On the Performance of Energy Criterion Method in Wi-Fi Transient Signal Detection

In the development of radiofrequency fingerprinting (RFF), one of the major challenges is to extract subtle and robust features from transmitted signals of wireless devices to be used in accurate identification of possible threats to the wireless network. To overcome this challenge, the use of the transient region of the transmitted signals could be one of the best options. For an efficient transient-based RFF, it is also necessary to accurately and precisely estimate the transient region of the signal. Here, the most important difficulty can be attributed to the detection of the transient starting point. Thus, several methods have been developed to detect transient start in the literature. Among them, the energy criterion method based on the instantaneous amplitude characteristics (EC-a) was shown to be superior in a recent study. The study reported the performance of the EC-a method for a set of Wi-Fi signals captured from a particular Wi-Fi device brand. However, since the transient pattern varies according to the type of wireless device, the device diversity needs to be increased to achieve more reliable results. Therefore, this study is aimed at assessing the efficiency of the EC-a method across a large set of Wi-Fi signals captured from various Wi-Fi devices for the first time. To this end, Wi-Fi signals are first captured from smartphones of five brands, for a wide range of signal-to-noise ratio (SNR) values defined as low (−3 to 5 dB), medium (5 to 15 dB), and high (15 to 30 dB). Then, the performance of the EC-a method and well-known methods was comparatively assessed, and the efficiency of the EC-a method was verified in terms of detection accuracy.

PAR4-Mediated PI3K/Akt and RhoA/ROCK Signaling Pathways Are Essential for Thrombin-Induced Morphological Changes in MEG-01 Cells

Thrombin stimulates platelets via a dual receptor system of protease-activated receptors (PARs): PAR1 and PAR4. PAR1 activation induces a rapid and transient signal associated with the initiation of platelet aggregation, whereas PAR4 activation results in a prolonged signal, required for later phases, that regulates the stable formation of thrombus. In this study, we observed differential signaling pathways for thrombin-induced PAR1 and PAR4 activation in a human megakaryoblastic leukemia cell line, MEG-01. Interestingly, thrombin induced both calcium signaling and morphological changes in MEG-01 cells via the activation of PAR1 and PAR4, and these intracellular events were very similar to those observed in platelets shown in previous studies. We developed a novel image-based assay to quantitatively measure the morphological changes in living cells, and observed the underlying mechanism for PAR1- and PAR4-mediated morphological changes in MEG-01 cells. Selective inhibition of PAR1 and PAR4 by vorapaxar and BMS-986120, respectively, showed that thrombin-induced morphological changes were primarily mediated by PAR4 activation. Treatment of a set of kinase inhibitors and 2-aminoethoxydiphenyl borate (2-APB) revealed that thrombin-mediated morphological changes were primarily regulated by calcium-independent pathways and PAR4 activation-induced PI3K/Akt and RhoA/ROCK signaling pathways in MEG-01 cells. These results indicate the importance of PAR4-mediated signaling pathways in thrombin-induced morphological changes in MEG-01 cells and provide a useful in vitro cellular model for platelet research.

A reliable islanding identification mechanism for DC microgrid using PCC transient signal

The islanding detection is a major problem for both AC and DC Microgrids. Failure to do so may result in problems such as system instability, increased non-detection zone, out-of-phase reclosing, personnel safety, and power quality deterioration. To address this issue, this paper presents a reliable island identification method for DC Microgrids that employs a Cumulative Sum of Rate of change of Voltage (CSROCOV) to reduce the non-recognition region. The proposed islanding protection scheme employs point of common coupling (PCC) transient signal to detect islands events. The voltage, power, and current sampling are accumulated from the PCC of the distributed generation terminals. The proposed scheme detects islanding in three test cases with varying power mismatching conditions, while non-islanding events are classified as capacitor switching and faults. The system is modelled and simulated in the MATLAB/Simulink environment, then islanding conditions are applied by turning off the main circuit breaker. Simulation results are presented to verify the methodology under different test cases. The robustness of the proposed scheme is also validated against measurement noise.

Automatic Detection of Slow Slip Events Using the PICCA: Application to Chilean GNSS Data

The detection of transient events related to slow earthquakes in GNSS positional time series is key to understanding seismogenic processes in subduction zones. Here, we present a novel Principal and Independent Components Correlation Analysis (PICCA) method that allows for the temporal and spatial detection of transient signals. The PICCA is based on an optimal combination of the principal (PCA) and independent component analysis (ICA) of positional time series of a GNSS network. We assume that the transient signal is mostly contained in one of the principal or independent components. To detect the transient, we applied a method where correlations between sliding windows of each PCA/ICA component and each time series are calculated, obtaining the stations affected by the slow slip event and the onset time from the resulting correlation peaks. We first tested and calibrated the method using synthetic signals from slow earthquakes of different magnitudes and durations and modelled their effect in the network of GNSS stations in Chile. Then, we analyzed three transient events related to slow earthquakes recorded in Chile, in the areas of Iquique, Copiapó, and Valparaíso. For synthetic data, a 150 days event was detected using the PCA-based method, while a 3 days event was detected using the ICA-based method. For the real data, a long-term transient was detected by PCA, while a 16 days transient was detected by ICA. It is concluded that simultaneous use of both signal separation methods (PICCA) is more effective when searching for transient events. The PCA method is more useful for long-term events, while the ICA method is better suited to recognize events of short duration. PICCA is a promising tool to detect transients of different characteristics in GNSS time series, which will be used in a next stage to generate a catalog of SSEs in Chile.

Study of Transmission Line Boundary Protection Using a Multilayer Perceptron Neural Network with Back Propagation and Wavelet Transform

Protection schemes are usually implemented in the planning of transmission line operations. These schemes are expected to protect not only the network of transmission lines but also the entire power systems network during fault conditions. However, it is often a challenge for these schemes to differentiate accurately between various fault locations. This study analyses the deficiencies identified in existing protection schemes and investigates a different method that proposes to overcome these shortcomings. The proposed scheme operates by performing a wavelet transform on the fault-generated signal, which reduces the signal into frequency components. These components are then used as the input data for a multilayer perceptron neural network with backpropagation that can classify between different fault locations in the system. The study uses the transient signal generated during fault conditions to identify faults. The scientific research paradigm was adopted for the study. It also adopted the deduction research approach as it requires data collection via simulation using the Simscape electrical sub-program of Simulink within Matrix laboratory (MATLAB). The outcome of the study shows that the simulation correctly classifies 70.59% of the faults when tested. This implies that the majority of the faults can be detected and accurately isolated using boundary protection of transmission lines with the help of wavelet transforms and a neural network. The outcome also shows that more accurate fault identification and classification are achievable by using neural network than by the conventional system currently in use.

Memory, switches, and logic gates through bistability in chemically fueled crystals

The ability to store information in chemical reaction networks is essential for evolution, calculations, and, more generally, for the complex behavior, we associate with life. In biology, cellular memory is regulated through transcriptional states that are bistable, i.e., a state that can either be on or off and can be flipped from one to another through a transient signal. Such memory circuits have been realized synthetically through the rewiring of genetic systems in vivo or through the rational design of reaction networks based on DNA and highly evolved enzymes in vitro. Completely bottom-up analogs based on small molecules are rare and hard to design and thus represent a challenge for systems chemistry. In this work, we show that bistability can be designed from an extremely simple non-equilibrium reaction cycle that is coupled to crystallization. The crystals exert the necessary feedback on the reaction cycle required for the bistability resulting in an on-state with assemblies and an off-state without. We can switch the state on and off, such that each state represents volatile memory that can be stored in continuously stirred tank reactors indefinitely despite the fact that molecules are turned over on a minute-timescale. We showcase the system’s abilities by creating a matrix display that can store images and by performing Boolean logic by coupling several switches together.

Potential Pitfalls in the Analysis and Structural Interpretation of Seismic Data from the Mars InSight Mission

ABSTRACT The Seismic Experiment for Interior Structure (SEIS) of the InSight mission to Mars has been providing direct information on Martian interior structure and dynamics of that planet since it landed. Compared with seismic recordings on the Earth, ground-motion measurements acquired by SEIS on Mars are not only made under dramatically different ambient noise conditions, but also include idiosyncratic signals that arise from coupling between different InSight sensors and spacecraft components. This work is to synthesize what is known about these signal types, illustrate how they can manifest in waveforms and noise correlations, and present pitfalls in structural interpretations based on standard seismic analysis methods. We show that glitches (a type of prominent transient signal) can produce artifacts in ambient noise correlations. Sustained signals that vary in frequency, such as lander modes that are affected by variations in temperature and wind conditions over the course of the Martian sol, can also contaminate ambient noise results. Therefore, both types of signals have the potential to bias interpretation in terms of subsurface layering. We illustrate that signal processing in the presence of identified nonseismic signals must be informed by an understanding of the underlying physical processes in order for high-fidelity waveforms of ground motion to be extracted. Whereas the origins of the most idiosyncratic signals are well understood, the 2.4 Hz resonance remains debated, and the literature does not contain an explanation of its fine spectral structure. Even though the selection of idiosyncratic signal types discussed in this article may not be exhaustive, we provide guidance on the best practices for enhancing the robustness of structural interpretations.

Regulation of EGFR signalling by palmitoylation and its role in tumorigenesis

The epidermal growth factor receptor (EGFR) is an essential driver of oncogenic signalling, and EGFR inhibitors are some of the earliest examples of successful targeted therapies in multiple types of cancer. The tractability of EGFR as a therapeutic target is overshadowed by the inevitable drug resistance that develops. Overcoming resistance mechanisms requires a deeper understanding of EGFR regulation in cancer cells. In this review, we discuss our recent discovery that the palmitoyltransferase DHHC20 palmitoylates EGFR on the C-terminal domain and plays a critical role in signal regulation during oncogenesis. Inhibiting DHHC20 expression or mutating the palmitoylation site on EGFR alters the EGF-induced signalling kinetics from a transient signal to a sustained signal. The change in signalling is accompanied by a decrease in cell proliferation in multiple human cancer cell lines. Our in vivo studies demonstrate that ablating the gene Zdhhc20 by CRISPR/Cas9-mediated inhibition in a mouse model of oncogenic Kras-driven lung adenocarcinoma potently inhibits tumorigenesis. The negative effect on tumorigenesis is mediated by EGFR since the expression of a palmitoylation-resistant mutant form of EGFR also inhibits Kras-driven lung adenocarcinoma. Finally, reducing EGFR palmitoylation increases the sensitivity of multiple cancer cell lines to existing inhibitors of EGFR and downstream signalling effector pathways. We will discuss the implications of these effects and strategies for targeting these new vulnerabilities.