Mixed-signal Research Articles

A Revolution In Design

Agile Analog is helping to revolutionise the design of analogue and mixed-signal circuits.

Microwave-controlled coherence effects and the generation of four-wave mixing signal in a five-level atomic system involving Rydberg states

Microwave-controlled coherence effects and the generation of four-wave mixing signal in a five-level atomic system involving Rydberg states

Graph modeling for vocal melody extraction from polyphonic music

In this paper, a vocal melody extraction method based on graph modeling is proposed. First, constant-Q transform of mixed audio signal is applied. Then, amplitude spectra of several adjacent frames are concatenated together to construct the input feature. Afterwards, an undirected graph is constructed to model the melody extraction issue, and the frame-wise melodic pitches are estimated by a graph convolutional network (GCN), where the pitch estimation issue is regarded as a multi-class classification problem. The frequency bins are viewed as nodes and the underlying connection relationships of the frequency bins are defined as edges. Finally, the quantized frame-wise pitches are fine-tuned according to the salience function defined at a certain range of the smoothed melody trajectory based on the pitches estimated by GCN. The proposed method addresses the vocal melody extraction issue in an explainable way where the edges are defined according to the underlying connection relationships of different frequency bins. Experimental results demonstrate that the proposed method achieves good performance with light weight parameters.

Real-time validation of downstream 50G/25G and 50G/100G flexible rate PON based on Miller encoding, NRZ, and PAM4 modulation

Experimental demonstration is given of a real-time flexible downstream 50 Gb/s passive optical network (PON) mixed with 25 Gb/s or 100 Gb/s signals. 25 Gb/s transmission is enabled by using 25 Gb/s Miller encoding. For back-to-back a lower sensitivity for 25 Gb/s Miller of ∼2.2dB is found relative to 50 Gb/s non-return-to-zero (NRZ). An extended reach of 40 km and up to ∼5dB optical power budget improvement after worst-case 20 km standard single-mode fiber over 50 Gb/s NRZ was shown for 25 Gb/s Miller. It was also shown that optical network units (ONUs) that have 9 dB additional optical received power because they experience a lower optical loss can receive 100 Gb/s four-level pulse amplitude modulation (PAM4) instead of NRZ in a flexible rate PON, which will double the bit-rate to these ONUs. 50 Gb/s NRZ transmission under mixed modulation (50% NRZ and 50% Miller or 50% PAM4) can operate with very low penalty. A power penalty of <0.4dB was found for 50 Gb/s NRZ after worst-case total dispersion (as specified in ITU-T G.9804) when mixed with 25 Gb/s Miller or 100 Gb/s PAM4. For back-to-back there was a smaller penalty of <0.2dB for 50 Gb/s NRZ under mixed modulation. The 25 Gb/s Miller signal spectrum is similar to 50 Gb/s NRZ and 100 Gb/s PAM4, which enables good performance of the real-time clock and data recovery with an integrated adaptive feed-forward-equalizer based equalizer under mixed signal modulation. The increased optical power budget enabled by the 25 Gb/s Miller encoding effectively improves power margins of the high-speed PON and will enable reliable transmission for ONUs deployed with out-of-spec (higher) loss and/or (larger) fiber reach. The additional margin provided by 25 Gb/s Miller encoding will also enable a safe-mode for ONUs that experience out-of-spec scenarios because of faults in the PON fiber network.

The interannual variability of the Indian Ocean subtropical mode water based on the Argo data

Gaining insight into the interannual variability of the Indian Ocean Subtropical Mode Water (IOSTMW) is essential for understanding ocean dynamics in the Southwest Indian Ocean, since it carries the signal of winter mixing and transports it into the ocean interior. As the number of Argo profiles in the Southwest Indian Ocean increases, it has become possible to study temporal variations in IOSTMW using observation data. We used Argo products to examine the interannual variability of the IOSTMW from 2005 to 2020. We examined various definitions to determine the most suitable definition for IOSTMW in this study, choosing to define the IOSTMW as a layer with a vertical temperature gradient of less than 1°C per 100 meters (dT/dz< 1°C/100 m) and a temperature range of 16°C–18°C because this correlates strongly with winter heat loss in the same year. This method is particularly useful for investigating how mode water captures anomalous winter mixing signals and advects them to the ocean interior via subduction. Furthermore, we found that summer stratification can play a role in either facilitating or hindering the formation of thick IOSTMW layers. Our study indicates that thin IOSTMW layers are primarily caused by extremely weak winter heat loss associated with anomalously weak latent heat, whereas thick IOSTMW formation is aided by weak summer stratification.

Redundancy of Centrality Measures in Financial Market Infrastructures

The concept of centrality is widely used to monitor systems with a network structure because it allows identifying their most influential participants. This monitoring task can be difficult if the number of system participants is considerably large or if the wide variety of centrality measures currently available produce non-coincident (or mixed) signals. This document uses robust principal component analysis to evaluate a set of centrality measures calculated for the financial institutions that participate in Colombia's four financial market infrastructures. The results obtained are used to construct general indices of centrality, using the most robust measures of centrality as inputs and leaving aside those considered redundant.

Governance by Uncertainty: Changing Patterns in China’s Environmental Enforcement

Governance in China is often characterized as dualistic. On the one hand, the state invests in stable, rule-based institutions to support effective policy implementation. Yet the state also engages in sudden regulatory campaigns, overstepping its own laws to implement rapid changes in key sectors. Businesses in China have developed coping mechanisms to weather these uncertainties; they cultivate political ties or learn to accommodate unexpected disruptions. Yet in the sphere of environmental policy implementation, businesses across the spectrum are starting to complain that local environmental enforcement feels unpredictable, even arbitrary. What constitutes unpredictable enforcement in an environment already characterized by high levels of regulatory uncertainty? What changes in environmental enforcement are driving these complaints? Using original data on nine years of city-level enforcement measures, I show that both campaign-style and rule-based enforcement have been increasing in intensity and frequency since the mid-2010s. Through qualitative evidence, I show how these two approaches work at cross purposes, generating mixed signals on which strategies businesses should use to reduce pollution or to mitigate regulatory uncertainty. Data shows that this is a nationwide pattern, which explains why business—even experienced, well-connected businesses—are complaining about arbitrary state action. This study draws attention to emerging stress tests of China’s dualistic governance, while delving into what these changes portend for state-business relations in China.

Experimental research on specially optical bistability based on atomic ensemble

Optical bistability (OB) phenomenon based on electromagnetic induced transparency has received widespread attention from researchers due to its good application prospects in ultrafast all-optical switches, high-speed all-optical exchange devices, optical storage, etc. In this paper, OB is realized in a thermal atomic ensemble by a probe transmission signal and a six-wave mixing signal. By adjusting the controllable parameters of the light field, such as the light field intensity, polarization state, incident angle of the light etc, the OB laws of related signal are discovered, analyzed, and summarized. These results will have potential application value in OB devices and signal processing.

The signaling effect of policy loans: Do commercial banks follow up or stand by?

This study provides evidence that the signaling effect of policy loans can influence commercial banks' loan decisions in China. After obtaining policy loans, firms convey mixed signals to commercial banks. These signals include the certification signal (indicating that firms are of high quality) and the endorsement signal (showing that firms have an implicit guarantee from the government), thus alleviating the information asymmetry between commercial banks and firms. Our results indicate that commercial banks follow policy banks and respond to the signaling effect of policy loans by granting more loans and relaxing loan terms.

Micro-Transfer Printing for Heterogeneous Integration of GaN and GaAs HEMTs

Here, we use the micro-transfer printing technique to demonstrate the device-level heterogeneous integration of two solid-state RF device technologies on the same interposer: GaN and GaAs high-electron-mobility transistors. The devices are released from their growth substrate using an epitaxial sacrificial layer while a thin polymer adhesion layer facilitates a strong bond between the target substrate and the compound semiconductor devices, allowing for post-transfer microfabrication processing. Transmission electron microscopy reveals no voids at the device/interposer interface and a polymer adhesion layer thickness of 5 ± 2 nm. No significant degradation in dc electrical characteristics is observed after device transfer for either device technology. Improvement in thermal performance of GaN devices was demonstrated when transferred to a diamond substrate, even with the thin polymer adhesion layer at the device/interposer interface, illustrating a pathway for enhanced thermal management for GaN and other high-output-power density semiconductor technologies. The ability to combine various solid-state technologies at the device level with high density provides an approach to meet next-generation demands for RF and mixed-signal circuits.

Bottom-Up and Top-Down Approaches for the Design of Neuromorphic Processing Systems: Tradeoffs and Synergies Between Natural and Artificial Intelligence

While Moore's law has driven exponential computing power expectations, its nearing end calls for new avenues for improving the overall system performance. One of these avenues is the exploration of alternative brain-inspired computing architectures that aim at achieving the flexibility and computational efficiency of biological neural processing systems. Within this context, neuromorphic engineering represents a paradigm shift in computing based on the implementation of spiking neural network architectures in which processing and memory are tightly co-located. In this paper, we provide a comprehensive overview of the field, highlighting the different levels of granularity at which this paradigm shift is realized and comparing design approaches that focus on replicating natural intelligence (bottom-up) versus those that aim at solving practical artificial intelligence applications (top-down). First, we present the analog, mixed-signal and digital circuit design styles, identifying the boundary between processing and memory through time multiplexing, in-memory computation, and novel devices. Then, we highlight the key tradeoffs for each of the bottom-up and top-down design approaches, survey their silicon implementations, and carry out detailed comparative analyses to extract design guidelines. Finally, we identify necessary synergies and missing elements required to achieve a competitive advantage for neuromorphic systems over conventional machine-learning accelerators in edge computing applications, and outline the key ingredients for a framework toward neuromorphic intelligence.

A Comprehensive Study of Different Techniques for Voltage References

In the analog and mixed-signal integrated circuits, voltage references that are independent of various factors such as temperature drift, noise, supply voltage, etc., and efficient in terms of power as well as area, are highly in demand to improve the efficiency of the overall circuits. Voltage references are one of those circuits that have applications in both high-power systems and low-power system-on-chip (SoC) designs for wireless connectivity like the internet of the things (IoT) or the internet of the medical things (IoMT). They are responsible for providing a stable bias or reference voltage. Thus, voltage reference influences directly or indirectly the performance of these systems. A comparative study between the techniques used in bandgap voltage references and CMOS voltage references, in terms of performance parameters such as line sensitivity, output noise, PSRR, temperature coefficient, etc., is presented in this paper so that we can choose the voltage references as per the applications and environment.

High Performance FPGA Implementation of Single MAC Adaptive Filter for Independent Component Analysis

Blind source separation (BSS) is the process of extracting sources from mixed data without or with limited awareness of the sources. This paper uses field programmable gate array (FPGA) to create an effective version of the Blind source separation algorithm (ICA) with a single Multiply Accumulate (MAC) adaptive filter and to optimize it. Recently, space research has paid a lot of attention to this technique. We address this problem in two sections. The first approach is ICA, which seeks a linear revolution that can enhance the mutual independence of the mixture to distinguish the source signals from mixed signals. The second is a powerful flexible finite impulse response (FIR) filter construction that makes use of a MAC core and is adaptable. The adjustable coefficient filters have been used in the proposed study to determine the undiscovered system utilizing an optimal least mean square (LMS) technique. The filter tap under consideration in this paper includes 32 taps, and hardware description language (HDL) and FPGA devices were used to carry out the analysis and synthesis of it. When compared to the described architecture, the executed filter architecture uses 80% fewer resources and increases clock frequency by nearly five times, and speed is increased up to 32%.

Recombination smooths the time signal disrupted by latency inwithin-host HIV phylogenies.

Within-host Human immunodeficiency virus (HIV) evolution involves several features that may disrupt standard phylogenetic reconstruction. One important feature is reactivation of latently integrated provirus, which has the potential to disrupt the temporal signal, leading to variation in the branch lengths and apparent evolutionary rates in a tree. Yet, real within-host HIV phylogenies tend to show clear, ladder-like trees structured by the time of sampling. Another important feature is recombination, which violates the fundamental assumption that evolutionary history can be represented by a single bifurcating tree. Thus, recombination complicates the within-host HIV dynamic by mixing genomes and creating evolutionary loop structures that cannot be represented in a bifurcating tree. In this paper, we develop a coalescent-based simulator of within-host HIV evolution that includes latency, recombination, and effective population size dynamics that allows us to study the relationship between the true, complex genealogy of within-host HIV evolution, encoded as an ancestral recombination graph (ARG), and the observed phylogenetic tree. To compare our ARG results to the familiar phylogeny format, we calculate the expected bifurcating tree after decomposing the ARG into all unique site trees, their combined distance matrix, and the overall corresponding bifurcating tree. While latency and recombination separately disrupt the phylogenetic signal, remarkably, we find that recombination recovers the temporal signal of within-host HIV evolution caused by latency by mixing fragments of old, latent genomes into the contemporary population. In effect, recombination averages over extant heterogeneity, whether it stems from mixed time signals or population bottlenecks. Furthermore, we establish that the signals of latency and recombination can be observed in phylogenetic trees despite being an incorrect representation of the true evolutionary history. Using an approximate Bayesian computation method, we develop a set of statistical probes to tune our simulation model to nine longitudinally sampled within-host HIV phylogenies. Because ARGs are exceedingly difficult to infer from real HIV data, our simulation system allows investigating effects of latency, recombination, and population size bottlenecks by matching decomposed ARGs to real data as observed in standard phylogenies.

Underdetermined Blind Source Separation Method Based on a Two-Stage Single-Source Point Screening

Underdetermined blind source separation is a signal processing technique that is more suitable for practical applications and aims to separate the source signals from the mixed signals. The mixing matrix estimation is a major step in the underdetermined blind source separation. Since the current methods for estimating the mixing matrix have the disadvantages of insufficient accuracy or weak noise immunity, a two-stage single-source point screening that combines the cosine angle algorithm and the L1-norm optimization algorithm is proposed. During the first stage, the first-stage single-source points are extracted from the original mixed signals using the cosine angle algorithm. During the second stage, based on the L1-norm optimization algorithm, the reference single-source points are extracted from the original mixed signals. The reference single-source points are then clustered to obtain the clustering center, which is defined as the reference center. In combination with the reference center, the deviation and interference points in the first-stage single-source points are eliminated by the cosine distance. The remaining signal points are considered as the second-stage single-source points, which are clustered to obtain the mixing matrix estimation. Experiments on simulated and speech signals show that the proposed method can obtain more accurate and robust mixing matrix estimation, leading to better separation of the source signals.

Multiple Signal TDOA/FDOA Joint Estimation with Coherent Integration

Passive localization relies significantly on the estimation of the Time Difference of Arrival (TDOA) and Frequency Difference of Arrival (FDOA) to accurately determine the location of a target. The precision of TDOA and FDOA estimation is affected by signal parameters of time and frequency distribution. In case of multiple signals arising at different frequency bands and intercepted simultaneously by spatially separate sensors covering a wide frequency band, the traditional method is first to separate the signals from the mixed wideband signal through digital down conversion (DDC), which brings multiple narrowband signals, and then the estimation of TDOA and FDOA of each narrowband signal can be performed using cross ambiguity function (CAF). The paper introduces a novel approach for estimating TDOA and FDOA of multiple signals simultaneously, which employs a coherent integration method. First, the cross ambiguity function for each signal is realized with the narrowband signal as the same as the traditional method. Next, the phase relation of each CAF is analyzed, then the joint CAF can be obtained with phase compensation, from which multiple signal TDOA and FDOA estimations will be implemented simultaneously. Numerical simulations are performed to compare the two methods, and the results demonstrate the superiority of the proposed algorithm.

Imaging evaluation of cerebrospinal fluid otorrhea associated with inner ear malformation in children

Objective: To explore the imaging evaluation of cerebrospinal fluid (CSF) otorrhea associated with inner ear malformation (IEM) in children. Methods: The clinical data of 28 children with CSF otorrhea associated with IEM confirmed by surgical exploration in Beijing Children's Hospital, from Nov, 2016 to Jan, 2021, were analyzed retrospectively,including 16 boys and 12 girls, aged from 8-month to 15-year and 8-month old, with a median age of 4-year old. The shapes of stapes were observed during the exploration surgery, and the imaging features of temporal bone high resolution CT(HRCT) and inner ear MRI pre- and post-operation were analyzed. Results: In 28 children with CSF otorrhea, 89.3%(25/28) had stapes footplates defect during exploration. Preoperative CT showed indirect signs such as IEM, tympanic membrane bulging, soft tissue in the tympanum and mastoid cavity. IEM included four kinds: incomplete partition type I (IP-Ⅰ), common cavity (CC), incomplete partition type Ⅱ (IP-Ⅱ), and cochlear aplasia (CA); 100%(28/28) presented with vestibule dilation; 85.7%(24/28) with a defect in the lamina cribrosa of the internal auditory canal. The direct diagnostic sign of CSF otorrrhea could be seen in 73.9%(17/23) pre-operative MRI: two T2-weighted hyperintense signals between vestibule and middle ear cavity were connected by slightly lower or mixed intense T2-weighted signals, and obvious in the coronal-plane; 100%(23/23) hyperintense T2-weighted signals in the tympanum connected with those in the Eustachian tube.In post-operative CT, the soft tissues in the tympanum and mastoid cavity decreased or disappeared as early as one week. In post-operative MRI, the hyperintense T2-weighted signals of tympanum and mastoid decreased or disappeared in 3 days to 1 month,soft tissues tamponade with moderate intense T2-weighted signal were seen in the vestibule in 1-4 months. Conclusions: IP-Ⅰ, CC, IP-Ⅱ and CA with dilated vestibule can lead to CSF otorrhea. Combined with special medical history, T2-weighted signal of inner ear MRI can provide diagnostic basie for most children with IEM and CSF otorrhea.HRCT and MRI of inner ear can also be used to evaluate the effect of surgery.

A Battery-Less Non-Hybrid Six-Port RFID-Based Wireless Sensor Architecture for IoT Applications

This paper introduces a novel battery-less wireless sensor architecture, which is based on the principle of direct frequency conversion. The proposed architecture uses a non-hybrid six-port structure to integrate a UHF RFID chip with sensing elements to create a sensor node. The RFID chip provides a unique identification to the sensor node and the sensing element enables the reading of environmental conditions. The non-hybrid six-port structure unequally divides an incoming RFID interrogator signal into an in-phase and quadrature branch. Using the novel unequal distribution in a six-port allows higher power being directed to the RFID chip to ensure a longer read range. The I and Q signals reflected by the RFID chip and the sensing element, respectively, are mixed without the use of a lossy or an active mixer. The mixed signal’s amplitude and phase are directly dependent on the values of the attached sensing element. To read the value of the sensing element wirelessly at a reader, an IQ demodulator is used, which determines the phase of the backscattered signal. As a result, various sensed parameters such as light intensity, voltage, or force may be read wirelessly without requiring a battery at the node. To demonstrate the performance, a pin diode is used as a sensing element to read voltages wirelessly at a distance of up to 2.45 m.

Establishment of an Electro-Optical Mixing Design on a Photonic SOA-MZI Using a Differential Modulation Arrangement.

We design and evaluate two experimental systems for a single and simultaneous electro-optical semiconductor optical amplifier Mach-Zehnder interferometer (SOA-MZI) mixing system based on the differential modulation mode. These systems and the optimization of their optical and electrical performance largely depend on characteristics of an optical pulse source (OPS), operating at a frequency of f= 39 GHz and a pulse width of 1 ps. The passive power stability of the electro-optical mixing output over one hour is better than 0.3% RMS (root mean square), which is excellent. Additionally, we generate up to 22 dBm of the total average output power with an optical conversion gain of 32 dB, while achieving a record output optical signal to noise ratio (OSNR) up to 77 dB. On the other hand, at the SOA-MZI output, the 128 quadratic amplitude modulation (128-QAM) signal at an intermediate frequency (IF), f1, is up-mixed to higher output frequencies nf ± f1. The advantages of the resulting 128-QAM mixed signal during electrical conversion gains (ECGs) and error vector magnitudes (EVMs) are also evaluated. The performed empirical SOA-MZI mixing can operate up to 118.5 GHz in its high-frequency range. The positive and almost constant conversion gains are achieved. Indeed, the obtained conversion gain values are very close across the entire range of output frequencies. The largest frequency range achieved during experimental work is 118.5 GHz, where the EVM achieves 6% at a symbol rate of 10 GSymb/s. Moreover, the peak data rate of the 128-QAM up mixed signal can reach 70 GBit/s. Finally, the study of the simultaneous electro-optical mixing system is accepted with unmatched performance improvement.

Digital twin-driven fault diagnosis method for composite faults by combining virtual and real data

The subsea production system is essential for the subsea production of oil and gas. Real-time monitoring can ensure safe production. The subsea production control system is the core of the subsea production system and the top priority to be monitored. Troubleshooting the subsea production system involves lifting the component to the platform to repair it. And then send it back subsea. There is no doubt that it is expensive. In this case, some faults will not be handled temporarily if they do not affect the system operation seriously. This leads to multiple faults existing simultaneously, resulting in mixed signals. That makes it difficult to distinguish the state of the subsea production system. Thus, a fault diagnosis method for composite faults is needed. A digital twin-driven fault diagnosis method for composite faults is proposed by combining virtual and real data. Bernoulli Equation combines loss, control, and state parameters to build a digital twin model. A cross-validation enhanced fault diagnosis method is applied to single faults. A Bayesian network-based fault diagnosis model combining virtual and real data is built for composite faults. Field data from an offshore platform in the South Sea of China is used to demonstrate the effect of the proposed method. The results indicate that the method is extremely effective for composite faults of the subsea production control system.