Wideband high dynamic range surveillance

In small target detection under strong sea clutter or impact signal detection under machinery fault diagnosis, a weak sinusoidal signal with random amplitude is often contaminated by heavier chaotic noise, and the target information is difficult to detect. Traditional solutions, such as neural networks or stochastic resonance, can not effectively extract heteroscedasticity of data, which leads to weak signals not being detected. To overcome these limitations and improve the detection efficiency, an empirical likelihood ratio statistical method for detecting weak sinusoidal signals with random amplitude under strong chaotic interference is proposed. First, based on the reconstruction in the phase space of the 1‐D observed time series signal with embedding dimension and time delay, the presented method obtains a multivariate special temporal series as an input. Subsequently, the chaotic single index (CSI) statistical model is established for single‐step prediction, and it can be estimated by the nonparametric locally linear algorithm for minimizing the mean squares error. Finally, the empirical likelihood ratio statistical method is applied to detect weak sinusoidal signals with random amplitude. Simulated data and real data experiment results show that the proposed CSI model can better capture the weak target signal and detect effectively weak target signal under the chaotic interference.

This paper presents a theoretical analysis in discrete time for a multi-tier weak radiofrequency (RF) signal estimation process with N simultaneous signals. Discrete time dynamic sampling is introduced and is shown to provide the capability to extract signal parameter values with increased accuracy compared with accuracy of estimates obtained in prior work. This paper advances phase measurement approaches by proposing discrete time dynamic sampling which our paper shows offers the desirable capability for more accurate weak signal parameter estimates. For N=2 simultaneous signals with a strong signal at 850 MHz and a weak signal at 855 MHz, the results show that dynamically sampling the instantaneous frequency at 24 times the Nyquist rate provides weak signal frequency estimates that are within 1.7 times 10^{-5} of the actual weak signal frequency and weak signal amplitude estimates that are within 428 PPM of the actual weak signal amplitude. Results are also presented for situations with N=2 simultaneous 5G signals. In one case, the strong signal is 3950 MHz, and the weak signal is 3955 MHz; in the other case the strong case is 5950 MHz, and the weak signal is 5955 MHz. The results for these cases show that estimates obtained with dynamic sampling are more accurate than estimates provided using a single sample rate of 65 MSPS. This work has promising applications for weak signal parameters estimation using instantaneous frequency measurements.

This study aims to propose methods for identifying entrepreneurial discovery processes with weak/strong signals of technological changes and incorporating technology foresight in the design and planning of the Smart Specialization Strategy (S3). For this purpose, we first analyse patent abstracts from 2000 to 2009, obtained from the European Patent Office and use a keyword-based text mining approach to collect weak and strong technology signals; the word2vec algorithm is also employed to group weak signal keywords. We then utilize Correlation Explanation (CorEx) topic modelling to link technology weak/strong signals to invention activities for the period 2010-2018 and use the ANOVA statistical method to examine the relationship between technology weak/strong signals and patent values. The results suggest that patents related to weak rather than strong signals are more likely to be high-impact innovations and to serve as a basis for future technological developments. Furthermore, we use latent Dirichlet allocation (LDA) topic modelling to analyse patent activities related to weak/strong technology signals and compute regional topic weights. Finally, we present implications of the research.

We characterized the expression levels of the retinoid Z receptor alpha (RZR alpha), RORalpha mRNA isoforms (RORalpha1, RORalpha2, and RORalpha3), and both melatonin receptor MT1 and hydroxindole-O-methyltransferase (HIOMT) genes. For this purpose, the following human peripheral blood mononuclear cells populations were isolated: monocytes (CD14+ cells), B lymphocytes (CD19+ cells), T helper lymphocytes (CD14(-) CD4+), cytotoxic T lymphocytes (CD56(-) CD8+ cells), and natural killer (NK) lymphocytes (CD56+ cells). PBMCs subsets were obtained by Dynabeads M-450 (Dynal) isolation procedure. We observed a strong gene expression signal for RZRalpha in all subpopulations studied, whereas both RORalpha1 and RORalpha2 transcripts were amplified only in CD8+ cells. Specific signal for RORalpha2 was obtained in all subpopulations studied, but we were not able to detect the RORalpha3 mRNA transcript in human immune cells studied. A weaker signal (especially in CD19+ cells) was also detected in all subsets of cells for the MT1 gene. With regard to HIOMT, a strong signal was achieved among all but one subpopulation of cells; the only exception was CD14+ cells. Thus, in addition to its classical function in the nervous and endocrine system, melatonin could act directly as a paracrine and/or autocrine agent in the human immune system.

Thymic selection eliminates T cells expressing TCRs with a high affinity for self-antigen. However, many of the most promising tumor antigens for T cell-based cancer immunotherapies are unmodified self-antigens. Therefore, tumor-specific T cells often have a low avidity for tumor antigens, thus limiting the efficacy of this approach. TCR gene therapy using TCR genes that have been modified for increased affinity can overcome this limitation. The generation of enhanced affinity TCRs generally involves saturation mutagenesis targeting the complementarity determining regions of the TCR genes to be modified. However, this approach requires considerable trial and error, and is still not always successful at generating a higher affinity TCR without promiscuous off-target activity. We have developed a system for selecting high affinity tumor antigen-specific TCRs from a population of T cells selected by agonist signaling during T cell development in vitro. The αβ/γδ lineage choice is in part dictated by the strength of TCR/pre-TCR signaling during early T cell development, such that the pre-TCR provides a weak signal that drives αβ T cell development while the mature γδ TCR provides a stronger signal that drives γδ T cell development. In TCR transgenic mice, early expression of the transgenic TCRα chain prior to β-selection can also provide a stronger γδ-like signal through the mature αβ TCR, leading to the development of lineage redirected TCRαβ+ CD4/CD8 double negative (DN) γδ wanna-be” cells. The development of these DN TCRαβ+ cells is enhanced in the presence of cognate antigen or TCR crosslinking; suggesting that early expression of an antigen-specific TCRα chain before β-selection could promote the generation of DN TCRαβ+ cells in the presence of antigen, through a process in which the introduced TCRα chain pairs with an endogenous TCRβ chain to produce an αβ TCR with high affinity for the target antigen. To test this hypothesis, the TCRα chain from a tumor antigen-specific TCR was transduced into hematopoietic progenitor cells and a library of TCRβ chains was isolated from the lineage redirected DN TCRαβ+ T cells that developed. Several TCRβchains of varying CDR3 lengths were isolated that confer enhanced affinity for the target antigen when paired with the introduced TCRα chain. This abstract is also presented as Poster A13. Citation Format: Thomas M. Schmitt, Philip D. Greenberg, David H. Aggen, David Kranz, Sebastian Ochsenreither. Early TCRα expression during T cell development for the generation of enhanced-affinity TCRs for TCR gene therapy. [abstract]. In: Proceedings of the AACR Special Conference on Tumor Immunology: Multidisciplinary Science Driving Basic and Clinical Advances; Dec 2-5, 2012; Miami, FL. Philadelphia (PA): AACR; Cancer Res 2013;73(1 Suppl):Abstract nr PR12.

The strategy for any organization can be set from the outside in, according to the school of positioning; according to this, the supply of products and / or services has to adapt to changes that occur in the environment. According to the movement schools of thought, a strategy can be set from the inside out in which case the organization influences the surroundings with its offer of products and / or services. Given the importance of weak signals in the definition of the strategy, the research goal is to treat the issue of pre-emptive strategy in the context of strategic decision making. An answer is provided for the following questions: (1) What is a weak signal? (2) How to tell if it is anticipative, relevant and useful for the decision makers? and (3) How to turn a weak signal in an effective anticipative clue? Based on investigator's experience in strategic consulting for small, medium and large companies, the weak and strong signals are explained and a set of criteria which are representative of their identification is presented.

Strong interference will affect direction of arrival (DOA) estimation of weak desired signal and even cause DOA estimation failure. This paper investigates the weak signal DOA estimation for an antenna array under strong interference signals, and proposed a novel DOA estimation method for strong interference source suppression and weighted l1-norm sparse representation. A parallel adaptive beamforming algorithm based on power inversion is used to suppress strong interference and form new array data. To reduce spurious peaks in the spectrum under strong interference, a weighted matrix is determined by the optimized subspace algorithm for the subspace projection. Then, the DOA estimation, which is calculated by weighted l1-norm sparse representation, is formed by the weighted matrix and new array data. In this paper, the superiority of the proposed algorithm is illustrated by an example of unmanned aerial vehicle (UAV) video signal DOA estimation under strong interference signals. The simulated results of an orthogonal frequency division multiplexing signal indicate that the proposed algorithm shows merits of fewer snapshots, a sharper main lobe, a lower average noise spectrum value, higher DOA estimation accuracy and success rate. For validation, an outdoor experiment was conducted which demonstrated that the proposed algorithm is superior to other algorithms and can be used for DOA estimation of UAV video signals under strong WiFi interference. Both the simulations and experiments verify that the proposed algorithm can effectively suppress strong interference and achieve better DOA estimation performance for weak signals.

Single User, Transmission Control Protocol Downstream Throughput (TCPDST) probability models in an IEEE802.11b WLAN have been developed, validated and evaluated for performance. Measurement of single user TCPDST were taken using Tamosoft throughput test while that of signal to noise ratio (SNR) were taken using inSSIDer 2.1. The Tamosoft throughput tests were conducted using different quality of service (QoS) traffic. These QoS traffic (which were sent through an infrastructure based network) correspond to different wireless multimedia tags. Measurements were taken in free space, small offices and open corridor environments. By assuming a normal distribution, single user TCPDST Cumulative distribution function (CDF) probability models were developed for different signal categories namely: (i) all the SNR considered, (ii) strong signals only, (iii) grey signals only and (iv) weak signals only. The models were validated and their performances evaluated using root mean square (RMS) errors. RMS errors were computed by comparing model predicted values with validation data. The RMS errors for single user CDF all signals model was 0.1466%. RMS errors for strong signals models, grey signals model and weak signals model respectively were 0.1466%, 0.6756% and 0.1233% indicating acceptable performances. All signals, strong signals, grey signals and weak signals CDF probability models predicted probabilities of obtaining TCPDST values greater than 5Mbps as 74.79%, 90.55%, 13.00% and 4.77% respectively while probabilities of obtaining TCPDST values less than 2Mbps were predicted as 4.91%, 0.00%, 18.98% and 52.41% respectively. These probability models will provide additional useful information needed to design efficient distributed data networks.
 Keywords: Throughput, TCP, WLAN, probability models

Globulins are an important group of seed storage proteins in dicotyledonous plants. They are synthesized during seed development, assembled into very compact protein complexes, and finally stored in protein storage vacuoles (PSVs). Here, we report a proteomic investigation on the native composition and structure of cruciferin, the 12 S globulin of Brassica napus. PSVs were directly purified from mature seeds by differential centrifugations. Upon analyses by blue native (BN) PAGE, two major types of cruciferin complexes of ∼ 300-390 kDa and of ∼470 kDa are resolved. Analyses by two-dimensional BN/SDS-PAGE revealed that both types of complexes are composed of several copies of the cruciferin α and β polypeptide chains, which are present in various isoforms. Protein analyses by two-dimensional isoelectric focusing (IEF)/SDS-PAGE not only revealed different α and β isoforms but also several further versions of the two polypeptide chains that most likely differ with respect to posttranslational modifications. Overall, more than 30 distinct forms of cruciferin were identified by mass spectrometry. To obtain insights into the structure of the cruciferin holocomplex, a native PSV fraction was analyzed by single particle electron microscopy. More than 20,000 images were collected, classified, and used for the calculation of detailed projection maps of the complex. In contrast to previous reports on globulin structure in other plant species, the cruciferin complex of Brassica napus has an octameric barrel-like structure, which represents a very compact building block optimized for maximal storage of amino acids within minimal space.

An application of the wavelet transform to electrocardiography is described in the paper. To detect the weak signals buried in noises is a fundamental and important problem. The proposed technique is based on the decomposition of the ECG signal into a set of basic functions covering the time- frequency domain. It has been found that digital filters are not suitable for processing weak signals in noises, while discrete wavelet transform (DWT) is used to analyze weak digital signal and extract small-features. In this paper, a new procedure is proposed, called sub-band filter. The performance for our method is investigated by detecting the ECG signal in white noise. The results show that the DTW is a quite effective method for the extraction features of weak signal and improving the ratio of signal to noise.

Diffusion-weighted MR imaging findings of ovarian adenocarcinofibromas and adenofibromas

The network signal and its intensity determine the connectivity and fluency of the network. Previous studies revealed that problematic Internet users (PIUs) exhibit an automatic detection advantage toward network signal cues. The aim of present study was to test whether this advantage effect is affected by network signal intensity. This study recruited 30 PIUs and 30 healthy controls. The event-related potentials (ERPs) were recorded when participants performed a task-irrelevant deviant–standard reverse oddball task. Strong and weak network signal images were used as standard and deviant stimuli, respectively, to evoke visual mismatch negativity (vMMN). Results showed that the vMMNs elicited by the intensity change of network signal cues in PIUs were considerably enhanced compared with that in healthy controls. Meanwhile, the vMMN elicited by strong network signal cues in PIUs was larger than that by weak network signal cues and was correlated with the scores of Young’s Internet Addiction Test and craving. However, the healthy controls did not show these effects. These results imply that the automatic detection advantage effect of PIUs for strong network signal cues was more pronounced compared with weak network signal cues, and the enhanced vMMNs can be considered as a candidate biomarker of PIU and the severity of its symptoms.

Smart manufacturing employs embedded systems such as CNC machine tools, programable logic controllers, automated guided vehicles, robots, digital measuring instruments, cyber-physical systems, and digital twins. These systems collectively perform high-level cognitive tasks (monitoring, understanding, deciding, and adapting) by making sense of sensor signals. When sensor signals are exchanged through the abovementioned embedded systems, a phenomenon called time latency or delay occurs. As a result, the signal at its origin (e.g., machine tools) and signal received at the receiver end (e.g., digital twin) differ. The time and frequency domain-based conventional signal processing cannot adequately address the delay-centric issues. Instead, these issues can be addressed by the delay domain, as suggested in the literature. Based on this consideration, this study first processes arbitrary signals in time, frequency, and delay domains and elucidates the significance of delay domain over time and frequency domains. Afterward, real-life signals collected while machining different materials are analyzed using frequency and delay domains to reconfirm its (the delay domain’s) significance in real-life settings. In both cases, it is found that the delay domain is more informative and reliable than the time and frequency domains when the delay is unavoidable. Moreover, the delay domain can act as a signature of a machining situation, distinguishing it (the situation) from others. Therefore, computational arrangements enabling delay domain-based signal processing must be enacted to effectively functionalize the smart manufacturing-centric embedded systems.

Blocking and desensitization are among the nonlinear effects that result when bipolar and MOSFET differential amplifiers are operating in a multisignal electromagnetic interference (EMI) environment. When a weak desired signal is processed along with a strong undesired interferer signal, the weak signal may experience a reduced gain; that is, desensitization. If the amplitude of the undesired signal is sufficiently large, the gain of the desired signal may drop to zero; that is, blocking. Using a Fourier-series model for the differential input-voltage/output-current characteristics, closed-form expressions for predicting the desensitization in bipolar and MOSFET differential amplifiers are presented. These expressions are valid over a wide range of desired and blocking interfering signals amplitudes.

It is important to examine in detail how the distribution of academic research topics related to renewable energy is structured and which topics are likely to receive new attention in the future in order for scientists to contribute to the development of renewable energy. This study uses an advanced probabilistic topic modeling to statistically examine the temporal changes of renewable energy topics by using academic abstracts from 2010–2019 and explores the properties of the topics from the perspective of future signs such as weak signals. As a result, in strong signals, methods for optimally integrating renewable energy into the power grid are paid great attention. In weak signals, interest in large-capacity energy storage systems such as hydrogen, supercapacitors, and compressed air energy storage showed a high rate of increase. In not-strong-but-well-known signals, comprehensive topics have been included, such as renewable energy potential, barriers, and policies. The approach of this study is applicable not only to renewable energy but also to other subjects.