Search Results (120)

Non-mechanical beam steering is required for holographic displays, free-space optical communication, and chip-scale LiDAR. Optical phased arrays (OPAs), which allow for inertia-free, high-speed beam control via electronic phase control, are an important research topic. The present study investigates the primary material platform for on-chip OPAs: Liquid crystal OPAs (LC-OPAs) employ electrically tunable refractive indices for low-voltage operation; lithium niobate OPAs (LN-OPAs) utilize high electro-optic coefficients for high-speed, low-power consumption, and large-bandwidth operation; and silicon-based OPAs (Si-OPAs) apply mature photonic integration to achieve high integration density and GHz-range steering. The paper thoroughly examines OPA basics, recent material-specific advancements, performance benchmarks, outstanding issues, and future prospects. Full article

The ground small-loop transient electromagnetism (TEM) provides a basis for detecting shallow underground space. However, the strong primary field interference from the transmitting coil to the receiving coil, along with the transition process of the receiving coil, can cause serious distortion of the early secondary field signals. This leads to the loss of effective shallow underground information. In this paper, we utilize the eccentric self-compensating structure to weaken the primary field interference. Aiming at the current position sensitivity of the eccentric structure, we propose a statistical method to realize the robustness analysis of the eccentric structure and find the optimal eccentric position where the primary field coupling between the transmitting and receiving coil is approximated to be zero. To address the impact of the coil transition process, a double receiving coils structure is proposed. This ensures that the number of turns, the secondary field flux and the secondary field response strength in the single receiving coil structure remain unchanged. Compared with the conventional eccentric structure of a single receiving coil, the bandwidth of the receiving coil sensor was increased from 103.5 kHz to 218.3 kHz, and the Signal-to-Noise Ratio (SNR) of the measured early secondary field signals improved from 18.5 to 27.9, representing a 50.81% increase in SNR. This study not only reduces primary field interference but also reduces the impact of the coil transition process, thereby capturing more early secondary field signals and enhancing the shallow detection resolution of the ground TEM. Full article

The extensive utilization of the Snappy compression algorithm in digital devices such as smartphones, IoT, and digital cameras has played a crucial role in alleviating demands on network bandwidth and storage space. This paper presents an improved Snappy compression algorithm optimized for implementation on field programmable gate arrays (FPGAs). The proposed algorithm enhances the compression ratio by refining the encoding format of Snappy and introduces an innovative approach utilizing fingerprints within the dictionary to minimize storage space requirements. Additionally, the algorithm incorporates a pipeline structure to optimize performance. Experimental results demonstrate that the proposed algorithm achieves a throughput of 1.6 GB/s for eight hardware kernels. The average compression ratio is 2.27, representing a 6.1% improvement over the state-of-the-art Snappy FPGA implementation. Notably, the proposed algorithm architecture consumes fewer on-chip storage resources compared to other advanced algorithms, striking a balance between logic and storage resource utilization. This optimization leads to higher FPGA resource utilization efficiency. Our design addresses the growing demand for efficient lossless data compression solutions in consumer electronics, ultimately contributing to advancements in modern digital ecosystems. Full article

An efficient optimization method for designing defected ground structure (DGS)-based common-mode filters (CMFs) is proposed, utilizing equation-based transmission line models integrated with a genetic algorithm (GA). Designing an optimal DGS-based CMF using full-wave simulation tools is time-consuming due to its process-intensive nature. The proposed optimization method implements transmission line theory to allow for direct S-parameter calculation, enabling integration with an optimization algorithm to identify optimal parameters within a confined 5 mm × 10 mm design space. This work demonstrates a compact asymmetric DGS design to illustrate the method’s capability. The resulting compact asymmetric DGS-based CMF achieves wideband common-mode suppression with a –10 dB bandwidth from 3.18 GHz to 12.89 GHz. The optimization method significantly reduces design time by minimizing the need for lengthy and repetitive full-wave simulations. The measured S-parameters of the fabricated CMF closely match the simulated results, validating the model’s accuracy. Compared with traditional methods for designing DGS-based CMFs, the proposed method utilizes transmission line theory to optimize the design efficiently, providing a practical and efficient solution. Full article

This paper presents a novel framework for optimizing multimodal large language model (MLLM) inference through task offloading and resource allocation in UAV-assisted satellite edge computing (SEC) networks. MLLMs leverage transformer architectures to integrate heterogeneous data modalities for IoT applications, particularly real-time monitoring in remote areas. However, cloud computing dependency introduces latency, bandwidth, and privacy challenges, while IoT device limitations require efficient distributed computing solutions. SEC, utilizing low-earth orbit (LEO) satellites and unmanned aerial vehicles (UAVs), extends mobile edge computing to provide ubiquitous computational resources for remote IoTDs. We formulate the joint optimization of MLLM task offloading and resource allocation as a mixed-integer nonlinear programming (MINLP) problem, minimizing latency and energy consumption while optimizing offloading decisions, power allocation, and UAV trajectories. To address the dynamic SEC environment characterized by satellite mobility, we propose an action-decoupled soft actor–critic (AD-SAC) algorithm with discrete–continuous hybrid action spaces. The simulation results demonstrate that our approach significantly outperforms conventional deep reinforcement learning methods in convergence and system cost reduction compared to baseline algorithms. Full article

This paper presents the design, fabrication, calibration, and comprehensive characterization of a homemade tri-axial fluxgate magnetometer. The magnetometer, utilizing a ring core configuration, was developed to measure ultra-low magnetic fields with high sensitivity and stability. Critical stages from material selection to sensor geometry optimization are discussed in detail. A series of critical characterization processes were conducted, including zero-field voltage determination, scale factor calculation, resolution measurement, noise analysis, bias assessment, cross-field effect evaluation, temperature dependency, and bandwidth determination. The sensor demonstrated a minimum detectable magnetic field resolution of 2.2 nT with a noise level of 1.1 nT/√Hz at 1 Hz. Temperature dependency tests revealed minimal impact on sensor output with a maximum shift of 120 nT in the range of 60 °C, which was effectively compensated through calibration to less than 5 nT. Additionally, the paper introduces a model function in matrix form to relate the magnetometer’s output voltage to the measured magnetic field, incorporating temperature dependency and cross-field effects. This work highlights the importance of meticulous calibration and optimization in developing fluxgate magnetometers suitable for various applications, from space exploration to biomedical diagnostics. Full article

Accurate and non-invasive optical metrology of transparent objects is essential in several commercial and research applications, from fluid dynamics to biomedical imaging. In this work, a digital holography approach for thickness measurement of glass plate and temperature mapping of candle flame is presented that leverages a double-field-of-view (FOV) configuration combined with high spatial bandwidth utilization (SBU). By capturing a multiplexed hologram from two distinct objects in a single shot, the system overcomes the limitations inherent to single-view holography, enabling more comprehensive object information of thickness measurement and temperature-induced refractive index variations. The method integrates double-FOV digital holography with high SBU, allowing for accurate surface profiling and mapping of complex optical path length changes caused by temperature gradients. The technique exhibits strong potential for applications in the glass industry and microfluidic thermometry, convection analysis, and combustion diagnostics, where precise thermal field measurements are crucial. This study introduces an efficient holographic framework that advances the capabilities of non-contact measurement applications by integrating double-FOV acquisition into a single shot with enhanced spatial bandwidth exploitation. The approach sets the groundwork for real-time, volumetric thermal imaging and expands the applicability of digital holography in both research and industrial settings. Full article

Vegetation productivity, as an essential global carbon sink, directly influences the variety and stability of ecosystems. Precise vegetation productivity monitoring and forecasting are crucial for the global carbon cycle. Traditional machine learning algorithms frequently experience overfitting when processing high-dimensional time-series data or substantial numbers of outliers, impeding the accurate prediction of various vegetation metrics. We propose a multimodal regression prediction model utilizing the TCLA framework—comprising the Transient Trigonometric Harris Hawks Optimizer (TTHHO), Convolutional Neural Networks (CNN), Least Squares Support Vector Machine (LSSVM), and Adaptive Bandwidth Kernel Density Estimation (ABKDE)—with the Hetao Irrigation District, a vast irrigation basin in China, serving as the study area. This model employs TTHHO to effectively navigate the search space and adaptively optimize network node positions, integrates CNN-LSSVM for feature extraction and regression analysis, and incorporates ABKDE for probability density function estimation and outlier detection, resulting in accurate interval probability prediction and enhanced model resilience to interference. Experimental data indicate that the TCLA model improves prediction accuracy by 10.57–26.47% compared to conventional models (Long Short-Term Memory (LSTM), Transformer). In the presence of 5–15% outliers, the fusion of multimodal data results in a substantial drop in RMSE (p < 0.05), with a reduction of 45.18–69.66%, yielding values between 0.079 and 0.137, thereby demonstrating the model’s high robustness and resistance to interference in predicting the next three years. This work introduces a scientific approach for precisely forecasting alterations in regional vegetation productivity using the proposed multimodal TCLA model, significantly enhancing global vegetation resource management and ecological conservation techniques. Full article

Existing databases supporting Online Transaction Processing (OLTP) workloads based on non-volatile memory (NVM) almost all use Multi-Version Concurrency Control (MVCC) protocol to ensure data consistency. MVCC allows multiple transactions to execute concurrently without lock conflicts, reducing the wait time between read and write operations, and thereby significantly increasing the throughput of NVM OLTP engines. However, it requires garbage collection (GC) to clean up the obsolete tuple versions to prevent storage overflow, which consumes additional system resources. Furthermore, existing GC approaches in NVM OLTP engines are inefficient because they are based on methods designed for dynamic random access memory (DRAM) OLTP engines, without considering the significant differences in read/write bandwidth and cache line size between NVM and DRAM. These approaches either involve excessive random NVM access (traversing tuple versions) or lead to too many additional NVM write operations, both of which degrade the performance and durability of NVM. In this paper, we propose TB-Collect, a high-performance GC approach specifically designed for NVM OLTP engines. On the one hand, TB-Collect separates tuple headers and contents, storing data in an append-only manner, which greatly reduces NVM writes. On the other hand, TB-Collect performs GC at the block level, eliminating the need to traverse tuple versions and improving the utilization of reclaimed space. We have implemented TB-Collect on DBx1000 and MySQL. Experimental results show that TB-Collect achieves 1.15 to 1.58 times the throughput of existing methods when running TPCC and YCSB workloads. Full article

This paper proposes a free-space time–frequency phase (TFP)-synchronization transmission architecture based on optoelectronic hybrid technology, addressing the high-precision TFP synchronization and high-speed communication requirements between mobile platforms in distributed collaborative positioning and other applications. The proposed scheme utilizes symmetric free-space optical (FSO) links to effectively suppress drift errors, integrating the high bandwidth of optical links and the high stability of microwave links, enabling one-to-many networking synchronization between mobile platforms. The system adopts optical wireless transmission technology based on pseudo-code regenerative ranging, integrating 1.5 Gbps high-speed data transmission with high-precision TFP-synchronization functionality. An experimental system consisting of a main station and two auxiliary stations was established in an outdoor mobile platform scenario. Experimental results show that while achieving high-speed communication, the frequency synchronization precision is 0.0131 ppb, frequency stability is in the order of 10⁻¹⁰@1 s, and phase synchronization precision is approximately 3.56°. The system achieves time synchronization precision at the picosecond level. The proposed technology is highly suitable for high-precision synchronization communication in scenarios lacking fiber-optic infrastructure, effectively fulfilling rigorous requirements in mobile platform applications such as distributed collaborative positioning. Full article

This work presents a cost-effective optical frequency comb generator (CEOFCG) solution for generating multiple, equally spaced carriers in wavelength-division-multiplexing coherent optical fiber communication systems (WDM-COFCS). It enables the replacement of multiple laser sources with a single continuous-wave laser, eliminating the need for additional amplification and filtering setups. The CEOFCG provides stable multicarrier spacing, broad phase coherence, and compatibility with advanced modulation formats, enhancing the performance of WDM-COFCS. Digital signal processing (DSP) techniques, including digital filtering, detection, and impairment compensation, contribute to high transmission and spectral efficiency (SE). The results demonstrate the potential of CEOFCG in achieving cost reduction, complexity reduction, high SE, and optimal utilization of optical fiber bandwidth, particularly in higher-order QAM-based COFCS. Full article

With the rise of digital video technologies and the proliferation of processing methods and storage systems, video-surveillance systems have received increasing attention over the last decade. However, the spread of cameras installed in public and private spaces makes it more difficult for human operators to perform real-time analysis of the large amounts of data produced by surveillance systems. Due to the advancement of artificial intelligence methods, many automatic video analysis tasks like violence detection have been studied from a research perspective, and are even beginning to be commercialized in industrial solutions. Nevertheless, most of these solutions adopt centralized architectures with costly servers utilized to process streaming videos sent from different cameras. Centralized architectures do not present the ideal solution due to the high cost, processing time issues, and network bandwidth overhead. In this paper, we propose a lightweight autonomous system for the detection and geolocation of violent acts. Our proposed system, named LAVID, is based on a depthwise separable convolution model (DSCNN) combined with a bidirectional long-short-term memory network (BiLSTM) and implemented on a lightweight smart camera. We provide in this study a lightweight video-surveillance system consisting of low-cost autonomous smart cameras that are capable of detecting and identifying harmful behavior and geolocate violent acts that occur over a covered area in real-time. Our proposed system, implemented using Raspberry Pi boards, represents a cost-effective solution with interoperability features making it an ideal IoT solution to be integrated with other smart city infrastructure. Furthermore, our approach, implemented using optimized deep learning models and evaluated on several public datasets, has shown good results in term of accuracy compared to state of the art methods while optimizing reducing power and computational requirements. Full article

Digitalization is transforming the maritime sector, and the amount and variety of data generated is increasing rapidly. Effective data utilization is crucial for data-driven services such as for highly automated maritime systems and efficient traffic coordination. However, these applications depend on heterogeneous, distributed data sources managed by different actors, making secure and sovereign information sharing difficult. This paper investigates how maritime data can be exchanged reliably and securely without jeopardizing data sovereignty. Based on the existing literature, we identify the main challenges and current research gap in sharing maritime information, emphasizing the importance of data availability. From this, we derive requirements for a secure and sovereign infrastructure for data exchange. To address these challenges, we propose a fully decentralized architecture for the maritime sector based on the concept of a data space. Our approach integrates protocols to improve data availability while minimizing data volume, considering maritime constraints such as volatile connectivity, low bandwidth and existing standards. We evaluate our architecture through a maritime traffic management case study and demonstrate its ability to enable secure and sovereign exchange of heterogeneous data. The results confirm that our solution reliably supports distributed data collection and enables data-driven, value-added services, which in turn will improve the safety and efficiency of the maritime domain in the near future. Full article

In strip intercropping, increasing bandwidth enhances light energy utilization and facilitates mechanized production, yet it constrains the realization of maize yield advantages. The impact of bandwidth on the ear differentiation and development and yield formation requires further investigation. In this study, different bandwidths (T1, 1.6 m, T2, 2.0 m, T3, 2.4 m, and T4, 2.8 m) were arranged, and monoculture maize with varying row spacings (K1, 0.8 m, K2, 1.0 m, K3, 1.2 m, and K4, 1.4 m) was used as the control. The results show that increasing bandwidth inhibited the ear differentiation. The proportion of dry matter partitioning to leaves increased and to ears decreased, resulting in shorter ear length and higher floret and grain abortion rates. Maize yield losses amounted to 26.9% and 31.6% in T4 compared to K4 and T1, respectively. Moreover, the bandwidth did not affect the fertilized florets due to the smaller anthesis–silking interval created by the simultaneous effect. We concluded that the appropriate bandwidth, 1.6 m and 2.0 m, can stabilize the dry matter partitioning to the ear; stabilize ear length, floret, and grain abortion rate; and stabilize the maize yield. Full article

This paper presents a method to design a compact high-gain multiple-input, multiple-output (MIMO) patch antenna. In a one-dimensional large-scale MIMO array, the conventional approach of using multiple patches and decoupling networks significantly increases the antenna size. To address this, this paper utilizes compact self-decoupled patches, enabling extremely small element spacing while maintaining high isolation levels. Accordingly, a compact size feature can be obtained. Then, gain and bandwidth enhancements are realized with a combination of multiple patches and T-junction dividers. The feasibility of the proposed concept is validated by measurement on a two-element MIMO antenna. The measured results demonstrate that the proposed antennas have good operation characteristics at 4.8 GHz with a small element spacing of 0.008$\lambda$. The measured operating bandwidth is about 5%, with isolation of better than 19 dB. A maximum broadside gain of 7.2 dBi can also be yielded. Full article