Mixed-signal Research Articles

Robust estimation of cancer and immune cell-type proportions from bulk tumor ATAC-Seq data

Assay for Transposase-Accessible Chromatin sequencing (ATAC-Seq) is a widely used technique to explore gene regulatory mechanisms. For most ATAC-Seq data from healthy and diseased tissues such as tumors, chromatin accessibility measurement represents a mixed signal from multiple cell types. In this work, we derive reliable chromatin accessibility marker peaks and reference profiles for most non-malignant cell types frequently observed in the microenvironment of human tumors. We then integrate these data into the EPIC deconvolution framework (Racle et al., 2017) to quantify cell-type heterogeneity in bulk ATAC-Seq data. Our EPIC-ATAC tool accurately predicts non-malignant and malignant cell fractions in tumor samples. When applied to a human breast cancer cohort, EPIC-ATAC accurately infers the immune contexture of the main breast cancer subtypes.

Candidate Selection in Teams: Be the Best or Surround Yourself with the Best?

ABSTRACT This study investigates, via an experiment, how the decentralization of a firm’s selection process affects the caliber of the chosen candidate in a team-based environment. We predict and find that, when decision makers have comprehensive and unambiguous candidate-specific information regarding who is the best for the job, the quality of the selected candidates is lower under a decentralized versus centralized selection process. We also find that nonpecuniary status concerns drive the effect. Results of two boundary conditions reveal that, as the clarity of information regarding who is the best candidate for the job decreases (due to decision-makers having weaker or mixed signals about job candidates), the quality of selected candidates becomes no worse under a decentralized than under a centralized selection process. Overall, our results indicate that nonpecuniary status considerations and information environment can influence candidate selection decisions in organizations. Data Availability: Data are available from the authors upon request.

Ultrafast four-wave-mixing spectroscopy with two vacuum fields and coincidence-double-heterodyne detection

In classical macroscopic ultrafast optical four-wave mixing signals, phase matching selects three classes of light–matter interaction pathways: double quantum coherence; non-rephasing; and photon-echo. Multiple pathways contribute to each of these signals. We show that a coincidence-double-heterodyne detection scheme that employs two classical and two vacuum fields can isolate a single pathway contribution to each of these signals. We further demonstrate the advantage of the proposed technique by comparing it with the classical photon-echo signal for a model Frenkel-exciton dimer.

Low-Temperature Drift Bandgap Reference Based on Exponential Compensation Design

Abstract In analogue and mixed-signal systems, a stable and accurate reference voltage source is essential to ensure system performance. In this paper, a high-precision bandgap reference (BGR) with a high power supply ripple rejection ratio (PSRR) and lowtemperature drift is proposed, which mainly consists of a core circuit, an exponential temperature compensation circuit, a PSRR enhancement circuit and a start-up circuit. The PSRR is improved by using a PSRR enhancement circuit and a low-pass filter. An exponential compensation circuit is used to reduce the temperature drift coefficient of the bandgap reference. Based on a 0.18 μm BCD process for design and simulation, the output reference voltage is 1.203 V with a temperature drift coefficient of 2.635 ppm/°C over the temperature range of −40°C to 125°C. The PSRR is −144.4 dB at a frequency of 10 Hz, and −43.8 dB at a frequency of 1 MHz, and the layout area is 316 μm × 156 μm.

A speech separation model improved based on Conv-TasNet network

Abstract In the field of single-channel speech separation, the extraction and separation of features from mixed audio have always been the focus and difficulty of research. Currently, mainstream methods mainly suffer from poor generalization ability and issues such as inadequate feature extraction, which leads to the models’ inferior separation capability. This paper proposes an improved DConv-TasNet network model, focusing on the optimization of the encoder/decoder modules and separation modules and utilizing deep dilated encoders/decoders to extract features from mixed speech signals. It enhances feature extraction capability and generalization compared to conventional encoders/decoders. In terms of the separation module, improvements were made to the convolutional blocks within the module by enhancing feature extraction in the channel dimension, leading to improved performance of the separation network. Validation of the model’s performance was conducted using the WSJ0-Mix2 dataset, demonstrating superior performance compared to the Conv-TasNet network.

An improved finite set model predictive control of SRM drive based on a voltage vectors strategy for low torque ripple

The robust rotor structure and fault-tolerance characteristics of the Switched Reluctance Motors (SRMs) are the best choice for next-generation Electric Vehicle (EV) applications. This machine has few restraints like high torque and flux ripples. However, the existing Model Predictive Control (MPC) using multiple control objectives and maximum sectors in the switching table results in high torque ripples due to the improper sector partition, Voltage Vectors (VVs) and weight factor (K1) selection. This paper proposes a Finite Set-Model Predictive Control (FS-MPC) for an analytical model of a non-linearity SRM machine to analyze the torque ripple performance. The proposed VVs are derived using sector partition based on the rotor position. The control is designed as a single cost function with the weighting factor contributing to smooth torque by selecting optimal control signals. Simulation studies and experiments with a four-phase 8/6 SRM drive verifies the enhanced FS-MPC for real-time implementation. The dynamic speed and ripple values of SRM Drives are measured using a mixed signal oscilloscope and the sensor probes. The laboratory outcomes calculate the analytical equations to validate the findings. The calculated value of torque ripple is 9 % through this FS-MPC. The study reveals that the proposed method is well suited for torque ripple reduction than flux ripples.

The Use of Diffusion Tensor Imaging in the Differentiation and Surgical Planning of Suprasellar Hypothalamic-Opticochiasmatic Glioma and Craniopharyngioma in Children

Background and ObjectivesSuprasellar hypothalamic-opticochiasmatic glioma (HOCG) and craniopharyngioma (CP) have similar appearances on conventional MRI and are difficult to distinguish. Moreover, these tumors are situated near vital structures like the optic chiasm and hypothalamus, rendering conventional surgery susceptible to significant complications. We mainly discussed the surgical application value and diagnostic value of diffusion tensor imaging (DTI) in HOCG and CP. MethodsThe retrospective analysis of 13 cases of HOCG and 16 cases of CP was conducted. All patients underwent conventional MRI and DTI prior to surgery, and were pathologically diagnosed postoperatively. ResultsBoth CP and HOCG appeared as heterogeneous mixed signal masses on conventional MRI. For HOCGs, fiber tractography revealed two different growth patterns of the tumor: infiltrative type and inflated type. The surgical approach and risk levels differ between these growth patterns. Additionally, fiber tractography demonstrates significant differences compared to CPs. The surgical approach and extent of resection for all cases of these two tumors were guided by DTI. ConclusionDTI enhances the accuracy of HOCG and CP differentiation. Furthermore, patterns of tractography described in this study assist neurosurgeons in delineating the surgical pathway and tumor resection range without damaging important fiber bundles, thereby avoiding permanent neurological deficits and improving survival quality for patients.

A Comprehensive Analog–Mixed Signal (AMS) Simulations Environment

The analog–mixed signal simulation environment, used for verifying non-volatile memory macrocells, is presented. It has been adopted over the last decade, providing excellent results in verification coverage, efficiency, and flexibility. This methodology ensures a smooth and effective transition from full transistor/fully analog simulations to fully digital simulations while maintaining most of the environment’s features. This allows verification designers to exchange data, stimuli, and results, thereby enhancing debugging capabilities and reducing simulation time.

'Are We Sure That He Knew That You Don't Want to Have Sex?': Discursive Constructions of the Suspect in Police Interviews with Rape Complainants.

Recent statistics reveal alarming flaws in the Criminal Justice System's (CJS) handling of rape cases, undermining the pursuit of justice for complainants seeking legal redress. This paper takes a novel approach to explore police rape stereotype use in interviews with rape complainants, utilising critical discourse analysis and conversation analysis and discursive psychology to understand and critique the balance of power within an interview and how this might impact attrition and prosecution decisions. Ten police interviews with rape complainants were analysed with several suspect discursive constructions present throughout, including the interviewer constructing the suspect as misunderstanding, the complainant as miscommunicating non-consent, or agentless and passive talk. A significant and original finding was the way constructions interacted with the spectrum of stranger-to-partner rapes. In stranger rape cases, passive language often obscures the suspect and emphasises the complainant's behaviour. Acquaintance rapes frequently involved misunderstandings centred on visible distress and mixed signals. Partner rapes highlighted issues around consent and coercion, with officers often ignorant of coercive control and domestic abuse. These findings align with Operation Bluestone Soteria (OSB); thus, the recommendations align with those made by OSB's Pillar One.

Music source feature extraction based on improved attention mechanism and phase feature

Music source feature extraction is an important research direction in music information retrieval and music recommendation system. To extract the features of music sources more effectively, the study introduces the jump attention mechanism and combines it with the convolutional attention module. Also, a feature extraction module based on Unet + + and spatial attention module is proposed. In addition, the phase feature information of the mixed music signals is utilized to improve the network performance. Results showed that this model was studied to perform well in music source separation experiments of vocals and accompaniment. For vocal separation on the MIR-1K dataset, the model achieves 11.25 dB, 17.34 dB, and 13.83 dB for each metric, respectively. Meanwhile, for drum separation on the DSD100 dataset, the model achieves a median signal-to-source distortion ratio of 4.36 dB, which is 2.91 dB better than that of the Spectral Hierarchical Network model. For the separation of the bass sound and the human voice, the model's in the separation of bass and human voice, the median distortion ratio of the model is as high as 4.87 dB and 6.09 dB, which is better than that of the Spectral Hierarchical Network model. This indicates the significant performance advantages in feature extraction and separation of music sources, and it has important application values in music production and speech recognition.

A Method for the Pattern Recognition of Acoustic Emission Signals Using Blind Source Separation and a CNN for Online Corrosion Monitoring in Pipelines with Interference from Flow-Induced Noise.

As a critical component in industrial production, pipelines face the risk of failure due to long-term corrosion. In recent years, acoustic emission (AE) technology has demonstrated significant potential in online pipeline monitoring. However, the interference of flow-induced noise seriously hinders the application of acoustic emission technology in pipeline corrosion monitoring. Therefore, a pattern-recognition model for online pipeline AE monitoring signals based on blind source separation (BSS) and a convolutional neural network (CNN) is proposed. First, the singular spectrum analysis (SSA) was employed to transform the original AE signal into multiple observed signals. An independent component analysis (ICA) was then utilized to separate the source signals from the mixed signals. Subsequently, the Hilbert-Huang transform (HHT) was applied to each source signal to obtain a joint time-frequency domain map and to construct and compress it. Finally, the mapping relationship between the pipeline sources and AE signals was established based on the CNN for the precise identification of corrosion signals. The experimental data indicate that when the average amplitude of flow-induced noise signals is within three times that of corrosion signals, the separation of mixed signals is effective, and the overall recognition accuracy of the model exceeds 90%.

Engineering wafer scale single-crystalline Si growth on epitaxial Gd2O3/Si(111) substrate using radio frequency sputtering for silicon on insulator application

Silicon on Insulator (SOI) technology has been the promising solution for the On-chip low-power mixed-signal systems. However, the traditional smart-cut method for SOI wafer fabrication is costly. Hence, in this work, we present an optimized methodology to fabricate a wafer scale single-crystalline Si on epitaxial Gd2O3/Si(111) substrate (SOXI) using a low-cost, high volume manufacturable (HVM)-friendly Radio Frequency magnetron sputtering technique for SOI application. The grown Si and Gd2O3 layers are physically characterized using high-resolution X-ray Diffraction and high-resolution Transmission Electron Microscopy (HRTEM). The Grazing Incidence X-ray Diffraction, Pole Figure, and HRTEM imaging confirm the formation of an epitaxial Top-Si layer on the epitaxial-Gd2O3/Si(111) substrate. Hence, we demonstrate a low-cost, HVM-friendly technique for the fabrication of SOXI wafers.

A Review of Current Differencing Buffered Amplifiers: Performance Metrics and Technological Advances

Current Differencing Buffered Amplifiers (CDBAs) are a critical class of analog circuit components capable of handling both current and voltage signals with minimal power consumption. Due to their low impedance voltage output, they play a significant role in modern electronics for developing high-performance, high-precision analog and mixed-signal circuits. But, designing and characterizing CDBAs pose several challenges, such as ensuring stability at high frequencies, minimizing noise impact for high-precision applications, and enhancing adaptability. Integrating CDBAs with other analog components to create multifunctional integrated circuits opens many opportunities in the analog signal-processing domain. This paper reviews the evolution and applications of CDBAs in analog signal processing. Various implementation schemes, including those using commercial Current Feedback Amplifiers (CFAs) and novel CMOS configurations, are analyzed for their performance metrics such as supply voltage, power dissipation, input/output impedances, and technology node. Future trends and challenges in advancing CDBA technology towards higher integration and lower-voltage operation are discussed, highlighting potential applications in next-generation electronics.

On the impact of interlayer misalignment for dual-layer data detection in three dimensional magnetic recording

Three-dimensional magnetic recording (3DMR) is a crucial technology for significantly increasing the storage capacity of hard disk drives (HDDs). However, the presence of inter-symbol interference (ISI), intertrack interference (ITI), and interlayer interference (ILI) poses significant challenges to the accurate detection of data stored on multiple layers. This study addresses the impact of interlayer misalignment on the bit error rate (BER) performance in 3DMR systems. We evaluate the performance based on a neural network estimator for reconstructing the top-layer read response signal using feedback from a Viterbi detector. This enables the separation of bottom-layer signals by subtraction from the mixed readback signal. We introduce a dual-layer partial response maximum likelihood (PRML) detector for simultaneous bit retrieval from both layers. Furthermore, we investigate methods of a per-layer binary classifier and a dual-layer four-class classifier based on neural networks. Our study demonstrates the BER performance of these detection schemes influenced by the interlayer misalignment, especially when the offset is 0, 10%, 50%, and 90% of the bit dimensions between the two layers. The results show that the neural network-based reconstruction and separation method achieves better bottom-layer BER performance under a slight interlayer misalignment. The BER performance benefits more from a mild downtrack offset than the crosstrack offset. The neural network-based separation detection and the dual-layer PRML achieve the lowest top-layer BER and the worst bottom-layer BER when the interlayer misalignment is half a bit.

Moisture-wicking Janus-structure electronic knitted fabric for multimodal wearable mechanical sensing

Multimodal mechanical sensors that can recognize bending direction and exhibit excellent wearable performance are crucial for the fields of physical exercise and healthcare. This study fabricated a knitted multimodal sensor with porous and wetting gradients using hydrophobic core-spun yarns and hydrophilic CNT/WPU conductive yarns in plain stitch and partial tuck stitch. The electronic fabric demonstrated reliable strain sensing unaffected by compression disturbances and displayed a selective response to directional bending. The sensor exhibits a 50% sensing range with a GFmax of −4.41 during strain deformation detection. In terms of bending detection, one side achieves a 10% sensing range (−51.68°) with a GFmax of −6.89, while the other side achieves a 20% sensing range (73.72°) with a GFmax of 8.64. Encouragingly, deep learning incorporated with the sensor achieves an impressive recognition rate of 98.16% for mixed signals with different force levels. Furthermore, it possesses excellent wearable properties such as breathability, moisture permeability, perspiration remove, quick-drying and washability, suitable for applications like activity monitoring, medical rehabilitation evaluation, and gesture recognition. This work presents a promising pathway for the advancement of next-generation multifunctional electronic textiles.

Magnified interaural level differences enhance spatial release from masking in bilateral cochlear implant users.

Bilateral cochlear implant (BiCI) usage makes binaural benefits a possibility for implant users. Yet, limited access to interaural time difference (ITD) cues and reduced saliency of interaural level difference (ILD) cues restricts perceptual benefits of spatially separating a target from masker sounds for BiCI users. Here, we explore whether magnifying ILD cues improves intelligibility of masked speech for BiCI listeners in a "symmetrical-masker" configuration, which controls for long-term positive target-to-masker ratio (TMR) at the ear nearer the target from naturally occurring ILD cues. We magnified ILDs by estimating moment-to-moment ITDs in 1-octave-wide frequency bands, and applying corresponding ILDs to the target-masker mixtures reaching the two ears at each time in each frequency band. We conducted two experiments, one with NH listeners using vocoded stimuli and one with BiCI users. ILD magnification significantly improved intelligibility in both experiments. BiCI listeners showed no benefit of spatial separation between target and maskers with natural ILDs, even for the largest target-masker separation. Because ILD magnification is applied to the mixed signals at each ear, the strategy does not alter the TMR in either ear at any time; improvements to masked speech intelligibility are thus likely from improved perceptual separation of the competing sources.

Magnetic resonance imaging of the dromedary camel carpus

BackgroundThe dromedary camel (Camelus dromedarius) carpal joint presents multiple joints and constitutes several bones and soft tissues. Radiography and/or ultrasonography of the carpus are challenging due to structural superimposition. High-field magnetic resonance imaging (MRI) technique precludes superimposed tissues and offers high soft tissue contrast in multiple sequences and planes. Hence, understanding the normal MRI anatomy is crucial during clinical investigations. Magnetic resonance imaging is highly sensitive for investigation of soft tissues and articular cartilage; therefore, it is extensively used for outlining joint anatomy and evaluation of a wide range of musculoskeletal conditions. MRI images of a specific anatomical region acquired by using multiple sequences in various planes are necessary for a complete MRI examination. Given the dearth of information on the MRI features of the dromedary camel carpus, the current study demonstrates the MRI appearance of the clinically significant structures in the camel carpus in various sequences and planes using a high-field 1.5 Tesla superconducting magnet. For this purpose, twelve cadaveric forelimbs, obtained from 6 clinically sound lameness free adult dromedary camels, were examined.ResultsThe cortex and medulla of the radius, carpal bones and metacarpus were evaluated. Articular cartilage of the carpal joints was depicted and showed intermediate intensity. Carpal tendons expressed lower signal intensity in all pulse sequences. The collateral and inter-carpal ligaments showed mixed signal intensity.ConclusionsThe obtained data outlines the validation of MRI for investigation of the camel carpus and could set as a reference for interpretation in clinical patients.

Demonstration of 800°C SiC MOSFETs for Extreme Temperature Applications

Silicon Carbide (SiC) enhancement mode MOS electronics offer several benefits for realizing analog, digital and mixed signal electronics, but limitations on gate dielectric reliability has limited the adoption of MOS in high temperature application. In this work, we report GE’s lateral SiC MOSFETs exceeding previously reported temperature capabilities for SiC MOSFETs with experimental results that have shown that >500°C operation of SiC MOSFETS is possible with >400 hours demonstrated at 620°C, and short-term functionality demonstrated to 800°C. The result shows that MOS-based SiC electronics can continue to be a viable choice for circuit implementations at extreme temperatures >600°C.

Predicting time-of-flight with Cerenkov light in BGO: a three-stage network approach with multiple timing kernels prior

Objective.In the quest for enhanced image quality in positron emission tomography (PET) reconstruction, the introduction of time-of-flight (TOF) constraints in TOF-PET reconstruction offers superior signal-to-noise ratio. By employing BGO detectors capable of simultaneously emitting prompt Cerenkov light and scintillation light, this approach combines the high time resolution of prompt photons with the high energy resolution of scintillation light, thereby presenting a promising avenue for acquiring more precise TOF information.Approach.In Stage One, we train a raw method capable of predicting TOF information based on coincidence waveform pairs. In Stage Two, the data is categorized into 25 classes based on signal rise time, and the pre-trained raw method is utilized to obtain TOF kernels for each of the 25 classes, thereby generating prior knowledge. Within Stage Three, our proposed deep learning (DL) module, combined with a bias fine-tuning module, utilizes the kernel prior to provide bias compensation values for the data, thereby refining the first-stage outputs and obtaining more accurate TOF predictions.Main results.The three-stage network built upon the LED method resulted in improvements of 11.7 ps and 41.8 ps for full width at half maximum (FWHM) and full width at tenth maximum (FWTM), respectively. Optimal performance was achieved with FWHM of 128.2 ps and FWTM of 286.6 ps when CNN and Transformer were utilized in Stages One and Three, respectively. Further enhancements of 2.3 ps and 3.5 ps for FWHM and FWTM were attained through data augmentation methods.Significance.This study employs neural networks to compensate for the timing delays in mixed (Cerenkov and scintillation photons) signals, combining multiple timing kernels as prior knowledge with DL models. This integration yields optimal predictive performance, offering a superior solution for TOF-PET research utilizing Cerenkov signals.

RF-GymCare: Introducing Respiratory Prior for RF Sensing in Gym Environments

Exercise-induced sudden death has become an increasingly serious health issue in recent years. Fortunately, respiratory monitoring can serve as an effective early prevention solution. In particular, radio frequency (RF) sensing has emerged as a promising solution due to its contactless nature and privacy preservation. Existing RF-based respiratory monitoring solutions for homes, offices, or vehicles would suffer from significant performance degradation when deployed in typical gym workouts, where respiratory and physical movements are mixed in the RF signals in an unpredictable manner, leading to a blind source separation (BSS) problem. What is worse, the prior assumptions of source independence in existing BSS algorithms no longer hold, leaving respiratory monitoring in gym scenarios as an open issue. In this paper, we propose RF-GymCare, which introduces respiratory priors into the BSS problem and utilizes knowledge distillation to transfer these priors, aiming to learn the demixing weight from mixed signals. With the demixing weight, the respiratory signal can be obtained through a linear combination of the mixed signals. Our experiments with 13 hours of data across nine types of exercises demonstrate its superiority over existing methods, achieving a median similarity of 0.75 and a median rate error of 0.5 respiration per minute (RPM), respectively.