A Low Power 1024-Channels Spike Detector Using Latch-Based RAM for Real-Time Brain Silicon Interfaces

High-density microelectrode arrays allow the neuroscientist to study a wider neurons population, however, this causes an increase of communication bandwidth. Given the limited resources available for an implantable silicon interface, an on-fly data reduction is mandatory to stay within the power/area constraints. This can be accomplished by implementing a spike detector aiming at sending only the useful information about spikes. We show that the novel non-linear energy operator called ASO in combination with a simple but robust noise estimate, achieves a good trade-off between performance and consumption. The features of the investigated technique make it a good candidate for implantable BMIs. Our proposal is tested both on synthetic and real datasets providing a good sensibility at low SNR. We also provide a 1024-channels VLSI implementation using a Random-Access Memory composed by latches to reduce as much as possible the power consumptions. The final architecture occupies an area of 2.3 mm2, dissipating 3.6 µW per channels. The comparison with the state of art shows that our proposal finds a place among other methods presented in literature, certifying its suitability for BMIs.

From Insect Vision to a Novel Bio-Inspired Algorithm for Image Denoising

Night active insects inspired the development of image enhancement methods that uncover the information contained in dim images or movies. Here, I describe a novel bionic night vision (NV) algorithm that operates in the spatial domain to remove noise from static images. The parameters of this NV algorithm can be automatically derived from global image statistics and a primitive type of noise estimate. In a first step, luminance values were ln-transformed, and then adaptive local means’ calculations were executed to remove the remaining noise without degrading fine image details and object contours. Its performance is comparable with several popular denoising methods and can be applied to grey-scale and color images. This novel algorithm can be executed in parallel at the level of pixels on programmable hardware.

A case study of seismograph self-noise test from Trillium 120QA seismometer and Reftek 130 data logger

Seismograph self-noise has become a de facto standard for instrument comparisons and their performance assessment and is considered as one of the most vital parameters for instrument comparison. For self-noise testing of modern force-balance feedback broadband seismometers, several factors have been thoroughly discussed and thought to be attributable to the self-noise estimate, including the data selection criteria, sensor alignment correction, timing error, correlation analysis method, and computational parameter selection during the computational process. This study focuses on some other factors, such as local site conditions, temperature insulating methods, and data logger self-noise interferences, with an aim to differentiate the self-noise contribution of these sources and their dependencies on time and frequency. A series of experiments were conducted at the Beijing National Earth Observatory using a Trillium 120QA seismometer and Reftek-130 data acquisition system at three different locations ranging from the ordinary equipment warehouse to global seismographic network level cave with a hard-rock base. Results show that noise-free site is necessary for the self-noise test in a frequency band greater than approximately 0.1 Hz. However, for a frequency band less than 0.1 Hz, the insulation method and installation procedures are far more important, although the influence of the site location cannot be neglected fully. A suitable preamp should be selected in the data logger configurations to ensure that the low-noise amplitude of the sensor signal is above the digitizer noise level.

Speech Enhancement for Secure Communication Using Coupled Spectral Subtraction and Wiener Filter

The encryption process for secure voice communication may degrade the speech quality when it is applied to the speech signals before encoding them through a conventional communication system such as GSM or radio trunking. This is because the encryption process usually includes a randomization of the speech signals, and hence, when the speech is decrypted, it may perceptibly be distorted, so satisfactory speech quality for communication is not achieved. To deal with this, we could apply a speech enhancement method to improve the quality of decrypted speech. However, many speech enhancement methods work by assuming noise is present all the time, so the voice activity detector (VAD) is applied to detect the non-speech period to update the noise estimate. Unfortunately, this assumption is not valid for the decrypted speech. Since the encryption process is applied only when speech is detected, distortions from the secure communication system are characteristically different. They exist when speech is present. Therefore, a noise estimator that is able to update noise even when speech is present is needed. However, most noise estimator techniques only adapt to slow changes of noise to avoid over-estimation of noise, making them unsuitable for this task. In this paper, we propose a speech enhancement technique to improve the quality of speech from secure communication. We use a combination of the Wiener filter and spectral subtraction for the noise estimator, so our method is better at tracking fast changes of noise without over-estimating them. Our experimental results on various communication channels indicate that our method is better than other popular noise estimators and speech enhancement methods.

Processing and quality control of FY-3C GNOS data used in numerical weather prediction applications

The Chinese radio occultation sounder GNOS (Global Navigation Occultation Sounder) is on the FY-3C satellite, which was launched on 23 September 2013. Currently, GNOS data are transmitted via the Global Telecommunications System (GTS), providing 450–500 profiles per day for numerical weather prediction applications. This paper describes the processing of the GNOS profiles with large biases related to L2 signal degradation. A new extrapolation procedure in bending angle space corrects the L2 bending angles using a thin ionosphere model and the fitting relationship between L1 and L2. We apply the approach to improve the L2 extrapolation of GNOS. The new method can effectively eliminate about 90 % of large departures. In addition to the procedure for the L2 degradation, this paper also describes our quality control (QC) for FY-3C GNOS. A noise estimate for the new L2 extrapolation can be used as a QC parameter to evaluate the performance of the extrapolation. A statistical comparison between GNOS bending angles and short-range ECMWF (European Centre for Medium-Range Weather Forecasts) forecast bending angles demonstrates that GNOS performs almost as well as the Global Navigation Satellite System (GNSS) Receiver for Atmospheric Sounding (GRAS), especially in the core region from around 10 to 35 km. The GNOS data with the new L2 extrapolation are suitable for assimilation into numerical weather prediction systems.

Tracking of Noise Tolerance to Predict Hearing Aid Satisfaction in Loud Noisy Environments

A method that tracked tolerable noise level (TNL) over time while maintaining subjective speech intelligibility was reported previously. Although this method was reliable and efficacious as a research tool, its clinical efficacy and predictive ability of real-life hearing aid satisfaction were not measured.The study evaluated an adaptive method to estimate TNL using slope and variance of tracked noise level as criteria in a clinical setting. The relationship between TNL and subjective hearing aid satisfaction in noisy environments was also investigated.A single-blinded, repeated-measures design.Seventeen experienced hearing aid wearers with bilateral mild-to-moderately-severe sensorineural hearing loss.Participants listened to 82-dB SPL continuous speech and tracked the background noise level that they could “put up with” while subjectively understanding >90% of the speech material. Two trials with each babble noise and continuous speech-shaped noise were measured in a single session. All four trials were completed aided using the participants’ own hearing aids. The stimuli were presented in the sound field with speech from 0° and noise from the 180° azimuth. The instantaneous tolerable noise level was measured using a custom program and scored in two ways; the averaged TNL (aTNL) over the 2-min trial and the estimated TNL (eTNL) as soon as the listeners reached a stable noise estimate. Correlation between TNL and proportion of satisfied noisy environments was examined using the MarkeTrak questionnaire.All listeners completed the tracking of noise tolerance procedure within 2 min with good reliability. Sixty-five percent of the listeners yielded a stable noise estimate after 59.9 sec of actual test time. The eTNL for all trials was 78.6 dB SPL (standard deviation [SD] = 4.4 dB). The aTNL for all trials was 78.0 dB SPL (SD = 3.3 dB) after 120 sec. The aTNL was 79.2 dB SPL (SD = 5.4 dB) for babble noise and 77.0 dB SPL (SD = 5.9 dB) for speech-shaped noise. High within-session test–retest reliability was evident. The 95% confidence interval was 1.5 dB for babble noise and 2.8 dB for continuous speech-shaped noise. No significant correlation was measured between overall hearing aid satisfaction and the aTNL (ρ = 0.20 for both noises); however, a significant relationship between aTNL and proportion of satisfied noisy situations was evident (ρ = 0.48 for babble noise and ρ = 0.55 for speech-shaped noise).The eTNL scoring method yielded similar results as the aTNL method although requiring only half the time for 65% of the listeners. This time efficiency, along with its reliability and the potential relationship between TNL and hearing aid satisfaction in noisy listening situations suggests that this procedure may be a good clinical tool to evaluate whether specific features on a hearing aid would improve noise tolerance and predict wearer satisfaction with the selected hearing aid in real-life loud noisy situations. A larger sample of hearing aid wearers is needed to further validate these potential uses.

Processing and quality control with FY-3C/GNOS data used in numerical weather prediction applications

The Chinese radio occultation sounder GNOS (Global Navigation Occultation Sounder) is on the FY-3C satellite, which was launched on September 23, 2013. Currently, GNOS data is transmitted via the Global Telecommunications System (GTS) providing 450–500 profiles per day for numerical weather prediction applications. This paper describes the processing for the GNOS profiles with large biases, related to L2 signal degradation. A new extrapolation procedure in bending angle space corrects the L2 bending angles, using a thin ionosphere model, and the fitting relationship between L1 and L2. We apply the approach to improve the L2 extrapolation of GNOS. The new method can effectively eliminate about 90 % of the large departures. In addition to the procedure for the L2 degradation, this paper also describes our quality control (QC) for FY-3C/GNOS. A noise estimate for the new L2 extrapolation can be used as a QC parameter to evaluate the performance of the extrapolation. Mean phase delays of L1 and L2 in the tangent height interval of 60 to 80 km are analysed and applied in the QC as well. A statistical comparison between GNOS and ECMWF (European Centre for Medium-Range Weather Forecasts) forecast data demonstrates that GNOS performs almost as well as GRAS, especially in the core region from around 10 to 35 km. The GNOS data with the new L2 extrapolation is suitable for assimilation into numerical weather prediction systems.

Quantifying the error level in computed magnetic amplitude data for 3D magnetization inversion

Three-dimensional inversion plays an important role in the quantitative interpretation of magnetic data in exploration problems, and magnetic amplitude data can be an effective tool in cases in which remanently magnetized materials are present. Because amplitude data are typically calculated from total-field anomaly data, the error levels must be characterized for inversions. Lack of knowledge of the error in amplitude data hinders the ability to properly estimate the data misfit associated with an inverse model and, therefore, the selection of the appropriate regularization parameter for a final model. To overcome these challenges, we have investigated the propagation of errors from total-field anomaly to amplitude data. Using parametric bootstrapping, we find that the standard deviation of the noise in amplitude data is approximately equal to that of the noise in total-field anomaly data when the amplitude data are derived from the conversion of total-field data to three orthogonal components. We then illustrate how the equivalent source method can be used to estimate the error in total-field anomaly data when needed. The obtained noise estimate can be applied to amplitude inversion to recover an optimal inverse model by applying the discrepancy principle. We test this method on synthetic and field data and determine its effectiveness.