Modulation Process Research Articles

An innovative methodology for segmenting vessel like structures using artificial intelligence and image processing

Innovation is currently driving enhanced performance and productivity across various fields through process automation. However, identifying intricate details in images can often pose challenges due to morphological variations or specific conditions. Here, artificial intelligence (AI) plays a crucial role by simplifying the segmentation of images. This is achieved by training algorithms to detect specific pixels, thereby recognizing details within images. In this study, an algorithm incorporating modules based on Efficient Sub-Pixel Convolutional Neural Network for image super-resolution, U-Net based Neural baseline for image segmentation, and image binarization for masking was developed. The combination of these modules aimed to identify capillary structures at pixel level. The method was applied on different datasets containing images of eye fundus, citrus leaves, printed circuit boards to test how well it could segment the capillary structures. Notably, the trained model exhibited versatility in recognizing capillary structures across various image types. When tested with the Set 5 and Set 14 datasets, a PSNR of 37.92 and SSIM of 0.9219 was achieved, surpassing significantly other image superresolution methods. The enhancement module processes the image using three different varaiables in the same way, which imposes a complexity of O(n) and takes 308,734 ms to execute; the segmentation module evaluates each pixel against its neighbors to correctly segment regions of interes, generating an quadratic complexity and taking 687,509 ms to execute; the masking module makes several runs through the whole image and in several occasions it calls processes of complexity at 581686 microseconds to execute, which makes it not only the most complex but also the most exhaustive part of the program. This versatility, rooted in its pixel-level operation, enables the algorithm to identify initially unnoticed details, enhancing its applicability across diverse image datasets. This innovation holds significant potential for precisely studying certain structures’ characteristics while enhancing and processing images with high fidelity through AI-driven machine learning algorithms.

Synthesis of Kantil Tone Using The Frequency Modulation Method

Music is a creative form of expression that utilizes sound arranged in specific patterns to create artistic works that are enjoyable to the listener. However, in music, excessive or continuous exposure to loud sounds can damage the hair cells in the ear, potentially leading to hearing loss or even deafness. One challenge in musical instrument craftsmanship is the variation in sound produced by different kantil artisans. These differences in sound output lead to inconsistencies in the rhythm of the angklung gamelan in Bali. This research addresses the issue by focusing on the process of synthesizing kantil sounds to achieve a more consistent output. The research begins by inputting audio files for each sound bar in format. The recorded audio data undergoes preprocessing using the Fast Fourier Transform (FFT) method, which extracts key features from the dataset, such as the fundamental frequency. Additionally, the Hilbert Transform is applied to obtain the optimal sound each blade, which will later be used in the Frequency Modulation process. Once preprocessing is completed on the dataset for each blade, the fundamental frequency and signal are acquired. To evaluate the accuracy of the synthesis, the Root Mean Square Error (RMSE) is calculated to compare the original signal with the synthesized signal. This step helps determine the degree of difference between the two signals. Ultimately, the result is a synthesized kantil sound that closely resembles the original, helping to standardize sound output among different craftsmen and ensuring consistency in musical performances

Meta-Learning Approaches for Context-Aware Decision Support in Smart Agriculture and Autonomous Systems

The increasing reliance on intelligent systems in agriculture and autonomous operations dictates the need for robust context-aware decision support systems (CADSS) to cope with heterogeneous and changing conditions. Conventional systems struggle with heterogeneous data and dynamic contexts, motivating the need for a meta-learning-driven solution. The paper proposes a CADSS-based meta-learning (CADSS-ML) framework to advance the adaptability and precision of the system in those domains. This proposed CADSS-ML framework develops hierarchical meta-learning using model-agnostic meta-learning (MAML) to allow fast task adaptation with ease and reptile algorithms for efficient optimization. Convolutional Neural Networks (CNN) have been used for feature extraction in spatial data, whereas transformer models have been used for temporal data. The Graph Neural Network (GNN) based module processes a sensor network topology, while an attention mechanism dynamically updates the weights on contextual variables. To perform decision-making, reinforcement learning with reward shaping guarantees adaptive and optimal control action outcomes. The proposed system also incorporates federated learning for data privacy across distributed sensor nodes. Experimental results confirm that improvements in prediction accuracy, decision reliability, and resource utilization are enhanced by 20%, 15%, and 12%, respectively, compared with baseline models. Some of the diverse applications of the proposed framework include precision farming, autonomous navigation, and smart irrigation.

Microstructure Control and Hot Cracking Prevention During Laser Additive Manufacturing of Cobalt-Based Superalloy

Hot cracking is a frequent and severe defect that occurs during laser additive manufacturing of superalloys. In this work, a pulsed-wave (PW) laser modulation process was employed to control the solidification microstructure and reduce the hot cracking susceptibility of laser additive manufactured cobalt-based superalloy. The effects of continuous-wave (CW) and PW laser processing modes on the dendrite morphology, element segregation, eutectic phase, and hot cracking of fabricated Co-based superalloys were investigated. Optical microscopy, scanning electron microscopy, and energy-dispersive X-ray spectroscopy were used to characterize the microstructural characteristics of samples. A two-color pyrometer was used to measure the molten pool temperature variation under different laser processing modes. The results show that coarse columnar dendrites, chain-like eutectic carbides, and hot cracks were observed in the CW sample. In contrast, the fine equiaxed crystals, discrete eutectic carbides, and low-level residual stresses were obtained to avoid hot cracks, owing to the high cooling rate and the periodic melting and solidification of the molten pool under the PW laser processing mode. This work provides a new method for controlling solidification structure and hot cracking of laser additive manufactured Co-based superalloy.

Ketamine differentially affects implicit and explicit memory processes in rats.

Ketamine, a non-competitive NMDA receptor antagonist, produces antidepressant effects at subanesthetic doses. The therapeutic effect, however, is often accompanied by cognitive side effects, including memory impairments. Yet, the specific effects of ketamine on different processes of implicit and explicit memory remain to be elucidated. We examined the effect of an antidepressant dose of ketamine (10mg/kg, IP) on the encoding, retrieval, and modulation processes of fear memory and spatial memory in adult Wistar rats. Ketamine was administered before the fear acquisition, retrieval, or extinction procedures in a Pavlovian fear conditioning task. In another set of experiments, it was administered before the training, probe trial, or reversal training phases of the Morris Water Maze (MWM). The antidepressant dose of ketamine partially impaired fear extinction when administered before the acquisition or retrieval. In contrast, it facilitated memory modulation and decreased the escape latency in the first day of reversal training in the MWM when administered before the training or reversal training sessions. Encoding or retrieval performance in either type of memory was not affected. These findings show that ketamine does not impair the acquisition or retrieval processes of cued fear or spatial memory; but exerts differential effects on memory modulation of these implicit and explicit memory paradigms, by disrupting fear extinction and facilitating reversal spatial learning.

Unveiling the dual anti-viral and anti-bacterial potential of Tridax procumbens: integrating system biology and molecular modeling for therapeutic insights

This study evaluates the dual anti-viral and anti-bacterial activity of Tridax procumbens using system biology and molecular docking targeting the tumor necrosis factor (TNF) signaling pathway. The bioactive potential of Tridax procumbens was studied using phytochemical databases, and the essential biomolecules were selected for the study. The principle of geneVenn diagrams and protein target prediction was used to identify overlapping molecular targets with viral and bacterial diseases, which was later validated using various database mining tools like GeneCards and OMIM. Cytoscape software was applied to construct protein-protein interaction networks, which showed that TNF, AKT1, EGFR, SRC, and ESR1 are the hub genes of the networks. The gene ontology (GO) enrichment analysis also recapitulated the modulated biological processes, cellular components, and molecular functions of Tridax procumbens compounds, including their action on the TNF signaling in inflammation and immune responses. Molecular docking studies showed strong binding affinities of compounds such as cynaroside and 5,7,2,3,4-Pentahydroxy-3,6-dimethoxyflavone 7-glucoside to TNF, and molecular dynamics simulations confirmed these interactions’ stability. The findings suggest that Tridax procumbens exerts its therapeutic effects by modulating the TNF signaling pathway, offering significant anti-viral and anti-bacterial properties. This integrative approach provides insights into the Tridax procumbens’s mechanisms of action, supporting its potential as a source of novel therapeutic agents against infectious and bacterial diseases. There is still a need for further experimental research to define the full spectrum of Tridax procumbens’ therapeutic application.

RIS-assisted differential transmitted spatial modulation design

In this paper, we propose a novel reconfigurable intelligent surface (RIS)-assisted wireless communication design called the RIS-assisted differential transmitted spatial modulation (DTSM) scheme. The encoding process of the differential spatial modulation (DSM) is integrated into the DTSM scheme, where only one transmit antenna is activated per time slot to transmit the M-ary phase shift keying (PSK) modulation symbol through the RIS. Due to the detection characteristics of DSM, the bit error rate (BER) performance remains satisfactory without requiring channel state information estimation, thereby enhancing robustness. The RIS application in the proposed scheme mitigates the effects of shadow area fading by adjusting the phase of the reflected signals to improve the signal-to-noise ratio at the receiver. In simulation results by comparing to other RIS-assisted spatial modulation schemes, we can find that the proposed DTSM scheme demonstrates good BER performance across various scenarios, including Nakagami-m fading, which also indicates its potential for practical applications.

Honey bee inspired resource allocation scheme for IoT-driven smart healthcare applications in fog-cloud paradigm.

The Internet of Things (IoT) paradigm is a foundational and integral factor for the development of smart applications in different sectors. These applications are comprised over set of interconnected modules that exchange data and realize the distributed data flow (DDF) model. The execution of these modules on distant cloud data-center is prone to quality of service (QoS) degradation. This is where fog computing philosophy comes in to bridge this gap and bring the computation closer to the IoT devices. However, resource management in fog and optimal allocation of fog devices to application modules is critical for better resource utilization and achieve QoS. Significant challenge in this regard is to manage the fog network dynamically to determine cost effective placement of application modules on resources. In this study, we propose the optimal placement strategy for smart health-care application modules on fog resources. The objective of this strategy is to ensure optimal execution in terms of latency, bandwidth and earliest completion time as compared to few baseline techniques. A honey bee inspired strategy has been proposed for allocation and utilization of the resource for application module processing. In order to model the application and measure the effectiveness of our strategy, iFogSim Java-based simulation classes have been extended and conduct the experiments that demonstrate the satisfactory results.

The covariance analysis of the periodically non-stationary random signal with narrow-band modulation of carrier harmonics

The periodically non-stationary random signals (PNRSs), whose carrier harmonics are mo-dulated by jointly stationary high-frequency random processes are analyzed. A representation of the signal in the form of a superposition of high-frequency components is obtained and it is shown that these components are jointly periodically non-stationary random processes. The random process is periodically non-stationary of the second order only in the case when some of the cross-covariance functions of its modulation processes are not equal to zero. The correlations of the PNRP spectral harmonics and the correlations of the modulating processes in series representation are equivalent. Evaluating the specific features of the auto- and cross-covariances for modulating processes as well as contribution of each pair to the covariance component values allows us to detect defects at early stages.

Enhancing urban flow prediction via mutual reinforcement with multi-scale regional information

Intelligent Transportation Systems (ITS) are essential for modern urban development, with urban flow prediction being a key component. Accurate flow prediction optimizes routes and resource allocation, benefiting residents, businesses, and the environment. However, few methods address the spatial–temporal heterogeneity of urban flows. Existing methods typically capture spatial features solely from urban flows, but spatial feature sensitivity becomes a bottleneck when dealing with small or noisy datasets. To address this issue, we propose a method for urban flow prediction via mutual reinforcement with multi-scale regional information (MR-UFP). Firstly, we employ spatial–temporal random masking and spatial–temporal contrastive learning pre-training to directly mine spatial–temporal heterogeneity from historical flow data. Secondly, we transform the task of spatial feature extraction and embedding for urban flow prediction into a mutual reinforcement task by multi-scale region classification auxiliary task. The adaptive environment fusion module and real-time graph processing dynamically correlate regional and environmental features during flow prediction. To balance the mutual reinforcement of the two tasks, we design a joint loss function to optimize feature embedding and feedback correction, ensuring robust and accurate urban flow prediction. Extensive experiments on two real-world datasets demonstrate that MR-UFP outperforms baseline models, showcasing its robustness and effectiveness even with minimal data.

The Relation between Mentalized Affectivity and Negative Emotions in Chinese College Students: The Moderating Effect of Internet Altruistic Behavior

This article aims to investigate the impact of mentalized affectivity on negative emotions among Chinese college students based on a questionnaire survey of 899 university students. The study adopts a mentalized affectivity scale, an internet altruistic behavior scale, and a self-analysis questionnaire. The study’s findings reveal a negative correlation between mentalized affectivity and depression, anxiety, and stress, but a positive correlation with variables linked to internet altruistic behavior in college students. Additionally, the study reveals a significant moderating effect of internet altruistic behavior on the relationship between mentalized affectivity and depression and anxiety. It is concluded that an increased level of affectivity can help modulate negative emotions and that internet altruistic behavior can play a moderator role in the modulation process.

Controllable temporal twisting polarization within an ultrafast laser pulse.

We propose a method for controlling the time-varying polarization of optical pulses by introducing a quarter-wave plate into a 4-f pulse shaper and using a spatial light modulator to impose a group delay. This setup enables the polarization state of the incident pulse to vary over time. Specifically, for linearly chirped incident pulses, the polarization ellipse twists uniformly over time, while its ellipticity changes monotonically. As the group delay increases, the pulse intensity gradually splits in the time domain, transitioning from a single pulse with uniformly twisting polarization to two circularly polarized pulses with opposite chirality. We provide a detailed and comprehensive explanation of this modulation process using analytical expressions.

Design and performance analysis of integrated sensing and communication scheme based on LoRa signals

LoRa (Long Range) has garnered widespread adoption in Internet of Things (IoT) communications due to its extensive transmission range and robust resistance to interference. Furthermore, its capability to linearly discern deviations across both frequency and time domains renders it an ideal signal for sensing applications. Consequently, this article proposes an Integrated Sensing and Communication (ISAC) scheme based on the LoRa signal. On the communication side, while the modulation and demodulation processes of the LoRa signal are currently kept confidential, this article has theoretically derived its principles and further implemented the communication functionality of LoRa through experimental simulations. On the sensing side, through theoretical derivation and experimental simulation, the influence of different modulation information on the peak amplitude and position of pulse compression was analyzed. Two signal processing schemes are proposed to overcome the impact of modulation information, thereby enabling the sensing function. The communication and sensing performance of the proposed ISAC scheme was evaluated. Experimental results demonstrated that, even in low signal-to-noise ratio (SNR) environments, the communication function maintained a low bit error rate (BER), while the sensing function achieved a high target detection rate, both of which exhibited excellent performance.

Study on the mechanism of laser-induced breakdown and damage performance of internal defects in transparent materials through local optical field modulation.

This paper establishes a photothermal damage model for bubble impurities affecting laser optical field modulation based on Mie scattering theory and incorporates the effects of optical field modulation. This model elucidates the evolution mechanism of synergistic damage in fused silica, with simulation results validated through experimental verification. A novel characterization of optical breakdown due to bubble impurities is proposed, occurring on a millisecond timescale through the dynamic evolution of combustion waves. The model delineates the influence of bubble size and spacing on optical field distribution, temperature, stress distribution, and their evolutionary behaviors. The modulation of the optical field due to double bubble impurities creates a localized "hot spot," resulting in a differential transverse contraction stress at the edges of the bubble impurities, thereby reducing the damage threshold of fused silica. The spacing of 1.1 λ represents the enhancement node for optical field modulation by double bubble impurities. Furthermore, localized oscillations in the optical field arise when the spacing between the double bubbles exceeds 1.1 λ, attributed to changes in the refractive index at the bubble defects and resonance oscillations generated by optical field modulation. This study not only enhances our understanding of the optical field modulation processes occurring at 1064 nm in the presence of bubble defects but also establishes a theoretical foundation for detecting internal defects at this wavelength without inducing surface damage.

Signal-to-noise ratio enhancement for digitalizing low amplitude wideband signals in photonic analog-to-digital converters.

Photonic analog-to-digital converters (PADCs) have been investigated for nearly five decades as a promising approach to overcome the bandwidth and jitter problem and bring ADC performance to new levels. However, low-amplitude signals often struggle to achieve full-scale quantization accuracy, posing a basic challenge for achieving high signal-to-noise ratio (SNR) digitization. Here, we established an optical carrier-to-sideband ratio (OCSR) based sampler model to achieve the optimal combination of the modulation, loss compensation, and photoelectric detection processes. The OCSR-based sampler features the advantages of high useful signal gain, low noise figure, and the ability to function over a very wide frequency range. The low-bias region is investigated, and the corresponding OCSR is selected as the transfer function for the Mach-Zehnder modulator (MZM). The OCSR-based sampler enables a higher gain of the radio frequency (RF) information signal sidebands. After the beating at the photodetector, the useful signal power reaches the digitizer's full scale to fully utilize the quantization accuracy, thereby enhancing the SNR of the whole system. In the experiment, a 20 GSa/s PADC with 4 interleaved sub-channels is configured out. Considerable advantages of the proposed OCSR-based sampler over conventional quadrature-biased sampler are demonstrated in comparative tests. A ∼5 dB enhancement in SNR and an increase of ∼0.8 effective number of bits (ENOB) are achieved under sinusoidal signals, and linear frequency modulation (LFM) signals with 8 GHz instantaneous bandwidth as well.

Low-light image enhancement guided by multi-domain features for detail and texture enhancement

Low-light image enhancement holds significant value in the fields of computer vision and image processing, such as in applications like surveillance photography and medical imaging. Images captured in low-light environments typically suffer from significant noise levels, low contrast, and color distortion. Although existing low-light image enhancement techniques can improve image brightness and contrast to some extent, they often introduce noise or result in over-enhancement, leading to the loss of detail and texture. This paper introduces an innovative approach to low-light image enhancement by fusing spatial and frequency domain features while optimizing them with multiple loss functions. The core of the algorithm lies in its multi-branch feature extraction, multi-loss function constraints, and a carefully designed model structure. In particular, the model employs an encoder-decoder architecture, where the encoder extracts spatial features from the image, the Fourier feature extraction module captures frequency domain information, and the histogram feature encoder-decoder module processes global brightness distribution. These extracted features are then fused and reconstructed in the decoder to produce the enhanced image. In terms of loss functions, the algorithm combines perceptual loss, structural similarity loss, Fourier loss, and histogram loss to ensure comprehensive and natural enhancement effects. The novelty of this algorithm lies not only in its multi-branch feature extraction design but also in its unique model structure, which synergistically improves image quality across different domains, effectively preventing over-enhancement, and ultimately achieving a balanced enhancement of brightness, details, and texture. Experimental results on multiple datasets, including SIDD, LOL, MIT-Adobe-FiveK, and LOL-v2-synthetic, demonstrate that the proposed method outperforms existing techniques in terms of image detail, texture, and brightness. Specifically, it achieves a PSNR of 27.52 dB on the LOL dataset, surpassing Wavelet Diffusion by 1.19 dB. Additionally, on the LOL-v2-synthetic dataset, it achieves a PSNR of 29.56 dB, exceeding Wavelet Diffusion by 3.06 dB. These results demonstrate a significant enhancement in the visual quality of low-light images.

SiPM-based LiDAR with multipulse sequence modulation and multithreshold signal processing.

A light detection and range technology (LiDAR) system that enables rapid ranging under extremely low signal-to-noise ratios (SNRs) during daylight conditions based on a SiPM (silicon photomultiplier) detector is proposed. The system emits a sequence of modulated laser pulses by controlling the semiconductor laser and then processes the SiPM response signals using dynamic multithreshold. The experimental result shows that, under extremely low SNR of daylight, the system achieves a 100% success rate in continuous ranging of a low reflectivity target at 125 m, with a ranging precision of less than 20 cm and a ranging time of less than 10 µs. Our system provides a significant reference value for achieving high-speed, noise-resistant, miniaturized, practical, and low-cost LiDAR based on a SiPM.

Posttraumatic Stress Disorder in Children: A Narrative Review

Method: This narrative review summarizes research from the years 2019-2024 on posttraumatic stress disorder (PTSD) and posttraumatic stress symptoms (PTSS) in children. Results: This research suggests that prevalence rates have been highly variable as a function of the stress experienced, for example, post-surgery (15%), following exposure to war (28%) and following head injury (34%). The negative effects have included cognitive dysfunction, aggression and other behavioral difficulties and the comorbidities include sleep disorders, somatic symptoms and depression. Predictors/risk factors have been the primary focus in this literature and have included parent risk factors including exposure to intimate partner violence, sexual and emotional abuse. Sensory modulation and cognitive processes have been implicated as, for example, negative attention bias and subjective threat. Injuries and hospitalizations have been predictors as has been exposure to explosions and earthquakes. The most frequently studied intervention has been cognitive behavioral therapy which has consistently reduced the symptoms of PTSD. Potential underlying biological mechanisms have included parent PTSD, pregnancy inflammation, and autonomic dysregulation including low cortisol and heartrate, although reduced cortical thickness (volume) in several areas of the brain has been the most frequently studied potential mechanism. Discussion: The studies in this review highlight the prevalence and severity of posttraumatic stress disorder in children. However, they have been primarily based on mixed-age child and adolescent samples and the results have varied by the type of stress experienced and the severity of the stress.

Evaluation of the effectiveness of incomplete phase pulse width modulation algorithms in the converter-electric motor system

Aim. This study evaluates the effectiveness of incomplete-phase algorithms for pulse width modulation of three-phase voltages in frequency control of electric drives, using integral current dispersion in the load as the criterion. Materials and methods. This study examines pulse width modulation processes in frequency-controlled electric drives, focusing on the converter-electric motor system. Methods from electric circuit theory are used, with proposed algorithms illustrated through drawings, and these drawing can be implemented in the matrix laboratory software. Results. The process of pulse width modulation in the converter-electric motor system is considered. It is demonstrated that to minimize the number of switching keys in the frequency converter, incomplete-phase pulse width modulation algorithms are beneficial. Expressions for local dispersion of the interphase current in single-phase and two-phase modulation are analyzed. An expression for local current dispersion in a three-phase bridge circuit load is derived, and the concept of integral current dispersion in a three-phase load is derived. Indicators for evaluating the effectiveness of algorithms for incomplete-phase pulse width modulation are proposed. Graphs the efficiency coefficient, characterizing incomplete-phase pulse width modulation with a minimum number of switching of key elements in the function of the amplitude coefficient and in the function of the relative frequency of modulation are constructed. Conclusion. The results aid in developing algorithms for controlling frequency converters in asynchronous electric drive systems.

Understanding inter-individual variability of experimental pain habituation and conditioned pain modulation in healthy individuals

Although reduced experimental pain habituation is proposed as a proxy of diminished endogenous pain modulatory capacity in chronic pain, prior studies show contradictory findings. Even across healthy participants, pain habituation varies substantially, which may relate to another measure of endogenous pain modulation, i.e., conditioned pain modulation (CPM). Hence, this study investigated the relationship between pain habituation and CPM. Pain habituation was assessed in 45 healthy participants between two blocks of 15–20 contact-heat stimuli applied to the hand. Habituation of subjective pain ratings and objective neurophysiological readouts (contact-heat evoked potential (CHEP) and palmar sympathetic skin response (SSR)) was investigated. CPM was assessed by comparing heat pain thresholds before and after hand immersion in a noxious cold (9 °C) and lukewarm water bath (32 °C, to control for repeated measures effects). Pain habituation showed a large variability, with subjective but not objective pain habituation correlating with cold-induced CPM effects (r = 0.50; p = 0.025). This correlation was not observed for ‘true’ CPM effects (corrected for repeated measures effects) nor for CPM effects induced by a lukewarm water bath. These findings suggest that the observed variability in subjective pain habituation may be influenced by both descending endogenous pain modulation and peripheral adaptation processes associated with repeated measures. Objective pain habituation readouts, i.e., CHEPs and SSRs, capture different, complementary aspects of endogenous pain modulation.