Weak Signal Research Articles

Multispectral multiplexed confocal FLIM for live cell imaging

Abstract Spectrally resolved fluorescence lifetime imaging with high spatial precision offers comprehensive information on species localization and behavior. It is challenging to resolve weak fluorescence signals in multiple dimensions (spatial, spectral, and temporal) at high frame rates, especially in dynamic live cell processes, as photobleaching and phototoxicity limit acceptable photon count rates. We developed a multiplexed confocal FLIM technique, which uses a prism-based imaging spectrometer to separate a 10x10 array of confocal foci into their spectral components. This allows the sampling of the spectra by a time-resolved image sensor to produce a multispectral time-resolved data set used for generating multispectral lifetime images. This system captures 300x300 pixel fluorescence lifetime images containing 12 unique spectral bands covering a 450-700 nm spectral range in 1.8 seconds of exposure. Its performance was demonstrated in fixed stained samples and in multispectral imaging of FLIM-FRET in live cells.

Direction of Arrival Estimation of Meteors Echoes using Array Radio Antennas

Array antennas have an interesting role in the radio astronomy field. The array antennas allow astronomers to obtain high-resolution signals with high sensitivity to weak signals. This paper estimates the meteors' positions entering the Earth's atmosphere and develops a simulation for array antenna radar to analyze the meteor's echoes. The GNU radio software was used to process the echoes, which is a free open-source software development toolkit that provides signal processing blocks to implement in radio projects. Then, the simulation determines the azimuth and elevation of the meteors. An improved Multiple Signal Classification (MUSIC) algorithm has been suggested to analyze these echoes. The detected power of each meteor echo has a Doppler frequency shift due to the high speed of the meteors, which impacts the accuracy of the Direction of Arrival (DOA) estimation. The Doppler shift was considered in this simulation, and the results showed that the suggested method has low complexity and high resolution and can estimate the meteors' position with the minimum error.

Multimodal signalling in manakins: lack of correlated evolution between acoustic, visual and behavioural traits

Sexual traits, such as visual adornments, sound‐based cues and courtship dances, are frequently displayed in combination as multimodal signals. Some hypotheses propose that different signals trade‐off with each other, potentially due to resource limitations (‘trade‐off’ or transfer hypothesis) or that these develop simultaneously to enhance communication effectiveness (‘joint evolution’ hypothesis). Alternatively, multiple cues may evolve independently to serve distinct functions or convey different information (‘multiple messages’ hypothesis). Here, we explored the interdependence between different signal modalities in manakins (Pipridae), which are known for great interspecific diversity in the degree of elaboration in courtship dance, song and plumage coloration. We also used comparative methods to explore the mode of evolution and the degree of conservatism of these signals. We found that song, plumage coloration and courtship dance evolved independently as there was no correlation between these traits. Song evolution in manakins was attracted to a single optimum and exhibited a weak phylogenetic signal in comparison with those of colour and behavioural diversity. The high support found for the speciational and κ models when analysing the multivariate estimates of trait variability suggests that these signals (especially colour and behaviour attributes) may have evolved in a punctuated manner, with large evolutionary changes at speciation events and minimal subsequent changes along the branches. Our results show that song, colour and dance diversity are each associated (or marginally associated) with different ecological factors, indicating that sexual signals in manakins might convey different information and/or respond to different selection pressures. Larger species and those with lower dispersal capacity tend to exhibit higher colour richness, which in turn differed among lek categories, being higher in species with ‘classic’ leks and lower in non‐lekking species. Overall, the present study supports the idea that correlation between signal modalities does not constitute a widespread pattern in songbirds.

Application of the AVMD-AIMCKD method to the enhancement of rolling bearing fault features

Abstract Rolling bearings are prone to failure due to harsh working environments, which can affect production efficiency. Given the background environmental noise interference, it is difficult to extract the fault characteristics of rolling bearings in the early stages of weak fault signals. Based on adaptive VMD combined with adaptive IMCKD (AVMD-AIMCKD), a rolling bearing fault diagnosis method is proposed in this paper. The vibration signal from the rolling element bearing is processed by adaptive variational modal decomposition (AVMD) to extract and select the time-frequency domain signal characteristics. The Adaptive Improved maximum correlated kurtosis deconvolution (AIMCKD) algorithm is used for noise reduction optimization to obtain the best extraction results. Because the traditional method requires input parameter values for VMD and IMCKD, it is not adaptive. The particle swarm optimization algorithm (PSO) is utilized in this study to optimize two variables: the penalty factor and decomposition mode number of variable mode decomposition (VMD). The filter length and shift order of the improved maximum correlated kurtosis deconvolution (IMCKD) are optimized to make it adaptive. The viability of the method is substantiated through simulations, and the efficacy and precision are validated by comparative studies. The experimental results show that the AVMD-AIMCKD method has high accuracy and robustness in rolling bearing fault diagnosis and provides an accurate reference for rolling bearing health monitoring in engineering practice. The AVMD-AIMCKD method effectively extracts the early fault features through adaptive optimization, overcomes the restrictions of traditional methods, and provides a more accurate and reliable tool for bearing fault signal diagnosis.

Using Horizon Scanning to Build Policy Resilience: Case of Waste Crime

ABSTRACTWaste crime is a pressing concern for the waste and resource industry as it is undermining investment, growth and jobs within the industry and threatening the natural environment. However, there is little knowledge of the scale of the problem, the types of criminality and motivations involved, and the precise nature of crime. Environmental regulators are building foresight capabilities to better understand the effect of current and future changes in markets, in technology and in the legislative environment on waste crime and associated behaviours. At the heart of this paper is the question: how can horizon scanning be adopted by environmental regulators to shape decision processes and build resilience to waste crime? We report our efforts to build a toolkit and guidance for conducting horizon scanning, aimed at supporting environmental regulators, investigators and intelligence analysts to build an understanding of—and interpretation of the consequences of—behavioural, market, technological and pollution trends in the waste sector. A review of the academic and grey literature provided insights to organisational approaches and design principles for public sector horizon scanning. Outputs guided discussion at a stakeholder workshop with waste regulators, criminal intelligence and industry professionals to explore institutional challenges and to agree broad design principles for a horizon scanning process. The toolkit supports environmental regulators in applying horizon scanning to policy and wider operational and delivery‐focused challenges; learning how to: (1) spot weak signals and emerging trends quickly, (2) examine the evidence around potential threats and opportunities for the future, and (3) take action on strategically important issues to minimise the impact of crime on the environment, society and business. The paper sets out further research needed to integrate horizon scanning with data analytics (e.g., predictive and hotspot analyses) to challenge assumptions about the patterns of change, based largely on historical trends, and to better manage these so there is greater adaptability to current and future trends.

The most detailed anatomical reconstruction of a Mesozoic coelacanth.

Although the split of coelacanths from other sarcopterygians is ancient, around 420 million years ago, the taxic diversity and the morphological disparity of the clade have remained relatively low, with a few exceptions. This supposedly slow evolutionary pace has earned the extant coelacanth Latimeria the nickname "living fossil". This status generated much interest in both extinct and extant coelacanths leading to the production of numerous anatomical studies. However, detailed descriptions of extinct taxa are made difficult due to the quality of the fossil material which generally prevents fine comparisons with the extant Latimeria. Here we describe a new genus and species of coelacanth, Graulia branchiodonta gen. et sp. nov. from the Middle Triassic of Eastern France, based on microtomographical imaging using synchrotron radiation. Through exquisite 3D preservation of the specimens, we reconstructed the skeletal anatomy of this new species at an unprecedented level of detail for an extinct coelacanth, and barely achieved for the extant Latimeria. In particular, we identified a well-developed trilobed ossified lung whose function is still uncertain. The skeletal anatomy of G. branchiodonta displays the general Bauplan of Mesozoic coelacanths and a phylogenetic analysis resolved it as a basal Mawsoniidae, shedding light on the early diversification of one of the two major lineages of Mesozoic coelacanths. However, despite its exquisite preservation, G. branchiodonta carries a weak phylogenetic signal, highlighting that the sudden radiation of coelacanths in the Early and Middle Triassic makes it currently difficult to detect synapomorphies and resolve phylogenetic interrelationships among coelacanths in the aftermath of the great Permo-Triassic biodiversity crisis.

Real-time non-line-of-sight computational imaging using spectrum filtering and motion compensation.

Non-line-of-sight (NLOS) imaging aims at recovering the shape and albedo of hidden objects. Despite recent advances, real-time video of complex and dynamic scenes remains a major challenge owing to the weak signal of multiply scattered light. Here we propose and demonstrate a framework of spectrum filtering and motion compensation to realize high-quality NLOS video for room-sized scenes. Spectrum filtering leverages a wave-based model for denoising and deblurring in the frequency domain, enabling computational image reconstruction with a small number of sampling points. Motion compensation tailored with an interleaved scanning scheme can compute high-resolution live video during the acquisition of low-quality image sequences. Together, we demonstrate live NLOS videos at 4 fps for a variety of dynamic real-life scenes. The results mark a substantial stride toward real-time, large-scale and low-power NLOS imaging and sensing applications.

Quadratic Unet for seismic random noise attenuation

In the field of seismic data processing, seismic denoising is essential to improve the quality of seismic data. Various deep learning methods have been proposed, showing promising performance for seismic denoising. However, most deep learning methods are based on lin ear neurons, resulting in limited expressive ability for complex seismic signals. To enhance denoising capability using non-linear neurons, we propose a supervised quadratic U-shaped network (QUnet) for seismic random noise attenuation. The quadratic neurons in QUnet are represented by a second-order polynomial of input data and weighted parameters. Nu merical synthetic seismic data experiments show that the proposed QUnet method achieves higher signal-to-noise ratio results and preserves the continuity of reflectors more effectively. We have also tested the effectiveness of QUnet on both prestack field data and post-stack seismic data. The detailed structures and weak signals of seismic data are better preserved by our proposed QUnet method.

Weakly supervised label learning flows

Supervised learning usually requires a large amount of labelled data. However, attaining ground-truth labels is costly for many tasks. Alternatively, weakly supervised methods learn with cheap weak signals that only approximately label some data. Many existing weakly supervised learning methods learn a deterministic function that estimates labels given the input data and weak signals. In this paper, we develop label learning flows (LLF), a general framework for weakly supervised learning problems. Our method is a generative model based on normalizing flows. The main idea of LLF is to optimize the conditional likelihoods of all possible labelings of the data within a constrained space defined by weak signals. We develop a training method for LLF that trains the conditional flow inversely and avoids estimating the labels. Once a model is trained, we can make predictions with a sampling algorithm. We apply LLF to three weakly supervised learning problems. Experiment results show that our method outperforms many baselines we compare against.

Sparsity-based direction-of-arrival estimation in the presence of near-field and far-field interferences for small-scale platform sonar arrays.

For the sonar arrays mounted on an unmanned underwater vehicle (UUV), the direction-of-arrival (DOA) estimation of the far-field (FF) weak sources is influenced by the near-field (NF) interferences generated from the radiated self-noise of the UUV and the FF interferences simultaneously. To address the problem, a sparsity-based DOA estimation method resistant to the NF and FF interferences is proposed in this paper. This method isolates the FF signals from the NF signals by sparse reconstruction. Additionally, subspace projection is applied to address the masking problem of the weak target signal by the strong interferences in the spatial domain, effectively enhancing the capacity of estimating the DOA of the weak target signal in the presence of strong interferences. Numerical simulations and experimental results demonstrate the effectiveness of the proposed method. Compared to other advanced DOA estimation methods, the proposed method exhibits better DOA estimation performance in the presence of strong NF and FF interferences.

Causal and Chronological Relationships Predict Memory Organization for Nonlinear Narratives.

While recounting an experience, one can employ multiple strategies to transition from one part to the next. For instance, if the event was learned out of linear order, one can recall events according to the time they were learned (temporal), similar events (semantic), events occurring nearby in time (chronological), or events produced by the current event (causal). To disentangle the importance of these factors, we had participants watch the nonlinear narrative, Memento, under different task instructions and presentation orders. For each scene of the film, we also separately computed semantic and causal networks. We then contrasted the evidence for temporal, semantic, chronological, or causal strategies during recall. Critically, there was stronger evidence for the causal and chronological strategies than semantic or temporal strategies. Moreover, the causal and chronological strategies outperformed the temporal one even when we asked participants to recall the film in the presented order, underscoring the fundamental nature of causal structure in scaffolding understanding and organizing recall. Nevertheless, time still marginally predicted recall transitions, suggesting it operates as a weak signal in the presence of more salient forms of structure. In addition, semantic and causal network properties predicted scene memorability, including a stronger role for incoming causes to an event than its outgoing effects. In summary, these findings highlight the importance of accounting for complex, causal networks in knowledge building and memory.

Ultratrace CO/SF6 Detection System Based on Differential Fourier Transform Infrared Spectroscopy Combined with the Weighted Sine Spectral Reconstruction Convolutional Neural Network (WSSR-CNN) Model.

Carbon monoxide (CO), as an indicator gas for fault diagnosis of gas-insulated switchgear, has the strongest absorption coefficient in the mid-infrared region, making Fourier transform infrared (FTIR) spectroscopy an ideal method for its concentration detection. However, there is extremely abundant absorption of SF6 in this region, resulting in a great challenge for CO/SF6 detection. To address this challenge, we report a high sensitivity online detection system for ultratrace levels of CO, which combines differential Fourier transform infrared (DFTIR) spectroscopy with a weighted sine spectral reconstruction convolutional neural network (WSSR-CNN). The differential technique is first introduced into FTIR for baseline correction of CO absorption signals. The novel WSSR method achieves feature extraction and denoising of weak spectral signals in strong interference by discretizing interfering signals and enhancing target signals. On this basis, the detection of CO concentration is realized using the CNN model. Finally, WSSR-CNN is compared with four other methods. Results showed that WSSR-CNN outperforms all four models with evaluation index R2 reaching 0.99982 and 0.99999 in the range of low concentration (0.096-0.986 ppm) and high concentration (1.984-52.193 ppm) and mean absolute percentage error of 0.97% and 0.22%, respectively. The lowest detection limit of the system at 1σ is 13 ppb, which is the best result reported so far for detecting CO/SF6 concentration based on FTIR.

Weak signal detection signifying foreign metal objects in wireless power transfer systems

Weak signal detection signifying foreign metal objects in wireless power transfer systems

Photonics-assisted self-interference cancellation for in-band full-duplex integrated sensing and communication transceiver

In-band full-duplex (IBFD) operation is essential for both sensing-centric and communication-centric integrated sensing and communications (ISAC) systems. Both types require the monostatic transceiver to overcome the technical challenge of self-interference (SI). To address this challenge, a photonics-assisted self-interference cancellation (SIC) scheme for an IBFD ISAC transceiver is proposed and experimentally demonstrated. By utilizing wavelength division multiplexing, the SI is cancelled by a cancellation reference with matched amplitude and time delay, using two counter-biased Mach-Zehnder modulators. In proof-of-concept experiments, the proposed IBFD ISAC transceiver is tested using a 10 GHz quadrature amplitude modulation (QAM) constant envelope linear frequency modulation orthogonal frequency division multiplexing (CE-LFM-OFDM) ISAC signal with a carrier frequency and a bandwidth of 10 GHz and 2 GHz, respectively. Experimental results show that cancellation depths of 35.29 dB and 32.59 dB are achieved with bandwidths of 1 GHz and 2 GHz, respectively, in the communication receiver. The corresponding weak signal of interest is successfully recovered after effective SIC in the wireless link. The ranging and imaging functions are also experimentally verified. The experimental results show that the cancellation depth of the SI after de-chirping is 23.6 dB when the center frequency and bandwidth of the CE-LFM-OFDM RF signal are 10 GHz and 2 GHz, respectively. A dynamic range increase of 23.84 dB is achieved in imaging function. The corresponding radar ranging resolution of 10 cm is also achieved for radar function. The proposed scheme cancels the SI in both communication and radar receivers, demonstrating excellent performance in the IBFD ISAC transceiver system.

Recent Advances in Organic Photodetectors

Organic photodetectors (OPDs) have garnered significant attention in fields such as image sensing, health monitoring, and wearable devices due to their exceptional performance. This review summarizes recent research advancements in materials, structures, performance, and applications of narrowband organic photodetectors, hybrid organic–inorganic perovskite photodetectors, flexible organic photodetectors (FOPDs), and photomultiplication type organic photodetectors (PM-OPDs). Organic semiconductors offer substantial potential in optoelectronic devices owing to their low cost, ease of processing, and tunable spectral response. Hybrid perovskite materials extend the spectral response range, FOPDs meet the demands of wearable devices, and PM-OPDs enhance sensitivity, allowing for the detection of weak light signals. Through innovations in materials, structural optimization, and improvements in manufacturing processes, the performance of OPDs has seen significant enhancement. This article also explores the application prospects of these technologies in medical monitoring, optical communications, and image sensing.

Simplified shielded MEG-MRI multimodal system with scalar-mode optically pumped magnetometers as MEG sensors

Magnetoencephalography (MEG) conventionally operates within high-performance magnetic shields due to the extremely weak magnetic field signals from the measured objects and the narrow dynamic range of the magnetic sensors employed for detection. This limitation results in elevated equipment costs and restricted usage. Additionally, the information obtained from MEG is functional images, and to analyze from which part of the brain the signals are coming, it is necessary to capture morphological images separately. When MEG and morphological imaging devices are separate, despite their individual high measurement accuracies, discrepancies in positional information may arise. In response, we have developed a low-field magnetic resonance imaging system that incorporates scalar-mode optically pumped magnetometers with a wide dynamic range and exceptionally high measurement sensitivity as sensors for MEG. Operating at low magnetic fields eliminates the need for superconducting coils in magnetic resonance imaging and the high-performance magnetic shields essential for MEG, promising a substantial cost reduction compared to traditional approaches. We achieved a noise level of about 16.7pT/Hz1/2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${16.7}\\,\\hbox {pT/Hz}^{1/2}$$\\end{document} with a single channel magnetometer, and reached a noise level of 367fT/cm/Hz1/2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${367}\\,\\hbox {fT/cm/Hz}^{1/2}$$\\end{document} with a baseline of 1 cm through differential measurements. We employed this system to conduct sequential OPM-based magnetic field measurements and MRI imaging, successfully demonstrating the compatibility of high OPM sensitivity with clear MRI acquisition.

Study on the Identification Method of Planar Geological Structures in Coal Mines Using Ground-Penetrating Radar

The underground detection environment in coal mines is complex, with numerous interference sources. Traditional ground-penetrating radar (GPR) methods suffer from limited detection range, high noise levels, and weak deep signals, making it extremely difficult to accurately identify geological structures without stable feature feedback. During research, it was found that the detection energy of the same target significantly changes with the antenna direction. Based on this phenomenon, this paper proposes a geological radar advanced detection method using spatial scanning. This method overcomes constraints imposed by the underground coal mine environment on detection equipment, enhancing both detection range and accuracy compared to traditional approaches. Experiments using this method revealed pea-shaped response characteristics of planar geological structures in radar images, and the mechanisms behind their formation were analyzed. Additionally, this paper studied the changes in response characteristics under changes in target inclination, providing a basis for understanding the spatial distribution of geological structures. Finally, application experiments in underground coal mine environments explored the practical potential of this method. Results indicate that, compared to drilling data, this method achieves identification accuracies of 91.88%, 90.42%, and 78.72% for the depth and spatial extent of geological structures, providing effective technical support for coal mining operations.

Research on AUV Multi-Node Networking Communication Based on Underwater Electric Field CSMA/CA Channel

To address the issues of high attenuation, weak reception signal, and channel blockage in the current electric field communication of underwater robots, research on autonomous underwater vehicle (AUV) multi-node networking communication based on underwater electric field Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) channel was conducted. This article, first through simulation, finds that the Optimized Link State Routing (OLSR) protocol has a smaller routing packet delay time and higher reliability compared to the Ad Hoc On-Demand Distance Vector (AODV) protocol on underwater electric field CSMA/CA channels. Then, a 2FSK underwater electric field communication system was established, and dynamic communication experiments were carried out between two AUV nodes. The experimental results showed that within a range of 0 to 3.5 m, this system can achieve underwater dynamic electric field communication with a bit error rate of 0 to 0.628%. Finally, to avoid channel blockage during underwater AUV multi-node communication, this article proposes a dynamic backoff method for AUV multi-node communication based on CSMA/CA. This system can achieve dynamic multi-node communication of underwater electric fields with an error rate ranging from 0 to 0.96%. The research results have engineering application prospects for underwater cluster operations.

Loose particle Detection: The optimal detection condition and weak loose particle impulse extraction

To achieve effective detection of loose particles within small cavities in microelectronic devices, this paper proposes the optimal detection conditions of loose particles in ceramic packaging for microelectronic devices based on simulation, and presents a method for weak signal reconstruction using weighted sparse representation. To address the challenge of ineffective detection of loose particles under recommended vibration conditions, a particle-cavity collision dynamics model is established. Subsequently, the detection laws of loose particles under different accelerations, frequencies, and cavity heights are studied through simulation, leading to the establishment of optimal detection conditions for microelectronic devices with varying cavity heights. To address the issue of weak impulses being submerged in background noise under optimal detection conditions, sparse representation is utilized for signal reconstruction. First, a Laplace wavelet dictionary is constructed according to the impulse characteristics. Second, the generalized minimax-concave (GMC) penalty function is applied as a sparse regularization term to preserve signal amplitude. Meanwhile, a weight matrix based on kurtosis and singular value is designed to threshold sparse coefficients. The results demonstrate that the proposed optimal detection conditions and signal reconstruction method effectively detect loose particles. Compared with other algorithms, the proposed method reduces background noise in the particle impact noise detection (PIND) signals while maintaining impulse amplitude; it also improves signal reconstruction accuracy and offers valuable insights into effective loose particle detection in microelectronic packaging devices.

Receiving Paths Improvement of Digital Phased Array Antennas Using Adaptive Dynamic Range

In contemporary radar technology, the observation and detection of objects with low radar cross-sections remains a significant challenge. A multi-functional radar model employing a digital phased array antenna system offers notable advantages over traditional radar in addressing this issue. Nonetheless, to fully capitalize on these benefits, improving the structure of the receiving path in digital transceiver modules is crucial. A method for improving the digital receiving path model by implementing a matched filter approach is introduced. Given that the return signals from objects are often lower than the internal noise, the analog part of the digital transceiver modules must ensure that its dynamic range aligns with the level of this noise and the weak signal. The output signal level of the analog part must correspond to the allowable input range of the analog-to-digital converter. Improvements in the receiving path to achieve a fully matched model can reduce errors in the phase parameters and amplitudes of the useful signal at the output. The simulation results presented in this paper demonstrate a reduction in amplitude error by approximately 1 dB and a phase error exceeding 1.5 degrees for the desired signal at the output of each receiving path. Consequently, these improvements are expected to enhance the overall quality and efficiency of the spatial and temporal accumulation processes in the digital phased array antenna system. Furthermore, to maintain the matched filter model, we also propose incorporating an adaptive “pseudo-expansion” of the linear gain range. This involves adding a feedback stage with an automatic and adaptive bias voltage adjustment for the intermediate-frequency preamplifier in the analog part of the receiving path. Simulations to qualitatively verify the validity of this proposal are conducted using data from practical operational radar system models.