Multichannel Technique Research Articles

Experimental investigation of the radial structure of energetic particle driven GAM in TCV

Abstract High amplitude energetic particle geodesic acoustic mode (EGAM) oscillations driven by Neutral Beam (NBI) injected in the direction counter to the toroidal plasma current are observed in the TCV tokamak. The modes appear at frequencies close to the geodesic acoustic mode (GAM) frequency, corresponding to the mode radii calculated without plasma elongation corrections. The spatial structure of the EGAM density oscillation is analyzed using multichannel Soft X-Ray (SXR) and broadband light emission diagnostics. The analysis of spatiotemporal emission data shows a non-rotating structure of the density oscillations. The non-rotating mode discrimination is the additional new capability of the multichannel spatiotemporal SXR data analysis technique in TCV. We present the discrimination method of assessing the standing character of the EGAM wave. The structure of EGAM density oscillations in TCV is consistent with the theoretical GAM poloidal structure, namely a m=1 standing wave with density oscillations amplitude proportional to sin(θ), where the poloidal angle θ is measured from the equatorial plane. The poloidal structure of magnetic the field oscillations is analyzed using a poloidal array of magnetic coils. The structure of the EGAM-induced magnetic field oscillations in TCV is a m=2 standing wave. Time evolution of EGAMs suggests that a nonlinear EGAM chirping is observed. The chirping depends on the EGAM radial location, which varies as the NBI deposition is varied through a vertical shift of the plasma magnetic axis. The chirping disappears at the plasma periphery. A fast periodic radial shift of the EGAMs radial location is also observed to occur during the single chirp.

A Study of Potential Applications of Student Emotion Recognition in Primary and Secondary Classrooms

Emotion recognition is critical to understanding students’ emotional states. However, problems such as crowded classroom environments, changing light, and occlusion often affect the accuracy of recognition. This study proposes an emotion recognition algorithm specifically for classroom environments. Firstly, the study adds the self-made MCC module and the Wise-IoU loss function to make object detection in the YOLOv8 model more accurate and efficient. Compared with the native YOL0v8x, it reduces the parameters by 16% and accelerates the inference speed by 20%. Secondly, in order to address the intricacies of the classroom setting and the specific requirements of the emotion recognition task, a multi-channel emotion recognition network (MultiEmoNet) has been developed. This network fuses skeletal, environmental, and facial information, and introduces a central loss function and an attention module AAM to enhance the feature extraction capability. The experimental results show that MultiEmoNet achieves a classification accuracy of 91.4% on a homemade classroom student emotion dataset, which is a 10% improvement over the single-channel classification algorithm. In addition, this study also demonstrates the dynamic changes in students’ emotions in the classroom through visual analysis, which helps teachers grasp students’ emotional states in real time. This paper validates the potential of multi-channel information-fusion deep learning techniques for classroom teaching analysis and provides new ideas and tools for future improvements to emotion recognition techniques.

All-fiber IPDA lidar for CH4 leakage monitoring using InGaAs/InP single-photon detector.

An integrated path differential absorption (IPDA) lidar for CH4 leakage monitoring is proposed and demonstrated. In the simplified all-fiber optical layout, a homemade InGaAs/InP single-photon detector (SPD) using multi-channel technique with multi-mode fiber coupling is used to increase the maximum count rate and coupling efficiency. The system is calibrated in intensity and frequency domains. Firstly, the fluctuation of the laser power is compensated. Secondly, the dead time, afterpulsing probability and dark counts of the SPD are corrected. A mean relative difference of 0.84% between SPD and PIN photodetector is achieved. Thirdly, non-linear frequency scanning of the laser is measured by homodyne detection and analyzed in joint time-frequency domain. In the symmetry-calibration process, the absorbance spectra of up and down scanning are compared. Maximum difference less than 1% with mean difference of 0.33% is achieved within a span of 4 GHz around the center of absorbance spectrum. Finally, a demonstration experiment over ten days is carried out to analyze the accuracy and stability of the system. A mean deviation of 0.03% with standard deviation of 0.46% is verified at a distance of 12 m and a time resolution of 1 s. By attenuating the laser power from 2 mW to 0.02 mW, the performance of the system is degraded to a mean deviation of 1.32% with standard deviation of 4.33%.

Holographic multiplexing recording with an orthogonal polarized array.

This study combines tensor polarization holography theory and multichannel recording techniques and proposes a novel polarization encoding method, the orthogonal polarized array (OPA). This method can efficiently and independently reconstruct polarization holograms using accurate OPA waves in the reference-based multiplexing technique when the reference waves in the reading process have the same polarization state as those in the recording process. The novelty is that the multiplexing dimensions of the OPA can reach any number without limitations. Based on theoretical analyses of polarization hologram superposition characteristics and material characteristics, a polarization multiplexing holographic memory with an orthogonal linearly polarized array recording configuration was designed. The experimental results were verified, demonstrating the independent reconstruction of three and five holograms under array waves with different polarization combinations. In addition to high-density optical data storage, this study provides a new paradigm for high-capacity optical displays and information encryption applications.

Synchronisation of thermal imaging and multi-channel EIS to interpret planar array PCB fuel cell performance

The planar design of printed circuit board (PCB) fuel cells offers the advantages of simple structure, rapid prototyping and easy manufacturing. However, uneven reactants and heat distribution in each PCB cell module often result in inconsistent overall performance. Current characterisation methods mainly focus on the study of the overall performance and lack the analysis of individual cells, which provide a limited understanding of localised electrochemical and thermal effects that might play a critical part in further optimising and diagnosing the cell performance of this category. We have developed a multi-channel impedance diagnostic technique for PCB fuel cells. Combining with thermal imaging, we could simultaneously diagnose individual open cathode cell modules by measuring the heat distribution and electrochemical impedance spectra (EIS) of 11 PCB cell modules. Hence, the correlation between heat distribution, internal resistance and performance of different sections of PCB fuel cells can be established. By comparing the fuel cell’s pristine and degraded, we demonstrated how this non-destructive technique could identify and locate cell modules with deteriorated performance, which would be beneficial to real-time diagnostics, quality control and optimisation. The multi-channel impedance measurement takes several seconds only through optimised multi-frequency perturbation and can be readily extended to stacks containing more cells by simply increasing the number of voltage sensors, which can be further adapted to other electrochemical devices.

Generalization of the Synthetic Aperture Radar Azimuth Multi-Aperture Processing Scheme—MAPS

This paper analyzes the advantages and the drawbacks of using the Synthetic Aperture Radar (SAR) azimuth multichannel technique known as Multi-Aperture Processing Scheme (MAPS), in a set of relevant application cases that are far from the canonical ones. In the scientific literature on this topic, equally distributed azimuth channels with the quasi-monostatic deployment are assumed. With this research, we aim at extending the models from the current literature to (i) a generic bistatic acquisition geometry, (ii) a set of cases where the number of receiving tiles is not the same for each channel, or (iii) the tiles are shared between adjacent channels thus creating an overlapping configuration. The paper introduces the mathematical models for the listed non-conventional MAPS cases. Dealing with the bistatic MAPS, we first solve the problem by interpreting multichannel acquisition as a bank of Linear Time Invariant (LTI) filters. Then, a more physical approach, based on discrimination of the direction of arrivals (DoAs) is pursued. The effectiveness of the two methods and the advantages of the second approach on the first are proved by using a simplified 1D end-to-end simulation. Even limiting to the monostatic configuration, the azimuth antenna tiles have always been supposed equally partitioned among the RX channels. Overcoming this limit has two advantages: (i) more MAPS possible solutions in case few azimuth tiles are available, as in the ROSE-L mission; (ii) the number of channels can be designed independently of the number of tiles, also allowing asymmetric solutions, useful for a phase array antenna with an odd number of tiles such as in the SAOCOM-1 mission. Conversely, sharing one or more receiving tiles in different receiving channels makes the input noise partially correlated. The drawback is an increase in the noise level. A trade-off is determined for the different solutions obtained using simulations with real mission parameters. The theoretical performance and the end-to-end simulations are compared.

Conquering Digital Marketing: Powerful Strategies for Accelerating Business Growth

Understanding digital marketing tactics is now crucial for company expansion in the digital age due to the quickly changing technology landscape. This study investigates the efficacy of modern digital marketing strategies with an emphasis on how they support significant company growth. This study is significant because it offers practical advice on how to use data analytics, customization, and cutting-edge technology like machine learning and artificial intelligence to maximize digital marketing initiatives. The main goal of this study is to determine and evaluate the best digital marketing tactics that spur company expansion. Using a thorough literature review methodology, the study assesses the effects of different digital marketing methods on business performance by looking through contemporary academic sources. Important results show that cross-channel and multi-channel marketing techniques greatly improve brand coherence and consumer engagement. Enhancing targeting accuracy and campaign efficacy requires the use of data analytics and artificial intelligence capabilities. While ethical principles and digital transformation are essential for maintaining consumer trust and competitive advantage, personalization and customer feedback integration play a critical role in driving engagement and conversion rates. Furthermore, social media, influencer marketing, and strong content tactics work well to increase brand awareness and adherence. Strategic agility is ensured by remaining up to date with emerging trends, and marketing outcomes are further enhanced through optimization of conversion rates and user experience design. These findings' implications imply that companies should take a multifaceted strategy to digital marketing, including cutting-edge technologies and emphasizing ethical and personalized practices. It is advised to use multi-channel marketing techniques, make use of AI and data analytics, prioritize ethical marketing, and continuously improve user experience and content. Future studies ought to investigate how new digital trends—like gamification and behavioral targeting—affect the efficiency of marketing campaigns and the expansion of companies. In addition to offering a foundation for companies hoping to succeed in the cutthroat digital economy, this research advances our understanding of successful digital marketing tactics.

Abstract Mo066: A Bifunctional Actuator Reverses Na V 1.5 Dysfunction Linked To Cardiac Arrhythmias

Cardiac voltage-gated sodium channels (Na V 1.5) are critical for the initiation of action potentials within the heart. Alterations in Na V 1.5 function significantly contribute to diverse inherited and acquired arrhythmias, such as Long QT syndrome (LQT), Brugada syndrome (BrS), mixed-syndrome, and heart failure. These pathologies are closely associated with two distinct modifications in Na V 1.5 function: (1) a gain-of-function phenotype, characterized by incomplete inactivation of Na V 1.5 leading to a sustained Na + current ( I NaL ), and (2) a loss-of-function phenotype resulting in diminished peak Na + current ( I NaP ). Despite the broad understanding of Na V 1.5 implication in cardiac pathophysiology, the development of effective therapeutic strategies to reverse these dysfunctions remains challenging. Here, we introduce a novel bifunctional actuator that inhibits I NaL and upregulates I NaP , as a common molecular strategy to reverse pathophysiological changes in Nav-linked cardiac arrhythmias. To do so, we use a genetically encoded approach by engineering a selective nanobody (nb82) targeting Na V 1.5, linked to (1) a peptide inhibitor targeting I NaL (FixR), and (2) the catalytic domain of a deubiquitinase (DUB) to enhance Na V 1.5 membrane expression. Changes in I NaP and I NaL were assessed using whole-cell and multichannel electrophysiology techniques in both wild-type and channelopathic mutant channels. Our results demonstrate that NbFixR-DUB effectively reduces I NaL while concurrently increasing I NaP for multiple channelopathic mutations. Overall, this approach holds considerable promise for rectifying both loss-of-function and gain-of-function phenotypes in Nav channelopathies.

Review on potential nerve guidance conduits design for spinal cord injuries

The current treatment of spinal cord injuries (SCI) focuses mainly on the elimination of secondary damage to the central nervous system. The performance of treatments targeted at neurological recovery is not satisfactory. Inspired by the treatment of peripheral nerve injuries using tissue engineering, the nerve guidance conduits became one of the potential designs to improve the rate of neurological recovery among patients. With the proper design like multichannel and fabrication techniques like 3D bioprinting, the hybrid biomaterial may provide good guidance for nerves to grow in long gaps under well-picked neuromorphic factors. The new technologies including vascularization and electrical stimulation give more possibilities for the construction of treatment. New materials like shape-memory polymers may improve the fabrication of NGCs. However, the combination of these innovative findings was not performed for lab tests and clinical tests. The commercialization of NGC for SCI is also in need of better standardization and regulation. There may be a comprehensive NGC-based design for SCI emerging in the future which improves the performance of neurological recovery.

High-resolution wide-swath SAR imaging with multifrequency pulse diversity mode in azimuth multichannel system

ABSTRACT High-resolution wide-swath (HRWS) imaging has emerged as a focal point in synthetic aperture radar (SAR) research. However, conventional SAR systems face challenges in achieving both high azimuth resolution and wide swath simultaneously due to the minimum antenna area constraint. In this paper, we propose a HRWS imaging method based on multifrequency pulse diversity (MFPD) and azimuth multichannel (AMC) technique, capable of resolving range and Doppler ambiguities concurrently. In MFPD mode, range ambiguous echoes can be separated by matched filters in the range frequency domain, as multifrequency pulses are transmitted by a single channel in the transmitter. To further enhance the ambiguity resolution ability of the system and address azimuth incoherence, a novel scheme applying the MFPD to AMC system is proposed, categorized into two distinct scenarios. 1) Uniform Sampling: This scenario satisfies the displaced phase centre antenna condition. Under this condition, high range resolution imaging can be achieved through spectrum splicing in range frequency domain, simultaneously resolving Doppler ambiguity. 2) Non-uniform Sampling: In this scenario, the ambiguous echoes are processed by spatial digital beamforming and spectrum splicing in two-dimensional frequency domain. Finally, the HRWS imaging can be obtained by performing the traditional SAR algorithm. Furthermore, the proposed method can synthesize a wideband signal from multifrequency signals, thereby enhancing the feasibility of super-high-resolution imaging. Simulation results demonstrate the effectiveness of the proposed method.

Atypical Antiadrenergic Effect of Refralon as a Mechanism of High Antiarrhythmic Effectiveness.

We studied the effect of Refralon on the electrophysiological properties of the supraventricular myocardium against the background of adrenergic (epinephrine) influence in the zone of the pulmonary veins, the area where 50-90% of atrial arrhythmias is triggered. The experiments were carried out on isolated tissue preparations of Wistar rats. The multichannel microelectrode array technique was used to record action potentials simultaneously in the atrium and in the ostium and distal parts of the pulmonary veins. Epinephrine application (12-50 nM) led to depolarization of the resting potential and the conduction block in the distal part of the pulmonary veins. Refralon (30 μg/kg) restored the resting potential in the distal part of the pulmonary veins. Against the background of epinephrine, Refralon did not significantly change the duration of the action potential at 90% repolarization in comparison with control. At the same time, the comparison drug E-4031 against the background of epinephrine significantly increased the duration of action potential in the atrium and in the ostium of the pulmonary veins, and sotalol increased it only in the ostium. Neither E-4031, nor sotalol restored conduction in their distal part. Refralon has a biphasic effect under conditions of adrenergic stimulation: the fast component is responsible for stabilizing the resting potential in the pulmonary vein and reduces the dispersion of action potential duration in the atrium and pulmonary vein and is also quickly washed away, and the slow component is responsible for the increase of the action potential duration and is slowly washed away.

Deep Learning-Based Integrated Circuit Surface Defect Detection: Addressing Information Density Imbalance for Industrial Application

In this study, we aimed to address the primary challenges encountered in industrial integrated circuit (IC) surface defect detection, particularly focusing on the imbalance in information density arising from difficulties in data sample collection. To this end, we have developed a new hybrid architecture model for IC surface defect detection (SDDM), based on ResNet and Vision Transformer (ViT). The core innovation of SDDM lies in the integration of the concepts of image information density and dataset information density, effectively identifying and processing areas of high information density through multi-channel image segmentation techniques. The convolution operations performed within each patch of the model help to precisely capture positional information, thereby meticulously differentiating the complex details on the surface defect detection of ICs. We optimized the model to make it more suitable for industrial applications, significantly reducing computational and operational costs. The experimental results confirmed that the improved SDDM model achieved an accuracy rate of 98.6% on datasets with uneven information density, effectively enhancing the productivity of IC packaging and testing companies, particularly in handling datasets with imbalanced information density.

Design and Analysis of a Bandwidth Aware Adaptive Multipath N-Channel Routing Protocol for 5G Internet of Things (IoT)

Large numbers of mobile wireless nodes that can move randomly and join or leave the network at any moment make up mobile ad-hoc networks. A significant number of messages are delivered during information exchange in populated regions because of the Internet of Things' (IoT) exponential increase in connected devices. Congestion can increase transmission latency and packet loss by causing congestion. More network size, increased network traffic, and high mobility that necessitate dynamic topology make this problem worse. An adaptive Multipath Multichannel Energy Efficient (AMMEE) routing strategy is proposed in this study, in which route selection strategies depend on forecasted energy consumption per packet, available bandwidth, queue length, and channel utilization. While multichannel uses a channel-ideal assignment process to lessen network collisions, multipath offers various paths and balances network strain. The link bandwidth is split up into a few sub-channels in the multichannel mechanism. To reduce network collisions, several source nodes simultaneously access the channel bandwidth. The cooperative multipath multichannel technique offers several paths from a single source or from several sources to the destination without colliding or becoming congested. The AMMEE routing approach is the basis for path selection. A load- and bandwidth-aware routing mechanism in the proposed AMMEE chooses the path based on node energy and forecasts their lifetime, which improves network dependability. The outcome demonstrates a comparative analysis of various multichannel medium access control (MMAC) techniques, including Parallel Rendezvous Multi Channel Medium Access Protocol (PRMMAC), Quality of Service Ad hoc On Demand Multipath Distance Vector (QoS-AOMDV), Q-learning-based Multipath Routing (QMR), and Topological Change Adaptive Ad hoc On-demand Multipath Distance Vector (TA-AOMDV) and the proposed AMMEE method. The results show that the AMMEE approach outperforms alternative systems. Doi: 10.28991/ESJ-2024-08-01-018 Full Text: PDF

High-temperature thermal disturbance elimination method based on multi-channel extraction and correction.

Digital image correlation (DIC) technology has been widely used in high-temperature measurement fields. However, due to the complexity of high-temperature environments, there are many interference factors that limit the development of high-temperature DIC technology, among which thermal disturbance is one of the most significant factors that severely affects the measurement accuracy of high-temperature DIC. In this paper, a multi-channel separation technique combined with a low-cost laser speckle device is proposed to eliminate thermal disturbance errors in high-temperature DIC measurements. First, a blue laser speckle generation system is independently designed to produce the most suitable speckle particles, and the best laser speckle is determined and projected onto the blue background white spot pattern. Then a green LED illuminates the sample to provide illumination for the sample's own grayscale characteristics. A color camera collects photos, and the obtained images are processed with channel separation to extract and calculate the displacement of different channels. Finally, the displacement fields of the green and blue channels are subtracted to separate the thermal disturbance error and correct the measurement values. In this paper, a laser speckle projection system is first assembled, followed by a comprehensive evaluation of the projected speckle and, finally, a DIC experimental system is constructed for verification experiments at both room temperature and high temperature, and the corrected values are compared with the true values. The results show that the corrected values are highly consistent with the true values, which verifies the reliability of the proposed method.

Unbiased multivariate multiscale sample entropy

The development of multi-channel data acquisition techniques has provided richer prior information for studying the nonlinear dynamic characteristics of complex systems. However, conventional nonlinear feature extraction algorithms prove unsuitable in the context of multi-channel data. Previously, the multivariate multiscale sample entropy (MMSE) algorithm was introduced for multi-channel data analysis. Although the MMSE algorithm generalized the multiscale sample entropy algorithm, presenting a novel method for multidimensional data analysis, it remains deficient in theoretical underpinning and suffers from shortcomings, such as missing cross-channel correlation information and having biased estimation results. In this paper, unbiased multivariate multiscale sample entropy algorithm (UMMSE) is proposed. UMMSE increases the embedding dimension from <i>M</i> to <i>M</i> + <i>p</i>. This increasing strategy facilitates the reconstruction of a deterministic phase space. By virtue of theoretical scrutiny grounded in probability theory and subsequent experimental validation, this paper illustrates the algorithm's effectiveness in extracting inter-channel correlation information through the integration of cross-channel conditional probabilities. The computation of similarities between sample points across different channels is recognized as a potential source of bias and instability in algorithms.Through simulation experiments, this study delineates the parameter selection range for the UMMSE algorithm. Subsequently, diverse simulation signals are employed to showcase the UMMSE algorithm’s efficacy in extracting both within-channel and cross-channel correlation information. Ultimately, this paper demonstrates that the new algorithm has the lowest computational cost compared with traditional MMSE algorithms.

Graph Convolutional Networks With Adaptive Neighborhood Awareness.

Graph convolutional networks (GCNs) can quickly and accurately learn graph representations and have shown powerful performance in many graph learning domains. Despite their effectiveness, neighborhood awareness remains essential and challenging for GCNs. Existing methods usually perform neighborhood-aware steps only from the node or hop level, which leads to a lack of capability to learn the neighborhood information of nodes from both global and local perspectives. Moreover, most methods learn the nodes' neighborhood information from a single view, ignoring the importance of multiple views. To address the above issues, we propose a multi-view adaptive neighborhood-aware approach to learn graph representations efficiently. Specifically, we propose three random feature masking variants to perturb some neighbors' information to promote the robustness of graph convolution operators at node-level neighborhood awareness and exploit the attention mechanism to select important neighbors from the hop level adaptively. We also utilize the multi-channel technique and introduce a proposed multi-view loss to perceive neighborhood information from multiple perspectives. Extensive experiments show that our method can better obtain graph representation and has high accuracy.

A real time digital vibration acceleration fiber sensing system based on a multi-carrier modulation/demodulation technique

A multi-longitudinal mode fiber laser sensor (MLMFLS) system based on digital modulation/demodulation technique for vibration acceleration detection is proposed. To the best of the authors’ knowledge, this is the first digital vibration acceleration demodulation system ever reported by a MLMFLS system. A few beat frequency signals (BFS) generated by the MLMFLS are used as signal carriers of applied vibration signals. Based on multi-channel digital down-conversion technique, the weak vibration signals modulated on the BFS are superposed by utilizing the digital universal software radio peripheral (USRP). The signal-to-noise ratio (SNR) of the output vibration signal is greatly enhanced. A spectral subtraction method is also utilized to improve the SNR of the demodulated vibration signal. The acceleration of the vibration signal is obtained after second order differential of the time domain vibration signal. By simply changing the position of window function of short-time Fourier transform, a real-time vibration frequency and acceleration sensing system is achieved. The measurement results demonstrated a minimum detectable acceleration of 0.03 g at vibration frequency of 1.0 Hz by the designed sensing system. It also has a simple structure, high signal-to-noise ratio and stability.

Research on theoretical and experimental of Interference processing based on three-channel mutual injection phase-locked laser

With the advantage of obtaining multiple phase-locked laser outputs at the same time without complicated modulation devices, the multi-channel mutual injection technique provides a new idea to solve the problem of excessive use of beam splitting devices in traditional multi-beam interference processing systems. In this paper, we analyzed the phase-locking mechanism of the mutual injection technique, studied in detail the locking process of the phase difference of three lasers with time when their energy is injected into each other, and discussed the coherence characteristics of the three Gaussian beams after convergence. Accordingly, we built a three-beam interferometric processing system based on the three-channel mutual injection solid-state laser to process the surface of silicon material. The surface of the material was observed by SEM to exhibit a clear structure of regular shaped microporous arrays with a period of 3.0 µm distributed according to the regular pattern of positive hexagon, which was consistent with the interference pattern obtained from the simulation. The comprehensive theoretical and experimental results show that the laser obtained based on the three-channel mutual injection technique has good coherence characteristics and can be effectively applied in interference processing. This opens up a new idea for the development of interference processing equipment and has positive significance for the development of multi-beam interference processing.

Multichannel Extracellular Recording in Freely Moving Mice.

The protocol aims to uncover the properties of neuronal firing and network local field potentials (LFPs) in behaving mice carrying out specific tasks by correlating the electrophysiological signals with spontaneous and/or specific behavior. This technique represents a valuable tool in studying the neuronal network activity underlying these behaviors. The article provides a detailed and complete procedure for electrode implantation and consequent extracellular recording in free-moving conscious mice. The study includes a detailed method for implanting the microelectrode arrays, capturing the LFP and neuronal spiking signals in the motor cortex (MC) using a multichannel system, and the subsequent offline data analysis. The advantage of multichannel recording in conscious animals is that a greater number of spiking neurons and neuronal subtypes can be obtained and compared, which allows the evaluation of the relationship between a specific behavior and the associated electrophysiological signals. Notably, the multichannel extracellular recording technique and the data analysis procedure described in the present study can be applied to other brain areas when conducting experiments in behaving mice.

Cluster Analysis of Seismicity in the Eastern Gulf of Corinth Based on a Waveform Template Matching Catalog.

The Corinth Rift, in Central Greece, is one of the most seismically active areas in Europe. In the eastern part of the Gulf of Corinth, which has been the site of numerous large and destructive earthquakes in both historic and modern times, a pronounced earthquake swarm occurred in 2020-2021 at the Perachora peninsula. Herein, we present an in-depth analysis of this sequence, employing a high-resolution relocated earthquake catalog, further enhanced by the application of a multi-channel template matching technique, producing additional detections of over 7600 events between January 2020 and June 2021. Single-station template matching enriches the original catalog thirty-fold, providing origin times and magnitudes for over 24,000 events. We explore the variable levels of spatial and temporal resolution in the catalogs of different completeness magnitudes and also of variable location uncertainties. We characterize the frequency-magnitude distributions using the Gutenberg-Richter scaling relation and discuss possible b-value temporal variations that appear during the swarm and their implications for the stress levels in the area. The evolution of the swarm is further analyzed through spatiotemporal clustering methods, while the temporal properties of multiplet families indicate that short-lived seismic bursts, associated with the swarm, dominate the catalogs. Multiplet families present clustering effects at all time scales, suggesting triggering by aseismic factors, such as fluid diffusion, rather than constant stress loading, in accordance with the spatiotemporal migration patterns of seismicity.