grounded pathway for self-powered sensing applications in built and industrial environments. Full article

Achieving precise sliding perception is crucial for enhancing human–machine interactions. Despite the extensive investigation of tactile sensors for static pressure detection, they still face challenges in detecting dynamic information such as sliding direction, speed, pressure and position in interactive touch scenarios. Herein, we propose a self-powered tactile interface that realizes motion-to-electricity generation by electrostatically regulating the carrier concentration and transport in the semiconductive layer with a top gate in sliding movement. This tactile sliding interface can distinguish various dynamic mechanical information by generating voltage signals related to the sliding direction, speed, pressure, and touch position without external bias voltage. By combining machine-learning algorithms, electrical signals of six representative sliding-touch interactions were accurately classified with a recognition accuracy of 98.33%. Furthermore, by integrating sensors into the smartphone’s side button, customizable functions such as volume control, screen unlocking, and music switching were achieved. This work provides an innovative mechanism for sliding sensing in interactive electronic and intelligent control systems. Full article

Satellite–terrestrial spectrum sensing plays a crucial role in enhancing spectrum efficiency through reusing spectra. However, in a satellite–terrestrial converged system, the large SNR range, non-Gaussian signal characteristics and noise uncertainty pose significant challenges for spectrum sensing. In this paper, we investigate a downlink spectrum sensing framework where multi-terrestrial BSs act as a secondary system to sense idle satellite spectra through a multi-domain feature-level sensing signal fusion. To enhance the characterization of signal/noise features, we provide a fusion strategy of multi-features including energy, power spectral density, cyclic autocorrelation function, higher-order moments, sparse ratio, and I/Q samples, constructing two feature tensors of statistical features and an I/Q component. Then, we propose a deep-learning-enabled multi-feature fusion spectrum sensing method (DL-MFFSSnet) based on a dual-branch deep neural network architecture with the constructed two feature tensors as inputs. In the statistical feature processing branch, CNN and channel self-attention are incorporated to capture intra-channel correlations and inter-channel relative contributions of different feature modalities. In the I/Q branch, multi-scale dilated convolutions and spatial self-attention are introduced to analyze dependencies across different temporal positions and multi-scale spatial features. The feature map extracted from both branches passed through fully connected layers for deepwise feature fusion, achieving accurate spectrum sensing. Extensive simulation results demonstrate that the DL-MFFSSnet method outperforms the existing state-of-the-art algorithms. Full article

To address the challenges of traditional flexible humidity sensors, such as reliance on external power supply, complex fabrication processes, and poor adaptability to energy-limited scenarios, this study successfully developed a low-cost, easily scalable, self-powered flexible humidity sensor based on hydroxypropyl trimethyl ammonium chitosan/lithium chloride (HACC/LiCl) composite electrolyte using a screen-printing process. The device employs A4 paper as the flexible substrate, and interdigitated manganese dioxide (MnO₂) positive electrodes, zinc (Zn) negative electrodes, and HACC/LiCl composite electrolyte layers are sequentially fabricated via screen-printing, ultimately constructing a simple primary battery structure. Through a series of performance screening and optimization, 0.1 mol/L LiCl-modified HACC (HL-1) is identified as the optimal electrolyte system. The test results show that the HL-1 sensor exhibits a wide humidity detection range of 11~97% relative humidity (RH), with the output voltage displaying a good quadratic function relationship with humidity (R² = 0.996), and a peak output voltage of up to 1.2 V. The device possesses excellent cyclic stability and long-term stability, with no significant fluctuation in output voltage under different bending deformation states. This sensor demonstrates broad application prospects in fields such as respiratory monitoring and non-contact sensing, providing a feasible technical path for the development of low-cost passive humidity monitoring equipment. Full article

Background/Objectives: Creativity has become an essential skill for children and adolescents to cope with the challenges of contemporary society. Beyond academic success, creativity is closely linked to well-being, social adjustment, and personal development. Schools, therefore, play a crucial role in creating conditions that allow students to explore ideas, express themselves, and develop socioemotional resources. This study aimed to examine how self-perceived creativity relates to educational, socioemotional, and well-being factors in Portuguese students, to identify different creativity profiles, and to explore the main variables that predict creativity. Methods: This cross-sectional study was based on secondary analyses of national data from the project Psychological Health and Well-being|School Observatory. The sample included 3011 students aged between 9 and 20 years (M = 13.62; SD = 2.53), from grades 5 to 12. Data were collected using validated instruments: the OECD Socioemotional Skills Survey (SSES), the Positive Youth Development (PYD) scale, and the WHO-5 Well-Being Index. Analyses included group comparisons, cluster analysis to identify self-perceived creativity profiles, correlation analyses, and multiple regression models. Results: Self-perceived creativity did not differ between boys and girls, but it decreased significantly with higher grade levels. Three profiles were identified: low, medium, and high self-perceived creativity. Students with higher self-perceived creativity reported better well-being, more positive relationships with teachers, a stronger sense of belonging at school, and higher parental educational levels. Self-perceived creativity was positively associated with socioemotional skills such as curiosity, sociability, and optimism, as well as with PYD dimensions and well-being. Negative associations were found with age and test anxiety. Socioemotional variables were the strongest predictors of creativity, explaining 39% of its variance. Conclusions: These results show that creativity is closely connected to students’ socioemotional development. Investing in emotional skills, supportive relationships, and positive school environments may be a powerful way to foster creativity and promote healthier, more balanced development. This has important implications for educational practice and policy. Full article

Triboelectric nanogenerators (TENGs) have been gaining significant attention owing to their excellent energy conversion efficiency and their integration towards a large number of practical applications in energy harvesting, wearables, and self-powered sensing. In recent advancements, the utilization of flexible triboelectric composite films can help to enhance the TENG’s electrical output performance, as they possess excellent mechanical and dielectric properties and tunable surface characteristics. Moreover, by combining flexible active layers with triboelectric nanogenerators, the advantages of each component result in sensor devices which offer superior characteristics, including high sensitivity, biocompatibility, less weight, and mechanical flexibility. This review mainly focuses on the applications of TENGs in mechanical energy harvesting, self-powered wearable sensor systems, as well as the latest research progress in the TENG field. The working principles of TENG will be first explained in detail, including four basic operational modes of TENG, simulation results, and the working mechanism of the contact–separation mode TENGs. The fabrication techniques of triboelectric flexible films, along with TENG construction, will then be introduced. Common applications of TENGs are based on mechanical energy harvesting and powering portable electronic devices, which will subsequently be classified and summarized. Additionally, the applications of various wearable and self-powered sensor applications are elucidated. Finally, the current limitations and future directions of the TENG will be explained in detail and proposed. By exploring these innovations, the review underscores the importance of triboelectric flexible film-based TENGs in driving the future of energy harvesting and sensor technologies. Full article