Number Of Channels Research Articles

Large area tiles of position-sensitive silicon photomultipliers

Silicon photomultipliers (SiPMs) are highly sensitive photodetectors emerging as the technology of choice for many applications. In FBK, we developed a position sensitive SiPM technology, called “linearly graded” (LG-SiPM), which is based on an avalanche-current position-weighted partitioning approach. In particular, we recently developed a new version of LG-SiPM with a larger chip area (10 × 10 mm2) and based on NUV-HD technology, having a peak photon detection efficiency at 420 nm. These SiPMs have been characterized in terms of noise, detection efficiency and position resolution.We also developed tiles of 2x2 and 3x3 elements with such SiPMs, reaching very large sensitive areas of about 20 × 20 mm2 and 30 × 30 mm2. In such large area tiles we implemented a “smart channel” configuration, being able to have just 6 output channels and 8 channels respectively, with a position resolution below 0.5 mm. These detectors provide great advantages in all application requiring position resolution but with a small number of readout channels and reduced system complexity.

A possible path to persistent re-entry waves at the outlet of the left pulmonary vein

Atrial fibrillation (AF) is the most common form of cardiac arrhythmia, often evolving from paroxysmal episodes to persistent stages over an extended timeframe. While various factors contribute to this progression, the precise biophysical mechanisms driving it remain unclear. Here we explore how rapid firing of cardiomyocytes at the outlet of the pulmonary vein of the left atria can create a substrate for a persistent re-entry wave. This is grounded in a recently formulated mathematical model of the regulation of calcium ion channel density by intracellular calcium concentration. According to the model, the number of calcium channels is controlled by the intracellular calcium concentration. In particular, if the concentration increases above a certain target level, the calcium current is weakened to restore the target level of calcium. During rapid pacing, the intracellular calcium concentration of the cardiomyocytes increases leading to a substantial reduction of the calcium current across the membrane of the myocytes, which again reduces the action potential duration. In a spatially resolved cell-based model of the outlet of the pulmonary vein of the left atria, we show that the reduced action potential duration can lead to re-entry. Initiated by rapid pacing, often stemming from paroxysmal AF episodes lasting several days, the reduction in calcium current is a critical factor. Our findings illustrate how such episodes can foster a conducive environment for persistent AF through electrical remodeling, characterized by diminished calcium currents. This underscores the importance of promptly addressing early AF episodes to prevent their progression to chronic stages.

Thermal performance of a two-pass microchannel evaporator

Microchannel evaporator is one of the key components in many thermal systems such as heat exchanger of HAVC and refrigeration system. The evaporation heat transfer performance of microchannel evaporator, which usually has many flow paths, is highly affected by the distribution of liquid flow that will seriously deteriorate the flow boiling performance due to the lack of liquid supply, local dryout, etc. In this study, a novel two-pass microchannel evaporator (L × W × H = 103 × 64 × 20 mm) is designed and fabricated. The width, height and length of each individual channel are 0.6, 1 and 65 mm, respectively. To significantly improve liquid redistribution after the first-pass, evenly distributed spacers with different diameter holes were placed in the connecting flow path. Additionally, the channel number ratio between the first and second pass plays an important role. Therefore, three different channel ratios of 1:1, 1:2, and 3:5 were selected in this study. The heat transfer and flow characteristics of the two-pass microchannel evaporator using working fluid HFE-7100 were experimentally studied for flow rate varying from 30 ml/min to 110 ml/min. The inlet subcooling keeps about 30 K. Also, visualization studies were conducted for the explanations. The experimental results indicate that the thermal performance is superior in microchannel evaporator with channel ratio of 1:2. By considering Reynolds number of each channel in the first pass, microchannel evaporator with channel ratio of 1:1 outperforms other two evaporators. Furthermore, the wall temperature uniformity of this evaporator is better than others. The achieved heat flux of 20 W/cm2 is much higher than the reported studies. The overall two-phase HTC of this microchannel evaporator is about 4.5 kW/m2K.

Performance analysis of the dual-hop mixed RF/UWOC system with fog channel and turbulence based on diversity

ABSTRACT In this paper, the performance of a dual-hop mixed radio frequency (RF)/underwater wireless optical communication (UWOC) system with amplifying and forwarding (AF) relaying is investigated. The diversity combining technology were combined with unifying Fisher-Snedecor ( F ), extended generalized- K (EGK), and α − μ distribution for atmospheric turbulence in foggy conditions to analyze the probability density function (PDF) of RF links. The UWOC link experiences mixed exponential generalized gamma (EGG) fading. By utilizing diversity combination methods and considering the statistical effects of atmospheric turbulence, random fog, and underwater turbulence, we derive the expression for the PDF of the RF/UWOC system. In addition, the tight asymptotic expression for the outage probability and average bit error rate under high signal-to-noise ratio (SNR) is provided. The analytical expressions are verified by Monte Carlo method, and the effects of channel number on RF/UWOC system performance under various atmospheric, underwater and weather conditions are demonstrated.

Research on temperature field and thermal deformation characteristics of casting rollers in twin-roll casting process

The twin-roll casting (TRC) process is considered a revolutionary technology in the metallurgical field and has garnered significant attention and research in recent years. The casting rollers used in the TRC process are crucial for achieving the desired shape of the casting strip. This study investigates the temperature distribution and thermal deformation of casting rollers in the TRC process using numerical simulation. A novel model for predicting the thermal resistance distribution at the circumferential interface between the casting roller and the molten pool has been introduced. The study examined the effects of various factors including casting roller diameter, water channel position, number, and diameter on temperature, thermal deformation, and heat flux. The findings reveal that increasing the casting roller diameter from 460 mm to 500 mm or augmenting the distance from the water channel to the casting roller surface from 35 mm to 55 mm resulted in a 37 % and 30 % increase in the maximum surface temperature of the casting roller, respectively. Additionally, an increase in the number of water channels from 30 to 50 and a growth in their diameter from 20 mm to 28 mm resulted in the average heat flux of the casting roller remaining stable, with fluctuations less than 1 MW/m2. Leveraging the insights gained from the thermal deformation behavior of the casting roller and the ideal shape of the casting strip, a comprehensive control approach for the casting roller profile was developed. The outcomes of this research provide a valuable theoretical framework for enhancing the design of casting roller structures in the TRC process.

Avoidance of specific calibration sessions in motor intention recognition for exoskeleton-supported rehabilitation through transfer learning on EEG data

Exoskeleton-based support for patients requires the learning of individual machine-learning models to recognize movement intentions of patients based on the electroencephalogram (EEG). A major issue in EEG-based movement intention recognition is the long calibration time required to train a model. In this paper, we propose a transfer learning approach that eliminates the need for a calibration session. This approach is validated on healthy subjects in this study. We will use the proposed approach in our future rehabilitation application, where the movement intention of the affected arm of a patient can be inferred from the EEG data recorded during bilateral arm movements enabled by the exoskeleton mirroring arm movements from the unaffected to the affected arm. For the initial evaluation, we compared two trained models for predicting unilateral and bilateral movement intentions without applying a classifier transfer. For the main evaluation, we predicted unilateral movement intentions without a calibration session by transferring the classifier trained on data from bilateral movement intentions. Our results showed that the classification performance for the transfer case was comparable to that in the non-transfer case, even with only 4 or 8 EEG channels. Our results contribute to robotic rehabilitation by eliminating the need for a calibration session, since EEG data for training is recorded during the rehabilitation session, and only a small number of EEG channels are required for model training.

Welding Seam Tracking and Inspection Robot Based on Improved YOLOv8s-Seg Model.

A weld is the main connection form of special equipment, and a weld is also the most vulnerable part of special equipment. Therefore, an effective detection of a weld is of great significance to improve the safety of special equipment. The traditional inspection method is not only time-consuming and labor-intensive, but also expensive. The welding seam tracking and inspection robot can greatly improve the inspection efficiency and save on inspection costs. Therefore, this paper proposes a welding seam tracking and inspection robot based on YOLOv8s-seg. Firstly, the MobileNetV3 lightweight backbone network is used to replace the backbone part of YOLOv8s-seg to reduce the model parameters. Secondly, we reconstruct C2f and prune the number of output channels of the new building module C2fGhost. Finally, in order to make up for the precision loss caused by the lightweight model, we add an EMA attention mechanism after each detection layer in the neck part of the model. The experimental results show that the accuracy of weld recognition reaches 97.8%, and the model size is only 4.88 MB. The improved model is embedded in Jetson nano, a robot control system for seam tracking and detection, and TensorRT is used to accelerate the reasoning of the model. The total reasoning time from image segmentation to path fitting is only 54 ms, which meets the real-time requirements of the robot for seam tracking and detection, and realizes the path planning of the robot for inspecting the seam efficiently and accurately.

Exploring the potential of water channels for developing genetically encoded reporters and biosensors for diffusion-weighted MRI

Genetically encoded reporters for magnetic resonance imaging (MRI) offer a valuable technology for making molecular-scale measurements of biological processes within living organisms with high anatomical resolution and whole-organ coverage without relying on ionizing radiation. However, most MRI reporters rely on synthetic contrast agents, typically paramagnetic metals and metal complexes, which often need to be supplemented exogenously to create optimal contrast. To eliminate the need for synthetic contrast agents, we previously introduced aquaporin-1, a mammalian water channel, as a new reporter gene for the fully autonomous detection of genetically labeled cells using diffusion-weighted MRI. In this study, we aimed to expand the toolbox of diffusion-based genetic reporters by modulating aquaporin membrane trafficking and harnessing the evolutionary diversity of water channels across species. We identified a number of new water channels that functioned as diffusion-weighted reporter genes. In addition, we show that loss-of-function variants of yeast and human aquaporins can be leveraged to design first-in-class diffusion-based sensors for detecting the activity of a model protease within living cells.

Design and performance analysis of double gate vertically stacked MoS2 nanosheet field effect transistor

In this work we report a vertically stacked nanosheet Field Effect Transistor (NSFET) in double gate configuration using transition metal dichalcogenide (TMD) based molybdenum disulphide (MoS2) as the conducting channel. The performance of the NSFET is analysed for number of channels, different channel thickness, different source/drain contacts. The performance of the device at different temperatures (T) also analysed. The proposed NSFET with three vertically stacked channels, exhibits a ON current (ION) of 30.6 μA μm−1, Subthreshold swing (SS) of 69 mV/dec and ON to OFF current ratio of more than 108 at Vds = 1V. Further the ION can be improved with multi-layer channel thickness. The performance of the vertically stacked MoS2 NSFET in junction less (JL) and inversion mode (IM) is compared, it is concluded from the simulations that JL vertically stacked MoS2 NSFET more immune to short channel effects such as threshold voltage (Vth) roll-off and drain induced barrier lowering (DIBL).

17-Cell battery monitoring analog front end with high sampling accuracy for battery pack applications

Battery management system (BMS) is a critical aspect to ensure the safe, reliable operation of lithium ion batteries for battery pack applications. Although some battery pack monitoring schemes have increased the number of measurable channels in a chip for higher voltage battery packs, the issue of accuracy caused by the common mode voltage difference in each unit of series battery pack remains unsolved. A high voltage multiplexer of 17-cell battery monitoring analog front end (AFE) is adopted to acquire each cell voltage for accurate monitoring. Besides, a current compensation scheme is proposed to tackle the current leakage in each channel to further improve the acquisition accuracy. The proposed scheme is implemented in 0.18 μm high-voltage BCD process, and occupies a die size of 2.92 × 3.23 mm2. The experimental results reveal that the sensing error of 17 battery cells is within ±2 mV under the input cell voltage from 1 to 5V.

Efficient YOLOv8 algorithm for extreme small-scale object detection

The past decade has witnessed significant progress in detecting small objects that are often distributed with large-scale variations in arbitrary orientations. However, most of the existing models are unable to detect extremely small objects, due to (1) tiny-scale objects with more blurring in comparison to ground-view objects provide less important information for precise and robust recognition; (2) Unevenly distributed objects make detection inefficient, particularly in regions occupied by dense objects, which lead to the common inconsistency between the classification score and localization accuracy. This research proposes an efficient YOLOv8 model with a new backbone network, and anchor-free head to address these issues. Bottleneck layers are used in the backbone, allowing the model to capture more complicated patterns and reduce the dimensionality of the feature maps while maintaining the same number of channels. Whereas an anchor-free head detection module does not require pre-defined anchor boxes, they directly predict objects’ center points and sizes and their corresponding class probabilities. This proposed model is experimentally verified on the VisDrone dataset and achieved higher mAP (45.9 %), and precision (76.8 %). These experimental results suggest that the proposed model outperforms existing models in detecting x-small items.

Rapid Mental Workload Detection of Air Traffic Controllers with Three EEG Sensors.

Air traffic controllers' mental workload significantly impacts their operational efficiency and safety. Detecting their mental workload rapidly and accurately is crucial for preventing aviation accidents. This study introduces a mental workload detection model for controllers based on power spectrum features related to gamma waves. The model selects the feature with the highest classification accuracy, β + θ + α + γ, and utilizes the mRMR (Max-Relevance and Min-Redundancy) algorithm for channel selection. Furthermore, the channels that were less affected by ICA processing were identified, and the reliability of this result was demonstrated by artifact analysis brought about by EMG, ECG, etc. Finally, a model for rapid mental workload detection for controllers was developed and the detection rate for the 34 subjects reached 1, and the accuracy for the remaining subjects was as low as 0.986. In conclusion, we validated the usability of the mRMR algorithm in channel selection and proposed a rapid method for detecting mental workload in air traffic controllers using only three EEG channels. By reducing the number of EEG channels and shortening the data processing time, this approach simplifies equipment application and maintains detection accuracy, enhancing practical usability.

A hybrid model for the classification of Autism Spectrum Disorder using Mu rhythm in EEG.

Autism Spectrum Disorder (ASD) is a condition with social interaction, communication, and behavioral difficulties. Diagnostic methods mostly rely on subjective evaluations and can lack objectivity. In this research Machine learning (ML) and deep learning (DL) techniques are used to enhance ASD classification. This study focuses on improving ASD and TD classification accuracy with a minimal number of EEG channels. ML and DL models are used with EEG data, including Mu Rhythm from the Sensory Motor Cortex (SMC) for classification. Non-linear features in time and frequency domains are extracted and ML models are applied for classification. The EEG 1D data is transformed into images using Independent Component Analysis-Second Order Blind Identification (ICA-SOBI), Spectrogram, and Continuous Wavelet Transform (CWT). Stacking Classifier employed with non-linear features yields precision, recall, F1-score, and accuracy rates of 78%, 79%, 78%, and 78% respectively. Including entropy and fuzzy entropy features further improves accuracy to 81.4%. In addition, DL models, employing SOBI, CWT, and spectrogram plots, achieve precision, recall, F1-score, and accuracy of 75%, 75%, 74%, and 75% respectively. The hybrid model, which combined deep learning features from spectrogram and CWT with machine learning, exhibits prominent improvement, attained precision, recall, F1-score, and accuracy of 94%, 94%, 94%, and 94% respectively. Incorporating entropy and fuzzy entropy features further improved the accuracy to 96.9%. This study underscores the potential of ML and DL techniques in improving the classification of ASD and TD individuals, particularly when utilizing a minimal set of EEG channels.

A two-stream-network based steganalysis network: TSNet

The three-stage framework consisting of preprocessing, feature extraction and classification is the most commonly used structure in traditional deep learning based steganalysis networks (SNs). Preprocessing, as the core part of the three-stage framework, typically adopts fixed high-pass filters to generate filter residuals, and exploits a feature enhancement module following high-pass filters to improve signal-to-noise ratio of the stego signal, thus strengthening the expression of steganographic features. However, fixed high-pass filters can only provide filter residuals through the linear relationships, rather than complex dependencies automatically learned, among local pixels, which suggests that fixed high-pass filters lack powerful ability of extracting comprehensive steganographic features from the beginning of SNs. Inspired by the motivation, a two-stream-network based SN called TSNet is proposed to extract comprehensive steganographic features from two different perspectives, namely automatic learning and fixed filtering. The discrete cosine transform attention mechanism (DCTAM)-based fusion module (DCTAM-FM) with considering the steganalysis properties is proposed to fuse two streams and enhance channel representation using DCTAM, which is conductive to improving the detection accuracy. Two streams aim to explore different types of residuals from two perspectives, but inevitably lead to a considerable number of parameters and high computational complexity. To this end, the feature extraction stage contains lightweight depthwise separable convolution blocks and the linearly increasing style of the number of channels. Combining the two-stream network, DCTAM-FM as well as the lightweight feature extraction stage, TSNet obtains deep composite features, which can fully express the comprehensive steganographic features, and thus brings a high detection accuracy. Experimental results from three benchmark datasets demonstrate that TSNet outperforms current state-of-the-art methods in detection accuracy and has an acceptable computational complexity.

Position-Sensitive Silicon Photomultiplier Arrays with Large-Area and Sub-Millimeter Resolution.

Silicon photomultipliers (SiPMs) are solid-state single-photon-sensitive detectors that show excellent performance in a wide range of applications. In FBK (Trento, Italy), we developed a position-sensitive SiPM technology, called "linearly graded" (LG-SiPM), which is based on an avalanche-current weighted-partitioning approach. It shows position reconstruction resolution below 250 μm on an 8 × 8 mm2 device area with four readout channels and minimal distortions. A recent development in terms of LG-SIPM is a larger chip version (10 × 10 mm2) based on FBK NUV-HD technology (near-ultraviolet sensitive), with a peak photon detection efficiency at 420 nm. Such a large-area detector with position sensitivity is very interesting in applications like MR-compatible PET, high-energy physics experiments, and readout of time-projection chambers, gamma and beta cameras, or scintillating fibers, with a reduced number of channels. These SiPMs were characterized in terms of noise, photon detection efficiency, and position resolution. We also developed tiles of 2 × 2 and 3 × 3 LG-SiPMs, reaching very large sensitive areas of 20 × 20 mm2 and 30 × 30 mm2. We implemented a "smart-channel" configuration, which allowed us to have just six output channels for the 2 × 2 elements and eight channels for the 3 × 3 element tiles, preserving a position resolution below 0.5 mm. These kinds of detectors provide a great advantage in compact and low-power applications by maintaining position sensitivity over large areas with a small number of channels.

MIMO optical communication systems based on broadband transmitters and receivers

A multiple-input multiple-output (MIMO) optical communication system is proposed. In the MIMO optical communication system, a series of broadband sources with different spectra act as the transmitters, and a compact imaging chip sensor accompanied by a disorder dispersion component and a calculating component serve as the receivers. Because the spectral ranges of the sources are overlapped, the number of the signal channels is not restricted by the bandwidth of the communication band and the characteristics of the sources. L1-norm regularization, L2-norm regularization, and the simulated annealing algorithms are used to solve matrix equations for decoding the signals at the receivers. A proof-of-concept experiment using on-off keying modulation has been done to prove the feasibility of the design. The experimental results demonstrate that the signals decoded by the receivers fit well with those generated from the transmitters. Besides the function of communications, the broadband sources are suitable to act as illumination sources because the white color lights in the visible band are more comfortable for human eyes compared with monochromatic lights used in traditional visible light communication (VLC) systems. We also prove that the MIMO optical communication system can operate in the infrared band. The infrared communication (IC) system has potential applications in situations requiring communication in a dark environment that cannot be realized by VLC systems.

A fully digital timing background calibration algorithm based on first-order auto-correlation for time-interleaved ADCs

This paper presents a fully digital background calibration method for time-interleaved (TI) analog-to-digital converters (ADCs). The timing detector employs a timing detection method based on an enhanced autocorrelation function, combined with matrix operations, to improve the accuracy of timing mismatch acquisition. The algorithm makes full use of the autocorrelation functions of each channel and proposes a more precise method for obtaining autocorrelation derivatives. Furthermore, the algorithm features a simple structure, low computational complexity, and can support timing calibration for any number of channels without requiring additional channels. The algorithm was validated using an ADC + FPGA combined system, with a 8-channel TI-ADC manufactured in 28 nm technology and a sampling rate of 20 GS/s. Results demonstrate that compared to the uncalibrated state, the algorithm significantly improves the SNDR and SFDR from 27.98 dB and 30.76 dB to 39.38 dB and 42.13 dB, respectively when the inputs are near the Nyquist frequency.

Random Forest Model-Based Inversion of Aerosol Vertical Profiles in China Using Orbiting Carbon Observatory-2 Oxygen A-Band Observations

Aerosol research is important for the protection of the ecological environment, the improvement of air quality, and as a response to climate change. In this study, a random forest (RF) estimation model of aerosol optical depth (AOD) and extinction coefficient vertical profiles was, respectively, established using Orbiting Carbon Observatory-2 (OCO-2) oxygen-A band (O2 A-band) data from China and its surrounding areas in 2016, combined with geographical information (longitude, latitude, and elevation) and viewing angle data. To address the high number of OCO-2 O2 A-band channels, principal component analysis (PCA) was employed for dimensionality reduction. The model was then applied to estimate the aerosol extinction coefficients for the region in 2017, and its validity was verified by comparing the estimated values with the Cloud-Aerosol Lidar Infrared Pathfinder Satellite Observations (CALIPSO) Level 2 extinction coefficients. In the comprehensive analysis of overall performance, an AOD model was initially constructed using variables, achieving a correlation coefficient (R) of 0.676. Subsequently, predictions for aerosol extinction coefficients were generated, revealing a satisfactory agreement between the predicted and the actual values in the vertical direction, with an R of 0.535 and a root mean square error (RMSE) of 0.107 km−1. Of the four seasons of the year, the model performs best in autumn (R = 0.557), while its performance was relatively lower in summer (R = 0.442). Height had a significant effect on the model, with both R and RMSE decreasing as height increased. Furthermore, the accuracy of aerosol profile inversion shows a dependence on AOD, with a better accuracy when AOD is less than 0.3 and RMSE can be less than 0.06 km−1.

Large-scale parameters framework with large convolutional kernel for encoding visual fMRI activity information.

Visual encoding models often use deep neural networks to describe the brain's visual cortex response to external stimuli. Inspired by biological findings, researchers found that large receptive fields built with large convolutional kernels improve convolutional encoding model performance. Inspired by scaling laws in recent years, this article investigates the performance of large convolutional kernel encoding models on larger parameter scales. This paper proposes a large-scale parameters framework with a sizeable convolutional kernel for encoding visual functional magnetic resonance imaging activity information. The proposed framework consists of three parts: First, the stimulus image feature extraction module is constructed using a large-kernel convolutional network while increasing channel numbers to expand the parameter size of the framework. Second, enlarging the input data during the training stage through the multi-subject fusion module to accommodate the increase in parameters. Third, the voxel mapping module maps from stimulus image features to functional magnetic resonance imaging signals. Compared to sizeable convolutional kernel visual encoding networks with base parameter scale, our visual encoding framework improves by approximately 7% on the Natural Scenes Dataset, the dedicated dataset for the Algonauts 2023 Challenge. We further analyze that our encoding framework made a trade-off between encoding performance and trainability. This paper confirms that expanding parameters in visual coding can bring performance improvements.

Models of digitalization of hr management systems in security-oriented organizations

The subject matter of the article is the study of operational processes of digitalization of HR-management of security-oriented systems in the current changing situation of our country. The goal of the work is to study the indicators of effective communication between employees and determine the number of potential communication channels, as well as to study automated communication between the personnel of security-oriented systems. The following tasks: formation of a centralized model for managing the operational processes of HR management of security-oriented systems, unlocking the potential of personnel in the digitalization of personnel processes of security-oriented systems. The methods are used: methods for implementing the continuous development of security-oriented systems, a centralized model for managing the operational processes of HR management of civil protection and an end-to-end process of automated HR communication within government agencies are developed and presented. The following results: determination of the number of potential communication channels, a graph of the number of stakeholders, a picture of the end-to-end process of automated HR communication within government agencies, a centralized model for managing HR operational processes of security-oriented systems, and the potential of personnel in the digitalization of HR processes. Conclusions: the driving forces behind the digital transformation of government agencies include meeting the expectations of the staff, digital transformation of the HR industry, and digital innovations in the civil service. Areas of digital transformation of HR management in security-oriented systems include a digital workplace (online employee profile), digital HR processes of the organization, digital services and online self-service of personnel. Among them, the digital HR process mainly focuses on training and development, open and effective communication within the civil service, and personnel evaluation functions. The digital transformation will lead to certain consequences, such as parallelization and transition between the old and new HR management systems, and will affect the productivity and digital ethics of the state.