Signal Transformation Research Articles

Pdr5: A master of asymmetry

Pdr5 is a founding member of a large (pdr) subfamily of clinically and agriculturally significant fungal ABC transporters. The tremendous power of yeast genetics combined with biochemical and structural approaches revealed the astonishing asymmetry of this efflux pump. Asymmetry is manifested in Pdr5′s ATP-binding sites, drug binding sites, signal transformation interface, and molecular exit gate. Even its mode of conformational switching is asymmetric with one half of the protein remaining nearly stationary. In the case of its ATP-binding sites, asymmetry is created by replacing a set of highly conserved residues with a characteristic set of deviant ones. This contrasts with the asymmetry of the molecular gate. There, a full complement of canonical residues is present, but structural features in the vicinity prevent some of these from forming a molecular plug during closure. Compared to their canonical-functioning counterparts, the deviant ATP site and these gating residues have different, essential functions. In addition to its remarkable asymmetry, the surprising observation that Pdr5 is a drug / proton co-transporter shines a new light on this remarkable protein.

Activity-dependent organization of prefrontal hub-networks for associative learning and signal transformation

Associative learning is crucial for adapting to environmental changes. Interactions among neuronal populations involving the dorso-medial prefrontal cortex (dmPFC) are proposed to regulate associative learning, but how these neuronal populations store and process information about the association remains unclear. Here we developed a pipeline for longitudinal two-photon imaging and computational dissection of neural population activities in male mouse dmPFC during fear-conditioning procedures, enabling us to detect learning-dependent changes in the dmPFC network topology. Using regularized regression methods and graphical modeling, we found that fear conditioning drove dmPFC reorganization to generate a neuronal ensemble encoding conditioned responses (CR) characterized by enhanced internal coactivity, functional connectivity, and association with conditioned stimuli (CS). Importantly, neurons strongly responding to unconditioned stimuli during conditioning subsequently became hubs of this novel associative network for the CS-to-CR transformation. Altogether, we demonstrate learning-dependent dynamic modulation of population coding structured on the activity-dependent formation of the hub network within the dmPFC.

A Wireless Gunshot Recognition System Based on Tri-Axis Accelerometer and Lightweight Deep Learning

Gun violence and misuse pose great threat to the public safety. Real time monitoring of guns usage and gunshot events are very promising for effective gun control. However, most available monitoring systems are installed in fixed location instead of the guns, which greatly limits the flexibility and coverage. In this study, we propose a wireless gun monitoring and gunshot recognition system based on a low-cost triaxial acceleration sensor, which can monitor the gun in real time and accurately recognize gunshot events. Addressing the limited resources of the embedded systems, we further propose an efficient gunshot recognition algorithm EfficientNetTime that combines the lightweight neural network and knowledge distillation, so as to enable the deployment on embedded devices. Firstly, a novel lightweight deep learning model is proposed as the basic model, which combines the advantages of one-dimensional convolution and depthwise separable convolution to effectively characterize the gunshot signal while decreasing the computing cost of convolution. Secondly, using the knowledge distillation, EfficientNetTime is used as the teacher model to generate a compressed student model that maintains accuracy and greatly reducing model size. Lastly, the EfficientNetTime student model can be deployed on resource-limited embedded systems. The proposed method can automatically extract features for end-to-end recognition and is robust to temporal transformations of input signals. Using a publicly available gunshot dataset, the proposed EfficientNetTime model is verified and compared against the state-of-the-art models. Experimental results demonstrate that the EfficientNetTime model surpasses other gunshot recognition methods in terms of the accuracy and model size.

Advances in the two-dimensional layer materials for cancer diagnosis and treatment: unique advantages beyond the microsphere.

In recent years, two-dimensional (2D) layer materials have shown great potential in the field of cancer diagnosis and treatment due to their unique structural, electronic, and chemical properties. These non-spherical materials have attracted increasing attention around the world because of its widely used biological characteristics. The application of 2D layer materials like lamellar graphene, transition metal dichalcogenides (TMDs), and black phosphorus (BPs) and so on have been developed for CT/MRI imaging, serum biosensing, drug targeting delivery, photothermal therapy, and photodynamic therapy. These unique applications for tumor are due to the multi-variable synthesis of 2D materials and the structural characteristics of good ductility different from microsphere. Based on the above considerations, the application of 2D materials in cancer is mainly carried out in the following three aspects: 1) In terms of accurate and rapid screening of tumor patients, we will focus on the enrichment of serum markers and sensitive signal transformation of 2D materials; 2) The progress of 2D nanomaterials in tumor MRI and CT imaging was described by comparing the performance of traditional contrast agents; 3) In the most important aspect, we will focus on the progress of 2D materials in the field of precision drug delivery and collaborative therapy, such as photothermal ablation, sonodynamic therapy, chemokinetic therapy, etc. In summary, this review provides a comprehensive overview of the advances in the application of 2D layer materials for tumor diagnosis and treatment, and emphasizes the performance difference between 2D materials and other types of nanoparticles (mainly spherical). With further research and development, these multifunctional layer materials hold great promise in the prospects, and challenges of 2D materials development are discussed.

A self-attention based contrastive learning method for bearing fault diagnosis

The shortage of labeled data is a major obstacle to the practical application of advanced fault diagnosis technologies, and the large amount of unlabeled data may be the key to solving this problem. This paper proposes a self-attention based contrastive leaning method for bearing fault diagnosis which utilizes the unlabeled data for self-supervised learning. Using the self-attention-based signal transformer as the backbone, the proposed method is able to learn feature extraction capability from a large number of unlabeled data by contrastive learning using only positive samples. Then using a small number of labeled data for fine-tuning, the proposed method can perform accurate fault diagnosis. Experiments using both run-to-failure and artificial fault vibration signal datasets show that the proposed method can not only outperform other semi-supervised or self-supervised learning methods but also exceed the accuracy of supervised learning methods in case of insufficient labels. The visualization shows the interpretability of the model and the feature extraction ability obtained from self-supervised pre-training.

Advances in the application of CRISPR-Cas technology in rapid detection of pathogen nucleic acid.

Clustered regularly interspaced short palindromic repeats (CRISPR) and CRISPR-associated proteins (Cas) are widely used as gene editing tools in biology, microbiology, and other fields. CRISPR is composed of highly conserved repetitive sequences and spacer sequences in tandem. The spacer sequence has homology with foreign nucleic acids such as viruses and plasmids; Cas effector proteins have endonucleases, and become a hotspot in the field of molecular diagnosis because they recognize and cut specific DNA or RNA sequences. Researchers have developed many diagnostic platforms with high sensitivity, high specificity, and low cost by using Cas proteins (Cas9, Cas12, Cas13, Cas14, etc.) in combination with signal amplification and transformation technologies (fluorescence method, lateral flow technology, etc.), providing a new way for rapid detection of pathogen nucleic acid. This paper introduces the biological mechanism and classification of CRISPR-Cas technology, summarizes the existing rapid detection technology for pathogen nucleic acid based on the trans cleavage activity of Cas, describes its characteristics, functions, and application scenarios, and prospects the future application of this technology.

Solving the Applied Problem of Random-Dot Images Analysis Using a Multidimensional Extension of Classic Catalan Numbers

The paper introduces the concept of generalized Catalan numbers as a useful tool for solving many theoretical and applied combinatorial-probabilistic problems. Combined with analytical transformation software algorithms, the generalized Catalan numbers simplify the solution of many problems in computer science and applied mathematics. In particular, they turn out to be an effective tool for solving problems related to the registration of random-dot images, in signal transformations of various degrees of smoothness, and when developing speed-optimal algorithms for searching for pulsed-point objects with a random generation time of super short pulses. The proposal of the authors to formulate the problems of enumerative combinatorics in a word-symbolic form naturally leads to multidimensional extensions of the classical Catalan numbers and has several advantages. The combined use of multidimensional Catalan numbers and high-performance computer algebra systems enables solving a number of complex applied problems related to the reliability of the registration of random-dot images.

Precipitation δ18O paced the seasonal δ18O variations of terrestrial snail body water and shells in the East Asian monsoon region

Terrestrial snails are sensitive to climate changes and their shell's oxygen isotope composition (δ18Oshell) is widely used in studies of paleoclimate reconstructions. However, the interpretation of δ18Oshell remains complex due to the combined effects from different factors such as precipitation δ18O (δ18Op), relative humidity (RH), and temperature. Furthermore, no systematic studies have addressed the transformation of oxygen isotope signals from rainfall to the aragonite shell via snail body water. Here we present the results of Cathaica fasciola body water δ18O (δ18OBW) and its content, which were sampled every two days throughout the growing season in 2021 in Xi'an. Meanwhile, the soil water δ18O (δ18OSW), δ18Op, rainfall amount, RH and temperature were also simultaneously monitored, and two high-resolution (at ∼0.3 mm interval) δ18Oshell series were obtained from two live snails collected in September and October 2021, respectively. These data reveal that the δ18OBW mimic the variations of δ18Op, and they are positively correlated (r2 = 0.84) for precipitation events and the correlation coefficient only increases slightly (r2 = 0.88) when RH is included in the multivariate analysis. During non-precipitation intervals, there is an obvious negative correlation between δ18OBW and RH. Moreover, the variations of two δ18Oshell series are broadly consistent with the theoretically calculated δ18Oshell from δ18OBW/δ18Op at seasonal scale. Collectively, this study demonstrates that δ18Op is the first-order control on the seasonal variations of δ18OBW and δ18Oshell, while RH play its role during non-precipitation periods. Importantly, this study lays good foundation for reconstructing terrestrial seasonal distribution of precipitation and extreme rainfall events using fossil shells in the geological past.

Multimodal transformers for wireless communications: A case study in beam prediction

Wireless communications at high-frequency bands with large antenna arrays face challenges in beam management, which can potentially be improved by multimodality sensing information from cameras, LiDAR, radar, and GPS. In this paper, we present a multimodal transformer deep learning framework for sensing-assisted beam prediction. We employ a convolutional neural network to extract the features from a sequence of images, point clouds, and radar raw data sampled over time. At each convolutional layer, we use transformer encoders to learn the hidden relations between feature tokens from different modalities and time instances over abstraction space and produce encoded vectors for the next-level feature extraction. We train the model on a combination of different modalities with supervised learning. We try to enhance the model over imbalanced data by utilizing focal loss and exponential moving average. We also evaluate data processing and augmentation techniques such as image enhancement, segmentation, background filtering, multimodal data flipping, radar signal transformation, and GPS angle calibration. Experimental results show that our solution trained on image and GPS data produces the best distance-based accuracy of predicted beams at 78.44%, with effective generalization to unseen day scenarios near 73% and night scenarios over 84%. This outperforms using other modalities and arbitrary data processing techniques, which demonstrates the effectiveness of transformers with feature fusion in performing radio beam prediction from images and GPS. Furthermore, our solution could be pretrained from large sequences of multimodality wireless data, on fine-tuning for multiple downstream radio network tasks.

Tumor microenvironment-mediated NIR-I-to-NIR-II transformation of Au self-assembly for theranostics.

The misdiagnosis of tumors due to insufficient penetration depth or signal interference and damage to normal tissues due to indiscriminate treatment are the biggest challenges in using photothermal agents for clinical translation. To overcome these limitations, a strategy of switching from the near-infrared (NIR)-I region to the NIR-II region was developed based on tumor microenvironment (TME)-mediated gold (Au) self-assembly. Using zeolitic imidazolate framework-8 (ZIF-8) metal-organic framework-coated gold nanorods (AuNRs@ZIF-8) as a model photothermal agent, we demonstrated that only a NIR-I photoacoustic imaging signal was observed in normal tissue because ZIF-8 could prevent the aggregation of AuNRs. However, when ZIF-8 dissociated in the TME, the AuNRs aggregated to activate NIR-II photoacoustic imaging and attenuate the NIR-I signal, thereby allowing an accurate diagnosis of tumors based on signal transformation. Notably, TME-activated NIR-II photothermal therapy could also inhibit tumor growth. Therefore, this TME-activated NIR-I-to-NIR-II switching strategy could improve the accuracy of deep-tumor diagnoses and avoid the injury caused by undifferentiated treatment. STATEMENT OF SIGNIFICANCE: Photothermal agents used for photoacoustic imaging and photothermal therapy have garnered great attention for tumor theranostics. However, always "turned on" near-infrared (NIR)-I laser (700-1000nm)-responsive photothermal agents face issues of penetration depth and damage to normal tissues. In contrast, tumor microenvironment-activated NIR-II "smart" photothermal agents exhibit deeper penetration depth and tumor selectivity. Therefore, a NIR-I-to-NIR-II switching strategy was developed based on tumor microenvironment-mediated Au self-assembly. This work provides a new strategy for developing tumor microenvironment-activated NIR-II smart photothermal agents.

Binlets: Data fusion-aware denoising enables accurate and unbiased quantification of multichannel signals

As monitoring multiple signals becomes more cost-effective, combining them through a data fusion-aware denoising method can produce a more robust estimation of the underlying process. Here, we present a method based on the Haar wavelet transform that trades off resolution against accuracy based on statistical significance. By taking advantage of correlations between channels, it offers a superior performance compared to denoising each channel separately. It outperforms standard wavelet methods when the magnitude of interest in the data-fusion process involves a non-linear transformation or reduction of a multichannel signal. We demonstrate its efficacy by benchmarking our method against standard wavelet thresholding for synthetic single and multichannel time series, and a multichannel two-dimensional image. The method has a simple interpretation as an adaptive binning of the signal, and neither requires training data nor specialized hardware to run fast. In addition, a reference Python implementation is available on GitHub and PyPI, making it simple to integrate into any analysis pipeline.

A Few-Shot Automatic Modulation Classification Method Based on Temporal Singular Spectrum Graph and Meta-Learning

Deep learning-based Automatic Modulation Classification (AMC) has achieved excellent classification accuracy. However, most deep learning-based AMC methods have an inherent drawback. They exhibit a strong dependency on massive labeled samples, which is precisely difficult to obtain in real-world scenarios. This paper presents a few-shot AMC approach that integrates signal transformation and meta-learning. The former enhances class separability, while the latter addresses challenges posed by limited sample sizes. The results of simulation experiments conducted on the RadioML.2018.01a dataset demonstrate that the proposed technique achieves a classification accuracy of 74.21% when using one sample per class and increases to 82.27% when using five samples. The few-shot AMC efficacy of this proposed approach exhibits an outperformance over the classical deep learning methods.

Efficient signal transmission algorithms for remote sensing gamma radiation over developed mobil WiMax network

SummaryThis paper overcomes two key issues within radiation monitoring networks. Secure and accurate transmission of detected gamma radiation in remote areas is the first challenge. The second one is related to the robustness of transmitted signal under different fading scenarios. These issues are handled during signal transmission over worldwide interoperability for microwave access (WiMax) networks. The signals are experimentally captured using a 137Cs point source, a NaI (TI) detector, and an analog‐to‐digital converter with 16 MS/s. For the first issue, several algorithms are implemented for secure and efficient transmission of gamma‐based signals. These algorithms include encoding of captured signal, decoding, interleaving, and frequency transformation of modulated signal. Encoding and decoding of target signals are conducted using the Reed Solomon technique and convolutional encoding with a Viterbi decoder. A superior algorithm for channel interleaver using the combinational interleaving method is introduced. Fast lifting transform (FLT) is applied for transforming modulated signal into frequency domain. The results declare that CC = 1/2 with BPSK‐1/2 coding rate introduces a better BER of 0.00068. A higher SNR of 1.5 dB is accomplished by the combinational interleaving algorithm. For the second issue, several models are suggested for overcoming channel degradations under gamma, exponential, Poisson, and geometric fading. Closed‐form expressions that describe outage probability, bit error rate (BER), and symbol error rate (SER) are suggested. Geometric and gamma fading achieve respectively a lower BER of 0.18 and 0.48. These channels demonstrate similar channel performances at 3.6 dB. The proposed models are validated through comparison with published work. Finally, the proposed algorithms enable higher data rate transmission of detected 137Cs signals under higher robustness using WiMax networks.

Frequency Comb Fiber Generator Based on Photonic Bandgap Amplifier

We report on a model of a fiber frequency comb generator that develops an approach to harmonically mode-locked fiber laser design based on dissipative four-wave mixing. In our version of this approach, we assume an amplifying one-dimensional photonic crystal as a key cavity element combining the properties of an intra-cavity filter and a power amplifier. Using standard equations describing the signal transformation in the ring cavity and the output fiber cascade, we have demonstrated the possibility of the application of the proposed model as a generator of broadband frequency comb with controllable line spacing.

Prognostic fault prevention by segmented digital transformation of manufacturing process signals

Faults during operation of a system can occur at any time. Contemporary fault diagnosis systems focus on identifying the problem. However, when faults occur, the system has already incurred losses, either as rejects or machine damage. In this paper, a new method that predicts future faults and subsequently prevents losses caused by the faults has been developed. The method makes use of a series of digital transformations of signals from the manufacturing process. An important finding of this research is to divide the continuous signal stream into data segments from which a parameter called sum standard deviation frequency (SSDF) can be extracted. SSDF reflects the change of inconspicuous conditions in the signal in different data segments over time. To predict how the signal stream will perform in the next minute, a new density peak clustering computational algorithm is developed to transform the SSDF database into “normal”, “marginal”, and “abnormal” records. Finally, a novel method “moving SSDF deep learning” (MSDL) concept has been proven to have a better prediction of future capability than four other prediction methods. The method has been applied to a 3D printing process in which belt tension affecting machine performance has been predicted 30 s ahead of the problem, giving sufficient time for the operator to adjust or stop the machine before a fault occurs.

A Universal Feature Extractor Based on Self-Supervised Pre-Training for Fault Diagnosis of Rotating Machinery under Limited Data

To address the limited data problem in real-world fault diagnosis, previous studies have primarily focused on semi-supervised learning and transfer learning methods. However, these approaches often struggle to obtain the necessary data, failing to fully leverage the potential of easily obtainable unlabeled data from other devices. In light of this, this paper proposes a novel network architecture, named Signal Bootstrap Your Own Latent (SBYOL), which utilizes unlabeled vibration signals to address the challenging issues of variable working conditions, strong noise, and limited data in rotating machinery fault diagnosis. The architecture consists of a self-supervised pre-training-based fault feature recognition network and a diagnosis network based on knowledge transfer. The fault feature recognition network uses ResNet-18 as the backbone network for self-supervised pre-training and transfers the trained fault feature extractor to the target diagnostic object. Additionally, a unique vibration signal data augmentation technique, time–frequency signal transformation (TFST), is proposed specifically for rotating machinery fault diagnosis, which addresses the key task of contrastive learning and achieves high-precision fault diagnosis with very few labeled samples. Experimental results demonstrate that the proposed diagnostic model outperforms other methods in both extremely limited sample and strong noise scenarios and can transfer unlabeled data utilization between similar and even different device types.

Modelling Regional Sustainable Development in Ukrainian Crisis and War

The article investigates the impact of force majeure crisis factors that appeared in the Ukrainian economy as a result of the global recession, the events of 2014-2021: annexation of Crimea, temporary occupation of part of the industrial territories of Donetsk and Luhansk regions, military operations in Donbas, during the fight against pandemic (COVID-19) and war in 2022 on the stable development of regions. Based on the analysis of official indicators of the State Statistics Service of Ukraine, it was proved that the said factors create additional multiplier and acceleration effects that adversely affect the dynamics of the gross regional product in the crisis conditions. It has been determined that the result of these effects is the transformation of crisis signals from a proactive to an active phase. The construction of a heat map of GRP correlation indicators made it possible to determine the existence of a disbalance between the economic and social development of the regions. Building a model of linear regression, allowed us to draw conclusions about the existing disbalance of GRP regions during the crisis and war.

Visible wavelength time-stretch optical coherence tomography.

Visible light optical coherence tomography (OCT) is an emerging non-invasive imaging modality that offers new opportunities for anatomical and functional imaging of biological tissues. Time-stretch dispersive Fourier transform, also known as photonic time-stretch, is an all-optical processing method that enables real-time Fourier transformation of ultrafast optical signals and allows for OCT at high A-scan rates. In this work, a working prototype of a photonic time-stretch OCT (TS-OCT) method in the visible wavelength region is proposed and experimentally demonstrated. The proposed visible-light TS-OCT system achieves unprecedented throughput of 100 giga voxels/second and OCT volume rate of 4,000 volumes/second and can be used to expand the range of applications of TS-OCT systems.

Feature engineering of EEG applied to mental disorders: a systematic mapping study

Around a third of the total population of Europe suffers from mental disorders. The use of electroencephalography (EEG) together with Machine Learning (ML) algorithms to diagnose mental disorders has recently been shown to be a prominent research area, as exposed by several reviews focused on the field. Nevertheless, previous to the application of ML algorithms, EEG data should be correctly preprocessed and prepared via Feature Engineering (FE). In fact, the choice of FE techniques can make the difference between an unusable ML model and a simple, effective model. In other words, it can be said that FE is crucial, especially when using complex, non-stationary data such as EEG. To this aim, in this paper we present a Systematic Mapping Study (SMS) focused on FE from EEG data used to identify mental disorders. Our SMS covers more than 900 papers, making it one of the most comprehensive to date, to the best of our knowledge. We gathered the mental disorder addressed, all the FE techniques used, and the Artificial Intelligence (AI) algorithm applied for classification from each paper. Our main contributions are: (i) we offer a starting point for new researchers on these topics, (ii) we extract the most used FE techniques to classify mental disorders, (iii) we show several graphical distributions of all used techniques, and (iv) we provide critical conclusions for detecting mental disorders. To provide a better overview of existing techniques, the FE process is divided into three parts: (i) signal transformation, (ii) feature extraction, and (iii) feature selection. Moreover, we classify and analyze the distribution of existing papers according to the mental disorder they treat, the FE processes used, and the ML techniques applied. As a result, we provide a valuable reference for the scientific community to identify which techniques have been proven and tested and where the gaps are located in the current state of the art.Graphical

Event‐triggered containment control for multi‐agent systems under hybrid cyber attacks

AbstractThis paper studies event‐triggered containment control problem of multi‐agent systems (MASs) under deception attacks and denial‐of‐service (DoS) attacks. First, to save limited network resources, an event‐triggered mechanism is proposed for MASs under hybrid cyber attacks. Different from the existing event‐triggered mechanisms, the negative influences of deception attacks and DoS attacks are considered in the proposed triggering function. The communication frequencies between agents are reduced. Then, based on the proposed event‐triggered mechanism, a corresponding control protocol is proposed to ensure that the followers will converge to the convex hull formed by the leaders under deception attacks and DoS attacks. Compared with the previous researches about containment control, in addition to hybrid cyber attacks being considered, the nonlinear functions related to the states of the agents are applied to describe the deception attack signals in the MAS. By orthogonal transformation of deception attack signals, the containment control problem under deception attacks and DoS attacks is reformulated as a stability problem. Then, the sufficient conditions on containment control can be obtained. Finally, a set of simulation example is used to verify the effectiveness of the proposed method.