Output Channels Research Articles

Antisaturation and Power Decoupling Control of Multiwinding Energy Harvester Based on Magnetomotive Force Compensation

With the rapid development of smart grids, numerous monitoring sensors have been widely used for the state inspection of power lines. To ensure reliable operation of these sensors, the problem of their power source needs to be solved urgently. In particular, non-invasive inductive energy harvesters clamped over the lines are suitable as power sources for the monitoring sensors. This paper presents the multiwinding energy harvester (MWEH), equipped with multiple energy output channels. To improve the anti-saturation ability of the magnetic core and eliminate power cross effects between the windings simultaneously, an auxiliary winding is introduced to regulate the magnetomotive force. The method of maintaining the unsaturated working state of the magnetic core and the power decoupling control strategy between the windings are proposed. Hence, the global power fluctuation can be eliminated with a constant unsaturated state when the load and turns of any winding change. The experimental results suggest that regulating the energy harvesting factor of the auxiliary winding can desaturate the magnetic core. Furthermore, based on the linear operation of the MWEH, the problem of power linkage of the windings can also be solved, which stabilizes the output power of one or more windings.

Deep Learning Based Prognostic Prediction in Oropharyngeal Cancer Patients Using Multiparametric MRI Inputs

While prognostic outcomes for oropharyngeal cancer (OPC) patients have improved in recent years, patients still face a non-negligible risk of disease recurrence or death. Accurately predicting post-therapy prognosis would be highly valuable for risk stratification and treatment guidance for OPC patients. Recent studies using PET/CT data have demonstrated the effectiveness of large-scale, end-to-end image-based deep learning (DL) models for predicting progression-free survival (PFS) in OPC patients. Multiparametric MRI (mpMRI), which combines anatomical and functional MRI sequences, has the potential to offer similar results, and has the added advantage of high-frequency longitudinal imaging capabilities, such as through MR-Linac devices. Therefore, this study aimed to develop a DL model using mpMRI data to predict PFS, and to evaluate the impact of anatomical and functional input channels on model performance. From a large-scale head and neck cancer database at MD Anderson Cancer Center, treatment-naïve OPC patients with available pre-radiotherapy mpMRI imaging were selected for this study. mpMRI images used for this study included T2-weighted images (T2) and apparent diffusion coefficient (ADC) maps. PFS event status was defined as having either a local, regional, or distant failure, and/or death; data were right censored if an event had not occurred. Images were resampled to the T2 resolution, normalized to a [-1,1] scale, and cropped to the field of view of the ADC image for use in DL models. A DL convolutional neural network model based on the DenseNet121 architecture from the Medical Open Network for AI (MONAI) Python package using a negative log-likelihood loss function was implemented. The model used mpMRI images as input channels and 20 output channels representing the different time intervals of the predicted PFS conditional probabilities of surviving that time interval; final PFS in days was obtained by summing the cumulative probability of surviving each interval times the interval duration. A 5-fold cross validation approach was used for model training and evaluation. Separate models using only T2, only ADC, and T2 + ADC channel inputs were compared. Model performance was measured using the C-index. Out of 1154 patients, 404 met inclusion criteria. The overall PFS event rate was 16%. Median C-index values from the 5-fold cross validation were 0.62, 0.67, and 0.69 for the ADC, T2, and T2+ADC models, respectively. Using large-scale datasets and open-source DL implementations, we find that OPC PFS prediction models using mpMRI data yield modest but comparable performance to existing models (i.e., state-of-the-art reference performance using PET/CT). Moreover, combining mpMRI channels may increase the performance of models for OPC prognostic prediction. Future work will involve integration of additional timepoints, additional mpMRI images, clinical variables, and saliency maps.

Candidate-nuclei for observation of a bound dineutron. Part I: The (n,2n) nuclear reaction

Current research has been performed for the stable isotopes within the 90 < A < 210 mass range in order to select properly those of them to be irradiated with neutrons for observation of a bound dineutron in the output channel. To this end, the neutron capture reaction cross sections were calculated in the (0 - 30) MeV energy range with the TALYS-1.96 code. That was performed in accordance with the purpose of this study to meet the criteria based on the (n, γ) resonance cross section analysis to find the appropriate candidates to be used for the possible future experimental confirmation of the formation of the dineutron in the (n,n2) nuclear reaction channel.

A configurable hardware-efficient ECG classification inference engine based on CNN for mobile healthcare applications

—Electrocardiogram (ECG) processors for healthcare have been widely used, however most of them can only adapt to specific applications, lacking flexibility. For achieving scalable on-chip ECG classification, a flexible inference engine based on one-dimensional (1-D) convolutional neural network (CNN) is proposed. By utilizing the proposed computing strategy based on systolic arrays, filter level parallelism and output channel parallelism are achieved. The configurable processing units (PUs) and modifiable instruction registers allow this inference engine to support computing of 1-D convolutional layers or fully connected layers with different scales. Based on the proposed data buffers system with multi-level storage structure, the input feature values and weight values are reused, thereby reducing hardware overhead and memory access power. This design has been validated on FPGA using our proposed two arrhythmia classification models that represent low energy consumption and high accuracy requirements, achieving accuracy of 98.9% and 99.3%, respectively, with energy consumption of 2.05 μJ and 14.27 μJ per classification at 200 MHz. The hardware implementation results indicate that good configurability enables our design to adapt to different ECG classification scenarios, effectively improving the hardware universality in mobile healthcare applications.

MISHEMT intrinsic voltage gain under multiple channel output characteristics

In this paper the MISHEMT device (metal/Si3N4/AlGaN/AlN/GaN - metal–insulator–semiconductor high electron mobility transistor) is studied focusing mainly on the impact of the multiple conductions on the intrinsic voltage gain (A v). It is shown that the total drain current is composed of three different drain current components, whereof one is related to the MIS channel and the other two are related to high electron mobility transistor (HEMT) channels. The device output characteristics present double drain voltage saturation that gives rise to a double plateau in the saturation region of the output characteristics. This behavior relies also on the gate voltage, so the output characteristics and analog parameters extraction are bias dependent. The intrinsic voltage gain increases thanks to the early voltage increment in the second plateau where HEMT conduction is dominant. Electron concentration profiles were simulated in order to investigate the device saturation regime.

Volitional inspiration is mediated by two independent output channels in the primary motor cortex.

The diaphragm is a multifunctional muscle that mediates both autonomic and volitional inspiration. It is critically involved in vocalization, postural stability, and expulsive core-trunk functions, such as coughing, hiccups, and vomiting. In macaque monkeys, we used retrograde transneuronal transport of rabies virus injected into the left hemidiaphragm to identify cortical neurons that have multisynaptic connections with phrenic motoneurons. Our research demonstrates that representation of the diaphragm in the primary motor cortex (M1) is split into two spatially separate and independent sites. No cortico-cortical connections are known to exist between these two sites. One site is located dorsal to the arm representation within the central sulcus and the second site is lateral to the arm. The dual representation of the diaphragm warrants a revision to the somatotopic map of M1. The dorsal diaphragm representation overlaps with trunk and axial musculature. It is ideally situated to coordinate with these muscles during volitional inspiration and in producing intra-abdominal pressure gradients. The lateral site overlaps the origin of M1 projections to a laryngeal muscle, the cricothyroid. This observation suggests that the coordinated control of laryngeal muscles and the diaphragm during vocalization may be achieved, in part, by co-localization of their representations in M1. The neural organization of the two diaphragm sites underlies a new perspective for interpreting functional imaging studies of respiration and/or vocalization. Furthermore, our results provide novel evidence supporting the concept that overlapping output channels within M1 are a prerequisite for the formation of muscle synergies underlying fine motor control.

Adaptive controller design and disturbance attenuation for minimum phase multiple‐input multiple‐output linear systems with noisy output measurements and with measured disturbances

SummaryIn this paper, we present a systematic procedure for robust adaptive control design for minimum phase uncertain multiple‐input multiple‐output linear systems that are right invertible and can be dynamically extended to a linear system with vector relative degree using a dynamic compensator that is known. For this class of systems, it is always possible to dynamically extend them, and/or integrate a select set of output channels, and/or padding dummy state variables to arrive at a system model that admits uniform vector relative degree and uniform observability indices that is further minimum phase according to Reference 13. We assume that the uniform vector relative degree is known and an upper bound for the uniform observability indices is known. We also assume that the unknown parameter vector lies in a convex compact set such that the high frequency gain matrix remains invertible for any parameter vector value in the set. These are the assumptions that allow for a successful design of a robust adaptive controller. A numerical example is included to fully illustrate the controller design and the effectiveness of the controller.

DRF codes: Deep SNR-robust feedback codes

We present a new Deep Neural Network (DNN)-based error correction code for fading channels with output feedback, called the Deep SNR-Robust Feedback (DRF) code. At the encoder, parity symbols are generated by a Long Short Term Memory (LSTM) network based on the message, as well as the past forward channel outputs observed by the transmitter in a noisy fashion. The decoder uses a bidirectional LSTM architecture along with a Signal to Noise Ratio (SNR)-aware attention NN to decode the message. The proposed code overcomes two major shortcomings of DNN-based codes over channels with passive output feedback: (i) the SNR-aware attention mechanism at the decoder enables reliable application of the same trained NN over a wide range of SNR values; (ii) curriculum training with batch size scheduling is used to speed up and stabilize training while improving the SNR-robustness of the resulting code. We show that the DRF codes outperform the existing DNN-based codes in terms of both the SNR-robustness and the error rate in an Additive White Gaussian Noise (AWGN) channel with noisy output feedback. In fading channels with perfect phase compensation at the receiver, DRF codes learn to efficiently exploit knowledge of the instantaneous fading amplitude (which is available to the encoder through feedback) to reduce the overhead and complexity associated with channel estimation at the decoder. Finally, we show the effectiveness of DRF codes in multicast channels with feedback, where linear feedback codes are known to be strictly suboptimal. These results show the feasibility of automatic design of new channel codes using DNN-based language models.

One Model to Synthesize Them All: Multi-Contrast Multi-Scale Transformer for Missing Data Imputation.

Multi-contrast magnetic resonance imaging (MRI) is widely used in clinical practice as each contrast provides complementary information. However, the availability of each imaging contrast may vary amongst patients, which poses challenges to radiologists and automated image analysis algorithms. A general approach for tackling this problem is missing data imputation, which aims to synthesize the missing contrasts from existing ones. While several convolutional neural networks (CNN) based algorithms have been proposed, they suffer from the fundamental limitations of CNN models, such as the requirement for fixed numbers of input and output channels, the inability to capture long-range dependencies, and the lack of interpretability. In this work, we formulate missing data imputation as a sequence-to-sequence learning problem and propose a multi-contrast multi-scale Transformer (MMT), which can take any subset of input contrasts and synthesize those that are missing. MMT consists of a multi-scale Transformer encoder that builds hierarchical representations of inputs combined with a multi-scale Transformer decoder that generates the outputs in a coarse-to-fine fashion. The proposed multi-contrast Swin Transformer blocks can efficiently capture intra- and inter-contrast dependencies for accurate image synthesis. Moreover, MMT is inherently interpretable as it allows us to understand the importance of each input contrast in different regions by analyzing the in-built attention maps of Transformer blocks in the decoder. Extensive experiments on two large-scale multi-contrast MRI datasets demonstrate that MMT outperforms the state-of-the-art methods quantitatively and qualitatively.

Microstrip diagram generating circuit for sector formation linear array element radiation patterns

A microstrip pattern-forming circuit designed to form sector radiation pattern of a linear antenna array. Proposed approximate method of device synthesis, including analysis of an infinite periodic system of related microstrip lines. Based on the found generalized parameters of the structure, the design was selected tive parameters of the circuit. Its electrodynamic analysis was carried out in the HFSS system, calculated in frequency band, the amplitude-phase distribution formed in the output channels. Designed, a sample device was manufactured and experimentally studied, providing in the frequency band up to 80% formation of the required amplitude-phase distribution. Based on received experimental data in the HFSS system, modeling of the diagram-forming scheme was carried out we are with a linear array of horns and it is shown that the elements of such an array have diagrams on straightness close to rectangular.

Fusion of RetinaFace and improved FaceNet for individual cow identification in natural scenes

Cows’ posture change is the fatal influencing factor for accurate identification of individual cows. To achieve non-contact, high-precision detection and identification of individual cows in farm environment, a cow individual identification method by the fusion of RetinaFace and improved FaceNet was proposed. MobileNet-enhanced RetinaFace was applied to ameliorate the impact of output channel quantity and convolution kernel dynamics using depthwise convolution combined with pointwise convolution. Regression predictions of bovine facial features and keypoints were generated under varying distances, scales and sizes. FaceNet's core feature network was enhanced through MobileNet integration, and the loss function was jointly optimized with Cross Entropy Loss and Triplet Loss to achieve a quicker and more stable convergence curve. The distances between the generated embedding vectors of cow facial features were corresponding to the similarity between cow faces, enabling accurate matching. RetinaFace exhibited detection false negative rates of 2.67%, 0.66%, 2.67%, and 3.33% under conditions of occlusion, no occlusion, low light, and bright light for cow facial detection. For cow facial pattern detection, the false negative rates for black and white patterns, pure black and pure white were 1.33%, 6.00% and 8.00%, respectively. Regarding cow facial posture changes, the false negative rates for face upward, bowing down, profile, and normal posture were 1.33%, 1.33%, 4.00% and 0.66%, respectively. Improved FaceNet model achieved an accuray of 99.50% on training set and 83.60% on test set. In comparison to YOLOX, the recognition model presented in this research demonstrated increased accuracy in cow facial detection under occlusion, no occlusion and strong lighting conditions by 2.67%, 0.40%, and 0.40%, respectively. Moreover, the accuracy for patterns with pure black and pure white tones surpassed that of YOLOX by 1.06% and 5.71%, correspondingly. Additionally, the accuracy rates for face upward, bowing down, profile and normal posture were higher than YOLOX by 2.00%, 3.34%, 2.66% and 0.40%, respectively. The proposed model demonstrates the proficiency in accurately identifying individual cows in natural scenes.

Generation of multiwavelength bismuth-doped fiber laser based on all-fiber Lyot filter

A stable multiwavelength fiber laser was proposed and demonstrated using a bismuth-doped fiber together with an all-fiber Lyot filter. The proposed multiwavelength bismuth-doped fiber laser (BDFL) spectrum can generate up to 21 output channels between 1309.88 nm and 1313.69 nm by carefully adjusting two polarization controllers (PCs). The multiwavelength BDFL shows good stability over time with a signal-to-noise ratio (SNR) of 48.69 dB, contributing to the average power fluctuations of 0.6 dB and wavelength drift of less than 0.1 nm in the laser output. In addition, the multiwavelength BDFL exhibits a free spectral range (FSR) of about 0.192 nm and a frequency bandwidth of 33.45 GHz. The characteristics of the multiwavelength BDFL can be observed by varying the pump power of the pump source, lasing output at different lengths of polarization maintaining fiber (PMF), and the generation in multiwavelengths using additional single mode-fiber (SMF).

Concatenated Forward Error Correction With KP4 and Single Parity Check Codes

Concatenated forward error correction is studied using an outer KP4 Reed-Solomon code with hard-decision decoding and inner single parity check (SPC) codes with Chase/Wagner soft-decision decoding. Analytical expressions are derived for the end-to-end frame and bit error rates for transmission over additive white Gaussian noise channels with binary phase-shift keying (BPSK) and quaternary amplitude shift keying (4-ASK), as well as with symbol interleavers and quantized channel outputs. The BPSK error rates are compared to those of two other inner codes: a two-dimensional product code with SPC component codes and an extended Hamming code. Simulation results for unit-memory inter-symbol interference channels and 4-ASK are also presented. The results show that the coding schemes achieve similar error rates, but SPC codes have the lowest complexity and permit flexible rate adaptation.

Birds multiplex spectral and temporal visual information via retinal On- and Off-channels

In vertebrate vision, early retinal circuits divide incoming visual information into functionally opposite elementary signals: On and Off, transient and sustained, chromatic and achromatic. Together these signals can yield an efficient representation of the scene for transmission to the brain via the optic nerve. However, this long-standing interpretation of retinal function is based on mammals, and it is unclear whether this functional arrangement is common to all vertebrates. Here we show that male poultry chicks use a fundamentally different strategy to communicate information from the eye to the brain. Rather than using functionally opposite pairs of retinal output channels, chicks encode the polarity, timing, and spectral composition of visual stimuli in a highly correlated manner: fast achromatic information is encoded by Off-circuits, and slow chromatic information overwhelmingly by On-circuits. Moreover, most retinal output channels combine On- and Off-circuits to simultaneously encode, or multiplex, both achromatic and chromatic information. Our results from birds conform to evidence from fish, amphibians, and reptiles which retain the full ancestral complement of four spectral types of cone photoreceptors.

Asynchronous Modal FRP

Over the past decade, a number of languages for functional reactive programming (FRP) have been suggested, which use modal types to ensure properties like causality, productivity and lack of space leaks. So far, almost all of these languages have included a modal operator for delay on a global clock. For some applications, however, a global clock is unnatural and leads to leaky abstractions as well as inefficient implementations. While modal languages without a global clock have been proposed, no operational properties have been proved about them, yet. This paper proposes Async RaTT, a new modal language for asynchronous FRP, equipped with an operational semantics mapping complete programs to machines that take asynchronous input signals and produce output signals. The main novelty of Async RaTT is a new modality for asynchronous delay, allowing each output channel to be associated at runtime with the set of input channels it depends on, thus causing the machine to only compute new output when necessary. We prove a series of operational properties including causality, productivity and lack of space leaks. We also show that, although the set of input channels associated with an output channel can change during execution, upper bounds on these can be determined statically by the type system.

DEVELOPMENT OF THE STAND FOR EXPERIMENTAL RESEARCH OF THE DOSER PUMP OF THE HYDROSTATIC STEERING SYSTEM OF SELF-PROPELLED AGRICULTURAL MACHINERY

Hydrovolumetric steering systems have today become widely used in heavy agricultural machines, such as combines, loaders, etc. The reliability and quality of operation of these machines largely depends on the quality of these systems and their elements. During the operation of these systems, a number of shortcomings were identified, which require additional research and the application of new design solutions. From the analysis of modern developments of the leading manufacturers of elements of these hydraulic systems, it follows that the main design changes are caused by the need to ensure the operability of these systems in abnormal or emergency situations. To check the performance of these devices in laboratory conditions, it is necessary to provide bench equipment with the ability to simulate abnormal situations and ensure the recording of the received information for the purpose of its further analysis and the formation of a set of measures aimed at eliminating the identified deficiencies and meeting the requirements of current standards. The authors have developed an original method of experimental research of individual steering control units for hydrostatic steering systems of special vehicles. It is proposed to create a load on the steering control unit in the form of a pressure difference in its output channels without using supporting hydraulic cylinders. To implement the proposed method, an electro-hydraulic scheme of the stand was developed. This makes it possible to analyze the quality of the steering unit's operation in conditions corresponding to different operating modes of the steering system, and to determine the corresponding characteristics of the steering unit under oncoming and passing loads.

Reconfigurable AWGR based on LNOI with a tunable central wavelength and bandwidth used in elastic optical networking.

Elastic optical networking introduces elasticity and adaptation into the optical domain, which highly depends on reconfigurable optical devices. In this paper, a tunable 4×4 arrayed waveguide grating router based on lithium niobate on insulator is designed. By using the electro-optic effect of lithium niobate, we design electrode regions with specific shapes in the array waveguide region to realize the tuning of the routing wavelength and bandwidth of the third output channel. The designed arrayed waveguide grating router (AWGR) has a dense channel spacing of 0.8nm, and the minimum insertion loss is 2.3dB. Experiments show that the tuning range of the central wavelength can reach 3.2nm, and the 3dB bandwidth can be expanded from 0.2 to 0.6nm.

A many-channel FPGA control system.

We describe a many-channel experiment control system based on a field-programmable gate array (FPGA). The system has 16 bit resolution on 10 analog 100 megasamples-per-second (MS/s) input channels, 14 analog 100 MS/s output channels, 16 slow analog input and output channels, dozens of digital inputs and outputs, and a touchscreen display for experiment control and monitoring. The system can support ten servo loops with 155ns latency and MHz bandwidths, in addition to as many as 30 lower bandwidth servos. We demonstrate infinite-impulse-response (IIR) proportional-integral-differential filters with 30ns latency by using only bit-shifts and additions. These IIR filters allow timing margin at 100 MS/s and use fewer FPGA resources than straightforward multiplier-based filters, facilitating many servos on a single FPGA. We present several specific applications: Hänsch-Couillaud laser locks with automatic lock acquisition and a slow dither correction of lock offsets, variable duty cycle temperature servos, and the generation of multiple synchronized arbitrary waveforms.

Fabrication Development for SPT-SLIM, a Superconducting Spectrometer for Line Intensity Mapping

Line Intensity Mapping (LIM) is a new observational technique that uses low-resolution observations of line emission to efficiently trace the large-scale structure of the Universe out to high redshift. Common mm/sub-mm emission lines are accessible from ground-based observatories, and the requirements on the detectors for LIM at mm-wavelengths are well matched to the capabilities of large-format arrays of superconducting sensors. We describe the development of an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$R$</tex-math></inline-formula> = <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\lambda / \Delta \lambda = 300$</tex-math></inline-formula> on-chip superconducting filter-bank spectrometer covering the 120–180 GHz band for future mm-LIM experiments, focusing on SPT-SLIM, a pathfinder LIM instrument for the South Pole Telescope. Radiation is coupled from the telescope optical system to the spectrometer chip via an array of feedhorn-coupled orthomode transducers. Superconducting microstrip transmission lines then carry the signal to an array of channelizing half-wavelength resonators, and the output of each spectral channel is sensed by a lumped element kinetic inductance detector (leKID). Key areas of development include incorporating new low-loss dielectrics to improve both the achievable spectral resolution and optical efficiency and development of a robust fabrication process to create a galvanic connection between ultra-pure superconducting thin-films to realize multi-material (hybrid) leKIDs. We provide an overview of the spectrometer design, fabrication process, and prototype devices.

EnRDeA U-Net Deep Learning of Semantic Segmentation on Intricate Noise Roads.

Road segmentation is beneficial to build a vision-controllable mission-oriented self-driving bot, e.g., the Self-Driving Sweeping Bot, or SDSB, for working in restricted areas. Using road segmentation, the bot itself and physical facilities may be protected and the sweeping efficiency of the SDSB promoted. However, roads in the real world are generally exposed to intricate noise conditions as a result of changing weather and climate effects; these include sunshine spots, shadowing caused by trees or physical facilities, traffic obstacles and signs, and cracks or sealing signs resulting from long-term road usage, as well as different types of road materials, such as cement or asphalt; all of these factors greatly influence the effectiveness of road segmentation. In this work, we investigate the extension of Primordial U-Net by the proposed EnRDeA U-Net, which uses an input channel applying a Residual U-Net block as an encoder and an attention gate in the output channel as a decoder, to validate a dataset of intricate road noises. In addition, we carry out a detailed analysis of the nets' features and segmentation performance to validate the intricate noises dataset on three U-Net extensions, i.e., the Primordial U-Net, Residual U-Net, and EnRDeA U-Net. Finally, the nets' structures, parameters, training losses, performance indexes, etc., are presented and discussed in the experimental results.