Processing Module Research Articles

A chip-integrated homodyne detection system with enhanced bandwidth performance for quantum applications

The rapid development of quantum technology has driven the need for high-performance quantum signal processing modules. Balanced homodyne detector (BHD) is one of the most promising options for practical quantum state measurement, providing substantial advantages of cost-effectiveness, no cooling requirement, and system compactness. However, due to the stringent requirements in BHD design, it typically suffers from a relatively small operating bandwidth which limits the overall speed of a quantum system. In this study, we propose comprehensive modelling for the BHD in quantum applications and enhance the performance of BHDs based on our modelling. Specifically, we utilise a photonic chip approach and optimise the electronic design to create the integrated BHD, which significantly boosts the 3 dB bandwidth to 4.75 GHz and achieves a shot-noise-limited bandwidth of 23 GHz. We demonstrate the capability of this setup to generate quantum random numbers at a rate of 240 Gbit s−1, highlighting its potential for ultra-high-speed quantum communication and quantum cryptography applications.

Dynamic water migration and flavor analysis of sea cucumber in the process of Sichuan pepper seasoning soak

Soaking in seasoning solution is the main process of sea cucumber seasoning. This study analyzed the dynamic changes in water migration and flavor substances in sea cucumbers during soaking in a Sichuan pepper solution. It was found that the sea cucumber experienced a process of water absorption followed by water loss during the 0–48 h soaking process. During this period, the flavor compounds in sea cucumbers showed different dynamic trends. A total of 46 volatiles were identified, of which 29 were key flavor compounds. Its flavor profiles tended to stabilize as soaking time increased. m-Xylene, d-Limonene, Eucalyptol, p-Xylene, Sabinene, Beta-Myrcene, and Beta-Phellandrene were the main characteristic substances contributing to the differences in sea cucumber flavor. Correlation analysis predicted the relationship between water migration and the dynamic shifts in flavor compounds. This study provides a crucial reference for future studies on the processing and flavor modulation of sea cucumber products.

Proactive safety hazard identification using visual–text semantic similarity for construction safety management

Automated safety management in construction can reduce injuries by identifying hazardous postures, actions, and missing personal protective equipment (PPE). However, existing computer vision (CV) methods have limitations in connecting recognition results to text-based safety rules. To address this issue, this paper presents a multi-modal framework that bridges the gap between construction image monitoring and safety knowledge. The framework includes an image processing module that utilizes CV and dense image captioning techniques, and a text processing module that employs natural language processing for semantic similarity evaluation. Experiments showed a mean average precision of 49.6% in dense captioning and an F1 score of 74.3% in hazard identification. While the proposed framework demonstrates a promising multi-modal approach towards automated safety hazard identification and reasoning, improvements in dataset size and model performance are still needed to enhance its effectiveness in real-world applications.

Multi-Resolution Learning and Semantic Edge Enhancement for Super-Resolution Semantic Segmentation of Urban Scene Images.

Super-resolution semantic segmentation (SRSS) is a technique that aims to obtain high-resolution semantic segmentation results based on resolution-reduced input images. SRSS can significantly reduce computational cost and enable efficient, high-resolution semantic segmentation on mobile devices with limited resources. Some of the existing methods require modifications of the original semantic segmentation network structure or add additional and complicated processing modules, which limits the flexibility of actual deployment. Furthermore, the lack of detailed information in the low-resolution input image renders existing methods susceptible to misdetection at the semantic edges. To address the above problems, we propose a simple but effective framework called multi-resolution learning and semantic edge enhancement-based super-resolution semantic segmentation (MS-SRSS) which can be applied to any existing encoder-decoder based semantic segmentation network. Specifically, a multi-resolution learning mechanism (MRL) is proposed that enables the feature encoder of the semantic segmentation network to improve its feature extraction ability. Furthermore, we introduce a semantic edge enhancement loss (SEE) to alleviate the false detection at the semantic edges. We conduct extensive experiments on the three challenging benchmarks, Cityscapes, Pascal Context, and Pascal VOC 2012, to verify the effectiveness of our proposed MS-SRSS method. The experimental results show that, compared with the existing methods, our method can obtain the new state-of-the-art semantic segmentation performance.

Clustered synapses develop in distinct dendritic domains in visual cortex before eye opening.

Synaptic inputs to cortical neurons are highly structured in adult sensory systems, such that neighboring synapses along dendrites are activated by similar stimuli. This organization of synaptic inputs, called synaptic clustering, is required for high-fidelity signal processing, and clustered synapses can already be observed before eye opening. However, how clustered inputs emerge during development is unknown. Here, we employed concurrent in vivo whole-cell patch-clamp and dendritic calcium imaging to map spontaneous synaptic inputs to dendrites of layer 2/3 neurons in the mouse primary visual cortex during the second postnatal week until eye opening. We found that the number of functional synapses and the frequency of transmission events increase several fold during this developmental period. At the beginning of the second postnatal week, synapses assemble specifically in confined dendritic segments, whereas other segments are devoid of synapses. By the end of the second postnatal week, just before eye opening, dendrites are almost entirely covered by domains of co-active synapses. Finally, co-activity with their neighbor synapses correlates with synaptic stabilization and potentiation. Thus, clustered synapses form in distinct functional domains presumably to equip dendrites with computational modules for high-capacity sensory processing when the eyes open.

Rolling bearing fault diagnosis method based on PE-DCM and ViT

Abstract Considering the issue of capturing the local and global contextual information and enhancing the parallel capability of bearing fault diagnosis in variable load and noise environments, a fault diagnosis method of rolling bearing based on PE-DCM and ViT is proposed. Firstly, the one-dimensional vibration signal is converted into a two-dimensional time-frequency diagram by continuous wavelet transform in the data processing module, and the model can understand the characteristics of the vibration signal more comprehensively. Secondly, a pyramid exponential expansion convolution module is established to extract the local features of fault information. Then, the global features of the fault information are learnt through the ViT (Vision Transformer) network, and the adaptive multi-attention is used to dynamically adjust the attention weights according to the features of the input data so as to inhibit noise or unimportant information. Finally, the experimental verification is carried out by using Case Western Reserve University and self-made MFS-bearing data set. The experimental results show that the method can better reflect the powerful image classification ability of the ViT network and has better noise resistance and generalization compared with other fault diagnosis methods.

An intelligent deep learning approach to spacecraft attitude control: The case of satellites

This paper presents a method using artificial intelligence (AI) and deep learning to transform an on- board camera into an intelligent star sensor. This method overcomes the problems and limitations of conventional star sensors, such as cost, size, weight, power consumption, camera constraints and orientation accuracy. The method proposes an intelligent processing module embedded in the camera to determine the attitude of the spacecraft, and uses two convolutional neural network models to improve the resolution of star images and determine the attitude of the spacecraft from these images. This approach guarantees high spacecraft orientation accuracy and real-time processing, contributing to the success of space missions. The off-line phases of collecting and pre-processing stellar images, building intelligent models and labeling training data are described in detail. The results show that this method offers improved performance over conventional star sensors, and presents significant contributions to scientific research and technological innovation in the field of space remote sensing, contributing to sustainable development and understanding of the universe.

Development of a new tuning method for an radio frequency quadrupole accelerator

Tuning is a critical step in the low-power test of the radio frequency quadrupole (RFQ) accelerator. A 163 MHz RFQ for boron neutron capture therapy (BNCT) at Lanzhou University has been fully assembled. Its designed output energy is 2.6 MeV, and the designed output current in continuous wave mode is 30 mA. Before the feeding of high power, a low-power tuning was performed. First, based on the analysis of the tuning algorithm, a new tuning code capable of tuning the RFQ longitudinal field distribution to any desired target field distribution has been developed using Matlab. The tuning code mainly consisted of two parts: the data processing module and the tuning module, including sub-functions such as smoothing processing function and Fourier expansion function. Among them, the smoothing function based on the Gaussian distribution can effectively eliminate random errors in the measured voltage distribution, thereby optimizing the tuning procedure. Then, a new bead-pull system which can directly track the cavity phase shift was built. The bead-pull system was mainly composed of a vector network analyzer, a stepper motor, etc. Finally, the field unflatness of the RFQ was controlled below 1% after 4 times of tuning. The experiment also measured the radio-frequency performance of the RFQ cavity after installing the fixed copper tuners. The results showed that the RFQ operation frequency was 162.937 MHz, the unflatness of the quadrupole voltage was ±0.74%, and the asymmetry of the dipole voltage was ±1.19%, all of which met the requirements of beam dynamics.

A study of association of the VNTR MIR-137 rs58335419 with schizophrenia

The MIR137 gene encodes microRNA-137 (miR-137), which is a brain-enriched miR that is highly expressed in various brain regions. miR-137 has been identified as a modulator of processes involved in the pathogenesis of neuropsychiatric disorders. Functional polymorphism of variable number of tandem repeats (VNTR) rs58335419 was found in the regulatory region of the MIR137 gene. It is associated with a change in the expression of miR-137 and, as a result, with an increased risk of developing psychopathologies, including schizophrenia. In this study, we for the first time have analyzed the distribution of frequencies of alleles and genotypes of VNTR MIR137 in a large sample from the Russian population. The association of VNTR with the risk of schizophrenia has been studied. It was found that the presence of VNTR alleles with more than three repeats, as well as a genotype homozygous for such alleles, is associated with an increased risk of developing schizophrenia (OR = 1.4, 95% CI: 1.01-1.95).

MiR-708-3p targetedly regulates LSD1 to promote osteoblast differentiation of hPDLSCs in periodontitis.

Periodontitis (PD) is a multifactorial inflammatory disease associated with periodontopathic bacteria. Lysine-specific demethylase 1 (LSD1), a type of histone demethylase, has been implicated in the modulation of the inflammatory response process in oral diseases by binding to miRNA targets. This study investigates the molecular mechanisms by which miRNA binds to LSD1 and its subsequent effect on osteogenic differentiation. First, human periodontal ligament stem cells (hPDLSCs) were isolated, cultured, and characterized. These cells were then subjected to lipopolysaccharide (LPS) treatment to induce inflammation, after which osteogenic differentiation was initiated. qPCR and western blot were employed to monitor changes in LSD1 expression. Subsequently, LSD1 was silenced in hPDLSCs to evaluate its impact on osteogenic differentiation. Through bioinformatics and dual luciferase reporter assay, miR-708-3p was predicted and confirmed as a target miRNA of LSD1. Subsequently, miR-708-3p expression was assessed, and its role in hPDLSCs in PD was evaluated through overexpression. Using chromatin immunoprecipitation (ChIP) and western blot assay, we explored the potential regulation of osterix (OSX) transcription by miR-708-3p and LSD1 via di-methylated H3K4 (H3K4me2). Finally, we investigated the role of OSX in hPDLSCs. Following LPS treatment of hPDLSCs, the expression of LSD1 increased, but this trend was reversed upon the induction of osteogenic differentiation. Silencing LSD1 strengthened the osteogenic differentiation of hPDLSCs. miR-708-3p was found to directly bind to and negatively regulate LSD1, leading to the repression of OSX transcription through demethylation of H3K4me2. Moreover, overexpression of miR-708-3p was found to promote hPDLSCs osteogenic differentiation in inflammatory microenvironment. However, the protective effect was partially attenuated by reduced expression of OSX. Our findings indicate that miR-708-3p targetedly regulates LSD1 to enhance OSX transcription via H3K4me2 methylation, ultimately promoting hPDLSCs osteogenic differentiation.

Widened Attention‐Enhanced Atrous Convolutional Network for Efficient Embedded Vision Applications under Resource Constraints

Onboard image analysis enables real‐time autonomous capabilities for unmanned platforms including aerial, ground, and aquatic drones. Performing classification on embedded systems, rather than transmitting data, allows rapid perception and decision‐making critical for time‐sensitive applications such as search and rescue, hazardous environment exploration, and military operations. To fully capitalize on these systems’ potential, specialized deep learning solutions are needed that balance accuracy and computational efficiency for time‐sensitive inference. This article introduces the widened attention‐enhanced atrous convolution‐based efficient network (WACEfNet), a new convolutional neural network designed specifically for real‐time visual classification challenges using resource‐constrained embedded devices. WACEfNet builds on EfficientNet and integrates innovative width‐wise feature processing, atrous convolutions, and attention modules to improve representational power without excessive overhead. Extensive benchmarking confirms state‐of‐the‐art performance from WACEfNet for aerial imaging applications while remaining suitable for embedded deployment. The improvements in accuracy and speed demonstrate the potential of customized deep learning advancements to unlock new capabilities for unmanned aerial vehicles and related embedded systems with tight size, weight, and power constraints. This research offers an optimized framework, combining widened residual learning and attention mechanisms, to meet the unique demands of high‐fidelity real‐time analytics across a variety of embedded perception paradigms.

A multiscale time-series decomposition learning for crude oil price forecasting

Crude oil price forecasting is important for market participants and policymakers. However, accurately tracking oil prices is quite a challenging task due to the complexity of temporal oil data and the nonlinear relationships involved in the forecasting task. In this study, a multiscale time-series decomposition learning framework is proposed to deal with this issue. First, a multiscale temporal processing module is designed to capture different frequency time-series patterns in historical data at various scales. Then, a multiscale decomposition technique is applied to decompose historical crude oil data into various temporal modes, involving global shared information across multiple scales, as well as local specific information that varies at each scale. Finally, a multiscale fusion mechanism is employed to combine these information, which are further used as inputs to construct nonlinear and complex predictive models for crude oil prices. A series of experiments conducted on Shanghai crude oil market demonstrate that the proposed approach outperforms several econometric and machine learning models.

BcRflow: a Nextflow pipeline for characterizing B cell receptor repertoires from non-targeted transcriptomic data.

B cells play a critical role in the adaptive recognition of foreign antigens through diverse receptor generation. While targeted immune sequencing methods are commonly used to profile B cell receptors (BCRs), they have limitations in cost and tissue availability. Analyzing B cell receptor profiling from non-targeted transcriptomics data is a promising alternative, but a systematic pipeline integrating tools for accurate immune repertoire extraction is lacking. Here, we present bcRflow, a Nextflow pipeline designed to characterize BCR repertoires from non-targeted transcriptomics data, with functional modules for alignment, processing, and visualization. bcRflow is a comprehensive, reproducible, and scalable pipeline that can run on high-performance computing clusters, cloud-based computing resources like Amazon Web Services (AWS), the Open OnDemand framework, or even local desktops. bcRflow utilizes institutional configurations provided by nf-core to ensure maximum portability and accessibility. To demonstrate the functionality of the bcRflow pipeline, we analyzed a public dataset of bulk transcriptomic samples from COVID-19 patients and healthy controls. We have shown that bcRflow streamlines the analysis of BCR repertoires from non-targeted transcriptomics data, providing valuable insights into the B cell immune response for biological and clinical research. bcRflow is available at https://github.com/Bioinformatics-Core-at-Childrens/bcRflow.

Evaluasi Program Pembelajaran Kurikulum Merdeka di Ma’had Utsman bin ‘Affan Binbaz 25 Jambi

This research aims to evaluate the implementation of the Merdeka Curriculum learning program at Ma'had Utsman bin 'Affan BINBAZ 25 Jambi. The aspects evaluated include: planning, implementation, and learning evaluation. The method used in this research is descriptive qualitative. The subjects in this research were 4 teachers, Ma'had Utsman bin 'Affan BINBAZ 25 Jambi, and 4 students. The data were analyzed using critical analysis techniques, with the Miles and Huberman model through data reduction, data presentation, and data verification. The results of the research show that the evaluation process for the Merdeka Curriculum Learning Program at Ma'had Utsman bin 'Affan BINBAZ 25 Jambi has not been implemented optimally. This can be seen from educators who 100% have not made teaching modules in the learning process, 50% of teachers who do not provide teaching materials in the form of learning media, 75% of teachers who do not create small groups in learning, and 75% of teachers who use objective and subjective tests.

Neurokinin1 − cholinergic receptor mechanisms in the medial Septum-Dorsal hippocampus axis mediates experimental neuropathic pain

The neurokinin-1 receptors (NK1Rs) in the forebrain medial septum (MS) region are localized exclusively on cholinergic neurons that partly project to the hippocampus and the cingulate cortex (Cg), regions implicated in nociception. In the present study, we explored the hypothesis that neurotransmission at septal NK1R and hippocampal cholinergic mechanisms mediate experimental neuropathic pain in the rodent chronic constriction injury model (CCI). Our investigations showed that intraseptal microinjection of substance P (SP) in rat evoked a peripheral hypersensitivity (PH)-like response in uninjured animals that was attenuated by systemic atropine sulphate, a muscarinic-cholinergic receptor antagonist. Conversely, pre-emptive destruction of septal cholinergic neurons attenuated the development of PH in the CCI model that also prevented the expression of cellular markers of nociception in the spinal cord and the forebrain. Likewise, anti-nociception was evoked on intraseptal microinjection of L-733,060, an antagonist at NK1Rs, and on bilateral or unilateral microinjection of the cholinergic receptor antagonists, atropine or mecamylamine, into the different regions of the dorsal hippocampus (dH) or on bilateral microinjection into the Cg. Interestingly, the effect of L-733,060 was accompanied with a widespread decreased in levels of CCI-induced nociceptive cellular markers in forebrain that was not secondary to behaviour, suggesting an active modulation of nociceptive processing by transmission at NK1R in the medial septum. The preceding suggest that the development and maintenance of neuropathic nociception is facilitated by septal NK1R-dH cholinergic mechanisms which co-ordinately affect nociceptive processing in the dH and the Cg. Additionally, the data points to a potential strategy for pain modulation that combines anticholinergics and anti-NKRs.

Design of Digital Lock-in Amplifier based on FPGA

Abstract In order to achieve high-precision measurement of strong noise background signal, a digital lock-in amplifier based on field programmable gate array (FPGA) is designed. The core of the lock-in amplifier consists of a signal processing module, an acquisition and quantisation module and a digital demodulation module. The signal processing module completes the amplification and gain adjustment of the input signal and modulates the signal to be measured into a specific frequency band, which is digitally quantised by the analog to digital converter (ADC) after anti-aliasing filtering and transmitted to the FPGA, and the signal to be measured is extracted by phase-sensitive detection through the mutual-correlation digital demodulation method, thus realising the detection of the weak signal. The performance of the lock-in amplifier is evaluated with a signal generator as the input source, and the results show that the signal-to-noise improvement ratio is 35dB and the relative measurement error of voltage is less than 0.03% after the digital lock-in amplifier is processed, which accurately restores the direct current (DC) signals in the noisy background, and provides an effective technical way for the accurate measurement of weak signals.

Achieving Better Category Separability for Hyperspectral Image Classification: A Spatial-Spectral Approach.

The task of hyperspectral image (HSI) classification has attracted extensive attention. The rich spectral information in HSIs not only provides more detailed information but also brings a lot of redundant information. Redundant information makes spectral curves of different categories have similar trends, which leads to poor category separability. In this article, we achieve better category separability from the perspective of increasing the difference between categories and reducing the variation within category, thus improving the classification accuracy. Specifically, we propose the template spectrum-based processing module from spectral perspective, which can effectively expose the unique characteristics of different categories and reduce the difficulty of model mining key features. Second, we design an adaptive dual attention network from spatial perspective, where the target pixel can adaptively aggregate high-level features by evaluating the confidence of effective information in different receptive fields. Compared with the single adjacency scheme, the adaptive dual attention mechanism makes the ability of target pixel to combine spatial information to reduce variation more stable. Finally, we designed a dispersion loss from the classifier's perspective. By supervising the learnable parameters of the final classification layer, the loss makes the category standard eigenvectors learned by the model more dispersed, which improves the category separability and reduces the rate of misclassification. Experiments on three common datasets show that our proposed method is superior to the comparison method.

Color image restoration by filtering methods: a review

Digital images are corrupted with noise, and image denoising is an important step in image processing modules. In this review, the latest developments in filtering methods for color image restoration are analyzed. These algorithms are compared in terms of objective image quality measures and divided into major classes, such as spatial domain, switching and wavelet filtering methods. These classes are based on the particular methodology used in image denoising algorithms and further subdivided to show their classification in terms of noise models utilized, application style, and stages the filters applied in images. In particular, we present a review of filtering methods in color image denoising, published over the past two decades. Our classification and succinct descriptions of color image restoration by these mathematical filtering techniques and their characterizations can help choose the appropriate ones for various downstream image processing tasks.

EmoConfident Interviewer :An AI Mock Interview Evaluator

Abstract: Interviews hold great significance for candidates as it's the moment when their hard work is put to the test in hopes of achieving their desired and successful life results. It plays a crucial role in our educational system and hiring process by helping to identify the best applicant based on the necessary abilities. We can do better by improving our communication and confidence skills through mock interviews. This article introduced new way to practice for interviews using AI-based mock interview platform. The three characteristics that our system will utilize to evaluate the user are emotions, confidence, and knowledge base. A deep learning CNN algorithm uses facial expressions to determine emotion classify the emotion into one of the seven categories, and the basis for evaluating confidence is voice recognition through the use Python modules for Pydub audio and natural language processing. A web scraping module will map keywords to internet resources by extracting them from incoming answers. Semantic analysis technique is utilized for knowledge assessment and keyword mapping. So, using this method will help the job candidate feel more confident and less stressed or anxious before the actual job interview

Adaptive image processing embedding to make the ecological tasks of deep learning more robust on camera traps images

Camera traps serve as a valuable tool for wildlife monitoring, generating a vast collection of images for ecologists to conduct ecological investigations, such as species identification and population estimation. However, the sheer volume of images poses a challenge, and the integration of deep learning into automated ecological investigation tasks remains complex, particularly when dealing with low-quality images in long-term monitoring programs. Existing approaches often struggle to strike a balance between image enhancement and deep learning for ecological tasks, thereby overlooking crucial information contained within low-quality images. This research introduces a pioneering adaptive image processing module (AIP) that seamlessly incorporates image processing into camera trap ecological tasks, elevating the performance of wildlife monitoring activities. Specifically, a differentiable image processing (DIP) module is presented to enhance low-quality images, with its parameters predicted by a Non-local based parameter predictor (NLPP). Additionally, an end-to-end approach based on hybrid data containing both original and synthetic data is proposed, encompassing adaptive image processing methods and downstream tasks for camera traps, adaptable to various scenarios. This approach effectively reduces the manual labor and time required for professional image processing. When applied to real-world camera trap images and synthetic image datasets, our method achieves an accuracy of 92.26% and 86.65% in classifying wildlife, respectively, demonstrating its robustness. By outperforming alternative methods under harsh conditions, the application of the adaptive image processing module instills greater confidence in deep learning applications within complex environments.