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This paper presents a solution for implementing a power measurement receiver made with PlutoSDR. The paper proposes the dynamic elimination of the contribution of internal noise to the measured signal level by compensating with the level of internal noise produced by the receiver in the adjacent RF band. Our approach is based on the fact that the internal noise level in the working band is approximately equal to that in the frequency band in the immediate vicinity. As far as we know, our work is the first to use the idea of measuring the noise in an adjacent band to estimate the internal noise of the measuring instrument. Another aspect necessary to ensure a complete measurement solution that does not require pre-measurement calibration concerns the compensation of the nonlinearities of the reception path. The compensation was automatically performed by modifying the amplification of the reception path depending on the working frequency. For high levels of the measured CW signal (-50 dBm ... -10 dBm) the solution allows obtaining measurement errors of less than 0.55 dB. From the perspective of measurement uncertainty, with a value of U = ±1 dB (k=2), the instrument with the method used fits very well into the requirements for pre -compliance EMC measurements. For levels lower than -60 dBm, the error starts to increase rapidly, so that at values of -70 dBm the signal can no longer be measured.

Static and dynamic cues within certain spatiotemporal proximity are used to evoke respective global percepts of form and motion. The limiting factors in this process are, first, internal noise, which indexes local orientation/direction detection, and, second, sampling efficiency, which relates to the processing and the representation of global orientation/direction. These parameters are quantified using the equivalent noise (EN) paradigm. EN has been implemented with just two levels: high and low noise. However, when using this simplified version, one must assume the shape of the overall noise dependence, as the intermediate points are missing. Here, we investigated whether two distinct EN methods, the 8-point and the simplified 2-point version, reveal comparable parameter estimates. This was performed for three different types of stimuli: random dot kinematograms, and static and dynamic translational Glass patterns, to investigate how constant internal noise estimates are, and how sampling efficiency might vary over tasks. The results indicated substantial compatibility between estimates over a wide range of external noise levels sampled with eight data points, and a simplified version producing two highly informative data points. Our findings support the use of a simplified procedure to estimate essential form-motion integration parameters, paving the way for rapid and critical applications to populations that cannot tolerate protracted measurements.

After a right hemisphere stroke, more than half of the patients are impaired in their capacity to produce or comprehend speech prosody. Yet, and despite its social-cognitive consequences for patients, aprosodia following stroke has received scant attention. In this report, we introduce a novel, simple psychophysical procedure which, by combining systematic digital manipulations of speech stimuli and reverse-correlation analysis, allows estimating the internal sensory representations that subtend how individual patients perceive speech prosody, and the level of internal noise that govern behavioral variability in how patients apply these representations. Tested on a sample of N = 22 right-hemisphere stroke survivors and N = 21 age-matched controls, the representation + noise model provides a promising alternative to the clinical gold standard for evaluating aprosodia (MEC): both parameters strongly associate with receptive, and not expressive, aprosodia measured by MEC within the patient group; they have better sensitivity than MEC for separating high-functioning patients from controls; and have good specificity with respect to non-prosody-related impairments of auditory attention and processing. Taken together, individual differences in either internal representation, internal noise, or both, paint a potent portrait of the variety of sensory/cognitive mechanisms that can explain impairments of prosody processing after stroke.

Stochastic Resonance (SR) is a phenomenon in which noise improves the performance of a system. With the addition of noise, a weak input signal to a nonlinear system, which may exceed its threshold, is transformed into an output signal. In the other words, noise-driven signal transfer is achieved. SR has been observed in nonlinear response systems, such as biological and artificial systems, and this review will focus mainly on examples of previous studies of mathematical models and experimental realization of SR using poly(hexylthiophene)-based organic field-effect transistors (OFETs). This phenomenon may contribute to signal processing with low energy consumption. However, the generation of SR requires a noise source. Therefore, the focus is on OFETs using materials such as organic materials with unstable electrical properties and critical elements due to unidirectional signal transmission, such as neural synapses. It has been reported that SR can be observed in OFETs by application of external noise. However, SR does not occur under conditions where the input signal exceeds the OFET threshold without external noise. Here, we present an example of a study that analyzes the behavior of SR in OFET systems and explain how SR can be made observable. At the same time, the role of internal noise in OFETs will be explained.

Portable transient electromagnetic (TEM) systems can be well adapted to various terrains, including mountainous, woodland, and other complex terrains. They are widely used for the detection of unexploded ordnance (UXO). As the core component of the portable TEM system, the sensor is constructed with a transmitting coil and a receiving coil. Based on the primary field of the transmitting coil and internal noise of the receiving coil, the design and testing of such a sensor is described in detail. Results indicate that the primary field of the transmitting coil depends on the diameter, mass, and power of the coil. A higher mass–power product and a larger diameter causes a stronger primary field. Reducing the number of turns and increasing the clamp voltage reduces the switch-off time of the transmitting current effectively. Increasing the cross-section of the wire reduces the power consumption, but greatly increases the coil’s weight. The study of the receiving coil shows that the internal noise of the sensor is dominated by the thermal noise of the damping resistor. Reducing the bandwidth of the system and increasing the size of the coil reduces the internal noise effectively. The cross-sectional area and the distance between the sections of the coil have little effect on the internal noise. A less damped state can effectively reduce signal distortion. Finally, a portable TEM sensor with both a transmitting coil (constructed with a diameter, number of turns, and transmitting current of 0.5 m, 30, and 5 A, respectively) and a receiving coil (constructed with a length and resonant frequency of 5.6 cm and 50 kHz, respectively) was built. The agreement between experimental and calculated results confirms the theory used in the sensor design. The responses of an 82 mm mortar shell at different distances were measured and inverted by the differential evolution (DE) algorithm to verify system performance. Results show that the sensor designed in this study can not only detect the 82 mm mortar shell within 1.2 m effectively but also locate the target precisely.

Human contrast discrimination performance is limited by transduction nonlinearities and variability of the neural representation (noise). Whereas the nonlinearities have been well-characterised, there is less agreement about the specifics of internal noise. Psychophysical models assume that it impacts late in sensory processing, whereas neuroimaging and intracranial electrophysiology studies suggest that the noise is much earlier. We investigated whether perceptually-relevant internal noise arises in early visual areas or later decision making areas. We recorded EEG and MEG during a two-interval-forced-choice contrast discrimination task and used multivariate pattern analysis to decode target/non-target and selected/non-selected intervals from evoked responses. We found that perceptual decisions could be decoded from both EEG and MEG signals, even when the stimuli in both intervals were physically identical. Above-chance decision classification started <100 ms after stimulus onset, suggesting that neural noise affects sensory signals early in the visual pathway. Classification accuracy increased over time, peaking at >500 ms. Applying multivariate analysis to separate anatomically-defined brain regions in MEG source space, we found that occipital regions were informative early on but then information spreads forwards across parietal and frontal regions. This is consistent with neural noise affecting sensory processing at multiple stages of perceptual decision making. We suggest how early sensory noise might be resolved with Birdsall's linearisation, in which a dominant noise source obscures subsequent nonlinearities, to allow the visual system to preserve the wide dynamic range of early areas whilst still benefitting from contrast-invariance at later stages. A preprint of this work is available at: https://doi.org/10.1101/364612. •M/EEG study of decision classification for 2IFC contrast discrimination.•Decoding of decisions around 100 ms implies early sensory noise.•Strongest decoding is later (>400 ms) and involves fronto-parietal regions.•Noise may impact after early nonlinearities to preserve their wide dynamic range.

The investigation depth of transient electromagnetic sensors can be effectively increased by reducing the system noise, which is mainly composed of sensor internal noise, electromagnetic interference (EMI), and environmental noise, etc. A high-sensitivity airborne transient electromagnetic (AEM) sensor with low sensor internal noise and good shielding effectiveness is of great importance for deep penetration. In this article, the design and optimization of such an AEM sensor is described in detail. To reduce sensor internal noise, a noise model with both a damping resistor and a preamplifier is established and analyzed. The results indicate that a sensor with a large diameter, low resonant frequency, and low sampling rate will have lower sensor internal noise. To improve the electromagnetic compatibility of the sensor, an electromagnetic shielding model for a central-tapped coil is established and discussed in detail. Previous studies have shown that unclosed shields with multiple layers and center grounding can effectively suppress EMI and eddy currents. According to these studies, an improved differential AEM sensor is constructed with a diameter, resultant effective area, resonant frequency, and normalized equivalent input noise of 1.1 m, 114 m2, 35.6 kHz, and 13.3 nV/m2, respectively. The accuracy of the noise model and the shielding effectiveness of the sensor have been verified experimentally. The results show a good agreement between calculated and measured results for the sensor internal noise. Additionally, over 20 dB shielding effectiveness is achieved in a complex electromagnetic environment. All of these results show a great improvement in sensor internal noise and shielding effectiveness.

Background: Visual perceptual learning plays a crucial role in shaping our understanding of how the human brain integrates visual cues to construct coherent perceptual experiences. The visual system is continually challenged to integrate a multitude of visual cues, including form and motion, to create a unified representation of the surrounding visual scene. This process involves both the processing of local signals and their integration into a coherent global percept. Over the past several decades, researchers have explored the mechanisms underlying this integration, focusing on concepts such as internal noise and sampling efficiency, which pertain to local and global processing, respectively. Objectives and Methods: In this study, we investigated the influence of visual perceptual learning on non-directional motion processing using dynamic Glass patterns (GPs) and modified Random-Dot Kinematograms (mRDKs). We also explored the mechanisms of learning transfer to different stimuli and tasks. Specifically, we aimed to assess whether visual perceptual learning based on illusory directional motion, triggered by form and motion cues (dynamic GPs), transfers to stimuli that elicit comparable illusory motion, such as mRDKs. Additionally, we examined whether training on form and motion coherence thresholds improves internal noise filtering and sampling efficiency. Results: Our results revealed significant learning effects on the trained task, enhancing the perception of dynamic GPs. Furthermore, there was a substantial learning transfer to the non-trained stimulus (mRDKs) and partial transfer to a different task. The data also showed differences in coherence thresholds between dynamic GPs and mRDKs, with GPs showing lower coherence thresholds than mRDKs. Finally, an interaction between visual stimulus type and session for sampling efficiency revealed that the effect of training session on participants’ performance varied depending on the type of visual stimulus, with dynamic GPs being influenced differently than mRDKs. Conclusion: These findings highlight the complexity of perceptual learning and suggest that the transfer of learning effects may be influenced by the specific characteristics of both the training stimuli and tasks, providing valuable insights for future research in visual processing.

In this study, a dynamic stochastic resonance (DSR)-based technique in spatial domain has been proposed for the enhancement of dark- and low-contrast images. Stochastic resonance (SR) is a phenomenon in which the performance of a system (low-contrast image) can be improved by addition of noise. However, in the proposed work, the internal noise of an image has been utilised to produce a noise-induced transition of a dark image from a state of low contrast to that of high contrast. DSR is applied in an iterative fashion by correlating the bistable system parameters of a double-well potential with the intensity values of a low-contrast image. Optimum output is ensured by adaptive computation of performance metrics – relative contrast enhancement factor (F), perceptual quality measures and colour enhancement factor. When compared with the existing enhancement techniques such as adaptive histogram equalisation, gamma correction, single-scale retinex, multi-scale retinex, modified high-pass filtering, edge-preserving multi-scale decomposition and automatic controls of popular imaging tools, the proposed technique gives significant performance in terms of contrast and colour enhancement as well as perceptual quality. Comparison with a spatial domain SR-based technique has also been illustrated.

The optimal template for signal detection in white additive noise is the signal itself: the ideal observer matches each stimulus against this template and selects the stimulus associated with largest match. In the noisy ideal observer, internal noise is added to the decision variable returned by the template. While the ideal observer represents an unrealistic approximation to the human visual process, the noisy ideal observer may be applicable under certain experimental conditions. For template values constrained to lie within a specified range, theory predicts that the template associated with a noisy ideal observer should be a clipped image of the signal, a result which we demonstrate analytically using variational calculus. It is currently unknown whether the human process conforms to theory. We report a targeted analysis of the theoretical prediction for an experimental protocol that maximizes template-matching on the part of human participants. We find indicative evidence to support the theoretical expectation when internal noise is compared across participants, but not within each participant. Our results indicate that implicit knowledge about internal variability in different individuals is reflected by their detection templates; no implicit knowledge is retained for internal-noise fluctuations experienced by a given participant during data collection. The results also indicate that template encoding is constrained by the dynamic range of weight specification, rather than the range of output values transduced by the template-matching process.