Noise Characterization and Robust Signal Detection in Yeast Pheromone Molecular Communication

Robust weak signal detection is quite a difficult problem in the presence of interferences and noise. Generally, adaptive beamforming is an efficient way of interference suppression. However, conventional adaptive beamforming, e.g. MVDR may degrade significantly due to mismatch in the practical applications. In this paper, a robust adaptive interference suppression method is presented and then the worst-case performance optimization is implemented on the eigenanalysis-based re-constructed covariance matrix. Experimental results show the proposed method can efficiently suppress the interferences and then be for robust adaptive beamforming and weak signal detection.

Proper and timely characterization of the safety profile of a pharmaceutical product under development is imperative for assessing the overall benefit-risk relationship of the product and for making key development decisions. For ongoing clinical development, a comprehensive and robust safety monitoring and safety signal detection program which is based upon quantitative statistical reasoning is critical. Methods presented here can be applied to safety signal detection and periodic safety monitoring. Various statistical properties, distributions, and models, all utilizing a Bayesian framework are considered and further examined in order to identify robust methods applicable to a broad set of scenarios and situations. Methods developed for incidence counts (including those with under-dispersed distributions) with variable time-at-risk and with underlying constant or non-constant hazard rates, are proposed and compared to traditional methods designed to assess adverse event incidence rates or binomial incidence proportions (which assume an underlying constant hazard rate and subsequent Poisson distribution for modeling event counts).

In this paper the dual topics of robust signal detection and robust estimation of a random variable are considered, where the data may be both dependent and nonstationary. We note that classical saddlepoint techniques for robustness do not readily apply in the dependent and/or nonstationary situation, and thus our results have application in a larger domain than what was feasible heretofore. In addition, our methods make possible the quantitative measurement of robustness and admit essentially arbitrary perturbations in an underlying joint statistical distribution away from the nominal. In particular, our methods show that the presence of dependency can result in a reduction of the robustness of the linear detector by approximately 50% and that appropriate censoring can improve this situation. We also show that, somewhat surprisingly, a weak amount of censoring can actually reduce robustness rather than increase it, even with dependent data that is "almost" independent. This calls into question the common practice, inspired by classical saddlepoint results for independent data, of employing censoring in cases where residual dependency is conceded. When applied to estimation, our work shows that for nominally Gaussian data, the conditional expectation estimator is optimal not only in terms of performance but also robustness (under appropriate performance measures), thus reinforcing the appeal of this estimator. On the other hand, for other performance measures, we also note that the conditional expectation estimator can be completely unrobust, regardless of whether the data is nominally Gaussian or not. Finally, our results establish a bound on estimator robustness.

Detecting weak signals in high-interference environments is a critical challenge across various fields, including radar and communication systems. Traditional methods often fail when the signal-to-noise ratio (SNR) is extremely low. Here, we propose two novel methods for weak signal detection using orbital angular momentum (OAM) waves. These methods leverage the unique periodic characteristics of synthetic waves composed of OAM and plane waves. By recombining a new electric field using the mean amplitude and phase of the synthetic wave, or the mean of the maximum and minimum amplitudes and the mean phase, we demonstrate the extraction of OAM waves from synthetic waves with an amplitude difference of 22 dB. This corresponds to a power ratio of 158.76 times between the interference and the OAM wave. Our methods enable the detection of weak linear frequency modulation (LFM) signals carried by OAM waves in strong noise backgrounds, even at an SNR as low as -30.98 dB. This represents a significant advancement, providing robust signal detection capabilities that could transform radar detection, radar anti-interference, and communication transmission. Our results are supported by both theoretical analysis and experimental validation.

In this paper, a reverberation robust Target Signal Detection (TSD) algorithm using two microphones is proposed. Most of traditional TSD algorithms are based on the assumption of free sound field and close-talking scene incorporate with multichannel system. They lack in achieving robustness in reverberant and noisy environment. The proposed TSD algorithm is based on Beam-to-Reference Ratio (BRR), and a novel estimator, Direct-to-Reverberate Ratio (DRR), is introduced to enlarge the basic assumption to reverberant and distant-talking scene. Spatial correlation information between microphones is used to estimate the DRR to revise threshold on each Time-Frequency (T-F) block and to form full-band likelihood using soft-decision information. Experimental results show that the proposed method performs robust in different reverberant environments with coherent interferences when target signal is from priori known direction-of-arrivals (DOA) in distant-talking scene.

In this paper, we propose a robust STBC-OFDM signal detection to combat intercarrier interference (ICI) over high move speed conditions. Successive interference cancellation (SIC) is an effective detection technique for STBC-OFDM systems. In this paper, a modified SIC detection is applied to the STBC-OFDM systems. Simulation results show that when the move speed is up to 250 km/hr and the SNR is 30dB, the modified SIC can achieve the performance of less than 10− 5. Moreover, the performance with some conventional methods are also evaluated.KeywordsMIMO-OFDMSTBC-OFDMsignal detectionSIC

Robust detection and localization of biomolecules inside cells is of great importance to better understand the functions related to them. Fluorescence microscopy and specific staining methods make biomolecules appear as point-like signals on image data, often acquired in 3D. Visual detection of such point-like signals can be time consuming and problematic if the 3D images are large, containing many, sometimes overlapping, signals. This sets a demand for robust automated methods for accurate detection of signals in 3D fluorescence microscopy. We propose a new 3D point-source signal detection method that is based on Fourier series. The method consists of two parts, a detector, which is a cosine filter to enhance the point-like signals, and a verifier, which is a sine filter to validate the result from the detector. Compared to conventional methods, our method shows better robustness to noise and good ability to resolve signals that are spatially close. Tests on image data show that the method has equivalent accuracy in signal detection in comparison to visual detection by experts. The proposed method can be used as an efficient point-like signal detection tool for various types of biological 3D image data.

This book contains a unified treatment of a class of problems of signal detection theory. This is the detection of signals in addi tive noise which is not required to have Gaussian probability den sity functions in its statistical description. For the most part the material developed here can be classified as belonging to the gen eral body of results of parametric theory. Thus the probability density functions of the observations are assumed to be known, at least to within a finite number of unknown parameters in a known functional form. Of course the focus is on noise which is not Gaussian; results for Gaussian noise in the problems treated here become special cases. The contents also form a bridge between the classical results of signal detection in Gaussian noise and those of nonparametric and robust signal detection, which are not con sidered in this book. Three canonical problems of signal detection in additive noise are covered here. These allow between them formulation of a range of specific detection problems arising in applications such as radar and sonar, binary signaling, and pattern recognition and classification. The simplest to state and perhaps the most widely studied of all is the problem of detecting a completely known deterministic signal in noise. Also considered here is the detection random non-deterministic signal in noise. Both of these situa of a tions may arise for observation processes of the low-pass type and also for processes of the band-pass type.

Addressing the global disparity in cancer care necessitates the development of rapid and affordable nucleic acid (NA) testing technologies. This need is particularly critical for cervical cancer. Molecular detection of human papillomavirus (HPV) has emerged as a highly accurate screening method, surpassing traditional Pap smears. However, implementing this transition in low- and middle-income countries has been challenging due to the high costs and centralized facilities required for current NA tests. Here, we present CreDiT (CRISPR Enhanced Digital Testing), an advanced diagnostic system for rapid, on-site NA detection. CreDiT integrates two major technical breakthroughs: i) a one-pot CRISPR strategy that simultaneously amplifies both target NAs and analytical signals, and ii) a robust fluorescent detection method based on digital communication (encoding/decoding) technology. These innovations enhance CreDiT's practical utility, offering a rapid assay (<35 minutes) that integrates NA extraction and detection in a single streamlined workflow. Furthermore, CreDiT's straightforward probe design enables easy incorporation of new NA targets, while its compact device provides robust signal detection. We adapted CreDiT for point-of-care HPV screening by designing probes for high-risk HPV genes (HPV16, HPV18, HPV45, HPV31, HPV33, HPV58) and oncoprotein mRNAs (E6, E7, p16INK4a) and developing a portable CreDiT device capable of processing 12 samples. CreDiT demonstrated sensitive detection of cell-derived HPV DNA targets down to single copies and accurately identified HPV types in every clinical cervical brushing specimen (n = 121) we tested. This technology has the potential to facilitate prompt and reliable triaging of high-risk HPV, overcoming pathology bottlenecks and circumventing geographical and socioeconomic barriers to effective cervical cancer screening in resource-limited regions. This work has spearheaded recent screening research efforts in Uganda and Ghana. Citation Format: Chang Yeol Lee, Hyunho Kim, Hanna Lee, Thomas Randall, Amy Ly, Hakho Lee, Cesar M. Castro. Rapid on-site nucleic acid detection using CRISPR and digital signal processing for portable and integrated cervical cancer screening in low resource settings [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6082.

Cosmic dawn represents a critical juncture in cosmic history when the first population of stars emerged. The astrophysical processes that govern this transformation need to be better understood. The detection of redshifted 21 cm radiation emitted from neutral hydrogen during this era offers a direct window into the thermal and ionization state of the Universe. This emission manifests as differential brightness between spin temperature and the cosmic microwave background. The SARAS experiment aims to detect the sky-averaged signal in the frequency range 40–200 MHz. SARAS’s unique design and operational strategy to float the antenna over a water body minimizes spectral features that may arise due to stratified ground beneath the antenna. However, the antenna environment can be prone to configuration changes due to variations in critical design parameters such as conductivity and antenna tilt. In this paper, we connect the variations in antenna properties to signal detection prospects. By using realistic simulations of a direction- and frequency-dependent radiation pattern of the SARAS antenna and its transfer function, we establish critical parameters and estimate bias in the detectability of different models of the global 21 cm signal. We find a correlation between the nature of chromaticity in antenna properties and the bias in the recovered spectral profiles of 21 cm signals. We also find stringent requirements for transfer function corrections, which can otherwise make detection prospects prohibitive. We finally explore a range of critical parameters that allow robust signal detection.

In this paper we have reviewed a new approach toward robust signal detection which is based on geometric concepts. By providing a quantitative measure of robustness, this approach can be an attractive alternative to employing classical saddlepoint techniques since important limitations associated with such techniques can be circumvented.

A new signal detection scheme based on joint Chebyshev polynomials acceleration and semi-iterative plus symmetric successive over relaxation (SSOR+CP) method is proposed to fast update the linear minimum mean square error (MMSE) detection when a user is added to or removed from the massive multipleinput multiple- output (MIMO) systems. Chebyshev polynomials (CP) acceleration and semi-iterative method not only is employed to construct a secondary iteration for the SSOR method to significantly accelerate the convergence rate, but also has inexpensive cost. Furthermore, by utilizing Schur complements and the block matrix inverse lemma, we can fast update a matrix inverse that further reduce the complexity by an order of magnitude in real massive MIMO systems with a dynamic set of users. Through numerical simulations, it is observed that the proposed hybrid iterative updates method, with only a few operations, is almost able to perform the optimal detection performance in contrast with recently proposed detection algorithms, even when the number of user equipments (UEs) significantly increased. Meanwhile, our hybrid iterative updates method can provide more robust signal detection in high-spatial correlation of massive MIMO channels.

Robustness in signal detection refers to the insensitivity of a detection procedure to deviations in the statistical model underlying the observed data. This paper presents a brief survey of some of the principal results that have been obtained in this area together with some related results for robust design based on statistical distance criteria.

Human afferent whole nerve signals recorded using an implanted nerve-cuff electrode were analyzed using two algorithms based on the statistical properties of the signals. The processing method typically described in the literature (Rectification and Bin-Integration-RBI) has serious shortcomings in processing these signals, which have very poor signal-to-noise ratios. Algorithms based on a Singular Value Decomposition (SVD) of the signal's 2nd and Higher-Order Statistics (HOS) have resulted in more robust signal detection. Reliable detection of afferent nerve signals is essential if such signals are to be of use in artificial sensory-based functional electrical stimulation neural prosthetics.

In this paper, we present a study of optimal training sequences for robust joint channel estimation and signal detection. Particularly, we study the case of virtual MIMO links, where there are more co-channel signals M than receive antennas N, i.e., N <M. A training sequence based on the one-dimensional chaotic Chebyshev map is presented herein. This sequence delivers robust performance in terms of Bit Error rate (BER), Normalized Mean-Squared-Error (NMSE) of the estimation and computation complexity. The proposed sequence exhibits an optimal performance by spanning only a minimal number of training symbols, i.e., L=M. The proposed chaotic-based training sequence performs adequatly on both i.i.d. and correlated Rayleigh Fading Channels without the need for a priori statistics of the channel.