Array Module Research Articles

Fundamental effects of array density and modulation frequency on image quality of diffuse optical tomography.

Diffuse optical tomography (DOT) provides three-dimensional image reconstruction of chromophore perturbations within a turbid volume. Two leading strategies to optimize DOT image quality include, (i) arrays of regular, interlacing, high-density (HD) grids of sources and detectors with closest spacing less than 15mm, or (ii) source modulated light of order ∼100MHz. However, the general principles for how these crucial design parameters of array density and modulation frequency may interact to provide an optimal system design have yet to be elucidated. Herein, we systematically evaluated how these design parameters effect image quality via multiple key metrics. Specifically, we simulated 32 system designs with realistic measurement noise and quantified localization error, spatial resolution, signal-to-noise, and localization depth of field for each of ∼85000 point spread functions in each model. We found that array density had a far stronger effect on image quality metrics than modulation frequency. Additionally, model fits for image quality metrics revealed that potential improvements diminish with regular arrays denser than 9mm closest spacing. Further, for a given array density, 300MHz source modulation provided the deepest reliable imaging compared to other frequencies. Our results indicate that both array density and modulation frequency affect the spatial sampling of tissue, which asymptotically saturates due to photon diffusivity within a turbid volume. In summary, our results provide comprehensive perspectives for optimizing future DOT system designs in applications from wearable functional brain imaging to breast tumor detection.

PV Array Reconfiguration for Global Maximum Power Optimizing under Partial Shading Conditions based on PV Switched System

Background: Due to the partial shading effects on different photovoltaic (PV) modules in a PV array, PV module operating conditions are inconsistent, and PV array output power is significantly reduced. Although the maximum power point (MPP) of a non-uniform irradiance PV array can be observed through global maximum power point tracking (GMPPT), no evaluation of the array's energy potential has been done. Objective: One of the most effective solutions to overcome the negative effects of partial shading in PV systems is the PV array reconfiguration process. To optimize the electrical structure of the PV array as the PV modules are partially shaded in a non-uniform manner, this study proposes a promising technique for dynamic reconfiguring a PV array in order to improve the extracted maximum power from a PV array under partial shading conditions (PSC). Methods: PV modules are rearranged by iteratively sorting them to allow the PV array with nonuniform irradiance to produce as much power as possible. This is conducted by applying a switching matrix to implement a PV-switched system approach. The proposed system with different PV array dimensions (e.g., 3×4, 4×6, and 5×8) is assessed in order to validate the proposed algorithm. A MATLAB/Simulink PV array developed model is used to find the global maximum power point for the different PV array dimensions in both pre-reconfiguration and post-reconfiguration states. Results: A detailed numerical comparison of the extracted power from the proposed system has been provided. Conclusion: The results show that the proposed system has the potential to extract the exact global maximum power for a PV-switched system under PSC, irrespective of array dimensions, according to simulation results.

Computational temporal ghost imaging based on complementary modulation

Abstract We report an experimental demonstration of temporal ghost imaging in which a digital micromirror device (DMD) and +1/−1 binary modulation have been combined to give an accurate reconstruction of a nonperiodic time object. Compared to the 0/1 modulation, the reconstruction signal can be improved greatly by +1/−1 binary modulation even with half of the measurements. Experimental results show that 0/1 binary temporal objects up to 4 kHz and sinusoidal time objects up to 1 kHz can be reconstructed by this method. The influences of modulation speed and array detector gray levels are also discussed.

Assessing impacts of observations on ocean circulation models with examples from coastal, shelf, and marginal seas

Ocean observing systems in coastal, shelf and marginal seas collect diverse oceanographic information supporting a wide range of socioeconomic needs, but observations are necessarily sparse in space and/or time due to practical limitations. Ocean analysis and forecast systems capitalize on such observations, producing data-constrained, four-dimensional oceanographic fields. Here we review efforts to quantify the impact of ocean observations, observing platforms, and networks of platforms on model products of the physical ocean state in coastal regions. Quantitative assessment must consider a variety of issues including observation operators that sample models, error of representativeness, and correlated uncertainty in observations. Observing System Experiments, Observing System Simulation Experiments, representer functions and array modes, observation impacts, and algorithms based on artificial intelligence all offer methods to evaluate data-based model performance improvements according to metrics that characterize oceanographic features of local interest. Applications from globally distributed coastal ocean modeling systems document broad adoption of quantitative methods, generally meaningful reductions in model-data discrepancies from observation assimilation, and support for assimilation of complementary data sets, including subsurface in situ observation platforms, across diverse coastal environments.

A Rapid Adaptation Approach for Dynamic Air-Writing Recognition Using Wearable Wristbands with Self-Supervised Contrastive Learning

HighlightsUtilizing self-supervised learning, the proposed wearable wristband with a four-channel sensing array and wireless transmission module is developed for tracking air-writing and dynamic gestures.The model can learn prior features from unlabeled signals of random wrist movements, significantly reducing the dependency on extensive labeled data for training.The wristband system rapidly adapts to multiple scenarios after fine-tuning using few-shot data, enhancing user interaction through natural and intuitive communication.

Ironing Out the Mechanism of gp130 Signaling.

gp130 functions as a shared signal-transducing subunit not only for interleukin (IL)-6 but also for eight other human cytokine receptor complexes. The IL-6 signaling pathway mediated through gp130 encompasses classical, trans, or cluster signaling, intricately regulated by a diverse array of modulators affecting IL-6, its receptor, and gp130. Currently, only a limited number of small molecule antagonists and agonists for gp130 are known. This review aims to comprehensively examine the current knowledge of these modulators and provide insights into their pharmacological properties, particularly in the context of cancer and other diseases. Notably, the prominent gp130 modulators SC144, bazedoxifene, and raloxifene are discussed in detail, with a specific focus on the discovery of SC144's iron-chelating properties. This adds a new dimension to the understanding of its pharmacological effects and therapeutic potential in conditions where iron homeostasis is significant. Our bioinformatic analysis of gp130 and genes related to iron homeostasis reveals insightful correlations, implicating the role of iron in the gp130 signaling pathway. Overall, this review contributes to the evolving understanding of gp130 modulation and its potential therapeutic applications in various disease contexts. SIGNIFICANCE STATEMENT: This perspective provides a timely and comprehensive analysis of advancements in gp130 signaling research, emphasizing the therapeutic implications of the currently available modulators. Bioinformatic analysis demonstrates potential interplay between gp130 and genes that regulate iron homeostasis, suggesting new therapeutic avenues. By combining original research findings with a broader discussion of gp130's therapeutic potential, this perspective significantly contributes to the field.

The KM3NeT Broadcast optical system network

The optical data transport system of the KM3NeT neutrino telescope at the bottom of the Mediterranean Sea will serve more than 6000 optical modules in the detector arrays with a point-to-point optical connection to the control stations onshore. The ARCA and ORCA detectors of KM3NeT are being installed at a depth of about 3500 m and 2500 m, respectively and their distance to the control stations is about 100 km and 40 km. The two detectors are optimised for the detection of cosmic neutrinos with energies above about 1 TeV (ARCA) and of atmospheric neutrinos with energies in the range 1 GeV– 1 TeV (ORCA). The expected maximum data rate is 200 Mbps per optical module. The implemented optical data transport system matches the layouts of the networks of electro-optical cables and junction boxes in the deep sea. For efficient use of the fibres in the system the technology of Dense Wavelength Division Multiplexing is applied. To comply with the scientific goals of KM3NeT, a time calibration of 1 ns between the many optical modules in the detector arrays is essential for the reconstruction of the neutrino events. For the first time in a deep sea neutrino telescope the White Rabbit protocol over Ethernet is used for the clock distribution. Downstream slow control and base module signals are broadcasted to all optical modules in the detector array. Synchronisation is achieved by communication between a White Rabbit master onshore and a White Rabbit slave unit inside the optical modules. In this contribution the KM3NeT broadcast optical data transport system is presented.

Vitreous Tissue Cryopreservation Using a Blood Vessel Model and Cryomacroscopy for Scale-Up Studies: Observations and Mathematical Modeling

Successful long term cryobanking of multicellular tissues and organs at deep subzero temperatures calls for the avoidance of ice cryoinjury by reliance upon ice-free cryopreservation techniques. However, the quality of the cryopreserved material is the direct result of its ability to survive a host of harmful mechanisms, chief among which is overcoming the trifecta effects of ice crystallization, toxicity, and mechanical stress. This study aims at exploring improved conditions to scale-up ice-free cryopreservation by combining DP6 as a base cryoprotective agent (CPA) solution with an array of synthetic ice modulators (SIMs). This study is conducted by integrating cryomacroscopy techniques, thermal modeling, solid mechanics analysis, and viability and contractility investigation to correlate physical effects, thermal outcomes, and cryobiology results. As an extension of previous work, this study aims at scale-up of established baseline blood vessel models, while comparing the relative toxicity and vitreous stability of 4ml and 10ml samples of DP6 containing either sucrose as a SIM, or the commercial synthetic ice blockers (X1000 and Z1000). Using that established protocol, the addition and removal of DP6+0.6M sucrose and DP6+1%X1000+1%Z1000 were both well tolerated in rabbit carotid and pig femoral artery models, when assessed for metabolic recovery and contractility. Using cryomacroscopy, it was demonstrated that DP6+0.6M sucrose provided a stable vitrification medium under marginal cooling and warming conditions that resulted in >50% survival rate. By contrast, DP6+1%X1000+1%Z1000 was subject to visible ice formation during cooling under the same thermal conditions, resulting in a significantly lower recovery of ∼20%. Thermal modeling is used in this study to verify the actual cooling and rewarming rates in the specimens, while thermo-mechanics analysis is used to explain why fractures were observed using cryomacroscopy when the specimens were contained in glass vials but not in plastic vials.

Enhanced sensitivity and broadband response in porous triple periodic minimal surface piezoresistive sensors for telemedicine applications

Wearable pressure sensors with outstanding performance have garnered significant attention in fields such as telemedicine. However, the development of sensors that achieve both adjustable high sensitivity and a wide response range remains a formidable challenge. To address these issues, we propose the development of a flexible piezoresistive sensor featuring simultaneous macro- and microstructures. This is achieved by constructing a complex macro-topological skeleton using Fused Deposition Molding (FDM) technology and integrating supercritical carbon dioxide (ScCO2) foaming technology for microscopic pore creation. The unique conductive network structure, coupled with the Triple Periodic Minimal Surface (TPMS) metamaterial framework and microscopic pore architecture, endows the piezoresistive sensors with exceptional sensing performance, including high sensitivity (12.13 MPa−1), a broad response range (exceeding 30 MPa), and long-term fatigue resistance (over 26,000 s cycles). In addition, the sensors are shielded from electromagnetic interference, with a maximum EMI shielding effectiveness of 53.2 dB for the TMFM with a thickness of 2.3 mm. The effects of varying porosities and foaming pressures on sensitivity were analyzed using finite element simulations. Owing to these remarkable features, multiple foot movement recognition was successfully demonstrated with volunteer participants by assembling sensing unit array modules. Based on this, an intelligent fall warning recognition system is developed using machine learning. This system accurately distinguishes four types of activity behaviors—standing, walking, spraining foot, and falling—with accuracy rates of 98.9 %, 97.1 %, 98.9 %, and 95.1 %, respectively. The system automatically generates an alarm pattern for detected fall behaviors. These findings suggest that the combination of TPMS skeleton and micropores structure offers a promising strategy for developing sensors with adjustable sensitivity and wide detection range for wearable devices. This technology holds significant potential for applications in telemedicine assistance, particularly for the elderly and other vulnerable populations.

A Hybrid-Integrated K-/Ka-Band Phased Array Module With Dual-Polarized Shared Aperture

A Hybrid-Integrated <i>K</i>-/<i>Ka</i>-Band Phased Array Module With Dual-Polarized Shared Aperture

High transmission efficiency long-wave infrared multispectral modulation array based on nanogap engineering

The long-wave infrared spectrum’s unique advantages in molecular fingerprinting and atmospheric transmission have led to its widespread application in detection, identification, medical diagnosis, and environmental monitoring. Consequently, there has been a strong focus on developing multispectral structure arrays with high transmission efficiency. In this study, we introduce a high-transmission-efficiency nanocoaxes field enhancement structures array based on complex nanogap engineering, exhibiting a transmission of 21.59 % within an open area of 3 % and accompanied by a 90-fold enhancement of the nanogap field. With the modulation of nanogaps, the absolute transmission can exceed 60 %. The experimental data and finite element simulation results of the spectrally tunable array confirm that the origin of resonance is attributed to the enhanced localized electromagnetic modes supported by nanogaps. Furthermore, we demonstrated the proposed nanocoaxes field-enhancement structures in practical applications such as molecular resonance absorption enhancement and material composition analysis. Our work not only provides a method for on-chip multispectral tuning in the long-wave infrared range but also contributes to further advancing the development of long-wave infrared nanophotonic structures.

The Effect of Partial Shadings on the Output Power of the Photovoltaic Modules Connected with Different Current and Voltage Characteristics

A mismatch in the output power of photovoltaic (PV) modules in a PV array can occur due to partial shading or a module replacement. Substitution of a module in a PV array with a new one might lead to different current and voltage characteristics between the new and existing modules and result in power losses. The amount of power loss might be increased more if the PV array experiences partial shading. For this reason, this study aims to investigate the effect of partial shadings on the power output of photovoltaic arrays with different current and voltage characteristics. The PV array under test consists of 25 units of solar modules with a total cross-tied (TCT) configuration. There are five shading conditions applied to the test PV array, i.e., the short narrow (ShN), the short wide (ShW), the long narrow (LnN), the long wide (LnW), and the diagonal. A magic square method is applied to reduce the power loss when the PV modules experience partial shading conditions. The results show that the power loss due to partial shadings, either on all identical modules or partially identical, is the same. The most significant power loss occurs in the long comprehensive shading scenario, where 80% of the modules experience shading, which is 41.30%.

A Novel Strategy for Multitype Fault Diagnosis in Photovoltaic Systems Using Multiple Regression Analysis and Support Vector Machines

This study focuses on analyzing common fault types in photovoltaic (PV) modules, employing fault diagnosis methods based on machine learning technology to enhance the accuracy and efficiency of diagnosing faults in solar power systems. Initially, we collected relevant data from the solar power system and used data analysis techniques to identify system faults, designing a human-machine monitoring interface for practical application. Furthermore, the experimental results proved that the system could accurately identify eight major types of faults, including solar panel output circuits, energy storage batteries, maximum power point tracking (MPPT) controllers, inverters, dust accumulation, loosening of mounting rack screws, damage to the mounting rack foundation, and deformation of the mounting rack structure. Particularly in the detection of dust accumulation, we developed a new method of estimating power generation from multiple regression analysis (MRA), which closely aligns the estimated power output with the actual power output, highlighting the significant impact of dust accumulation on the efficiency of solar power systems. Next, by integrating voltmeters and support vector machines (SVM) into the solar PV array modules, we are able to quickly and accurately measure and locate short-circuit and open-circuit faults in bypass diodes. Ultimately, the proposed PV fault diagnosis strategy includes diagnostics for dust accumulation and mounting frame faults, making it particularly suitable for areas with severe air pollution and frequent earthquakes, providing a comprehensive fault diagnosis solution.

Cable Eccentricity Detection Method Based on Magnetic Field.

Amid the rapid advancement of electronic information technology, the need for cable eccentricity measurement in the industry is increasing both in China and across the globe. Current detection methods have several flaws, including high costs, insufficient accuracy, and instability. In this paper, we introduce a magnetic field-based detection method for cable eccentricity that provides high precision and cost-effectiveness. We position three pairs of magnetic field-collection modules in a circular array to gather magnetic flux density information induced by the electrified cable. We apply the law of electromagnetic induction to calculate the cable eccentricity. Our method is non-contact, preserving the cable's integrity. Our method outperforms traditional detection methods, not only in achieving greater accuracy and stability but also in significantly lowering the detection cost. Simulations and experiments show that our method's error rate under specified conditions is 0~4%, with a maximum standard deviation of 0.11, confirming its precision and stability in detecting cable eccentricity. The effectiveness of our method is influenced by two factors: lift-off value and loading current intensity. Our method presents a novel concept and a dependable strategy for the progress of cable eccentricity-detection technology.

Machine learning and IoT system for real-time cough detection and classification

Our research investigates the application of machine learning and Internet of Things (IoT) technologies in healthcare, focusing on detecting and classifying coughing episodes. Leveraging deep learning architectures and a comprehensive IoT infrastructure, we developed an automated system capable of monitoring audio signals from a microphone array module to detect coughs and classify their types accurately. The study utilized the COUGHVID dataset for model training and evaluation, employing rigorous preprocessing techniques to ensure data integrity. Through comparative analysis, we identified MobileNet as the optimal model for cough detection, achieving promising results in accuracy, area under the ROC curve (AUC), and F1 score. Furthermore, our emphasis on privacy safeguards and remote medical examination facilitation underscores the practical implications of our research in enhancing healthcare delivery. Our study contributes to advancing technology-enabled healthcare solutions, offering valuable insights and solutions for improving patient care and outcomes.

How does module tracking for agrivoltaics differ from standard photovoltaics? Food, energy, and technoeconomic implications

Module tracking can be an effective approach for spatial-temporal sharing of sunlight between solar modules and crops in agrivoltaics (AV). For desired food-energy yield across various seasons and crops, tracking needs to be customized based on factors including crop type, module array density, economics and social preferences, and policy. Here, we explore customized tracking (CT) along single axis for AV to meet the desired food-energy yield and economic performance for a variety of module configurations and crop types. CT is implemented through a time multiplexing of standard tracking and anti-tracking along the day to balance sunlight between modules and crops, respectively. Module energy and spatial-temporal shading are modeled using the view factor approach while the crop response to shading is modeled empirically. Economic factors including module hardware/soft costs and crop profit are explored to model the economic performance using price-performance ratio. A case study done at Punjab, Pakistan shows that 5 hours of daily standard tracking around noon can ensure 80 % of the typical energy yield targets while maintaining 40 % and 80 % relative biomass yield for crops having high and moderate shade sensitivity, respectively. When module row spacing is increased 2–3 times relative to a typical ground mounted photovoltaic (GMPV) system, biomass yield for crops having high shade sensitivity can be substantially recovered while also improving economic performance. Due to a higher capital cost, 30–40 % increase in feed-in-tariff could be required to make the tracking AV economically competitive to a typical GMPV. The proposed approach can be highly effective for customized module tracking design and economic analysis for AV.

Spatial and temporal thermal management of a spintronic terahertz emitter

This work presents methods for addressing undesirable thermal effects induced by the pump beam of an oscillator laser to improve the efficiency of a terahertz (THz) spintronic emitter. We explore two approaches: spatial distribution of pump energy using a 2D lens array and temporal modulation of the pump duty cycle. Optimizing the spatial distribution approximately doubles the THz signal by increasing local heat dissipation, delaying the saturation limit. Similarly, temporal spreading of pump pulses by adjusting the duty cycle allows greater thermal relaxation within the heterostructure, enhancing the overall efficiency of THz wave generation.

Integrated photonic fractional convolution accelerator

An integrated photonic circuit architecture to perform a modified-convolution operation based on the discrete fractional Fourier transform (DFrFT) is introduced. This is accomplished by utilizing two nonuniformly coupled waveguide lattices with equally spaced eigenmode spectra, the lengths of which are chosen so that the DFrFT and its inverse operations are achieved. A programmable modulator array is interlaced so that the required fractional convolution operation is performed. Numerical simulations demonstrate that the proposed architecture can effectively perform smoothing and edge detection tasks even for noisy input signals, which is further verified by electromagnetic wave simulations. Notably, mild lattice defects do not jeopardize the architecture performance, showing its resilience to manufacturing errors.

Numerical simulation of dust deposition characteristics of photovoltaic arrays taking into account the effect of the row spacing of photovoltaic modules

Dust deposition on the surfaces of Photovoltaic (PV) arrays during their operation markedly affects their power generation efficiency. Previous research has overlooked the impact of the row spacing of PV modules on the actual dust deposition on PV arrays. This study investigates the dust deposition process and its behavior on PV arrays considering variations in row spacings, inlet wind speeds, dust particle sizes, and dust particle counts. By employing commercial Computational Fluid Dynamics (CFD) software, incorporating the SST k-ω turbulence model and discrete particle model, numerical simulations were performed to analyze the airflow field, dust particle trajectories, dust deposition patterns, and deposition rates on the PV array through grid-independent verification and numerical validation. At the same time, we performed a comparative analysis of the deposition rate considering the rebound of dust particles on the PV module surface versus the case where rebound is not considered. Results revealed that the maximum dust deposition rates at different inlet wind speeds were 6.84 %, 8.84 %, 11.0 %, and 14.6 %, corresponding to dust sizes of 50 μm, 100 μm, 120 μm, and 300 μm, respectively. Smaller dust particles exhibited lower deposition rates, while larger particles were influenced more by mass inertia and gravity, leading to predominant deposition on the front row of PV modules. Larger dust particles are more likely to deposit primarily on the front row of PV modules in a PV array. The tilt angles of 30°, 45°, and 60° were chosen to study the effects of different tilt angles on the dust deposition of PV arrays, and the results show that the dust deposition rate decreases as the tilt angle increases, and it is worth noting that the larger the tilt angle, the larger the dust deposition rate is when the dust particles are especially small as 5 μm. When wind speeds are low and dust particles are small, the dust deposition rate gradually rises as the row spacing increases. However, at higher wind speeds with small dust particles, the row spacing has minimal impact on the dust deposition rate. Conversely, with larger dust particles, increasing the row spacing results in a lower dust deposition rate. These findings underscore the significance of optimizing PV array design for enhanced power generation efficiency.

Flame intensity sensor based on the resistive and memory properties of spintronic memristor

A flame intensity sensor based on the resistive and memory properties of spintronic memristor is proposed. The sensor circuit mainly consists of the infrared radiation conversion module, the spintronic memristor array module, the amplifier and current limiting resistor. In order to verify the designed sensor, experiments were conducted under no flame, constant and gradually increasing flame combustion temperature, and the effects of the number of parallel memristor arrays, current limiting resistors, measurement distance and radiation wavelength on the sensor were analyzed. The experiments results demonstrate that the designed flame intensity sensor based on the resistive and memory properties of spintronic memristor can quantitatively measure the flame intensity. When the temperature change rate of flame combustion is 1℃/s, the sensor measurement range is 0–3.1 mW/cm2, and the sensitivity is 4.516kΩ/(mW/cm2). The rate of change in the resistance of the spintronic memristor array may be affected by the changes in flame intensity, but this does not affect the measurement range of the sensor.