Quality Of Signal Detection Research Articles

DeepChaos+: Signal Detection Quality Enhancement of High-Speed DP-16QAM Optical Fiber Communication Based on Chaos Masking Technique with Deep Generative Models

DeepChaos+: Signal Detection Quality Enhancement of High-Speed DP-16QAM Optical Fiber Communication Based on Chaos Masking Technique with Deep Generative Models

Chaotic resonance in an astrocyte-coupled excitable neuron

We study the chaotic resonance phenomenon whereby the response of a neuron to a weak signal is amplified with the help of chaotic current stemming from background activity in the brain. This resonance behavior exhibits a bell-shaped curve in terms of detection quality due to increasing chaotic current intensity. Recent experimental studies have shown that astrocytes, which are the most abundant types of glial cells, may be responsible for the regulation of electrophysiological events in neuronal medium. Hence, we consider here a realistic neuronal system which is constituted by a bipartite network consisting of an excitable neuron and an astrocyte. Our analysis reveals that signal detection quality can be greatly enhanced with the astrocyte contribution obtained by appropriate neuronal and astrocytic cell dynamics. We find that depolarization-induced astrocytic glutamate release is able to improve chaotic resonance performance considerably in the presence of an adequately strong interaction between the astrocyte and the excitable neuron receiving a weak signal with a relatively higher frequency. We also show that a moderate production rate of gliotransmitters is required for the astrocyte to affect resonance performance of the neuron. Except for those conditions where the facilitating effect of astrocyte is observed, it can also reduce signal detection performance in the neuron. Furthermore, we demonstrated that intrinsic neuronal excitability is regulated by the astrocyte, via a comparison of resonance behaviors under effects of bias and astrocytic current separately. Taken together, our findings provide a novel insight into the functioning of astrocyte-neuron circuits, in particular the encoding weak signals via chaotic resonance, and suggest that astrocytes play a key role in intrinsic regulation and selectivity in neuronal information processing.

Optimization of light scattering enhancement by gold nanoparticles in fused silica optical fiber.

A conventional distributed fiber optic sensing system offers close to linear sensitivity along the fiber length. However gold nanoparticles (NP) have been shown to be able to enhance the contrast ratio to improve the quality of signal detection. The challenge in improving the contrast of reflected signals is to optimise the nanoparticle doping concentration over the densed sensing length to make best use of the distributed fiber sensing hardware. In this paper, light enhancement by spherical gold NPs in the optical fibers was analyzed by considering the size-induced NP refractive index changes. This was achieved by building a new model to relate backscattered light from a gold NP suspension between the optical fiber end tips and backscattered light from gold NPs in the core of the optical fiber. The paper provides a model to determine the optimized sizes and concentrations of NPs for sensing at different desired penetration depths in the optical fiber.

Average quality description of energy detection for generalised multipath shadowed κ – μ fading channels

The research addresses the problem of analytical description of energy detection procedure being one of the most favourable mechanism of white-space detection for cognitive radio and Internet-of-Things as well as unknown signal detection in radars and Unmanned Ground/Aerial Vehicle applications. A novel closed-form analytic solutions for average quality metrics (average receiver operating characteristic and area under the AROC curve) are derived for a generic shadowed fading channel model. Numerical simulations verify the computational effectiveness of the derived expressions compared to straightforward numerical integration and analytical treatment demonstrated their consistency with the several specific simplified cases widely used in engineering practice. The results enable one to estimate signal detection quality on the stage of wireless communications' system design.

Uses of pharmacovigilance databases: An overview

Over the past decades, assessment of drug safety and of their benefits harms balance has been profoundly modified by the availability of large databases and computerized automated statistical approaches. Improvement of digital data storage capacity has been applied to pharmacovigilance reports. VigiBase, the international pharmacovigilance database, is now aggregating over 21 million individual case safety reports in 2020. Identification and investigation of drug safety signals -concerning notably rare and unknown adverse drug reactions- is one of the major tasks in pharmacovigilance that can be amplified by automated signal detection. Several quantitative statistical methods exist, each with its own strengths and limits. Integrating signal detection, pharmacovigilance databases can be used for a wide variety of retrospective observational studies illustrated here by concrete examples. Confirming these signals by orthogonal validation using pre-clinical platforms and prospective trials is helpful. Pharmacovigilance databases represent a considerable source of information. However, the quality of signal detection and of pharmacoepidemiology studies in the field of adverse drug reaction closely depends on the quality of the individual data recorded.

СРАВНИТЕЛЬНЫЙ АНАЛИЗ АЛГОРИТМОВ ОБНАРУЖЕНИЯ НЕИЗВЕСТНЫХ СИГНАЛОВ С УЧЁТОМ СОВОКУПНОСТИ ПОКАЗАТЕЛЕЙ КАЧЕСТВА

The subject of this article is the problem of detecting unknown signals in conditions of high a priori uncertainty, which can occur during the determination of unoccupied frequency channels in cognitive networks. It should be noted that various sources of radio emissions work on the air, which in turn complicates the solution of the problem of detection, since it is impossible to say what kind of signal will be received. Most existing algorithms require information about the signals for their operation. In practice, it is not possible to have such data on all sources of radio emission due to their diversity. The goal of the article is to study non-classical signal detection algorithms in conditions of high a priori uncertainty, when there is information only about noise, and signals are unknown. The task: to conduct a comparative analysis of unknown signal detection algorithms based on a set of quality indicators and to determine the set of Pareto-optimal detection algorithms, as well as to identify the best algorithm for a set of quality indicators. The method of statistical modeling of detection algorithms on samples of real signals and noise is performed. As a result, we obtained estimates of speed of work and quality of signal detection for a number of different variants of unknown signal detection algorithms. Possible variants of implementation of the detectors were summarized in the table. These variants were formed taking into account the dimension of the DPF sample and the number of implementations on which the decision is made. A comparative analysis of different types of detection algorithms is carried out taking into account the set of performance indicators and the quality of signal detection. It should be noted that the values of quality indicators of detection of unknown signals and performance indicators of the algorithms are related and contradictory. Conclusions. A multicriteria selection of a subset of Pareto-optimal variants is performed. Using the conditional preference criterion, the only preferred variant of the algorithm for detecting unknown signals is selected from the Pareto subset. The results of the research can be used in automated radio monitoring in cognitive radio networks

Vibrational resonance in a scale-free network with different coupling schemes

Abstract We investigate the phenomenon of vibrational resonance (VR) in neural populations, whereby weak low-frequency signals below the excitability threshold can be detected with the help of additional high-frequency driving. The considered dynamical elements consist of excitable FitzHugh–Nagumo neurons connected by electrical gap junctions and chemical synapses. The VR performance of these populations is studied in unweighted and weighted scale-free networks. We find that although the characteristic network features – coupling strength and average degree – do not dramatically affect the signal detection quality in unweighted electrically coupled neural populations, they have a strong influence on the required energy level of the high-frequency driving force. On the other hand, we observe that unweighted chemically coupled populations exhibit the opposite behavior, and the VR performance is significantly affected by these network features whereas the required energy remains on a comparable level. Furthermore, we show that the observed VR performance for unweighted networks can be either enhanced or worsened by degree-dependent coupling weights depending on the amount of heterogeneity.

Phased-locked signal detection system based on parallel optimal receivers by average risk criterion

This paper is devoted to the investigation of a developed sync system which is based on optimal receiver by average risk minimum criterion when detecting the reflected radar impulse under conditions of Gaussian and Johnson noises. M-sequence with single pulse phase-shift keying has been chosen as a reference signal. A transceiver has been implemented during the digital experiment. The use of developed self-tuning system allows applying the optimal methods of reflected radar signal detection when it is noncoherently received under conditions of Gaussian and non-Gaussian noises. It has been proved that the developed sync system has the signal detection quality advantage over the system based on matched filter.

Detection of algorithmic trading

We develop a new approach to reflect the behavior of algorithmic traders. Specifically, we provide an analytical and tractable way to infer patterns of quote volatility and price momentum consistent with different types of strategies employed by algorithmic traders, and we propose two ratios to quantify these patterns. Quote volatility ratio is based on the rate of oscillation of the best ask and best bid quotes over an extremely short period of time; whereas price momentum ratio is based on identifying patterns of rapid upward or downward movement in prices. The two ratios are evaluated across several asset classes. We further run a two-stage Artificial Neural Network experiment on the quote volatility ratio; the first stage is used to detect the quote volatility patterns resulting from algorithmic activity, while the second is used to validate the quality of signal detection provided by our measure.

Electrode modifications to lower electrode impedance and improve neural signal recording sensitivity

Objective. Although electrode size should be miniaturized to provide higher selectivity for neural signal recording and to avoid tissue damage, small sized electrodes induce high impedance, which decreases recording quality. In this work, the electrode surface was modified to increase the effective surface area to lower the electrode impedance and to improve the neural signal detection quality by optimizing plasma conditions. Approach. A tetrafluoromethane (CF4) plasma was used to increase the effective surface area of gold electrode sites of polyimide-based neural probes. In vitro electrode impedance and in vivo neural signal recording and stimulation were characterized. Main results. For 15 μm diameter (dia.) electrode size, the average surface roughness could be increased from 1.7 to 22 nm after plasma treatment, and the electrode impedance was decreased by 98%. Averaged background noise power in the range of 1 to 1000 Hz was decreased to −106 dB after the 30 μm dia. electrodes were plasma modified—lower than the noise level of −86 dB without plasma treatment. Neural probes with plasma-modified electrode sites of 15 and 30 μm dia. were implanted to the anterior cingulate cortex (ACC) region for acute recording of spontaneous and electrical evoked local field potential (LFP) of neural signals. Spontaneous LFP recorded in vivo by the plasma-modified electrodes of 30 μm dia. was two times higher compared to electrodes without treatment. For a stimulation current of 400 μA, electrically evoked LFP recorded by the plasma-modified electrodes was seven times higher than those without plasma exposure. Significance. A controllable technology was developed to increase the effective surface area of electrodes using a CF4 plasma. Plasma-modified electrodes improved the quality of the neural probe recording and more sensitive to record spontaneous and evoked LFP in the ACC region.

Statistical Signal Detection as a Routine Pharmacovigilance Practice: Effects of Periodicity and Resignalling Criteria on Quality and Workload.

IntroductionThe goal of signal detection in pharmacovigilance (PV) is to detect unknown causal associations between medicines and unexpected events. Statistical methods serve to detect signals and supplement traditional PV methods. Statistical signal detection (SSD) requires decisions about various settings that influence the quality and efficiency of SSD, as shown in several studies. To our knowledge, the effects of SSD periodicity and resignalling criteria on the quality and workload of routine SSD have not been published before.ObjectiveTo analyse the effects of different periodicities and resignalling criteria on signal detection quality and signal validation workload, and to test the impact of changing the signal threshold for number of cases.MethodsWe calculated signals of disproportionate reporting (SDRs) using thresholds of number of cases (N) ≥3, proportional reporting ratio ≥2 and Chi2 ≥ 4. We retrospectively simulated recurrent SDR calculation and validation with varying periodicity (quarterly vs. monthly), resignalling criteria, and N ≥ 3 vs. N ≥ 5.ResultsChanging the periodicity from quarterly to monthly increased the workload by 46.6 % (0 % signal loss). More restrictive resignalling criteria reduced the workload between 36.3 % (0 % signal loss) and 74.1 % (50 % signal loss). For N ≥ 3, the most efficient monthly SSD resignalling criterion reduced the workload by 36.3 % and detected all true signals earlier than quarterly SSD. N ≥ 5 reduced the workload between 13.8 and 21.4 % (0 % signal loss).ConclusionsIn real-life PV practice, signal detection and validation are recurrent periodic activities. Some true signals are only discovered upon resignalling. Our results demonstrate resignalling criteria with high signal detection quality and high efficiency. We found potential earlier detection of true signals using monthly SSD. Additional studies about resignalling should be performed to complement our findings.

Commentary on 'Antenatal cardiotocogram quality and interpretation using computers'.

Commentary on 'Antenatal cardiotocogram quality and interpretation using computers'.

Microwave Human Vocal Vibration Signal Detection Based on Doppler Radar Technology

A speech radar system is presented for extracting speech information from the vocal vibration signal of a human subject. Due to the tiny glottis motion of several millimeters, a coherent homodyne demodulator with high sensitivity is developed to detect reflected radio signal, phase modulated by the vibrating vocal cords. The signal detection quality and system circuit design are described. Measurements of vowels and words, both with the speech radar system and the conventional acoustic microphone system, were conducted and compared. The essential speech information can be reliably obtained from the proposed speech radar, making it more appealing for speech applications in high background acoustic noise environment.

The Influence of Stellate Ganglion Transcutaneous Electrical Nerve Stimulation on Signal Quality of Pulse Oximetry in Prehospital Trauma Care

Accurate monitoring of the peripheral arterial oxygen saturation has become an important tool in the prehospital emergency medicine. This monitoring requires an adequate plethysmographic pulsation. Signal quality is diminished by cold ambient temperature due to vasoconstriction. Blockade of the stellate ganglion can improve peripheral vascular perfusion and can be achieved by direct injection or transcutaneous electrical nerve stimulation (TENS) stimulation. We evaluated whether TENS on the stellate ganglion would reduce vasoconstriction and thereby improve signal detection quality of peripheral pulse oximetry. In our study, 53 patients with minor trauma who required transport to the hospital were enrolled. We recorded vital signs, including core and skin temperature before and after transport to the hospital. Pulse oximetry sensors were attached to the patient's second finger on both hands. TENS of the stellate ganglion was started on one side after the beginning of the transport. Pulse oximeter alerts, due to poor signal detection, were recorded for each side separately. On the hand treated with TENS we detected a significant reduction of alerts compared to the other side (mean alerts TENS 3.1 [1-15] versus control side 8.8 [1-28] P < 0.05). The duration of dropouts was shorter as well (mean duration TENS 77 [16-239] s versus control side 333 [78-1002] s). The data indicate that blockade of the stellate ganglion with TENS improves signal quality of pulse oximeters in the prehospital setting.

Optimal placement of a limited number of observations for period searches

Robotic telescopes present the opportunity for the sparse temporal placement of observations when period searching. We address the best way to place a limited number of observations to cover the dynamic range of frequencies required by an observer. We show that an observation distribution geometrically spaced in time can minimise aliasing effects arising from sparse sampling, substantially improving signal detection quality. The base of the geometric series is however a critical factor in the overall success of this strategy. Further, we show that for such an optimal distribution observations may be reordered, as long as the distribution of spacings is preserved, with almost no loss of quality. This implies that optimal observing strategies can retain significant flexibility in the face of scheduling constraints, by providing scope for on-the-fly adaptation. Finally, we present optimal geometric samplings for a wide range of common observing scenarios, with an emphasis on practical application by the observer at the telescope. Such a sampling represents the best practical empirical solution to the undersampling problem that we are aware of. The technique has applications to robotic telescope and satellite observing strategies, where target acquisition overheads mean that a greater total target exposure time (and hence signal-to-noise) can often in practice be achieved by limiting the number of observations.

Multistation data fusion for code-division multiple access wireless communications

Data fusion is the study of optimal information processing in distributed multisensor environments with the benefits of more reliable detection and higher immunity to noise and sensor failures. We investi- gate applications of the data fusion principle to the handoff problem in popular code-division multiple access (CDMA) cellular wireless commu- nication networks. When a mobile station traverses a cell boundary, cell handoff is required to make the transition of a mobile between cells smoother without degrading performance. CDMA multiple access allows soft handoff, in which the signal strength of the mobile is detected and tracked by the searchers from more than one base station. The cell receiving the strongest signal strength handles signal processing. We demonstrate that the signal detection quality in the handoff region can be further improved by fusing the detected results from multiple base sta- tions. A new fusion handoff strategy is designed, and its error perfor- mance measures are derived analytically in the presence of log-normal shadowing. Theoretical fading margin analysis shows that the fusion handoff approach offers better network performance over conventional hard and soft handoffs. © 2003 Society of Photo-Optical Instrumentation Engineers.