Abstract

A conventional push–push frequency doubler (FD) is simple to implement but suffers from limited total efficiency ($eta_{text{total}}$) at millimeter-wave (mm-Wave) frequencies due to negative feedback introduced by the gate–drain capacitance ($C_{mathrm{gd}}$). To overcome this limitation, a 42.5-58 GHz mm-Wave FD is proposed, utilizing cross-coupled gate-to-source feedback. In the proposed topology, the push-push NMOS frequency doubler employs cross-coupled gate-to-source feedback, which generates a second-harmonic voltage at the gate that is in phase with the drain’s second-harmonic current. This feedback effectively mitigates the inherent feedback caused by $C_{mathrm{gd}}$, and doubles the gate-to-source voltage of the fundamental harmonic, $V_{gs}[f_{0}]$. As a result, the amplitude of the drain $2f_{0}$ current generated by the nonlinear second harmonic transconductance $g_{m2,nonlinear}$ of the NMOS increases, leading to enhanced drain efficiency ($eta_{mathrm{D}}$) and $eta_{text{total}}$. The proposed FD is designed in a 65 nm CMOS process. Post-layout simulations demonstrate a peak $eta_{mathrm{D}}$ of 28.39%, $eta_{text{total}}$ of 13.7%, and a peak $2f_0$ output power of 6.27 dBm at 47.5 GHz, with a 12 dBm local oscillator (LO) input and 14.92 mW DC power consumption. The doubler achieves a 3 dB bandwidth of 42.5–58 GHz $(30.85%)$ and an efficiency bandwidth 43.75–54.66 GHz $(20.4%)$ where $eta_{text{total}} geq 10%$.

Abstract

Respiratory diseases such as asthma, bronchitis, and pneumonia remain a leading cause of mortality worldwide, significantly affecting both individual health and socioeconomic conditions. Early and accurate diagnosis is essential to mitigate these effects, yet traditional diagnostic techniques such as auscultation are subjective and rely on the expertise of trained physicians. To address these challenges and improve diagnostic accessibility, this work proposes a fully portable and low-cost system for the automated detection and classification of respiratory diseases. Lung sounds are captured using a microphone interfaced with a microcontroller. These recordings are then transmitted to an Edge hardware platform, where a Convolutional Neural Network (CNN) model processes Mel spectrogram representations of the audio for classification. The system leverages the publicly available HF Lung dataset for training and evaluation. The proposed system achieved a classification accuracy of 80.5%, with a sensitivity of 95.65% and a specificity of 98.80%. This portable approach demonstrates the potential for real-time, AI-powered respiratory diagnostics in remote or resource-limited settings, bridging the gap between clinical expertise and everyday health monitoring.

Abstract

Frequency Modulated Continuous Wave (FMCW) radars, known for their millimeter-level accuracy, are increasingly used in healthcare for non-contact monitoring of vital signs like Heart Rate (HR) and Breath Rate (BR). HR and BR are estimated using frequency domain analysis of the extracted respiration and cardiac activity from Radar Data. The focus of our work is to extract the waveforms of chest vibrations due to respiration and cardiac activity, for which higher time resolution is required. This work utilizes a signal processing technique known as Zero Frequency Filtering (ZFF) on the FMCW Radar Data. ZFF is known to have the best time resolution. The processing pipeline is implemented on the PYNQ-ZU platform to ensure increased portability. Experimental results show that the proposed system achieves an accuracy of over 95.26% for HR estimation and over 98.47% for BR estimation, with a latency of less than 2 ms.

Abstract

We explore language-agnostic deep text embeddings for severity classification of dysarthria in Amyotrophic Lateral Sclerosis (ALS). Speech recordings are transcribed by human and ASR and embeddings of the transcripts are considered. Though speech recognition accuracy has been studied for grading dysarthria severity, no effort has yet been made to utilize text embeddings of the transcripts. We perform severity classification at different granularity (2, 3, and 5-class) using data obtained from 47 ALS subjects. Experiments with dense neural network based classifiers suggest that, though text features achieve nearly equal performances as baseline speech features, like statistics of mel frequency cepstral coefficients (MFCC), for 2-class classification, speech features outperform for higher number of classes. Concatenation of text embeddings and MFCC statistics attains the best performances with mean F1 scores of 88%, 68%, and 53%, respectively, in 2, 3, and 5-class classification.

Abstract

Grammar error detection in one's speech, referred to as spoken grammar error detection (SGED), is a critical component for building computer-assisted language learning systems. Traditionally, ASR followed by text-based GED has been used, but ASR errors can limit SGED performance. In this work, we analyse SGED under three speaking conditions: spontaneous, semi-spontaneous, and memorized, using five state-of-the-art ASRs. We examine the decoded text and ASR cues such as confidence scores and aligner probabilities, out of which, for the latter one, the ground-truth grammatically correct (GGC) text is used. Experiments revealed that autoregressive ASRs show bias toward GGC text, leading to suboptimal performance. It is observed that there is an increase in performance in response to the decreased freedom of speech, with semi-spontaneous speech outperforming memorized speech. Aligner probabilities outperform confidence scores despite the alignment and overconfidence issues.

Abstract

As robotic manipulators start performing more daily tasks, striking can be a useful method for transporting objects because it significantly increases the reachable workspace. However, striking methods are underexplored compared to pick-and-place because of the difficulty of modeling and executing striking interactions. In this paper, we develop an algorithm for striking objects so that they stop at a target location. We start with an optimizer in simulation that solves for the striking velocity given the relative target position, and perform system identification to set the simulation parameters. However, real-world striking with this model does not have high accuracy because it is unclear which parameters should be considered for identification in practice. Therefore, we finally developed a residual learning approach that subsumes all unmodeled differences between the simulation and the real-world environment into action, that is, striking velocity residuals. Our real-world experiments show that the residual learning model results in 81.6% more accurate object strikes.

Abstract

Following up on our earlier study [J. Bardhan et al., Phys. Rev. D 107, 115001 (2023)], we investigate the LHC prospects of pair-produced vectorlike 𝐵 quarks decaying exotically to a new gauge-singlet (pseudo)scalar field Φ and a 𝑏 quark. After the electroweak symmetry breaking, the Φ decays predominantly to 𝑔⁡𝑔⁡/𝑏⁢𝑏 final states, leading to a fully hadronic 2⁢𝑏 +4⁢𝑗 or 6⁢𝑏 signature. Because of the large Standard Model background and the lack of leptonic handles, it is a difficult channel to probe. To overcome the challenge, we employ a hybrid deep learning model containing a graph neural network followed by a deep neural network. We estimate that such a state-of-the-art deep learning analysis pipeline can lead to a performance comparable to that in the semileptonic mode, taking the discovery (exclusion) reach up to about 𝑀_𝐵=1.8⁢(2.4)  TeV at HL-LHC when 𝐵 decays fully exotically, i.e., BR⁡(𝐵→𝑏⁢Φ)=100%.

Abstract

This study advances Kai Katahira’s Speculation Game, an agent-based model (ABM) for financial markets, by addressing its limitation in capturing order flow imbalance, a critical indicator of herd behavior. Although the original model successfully replicated key stylized facts of financial markets, it did not account for the persistence of order imbalance observed in real-world trading. Through a comprehensive analysis of two decades of BSE Sensex data, we establish the prevalence of order imbalance and its correlation with price fluctuations. To bridge this gap, we propose an extended model, Speculation Game with Information Cascade (SGIC), which integrates Self-Organized Criticality (SOC) through a sand-pile model, enabling agents to interact within a small-world network. Our proposed model not only reproduces the stylized facts captured by the original Speculation Game, but also successfully generates the additional stylized fact of order flow imbalance. These advances enhance the realism of ABMs in financial markets, providing deeper insights into the mechanisms driving herding and market fluctuations.

Abstract

The color gamut of visual scenes can encompass a multitude of hues, but how many distinct hues are individuals aware of? We examined this question in the context of texture perception, with a grid of stimuli composed of random colors chosen from color sets varying from two to six different hues or saturations. In a behavioral experiment, participants had to discriminate between differences in the number of hues present by identifying which of four color grids included a larger number of different colors. Color number discrimination was also assessed neurally, using electroencephalogram (EEG) frequency tagging, wherein a texture with an extra hue was shown as an “oddball,” once per second, in a stream of textures presented at six images per second. In both experiments, two versus three texture contrasts were readily distinguished, yet performance fell rapidly when the set size increased further. The results suggest that sensitivity to the density of the color gamut defining a texture is very restricted.

Abstract

Understanding user intent is essential for build- ing better human interaction agents, as it enables personalization, co-creation, and contextual adap- tation. However, existing approaches are either restricted to text environments, use human anno- tation, or just predict future user actions lacking the ability to reason explicitly about user goals. In this work, we introduce EARL (Early Action Reasoning for Latent intent), a theory of mind inspired inference-time algorithm that models user intent as an inverse planning problem, in- ferring latent goals from observed user actions. EARL hypothesizes potential user intent at mul- tiple stages during the course of task execution, enabling timely intervention and personalization. Evaluated on three diverse benchmarks namely Mind2Web, AiTz, and VideoGUI, and using two strong LLMs (Gemini-1.5-Pro and GPT-4o), we show that EARL consistently outperforms CoT- based LLM baselines in accurately deciphering user intent, especially under partial observations