Search Results (500)

Energy harvesting technology allows Internet of Things (IoT) devices to be powered continuously without needing battery charging or replacement. In addressing existing and emerging massive IoT energy supply challenges, this paper presents the design of multi-sourced multiple input and multiple output (MIMO) multiband hybrid wireless RF-perovskite photovoltaic energy harvesting subsystems for IoT application. The research findings evaluate the efficiency and power output of different RF configurations (1 to 16 antennas) within MIMO RF subsystems. A Delon quadruple rectifier in the RF energy harvesting system demonstrates a system-level power conversion efficiency of 51%. The research also explores the I-V and P-V characteristics of the adopted perovskite tandem cell. This results in an impressive array capable of producing 6.4 V and generating a maximum power of 650 mW. For the first time, the combined mathematical modelling of the system architecture is presented. The achieved efficiency of the combined system is 90% (for 8 MIMO) and 98% (for 16 MIMO) at 0 dBm input RF power. This novel study holds great promise for next-generation 5G/6G smart IoT passive electronics. Additionally, it establishes the hybrid RF-perovskite energy harvester as a promising, compact, and eco-friendly solution for efficiently powering IoT devices in smart cities. This work contributes to the development of sustainable, scalable, and smart energy solutions for IoT integration into smart city infrastructures. Full article

Single-stranded DNA (ssDNA) viruses are a diverse group of pathogens with broad host range, including bacteria, archaea, protists, fungi, plants, invertebrates, and vertebrates. Their small compact genomes have evolved to encode multiple proteins. This review focuses on the structure and functional diversity of the icosahedral capsids across the ssDNA viruses. To date, X-ray crystallography and cryo-electron microscopy structural studies have provided detailed capsid architectures for 8 of the 35 ssDNA virus families, illustrating variations in assembly mechanisms, symmetry, and structural adaptations of the capsid. However, common features include the conserved jelly-roll motif of the capsid protein and strategies for genome packaging, also showing evolutionary convergence. The ever-increasing availability of genomic sequences of ssDNA viruses and predictive protein modeling programs, such as using AlphaFold, allows for the extension of structural insights to the less-characterized families. Therefore, this review is a comparative analysis of the icosahedral ssDNA virus families and how the capsid proteins are arranged with different tessellations to form icosahedral spheres. It summarizes the current knowledge, emphasizing gaps in the structural characterization of the ssDNA capsidome, and it underscores the importance of continued exploration to understand the molecular underpinnings of capsid function and evolution. These insights have implications for virology, molecular biology, and therapeutic applications. Full article

Large Language Models (LLMs), like GPT and BERT, have significantly advanced Natural Language Processing (NLP), enabling high performance on complex tasks. However, their size and computational needs make LLMs unsuitable for deployment on resource-constrained devices, where efficiency, speed, and low power consumption are critical. Tiny Language Models (TLMs), also known as BabyLMs, offer compact alternatives by using advanced compression and optimization techniques to function effectively on devices such as smartphones, Internet of Things (IoT) systems, and embedded platforms. This paper provides a comprehensive survey of TLM architectures and methodologies, including key techniques such as knowledge distillation, quantization, and pruning. Additionally, it explores potential and emerging applications of TLMs in automation and control, covering areas such as edge computing, IoT, industrial automation, and healthcare. The survey discusses challenges unique to TLMs, such as trade-offs between model size and accuracy, limited generalization, and ethical considerations in deployment. Future research directions are also proposed, focusing on hybrid compression techniques, application-specific adaptations, and context-aware TLMs optimized for hardware-specific constraints. This paper aims to serve as a foundational resource for advancing TLMs capabilities across diverse real-world applications. Full article

This paper introduces a fully integrated memristive chaotic circuit, which is based on a voltage-controlled oscillator (VCO). The circuit employs a fully integrated architecture that offers reduced power consumption and a smaller footprint compared to the use of discrete components. Specifically, the VCO is utilized to generate the oscillatory signal, whereas the memristor emulator circuit serves as the nonlinear element. The memristor emulator circuit is constructed using a single operational transconductance amplifier (OTA), two transistors, and a grounded capacitor. This straightforward design contributes to diminished power usage within the chip’s area. The VCO incorporates a dual delay unit and implements current compensation to enhance the oscillation frequency and to broaden the VCO’s tunable range. Fabricated using the SMIC 180 nm CMOS process, this chaotic circuit occupies a mere 0.0072 mm² of chip area, demonstrating a design that is both efficient and compact. Simulation outcomes indicate that the proposed memristor emulator is capable of operating at a maximum frequency of 300 MHz. The memristive chaotic circuit is able to produce a chaotic oscillatory signal with an operational frequency ranging from 158 MHz to 286 MHz, powered by a supply of 0.9 V, and with a peak power consumption of 3.5553 mW. The Lyapunov exponent of the time series within the resultant chaotic signal spans from 0.2572 to 0.4341. Full article

The piezoelectric grating voltage sensor has garnered significant attention in the realm of intelligent sensing, attributed to its compact size, cost-effectiveness, robust electromagnetic interference (EMI) immunity, and high network integration capabilities. In this paper, we propose a PZT-FBG (piezoelectric ceramic–fiber Bragg grating) voltage–temperature demodulation optical path architecture. This scheme effectively utilizes the originally unused temperature compensation reference grating, repurposing it as a temperature measurement grating. By employing FBGs with identical or similar parameters, we experimentally validate two distinct optical path connection schemes, before and after optimization. The experimental results reveal that, when the input voltage ranges from 250 V to 1800 V at a frequency of 50 Hz, the goodness of fit for the three fundamental waveforms is 0.996, 0.999, and 0.992, respectively. Furthermore, the sensor’s frequency response was tested across a frequency range of 50 Hz to 20 kHz, demonstrating that the measurement system can effectively respond within the sensor’s operational frequency range. Additionally, temperature measurement experiments showed a goodness of fit of 0.997 for the central wavelength of the FBG as the temperature increased. This research indicates that the improved optical path connection method not only accomplishes a synchronous demodulation of both temperature and voltage parameters but also markedly enhances the linearity and resolution of the voltage sensor. This discovery offers novel insights for further refining sensor performance and broadening the applications of optical voltage sensors. Full article

The presence of non-tobacco-related materials can significantly compromise the quality of tobacco. To accurately detect non-tobacco-related materials, this study introduces a lightweight and real-time detection model derived from the YOLOv11 framework, named LRNTRM-YOLO. Initially, due to the sub-optimal accuracy in detecting diminutive non-tobacco-related materials, the model was augmented by incorporating an additional layer dedicated to enhancing the detection of small targets, thereby improving the overall accuracy. Furthermore, an attention mechanism was incorporated into the backbone network to focus on the features of the detection targets, thereby improving the detection efficacy of the model. Simultaneously, for the introduction of the SIoU loss function, the angular vector between the bounding box regressions was utilized to define the loss function, thus improving the training efficiency of the model. Following these enhancements, a channel pruning technique was employed to streamline the network, which not only reduced the parameter count but also expedited the inference process, yielding a more compact model for non-tobacco-related material detection. The experimental results on the NTRM dataset indicate that the LRNTRM-YOLO model achieved a mean average precision (mAP) of 92.9%, surpassing the baseline model by a margin of 4.8%. Additionally, there was a 68.3% reduction in the parameters and a 15.9% decrease in floating-point operations compared to the baseline model. Comparative analysis with prominent models confirmed the superiority of the proposed model in terms of its lightweight architecture, high accuracy, and real-time capabilities, thereby offering an innovative and practical solution for detecting non-tobacco-related materials in the future. Full article

Recent research efforts to mitigate the burden of biofouling in marine environments have focused on the development of environmentally friendly coatings that can provide long-lasting protective effects. In this study, the antifouling performance of novel polyurethane (PU)-based coatings containing cyclam-based Fe(III) complexes against Cobetia marina biofilm formation was investigated. Biofilm assays were performed over 42 days under controlled hydrodynamic conditions that mimicked marine environments. Colony-forming units (CFU) determination and flow cytometric (FC) analysis showed that PU-coated surfaces incorporating 1 wt.% of complexes with formula [{R₂(^4-CF3PhCH₂)₂Cyclam}FeCl₂]Cl (R = H, HOCH₂CH₂CH₂) significantly reduced both culturable and total cells of C. marina biofilms up to 50% (R = H) and 38% (R = HOCH₂CH₂CH₂) compared to PU-coated surface without complexes (control surface). The biofilm architecture was further analyzed using Optical Coherence Tomography (OCT), which showed that biofilms formed on the PU-coated surfaces containing cyclam-based Fe(III) complexes exhibited a significantly reduced thickness (58–61% reduction), biovolume (50–60% reduction), porosity (95–97% reduction), and contour coefficient (77% reduction) compared to the control surface, demonstrating a more uniform and compact structure. These findings were also supported by Confocal Laser Scanning Microscopy (CLSM) images, which showed a decrease in biofilm surface coverage on PU-coated surfaces containing cyclam-based Fe(III) complexes. Moreover, FC analysis revealed that exposure to PU-coated surfaces increases bacterial metabolic activity and induces ROS production. These results underscore the potential of these complexes to incorporate PU-coated surfaces as bioactive additives in coatings to effectively deter long-term bacterial colonization in marine environments, thereby addressing biofouling-related challenges. Full article

Photonic integrated circuits (PICs) are crucial for advanced applications in telecommunications, quantum computing, and biomedical fields. Silicon nitride (SiN)-based platforms are promising for PICs due to their transparency, low optical loss, and thermal stability. However, achieving efficient thermo-optic (TO) modulation on SiN remains challenging due to limited reconfigurability and high power requirements. This study aims to optimize TO phase shifters on SiN platforms to enhance power efficiency, reduce device footprint, and minimize insertion losses. We introduce a CMOS-compatible plasmo-photonic TO phase shifter using a SiOC material layer with a high TO coefficient combined with aluminum heaters on a SiN platform. We evaluate four interferometer architectures—symmetric and asymmetric Mach–Zehnder Interferometers (MZIs), an MZI with a ring resonator, and a single-arm design—through opto-thermal simulations to refine performance across power, losses, footprint, and switching speed metrics. The asymmetric MZI with ring resonator (A-MZI-RR) architecture demonstrated superior performance, with minimal power consumption (1.6 mW), low insertion loss (2.8 dB), and reduced length (14.4 μm), showing a favorable figure of merit compared to existing solutions. The optimized SiN-based TO switches show enhanced efficiency and compactness, supporting their potential for scalable, energy-efficient PICs suited to high-performance photonic applications. Full article

While photocrosslinked collagen hydrogels show promise in tissue engineering, conventional approaches for property control often require complex chemical modifications or concentration changes that alter their biochemical composition. Here, we present the first systematic investigation of light-intensity-dependent control in riboflavin phosphate (RFP)-mediated photocrosslinking as a novel, single-parameter approach to modulate hydrogel properties while preserving native biochemical environments. We systematically investigated the effects of varying light intensities (100 K, 50 K, and 10 K lux) during hydrogel fabrication through comprehensive structural, mechanical, and biological characterization. Scanning electron microscopy revealed unprecedented control over network architecture, where higher light intensities produced more uniform and compact networks, while swelling ratio analysis showed significant differences between 100 K lux (246 ± 2-fold) and 10 K lux (265 ± 4-fold) conditions. Most significantly, we discovered that intermediate intensity (50 K lux) uniquely optimized mechanical performance in physiological conditions, achieving storage modulus of about 220 Pa after 24 h swelling, compared to about 160 and 109 Pa for 100 K and 10 K lux conditions, respectively. Remarkably, cellular studies using NIH/3T3 fibroblasts demonstrated that lower light intensity (10 K lux) enhanced cell proliferation by 2.8-fold compared to 100 K lux conditions after 7 days of culture, with superior cell network formation in both 2D and 3D environments. This groundbreaking approach establishes light intensity as a powerful single parameter for precise control of both mechanical and biological properties, offering a transformative tool for tailoring collagen-based biomaterials in tissue engineering applications. Full article

Upon the advent of the big data era, information processing hardware platforms have undergone explosive development, facilitating unprecedented computational capabilities while significantly reducing energy consumption. However, conventional electronic computing hardware, despite significant upgrades in architecture optimization and chip scaling, still faces fundamental limitations in speed and energy efficiency due to Joule heating, electromagnetic crosstalk, and capacitance. A new type of information processing hardware is urgently needed for emerging data-intensive applications such as face identification, target tracking, and autonomous driving. Recently, integrated photonics computing architecture, which possesses remarkable compactness, wide bandwidth, low latency, and inherent parallelism, has harvested great attention due to its enormous potential to accelerate parallel data processing, such as digital image convolution. In this study, an integrated photonic processor based on a Mach-Zehnder interferometer (MZI) network is proposed and demonstrated. The processor, being scalable and compatible with complementary metal oxide semiconductors, facilitates mass production and seamless integration with other silicon-based optoelectronic devices. An experimental verification for digital image convolution is also performed, and the result deviations between our processor and a commercial 64-bit computer are less than 2.3%. Full article

Deep neural networks have been widely applied to fiber optic sensor systems, where the detection of external intrusion in metro tunnels is a major challenge; thus, how to achieve the optimal balance between resource consumption and accuracy is a critical issue. To address this issue, we propose a lightweight deep learning model, the Temporal Efficient Residual Network (TEResNet), for the detection of anomalous intrusion. In contrast to the majority of two-dimensional convolutional approaches, which require a deep architecture to encompass both low- and high-frequency domains, our methodology employs temporal convolutions and a compact residual network architecture. This allows the model to incorporate lower-level features into the higher-level feature formation in subsequent layers, leveraging informative features from the lower layers, and thus reducing the number of stacked layers for generating high-level features. As a result, the model achieves a superior performance with a relatively small number of layers. Moreover, the two-dimensional feature map is reduced in size to reduce the computational burden without adding parameters. This is crucial for enabling rapid intrusion detection. Experiments were conducted in the construction environment of the Guangzhou Metro, resulting in the creation of a dataset containing 6948 signal segments, which is publicly accessible. The results demonstrate that TEResNet outperforms the existing intrusion detection methods and advanced deep learning networks, achieving an accuracy of 97.12% and an F1 score of 96.15%. With only 48,009 learnable parameters, it provides an efficient and reliable solution for intrusion detection in metro tunnels, aligning with the growing demand for lightweight and robust information processing systems. Full article

Background/Objectives: This study aims to address the critical need for accessible, early, and accurate cardiac di-agnostics, especially in resource-limited or remote settings. By shifting focus from traditional multi-lead ECG analysis to single-lead ECG data, this research explores the potential of advanced deep learning models for classifying cardiac conditions, including Nor-mal, Abnormal, Previous Myocardial Infarction (PMI), and Myocardial Infarction (MI). Methods: Five state-of-the-art deep learning architectures—Inception, DenseNet201, MobileNetV2, NASNetLarge, and VGG16—were systematically evaluated on individual ECG leads. Key performance metrics, such as model accuracy, inference time, and size, were analyzed to determine the optimal configurations for practical applications. Results: VGG16 emerged as the most accurate model, achieving an F1-score of 98.11% on lead V4 with a prediction time of 4.2 ms and a size of 528 MB, making it suitable for high-precision clinical settings. MobileNetV2, with a compact size of 13.4 MB, offered a balanced performance, achieving a 97.24% F1-score with a faster inference time of 3.2 ms, positioning it as an ideal candidate for real-time monitoring and telehealth applications. Conclusions: This study bridges a critical gap in cardiac diagnostics by demonstrating the feasibility of lightweight, scalable, single-lead ECG analysis using advanced deep learning models. The findings pave the way for deploying portable diagnostic tools across diverse settings, enhancing the accessibility and efficiency of cardiac care globally. Full article

The rise of artificial intelligence (AI) necessitates ultra-fast computing, with on-chip terahertz (THz) communication emerging as a key enabler. It offers high bandwidth, low power consumption, dense interconnects, support for multi-core architectures, and 3D circuit integration. However, transitioning between different waveguides remains a major challenge in THz systems. In this paper, we propose a THz band mode converter that converts from a rectangular waveguide (RWG) (WR-0.43) in ${\mathrm{TE}}_{10}$ mode to a substrate-integrated waveguide (SIW) in ${\mathrm{TE}}_{20}$ mode. The converter comprises a tapered waveguide, a widened waveguide, a zigzag antenna, and an aperture coupling slot. The zigzag antenna effectively captures the electromagnetic (EM) energy from the RWG, which is then coupled to the aperture slot. This coupling generates a quasi-slotline mode for the electric field (E-field) along the longitudinal side of the aperture, exhibiting odd symmetry akin to the SIW’s ${\mathrm{TE}}_{20}$ mode. Consequently, the ${\mathrm{TE}}_{20}$ mode is excited in the symmetrical plane of the SIW and propagates transversely. Our work details the mode transition principle through simulations of the EM field distribution and model optimization. A back-to-back RWG ${\mathrm{TE}}_{10}{\mathrm{-to-TE}}_{10}$ mode converter is designed, demonstrating an insertion loss of approximately 5 dB over the wide frequency range band of 2.15–2.36 THz, showing a return loss of 10 dB. An on-chip antenna is proposed which is fed by a single higher-order mode of the SIW, achieving a maximum gain of 4.49 dB. Furthermore, a balun based on the proposed converter is designed, confirming the presence of the ${\mathrm{TE}}_{20}$ mode in the SIW. The proposed mode converter demonstrates its feasibility for integration into a THz-band high-speed circuit due to its efficient mode conversion and compact planar design. Full article

Shoot determinacy is a key trait affecting productivity in tomato, quantitatively governed by genes within the flowering pathway. Achieving an optimal balance of flowering signals is essential for shaping plant architecture and maximizing yield potential. However, the genetic resources and allelic diversity available for fine-tuning this balance remain limited. In this work, we demonstrate the potential for directly manipulating shoot architecture by simultaneously targeting the flowering activating FRUITFULL(FUL)-like genes, FUL2 and MADS-BOX PROTEIN 20 (MBP20), and the flowering-repressing gene SELFPRUNING (SP). Loss of MBP20 in the sp background leads to additional inflorescences, while determinacy is largely maintained. However, additional mutation of FUL2 results in mainly indeterminate plants, which have faster sympodial cycling, leading to more compact growth and increased flower production. Our results provide a path to quantitative tuning of the flowering signals with a direct impact on shoot architecture and productivity. Full article

This paper presents a low-noise CMOS transimpedance-limiting amplifier (CTLA) for application in LiDAR sensor systems. The proposed CTLA employs a dual-feedback architecture that combines the passive and active feedback mechanisms simultaneously, thereby enabling automatic limiting operations for input photocurrents exceeding 100 µA_pp (up to 1.06 mA_pp) without introducing signal distortions. This design methodology can eliminate the need for a power-hungry multi-stage limiting amplifier, hence significantly improving the power efficiency of LiDAR sensors. The practical implementation for this purpose is to insert a simple NMOS switch between the on-chip avalanche photodiode (APD) and the active feedback amplifier, which then can provide automatic on/off switching in response to variations of the input currents. In particular, the feedback resistor in the active feedback path should be carefully optimized to guarantee the circuit’s robustness and stability. To validate its practicality, the proposed CTLA chips were fabricated in a 180 nm CMOS process, demonstrating a transimpedance gain of 88.8 dBΩ, a −3 dB bandwidth of 629 MHz, a noise current spectral density of 2.31 pA/√Hz, an input dynamic range of 56.6 dB, and a power dissipation of 23.6 mW from a single 1.8 V supply. The chip core was realized within a compact area of 180 × 50 µm². The proposed CTLA shows a potential solution that is well-suited for power-efficient LiDAR sensor systems in real-world scenarios. Full article