Detailed Circuit Research Articles

A Unidirectional Single-Stage Three-Phase Soft-Switched Isolated DC–AC Converter

This paper presents a novel single-stage soft-switched high-frequency-link three-phase dc–ac converter topology. The topology supports unidirectional dc to ac power flow and is targeted for applications like grid integration of photovoltaic sources, fuel cell, etc. The high frequency magnetic isolation results in reduction of system volume, weight, and cost. Sine-wave pulsewidth modulation is implemented in dc-side converter. Though high-frequency switched, dc-side converter is soft switched for most part of the line cycle. The ac-side converter active switches are line frequency switched incurring negligible switching loss. The line frequency switching of ac-side converter facilitates use of high voltage blocking inherently slow semiconductor devices to generate high voltage ac output. In addition, a cascaded multilevel structure is presented in this paper for direct medium-voltage ac grid integration. A detailed circuit analysis considering nonidealities like transformer leakage and switch capacitances, is presented in this paper. A 6-kW three-phase laboratory prototype is built. The presented simulation and experimental results verify the operation of the proposed topologies.

<inline-formula> <tex-math notation="LaTeX">$\Delta-\Sigma$ </tex-math> </inline-formula> Noise-Shaping in 2-D Space-Time for Wideband Antenna Array Receivers

This paper proposes multi-dimensional signal processing methods to extend $\Delta-\Sigma $ modulation into the two-dimensional (2-D) space, time case. The proposed 2-D noise-shaping methods employ lossless discrete integrators (LDIs) to reduce the spectral overlap of the regions of support (ROSs) of array signals with that of both thermal and quantization noise in microwave and mm-wave array processing systems. Spatio–temporal low-pass filtering can then be used to improve the overall noise figure, linearity, and resolution of the resulting $N$ -port receiver. A proof-of-concept 65-port receiver that uses first-order $\Delta-\Sigma $ within both the low-noise amplifier and analog-to-digital converter (ADC) has been designed in the UMC 65-nm CMOS process. Detailed circuit simulations with wideband RF inputs at a center frequency of 4 GHz confirm that the proposed 2-D noise-shaping approach provides significant improvements in noise, linearity, and resolution. Specifically, noise figure (NF) improves by 1.5 dB, IIP3 improves by 15 dB, and the effective number of bits (ENOB) improves by 3.1 bits. Experimental results from a low-frequency board-level implementation of a 64-port ADC with first-order noise shaping are also presented.

Research of Drive Linear Induction Motor for Conveyor Train

In the work considering results of drive linear induction motor research for conveyor transport to mining industrial. The design of driving motor is described. The results of calculating motor features by detailed magnetic equivalent circuit method are applied. Studding influence of chose ferromagnetic material (massive steel 3, steel 20 and as well laminated steel 2111) of secondary element (SE) on features of the motor. Normal component to inductor of force acting by truck of conveyor train is evaluated. Obtained results of the motor are compared with manufacture’s date.

A Non-Isolated Hybrid-Modular DC-DC Converter for DC Grids: Small-Signal Modeling and Control

This paper presents small-signal modeling, stability analysis, and controller design of a non-isolated bidirectional hybrid-modular DC-DC Converter for DC grid applications. The DC-DC converter can be used to interconnect two different DC voltage levels in a medium-/high-voltage DC grid. Half-bridge Sub-Modules (SMs) and a high-voltage valve are the main components of the converter. The high-voltage valve can be implemented via employing series-connected Insulated-Gate Bipolar Transistors (IGBTs). Operation with zero voltage switching of the involved high-voltage valve is feasible, i.e., there is no concern pertinent to dynamic voltage sharing among the series-connected IGBTs. The power is transferred from one side to another through the involved SMs, where their capacitors are connected in series across the high-voltage side, while they are connected sequentially across the low-voltage side. In this paper, the state-space averaging technique is employed to derive the small-signal model of the presented converter for controller design. Closed-form expression of the duty cycle-to-inductor current transfer function is extracted. Comparison between simulation results of the small-signal model and the detailed circuit model is presented to authenticate the accuracy of the derived small-signal model. Finally, a scaled-down prototype is used to verify the accuracy of the small-signal model.

A USB high resolution lock-in photometer

A design is presented, in which the DDC112 current-input analogue-to-digital converter is combined with a PIC microcontroller and associated circuitry to give a simple and economical dual-beam photometer / electrometer. The PIC supervises the operation of the analogue-digital converter and performs a lock-in function using a rolling average filter, which enables intensity measurement to parts per million. It also synchronously controls a regulated current source to drive, typically, one or more power LEDs as light sources. It has been applied in various fluorescence and absorbance measurements and can be used with Cavity Enhanced Absorption Spectrometry to determine ultra-low absorbance. An expanded version has been created with a PIC32 processor handling eight channels for a PixelSensor multispectral detector. All circuit details and source code are made available.

Electrochemical properties of porous Sr0.86Ti0.65Fe0.35O3 oxygen electrodes in solid oxide cells: Impedance study of symmetrical electrodes

This work evaluates porous Sr0.86Ti0.65Fe0.35O3 (STF35) as a possible oxygen electrode material for Solid Oxide Cells. The powder synthesis was performed by solid state method. Characterization included DC electrical conductivity study of sintered bulk samples and impedance spectroscopy study of symmetrical electrodes deposited on gadolinium doped ceria substrates. Measurements were carried out in atmospheres with different pO2 levels: 0.1%–20% O2. Detailed equivalent circuit analysis was carried out in order to clarify the reaction pathway on porous electrode, which extends knowledge available for dense model electrodes. At 800 °C in 21% O2, the DC electrical conductivity of STF35 pellet was 0.6 S cm−1 and the polarization resistance of the electrode in the symmetrical cell was ∼100 mΩ cm2. Detailed impedance spectroscopy studies revealed that the largest contribution (∼80%) towards the polarization resistance is due to oxygen adsorption, which is limiting the oxygen reduction performance of the porous STF35 electrode. These results show the applicability of advanced impedance analysis methods (e.g. Distribution of Relaxation Times - DRT) for description of complex impedance electrode phenomena of porous electrodes.

Long-length horizons dynamic matrix predictive control for a MMC inverter

This paper proposes an indirect model predictive control (I-MPC) strategy that allows considering long-length horizons to control a modular multilevel converter (MMC) working as an inverter, where such a MMC has the purpose to transfer bidirectionally active power to the grid, and simultaneously compensate reactive power. In the proposed scheme the dynamic matrix control (DMC) technique is adopted to generate the direct power control (DPC), where new power references are computed by taking into account the system behavior from a detailed circuit or model. In addition, the DMC allows to present a hybrid modulation structure that involves a sinusoidal pulse width modulation (SPWM) together with a voltage capacitor balance strategy. Finally, with the aim to illustrate the feasibility of controlling highly redundant converters, the MMC, the hybrid modulation technique and the control scheme are tested on a real-time simulation environment OPAL-RT®.

Clustering Count-based RNA Methylation Data Using a Nonparametric Generative Model

Background: RNA methylome has been discovered as an important layer of gene regulation and can be profiled directly with count-based measurements from high-throughput sequencing data. Although the detailed regulatory circuit of the epitranscriptome remains uncharted, clustering effect in methylation status among different RNA methylation sites can be identified from transcriptome-wide RNA methylation profiles and may reflect the epitranscriptomic regulation. Count-based RNA methylation sequencing data has unique features, such as low reads coverage, which calls for novel clustering approaches. <P><P> Objective: Besides the low reads coverage, it is also necessary to keep the integer property to approach clustering analysis of count-based RNA methylation sequencing data. <P><P> Method: We proposed a nonparametric generative model together with its Gibbs sampling solution for clustering analysis. The proposed approach implements a beta-binomial mixture model to capture the clustering effect in methylation level with the original count-based measurements rather than an estimated continuous methylation level. Besides, it adopts a nonparametric Dirichlet process to automatically determine an optimal number of clusters so as to avoid the common model selection problem in clustering analysis. <P><P> Results: When tested on the simulated system, the method demonstrated improved clustering performance over hierarchical clustering, K-means, MClust, NMF and EMclust. It also revealed on real dataset two novel RNA N6-methyladenosine (m6A) co-methylation patterns that may be induced directly by METTL14 and WTAP, which are two known regulatory components of the RNA m6A methyltransferase complex. <P><P> Conclusion: Our proposed DPBBM method not only properly handles the count-based measurements of RNA methylation data from sites of very low reads coverage, but also learns an optimal number of clusters adaptively from the data analyzed. <P><P> Availability: The source code and documents of DPBBM R package are freely available through the Comprehensive R Archive Network (CRAN): https://cran.r-project.org/web/packages/DPBBM/.

ReRAM-Based Processing-in-Memory Architecture for Recurrent Neural Network Acceleration

We present a recurrent neural network (RNN) accelerator design with resistive random-access memory (ReRAM)-based processing-in-memory (PIM) architecture. Distinguished from prior ReRAM-based convolutional neural network accelerators, we redesign the system to make it suitable for RNN acceleration. We measure the system throughput and energy efficiency with the detailed circuit and device characterization. Reprogrammability is enabled with our design, and an RNN friendly pipeline is employed to increase the system throughput. We observe that on average the proposed system achieves $79{\times}$ improvement of computing efficiency compared with graphics processing unit baseline. Our simulation also indicates that to maintain high accuracy and computing efficiency, the read noise standard deviation should be less than 0.2, the device resistance should be at least 1 $\text{M}{\Omega }$ , and the device writes latency should be minimized.

A Receiver for Inductive Ear-to-Ear Communication

This paper proposes a receiver circuit for inductive ear-to-ear communication. Its input resonance structure, an LCR front-end, is optimized for both a data transmission rate of 100 kbit/s and maximum voltage excess. Following an analytical study, the optimal quality factor $Q_{\text {LCR}}$ is found to be 12.8. Subsequent amplification of ON-OFF keyed bits is performed with a four-stage JFET amplifier, introducing a total amplification of 68.6 dB at a sensitivity of $31.6~\mu \text{V}$ . Each stage is implemented in common-source JFET topology containing a current source in its drain path instead of the conventional drain resistor. This modification allows for a per-stage increase in voltage amplification by a factor of 1.87, avoiding extra quiescent current (detailed circuit theory is added). A hardware realization for signal demodulation reconstructs the baseband signal. In combination with a dedicated transmitter, a Hartley Oscillator, the inductive ear-to-ear transmission system demonstrates a reliable functionality over a distance of 18 cm. Operation is possible with a standard 1.5 V battery cell requiring low currents on a discrete benchtop prototype [transmitter: 0.8 mA; receiver: 4.0 mA]. Applying a carrier frequency of 3.175 MHz, the system is fully compliant with ITU regulation 5.115.

A neural microcircuit model for a scalable scale-invariant representation of time.

Scale-invariant timing has been observed in a wide range of behavioral experiments. The firing properties of recently described time cells provide a possible neural substrate for scale-invariant behavior. Earlier neural circuit models do not produce scale-invariant neural sequences. In this article, we present a biologically detailed network model based on an earlier mathematical algorithm. The simulations incorporate exponentially decaying persistent firing maintained by the calcium-activated nonspecific (CAN) cationic current and a network structure given by the inverse Laplace transform to generate time cells with scale-invariant firing rates. This model provides the first biologically detailed neural circuit for generating scale-invariant time cells. The circuit that implements the inverse Laplace transform merely consists of off-center/on-surround receptive fields. Critically, rescaling temporal sequences can be accomplished simply via cortical gain control (changing the slope of the f-I curve).

Visual physiology of the layer 4 cortical circuit in silico.

Despite advances in experimental techniques and accumulation of large datasets concerning the composition and properties of the cortex, quantitative modeling of cortical circuits under in-vivo-like conditions remains challenging. Here we report and publicly release a biophysically detailed circuit model of layer 4 in the mouse primary visual cortex, receiving thalamo-cortical visual inputs. The 45,000-neuron model was subjected to a battery of visual stimuli, and results were compared to published work and new in vivo experiments. Simulations reproduced a variety of observations, including effects of optogenetic perturbations. Critical to the agreement between responses in silico and in vivo were the rules of functional synaptic connectivity between neurons. Interestingly, after extreme simplification the model still performed satisfactorily on many measurements, although quantitative agreement with experiments suffered. These results emphasize the importance of functional rules of cortical wiring and enable a next generation of data-driven models of in vivo neural activity and computations.

Targeting the pedunculopontine nucleus in Parkinson's disease: Time to go back to the drawing board.

In summary, what we have learned about the PPN and MLR over the past few years undermines the simplistic model that underpins the rationale for PPN DBS. Our understanding of the relevant circuitry remains rudimentary. Further, much of what we know about these circuits is based on studies in rodents and felines. While it is likely that critical features of posture and locomotion control are phylogenetically conserved, additional non-human primate experiments are needed. Even if we understood the relevant motor circuits in detail, the small sizes of these structures, and the considerable heterogeneity of neurons within the PPN and surrounding structures, indicates that conventional DBS is too blunt an instrument to selectively target the relevant neurons. Finally, manipulating sick neurons adds an additional element of uncertainty. In the context of these facts, it is not surprising that the clinical results of PPN DBS are largely unimpressive.1 Technical refinements in conventional DBS targeting or technology are unlikely to overcome these obstacles. While basic research revealed significant obstacles to manipulating mesopontine neuronal populations, it also indicates circuits in this region are important in gait and balance, and likely relevant to PD. It is plausible that cell type specific manipulations in this region with optogenetic or chemogenetic methods might be useful.50 That said, deploying these technologies in well-designed clinical experiments face a number of hurdles, not the least of which is a much firmer grasp of the neural circuitry controlling gait and posture is required. We need also as well to know how this circuitry is disrupted in PD.51 At this point in time, human experimentation with PPN DBS should be reconsidered. Efforts should focus on sorting out precisely how the PPN/MLR and their afferent and efferent connections work, on what dysfunctions are characteristic of PD, and on developing the technologies needed to rectify these dysfunctions in PD patients.

A Voltage Quadrupler Rectifier Based Pulsewidth Modulated LLC Converter With Wide Output Range

LLC resonant converter is a soft switching frequency modulated dc–dc topology with its switching frequency near the resonant frequency. However, in applications where a wide output voltage is desired, it is extremely difficult to tune its operation close to the resonant frequency. In this paper, a novel pulsewidth modulated LLC type resonant converter based on voltage quadrupler rectifier is proposed. The proposed converter always operates at the resonant frequency and is able to achieve a wide output voltage range by modulating the duty cycle of the secondary side auxiliary mosfet . This brings the benefits of the decreased circulating current and corresponding conduction loss, as well as the simplification of the parameters selection and magnetic component design. Zero-voltage-switching and zero-current-switching are realized among all power mosfet s and all power diodes, respectively. Detailed circuit operation principles and modeling method are presented. A 1.3-kW converter prototype, generating 250–420 V output from 390-V dc-link is designed. Both the circuit functionality and the theoretical analysis are verified in the experimental results.

$$\varvec{\varDelta }$$–$$\varvec{\varSigma }$$ noise-shaping in 3-D space–time for 2-D wideband antenna array receivers

A novel delta–sigma ( $$\varDelta $$ – $$\varSigma $$ ) modulation method is proposed for extending noise-shaping to three dimensions: 2-D space and time. We show that a spatially-oversampled $$N_{x}\times N_{y}$$ antenna array coupled to a noise-shaped low-noise amplifier and analog-to-digital converter can diminish in-band additive noise and distortion by shaping the multi-dimensional spectrum towards higher spatial frequencies that are outside the space–time regions of support of all possible propagating electromagnetic waves. Detailed circuit simulations in a 65 nm CMOS process with wideband RF inputs at a center frequency of 4 GHz confirm that the proposed 3-D noise-shaping approach provides significant improvements in noise figure and resolution for 2-D array receivers.

Weighting Factor Design in Model Predictive Control of Power Electronic Converters: An Artificial Neural Network Approach

This paper proposes the use of an artificial neural network (ANN) for solving one of the ongoing research challenges in finite set-model predictive control (FS-MPC) of power electronics converters, i.e., the automated selection of weighting factors in cost function. The first step in this approach is to simulate a detailed converter circuit model or run experiments numerous times using different weighting factor combinations. The key performance metrics [e.g., average switching frequency ( $f_{{\rm sw}}$ ) of the converter, total harmonic distortion, etc.] are extracted from each simulation. This data is then used to train the ANN, which serves as a surrogate model of the converter that can provide fast and accurate estimates of the performance metrics for any weighting factor combination. Consequently, any arbitrary user-defined fitness function that combines the output metrics can be defined and the weighting factor combinations that optimize the given function can be explicitly found. The proposed methodology was verified on a practical weighting factor design problem in FS-MPC regulated voltage source converter for uninterruptible power supply system. Designed weighting factors for two exemplary fitness functions turned out to be robust to load variations and to yield close to expected performance when applied both to detailed simulation model (less than 3% error) and to experimental test bed (less than 10% error).

Characteristics of Scar-Related Ventricular Tachycardia Circuits Using Ultra-High-Density Mapping: A Multi-Center Study.

Ventricular tachycardia (VT) with structural heart disease is dependent on reentry within scar regions. We set out to assess the VT circuit in greater detail than has hitherto been possible, using ultra-high-density mapping. All ultra-high-density mapping guided VT ablation cases from 6 high-volume European centers were assessed. Maps were analyzed offline to generate activation maps of tachycardia circuits. Topography, conduction velocity, and voltage of the VT circuit were analyzed in complete maps. Thirty-six tachycardias in 31 patients were identified, 29 male and 27 ischemic. VT circuits and isthmuses were complex, 11 were single loop and 25 double loop; 3 had 2 entrances, 5 had 2 exits, and 15 had dead ends of activation. Isthmuses were defined by barriers, which included anatomic obstacles, lines of complete block, and slow conduction (in 27/36 isthmuses). Median conduction velocity was 0.08 m/s in entrance zones, 0.29 m/s in isthmus regions ( P<0.001), and 0.11 m/s in exit regions ( P=0.002). Median local voltage in the isthmus was 0.12 mV during tachycardia and 0.06 mV in paced/sinus rhythm. Two circuits were identifiable in 5 patients. The median timing of activation was 16% of diastole in entrances, 47% in the mid isthmus, and 77% in exits. VT circuits identified were complex, some of them having multiple entrances, exits, and dead ends. The barriers to conduction in the isthmus seem to be partly functional in 75% of circuits. Conduction velocity in the VT isthmus slowed at isthmus entrances and exits when compared with the mid isthmus. Isthmus voltage is often higher in VT than in sinus or paced rhythms.

Memristive Fully Convolutional Network: An Accurate Hardware Image-Segmentor in Deep Learning

As well known, fully convolutional network (FCN) becomes the state of the art for semantic segmentation in deep learning. Currently, new hardware designs for deep learning have focused on improving the speed and parallelism of processing units. This motivates memristive solutions, in which the memory units (i.e., memristors) have computing capabilities. However, designing a memristive deep learning network is challenging, since memristors work very differently from the traditional CMOS hardware. This paper proposes a complete solution to implement memristive FCN (MFCN). Voltage selectors are firstly utilized to realize max-pooling layers with the detailed MFCN deconvolution hardware circuit by the massively parallel structure, which is effective since the deconvolution kernel and the input feature are similar in size. Then, deconvolution calculation is realized by converting the image into a column matrix and converting the deconvolution kernel into a sparse matrix. Meanwhile, the convolution realization in MFCN is also studied with the traditional sliding window method rather than the large matrix theory to overcome the shortcoming of low efficiency. Moreover, the conductance values of memristors are predetermined in Tensorflow with ex-situ training method. In other words, we train MFCN in software, then download the trained parameters to the simulink system by writing memristor. The effectiveness of the designed MFCN scheme is verified with improved accuracy over some existing machine learning methods. The proposed scheme is also adapt to LFW dataset with three-classification tasks. However, the MFCN training is time consuming as the computational burden is heavy with thousands of weight parameters with just six layers. In future, it is necessary to sparsify the weight parameters and layers of the MFCN network to speed up computing.

Experience, memory, and the places they meet.

Studies of learning and memory have made significant advances in characterizing the mechanisms of single memories, formed when surprising and unpredictable events trigger synaptic modifications in response to tightly timed coincidental cues. Yet outside the laboratory setting, few natural experiences are wholly unique, and much of our behavior is shaped progressively through the interactions of perceived experiences, recently formed memories and distant acquired knowledge. Despite the necessity of these memory dynamics, relatively little is known about how previously established associations are accessed, updated, and applied to inform new learning at the appropriate moments in time. Such questions have historically been technically challenging to address because they require prolonged access to circuits linked to past episodes of learning; however, new techniques for function- and activity-based circuit mapping, developed and refined over several decades, have introduced novel opportunities to investigate both broad systems-level functions and detailed circuit mechanisms in complex neocortical systems. This review will focus particularly on insights from studies of the retrosplenial cortex, a large and heavily interconnected region of neocortex that has recently emerged as a candidate node for largescale information exchange over functionally diverse anatomical domains. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Voltage distribution in the windings of high temperature inverter-fed motors

PurposeHigh-temperature (HT°) motors are made with inorganic coils wound with a ceramic-coated wire. They must be carefully designed because the HT° insulating materials have a lower breakdown voltages than the polymers used for insulating standard machines.Design/methodology/approachThe voltage distribution between stator coils is computed with high-frequency (HF) equivalent circuits that consider the magnetic couplings and the stray capacitances. Two time scales are used for getting a fast computation of very short voltage spikes. For the first step, a medium time scale analysis is performed considering a simplified equivalent circuit made without any stray capacitance but with the full PWM pattern and the magnetic couplings. For the second step, a more detailed HF equivalent circuit computes voltage spikes during short critical time windows.FindingsThe computation made during the first step provides the critical time windows and the initial values of the state variables to the second one. The rise and fall time of the electronic switches have a minor influence on the maximum voltage stress. Conversely, the connection cable length and the common-mode capacitances have a large influence.Research limitations/implicationsHF equivalent circuits cannot be used with random windings but only to formed coils that have a deterministic position of turns.Practical implicationsThe proposed method can be used designing of HT° machine windings fed by PWM inverter and for improving the coils of standard machine used in aircraft’s low-pressure environments.Originality/valueThe influence of grounding system of the DC link is considered for computing the voltage spikes in the motor windings.