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The previously obscure CIC filter is now vital to many high-volume wireless communications tasks and equipment. Using CIC filters can cut costs, improve reliability, and help performance. Here's a primer to get you started. Cascaded integrator-comb (CIC) digital filters are computationally efficient implementations of narrowband lowpass filters and...
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... The IF signal is usually close to dc and tracking the variations in the former using the master clock operating at 125 MHz would result in sub-optimal usage of resources. Hence, we use a cascaded integrated comb (CIC) filter for down-sampling the signal [258,259]. When the factor by which the signal has to be down-sampled is large, CIC filters, used as a front end for FIR filters result in decreased number of filter taps required for anti-aliasing. ...
This thesis contains the detection of intrinsic spin-coherence from an ensemble of magneto-optically trapped (MOT) cold Rb atoms at 150 micro Kelvin temperature using Faraday rotation fluctuations measurement and by employing coherent Raman coupling. A coherent Raman coupling applied between the adjacent Zeeman states within the ground hyperfine level enhances the traditional spin noise (SN) signal strength by million folds in thermal atoms which allows us to detect the spin coherence in cold atoms with a good signal-to-noise ratio. This thesis also demonstrates the instantaneous measurement of atomic spin polarization when the spin system is subjected to a resonant laser field. The precise measurement of various atomic, nuclear, magnetic, and chemical properties and the demonstration of time-resolved precision magnetometry within the intrinsic SN measurement is also presented.
... We down-sample the SEEG signal from 1024 to 256 Hz by applying a low-pass filter to prevent aliasing (fifth-order Butterworth filter with a cut-off frequency of 128 Hz). This is followed by a cascaded integral comb with compensation filter [56] and decimation (down-sampling). Henceforth, the SEEG sampling rate will be 256 Hz, unless mentioned otherwise. ...
... Nevertheless, the model can still predict the heart rate using the non-CAN region contact points in other subjects. However, these contacts are in close proximity of the insula, and might still pick up signal from that area -this might be the reason for the relatively good performance, shown in Figure 7. Furthermore, using DeepLIFT attribution, we show that the heart rate is associated with the theta (4-8 Hz), alpha (8)(9)(10)(11)(12)(13), and lower beta (13-20 Hz) frequency ranges, as well as the higher frequencies (53)(54)(55)(56)(57)(58)(59)(60). Frequencies outside these ranges are less relevant to the predictive model. ...
Autonomic peripheral activity is partly governed by brain autonomic centers. However, there is still a lot of uncertainties regarding the precise link between peripheral and central autonomic biosignals. Clarifying these links could have a profound impact on the interpretability, and thus usefulness, of peripheral autonomic biosignals captured with wearable devices. In this study, we take advantage of a unique dataset consisting of intracranial stereo-electroencephalography (SEEG) and peripheral biosignals acquired simultaneously for several days from four subjects undergoing epilepsy monitoring. Compared to previous work, we apply a deep neural network to explore high-dimensional nonlinear correlations between the cerebral brainwaves and variations in heart rate and electrodermal activity (EDA). Further, neural network explainability methods were applied to identify most relevant brainwave frequencies, brain regions and temporal information to predict a specific biosignal. Strongest brain-peripheral correlations were observed from contacts located in the central autonomic network, in particular in the alpha, theta and 52 to 58 Hz frequency band. Furthermore, a temporal delay of 12 to 14 s between SEEG and EDA signal was observed. Finally, we believe that this pilot study demonstrates a promising approach to mapping brain-peripheral relationships in a data-driven manner by leveraging the expressiveness of deep neural networks.
... The IF signal is usually close to dc and tracking the variations in the former using the master clock operating at 125 MHz would result in sub-optimal usage of resources. Hence, we use a cascaded integrated comb (CIC) filter for down-sampling the signal [22], [23]. When the factor by which the signal has to be down-sampled is large, CIC filters, used as a front end for FIR filters result in decreased number of filter taps required for antialiasing. ...
We present the development and characterization of a generic, reconfigurable, low-cost ($<$ 350 USD) software-defined digital receiver system (DRS) for temporal correlation measurements in atomic spin ensembles. We demonstrate the use of the DRS as a component of a high resolution magnetometer. Digital receiver based fast Fourier transform spectrometers (FFTS) are generally superior in performance in terms of signal-to-noise ratio (SNR) compared to traditional swept-frequency spectrum analyzers (SFSA). In applications where the signals being analyzed are very narrow band in frequency domain, recording them at high speeds over a reduced bandwidth provides flexibility to study them for longer periods. We have built the DRS on the STEMLab 125-14 FPGA platform and it has two different modes of operation: FFT Spectrometer and real time raw voltage recording mode. We evaluate its performance by using it in atomic spin noise spectroscopy (SNS). We demonstrate that the SNR is improved by more than one order of magnitude with the FFTS as compared to that of the commercial SFSA. We also highlight that with this DRS operating in the triggered data acquisition mode one can achieve spin noise (SN) signal with high SNR in a recording time window as low as 100 msec. We make use of this feature to perform time resolved high-resolution magnetometry. While the receiver was initially developed for SNS experiments, it can be easily used for other atomic, molecular and optical (AMO) physics experiments as well.
... This is the non normalized moving average filter, which can be implemented in a recursive running-sum structure. It consists of two construction blocks: the integrator having the transfer function I (z) and the comb with a delay z −U having the transfer function C(z U ) [50]. Then by cascading N rectangular function filters, the B-spline interpolation of order N − 1 is implemented. ...
Sample rate conversion (SRC) is ubiquitous and critical function of software defined radio and other signal processing systems (speech coding and synthesis, computer simulation of continuous-time systems, etc..). In this paper, we present a survey on linear phase finite impulse response (FIR) based sampling rate conversion. Many different FIR-based SRC solutions exist, such as classical FIR, polyphase, Farrow, cascaded-integrator-comb, and Newton structures. Each one of these solutions is presented differently in the literature, and SRC reference books introducing the subject are often missing hardware implementation aspects. The main objective of this paper is to provide a simple and comprehensive overview of main FIR-based SRC techniques from theoretical to hardware implementation aspects. The state of the art of FIR-based SRC filters is summed-up through a concise derivation of the different solutions from a common root: linear phase FIR filters. Each SRC solution is presented from both theoretical and practical implementation points of view. The paper provides a succinct tutorial that introduces SRC, and helps identifying and implementing the appropriate FIR-based SRC architecture for any given applications.
... The IF signal is usually close to dc and tracking the variations in the former using the master clock operating at 125 MHz would result in sub-optimal usage of resources. Hence, we use a cascaded integrated comb (CIC) filter for down-sampling the signal [22], [23]. When the factor by which the signal has to be down-sampled is large, CIC filters, used as a front end for FIR filters result in decreased number of filter taps required for antialiasing. ...
We present the development and characterization of a generic, reconfigurable, low-cost (<
350 USD) software-defined digital receiver system (DRS) for temporal correlation measurements in atomic spin ensembles. We demonstrate the use of the DRS as a component of a high resolution magnetometer. Digital receiver based fast Fourier
transform spectrometers (FFTS) are generally superior in performance in terms of signal-to-noise ratio (SNR) compared to traditional swept-frequency spectrum analyzers (SFSA). In applications where the signals being analyzed are very narrow band in frequency omain, recording them at high speeds over a reduced bandwidth provides flexibility to study them for longer periods. We have built the DRS on the STEMLab 125-14 FPGA platform and
it has two different modes of operation: FFT Spectrometer and real time raw voltage recording mode. We evaluate its performance by using it in atomic
spin noise spectroscopy (SNS). We demonstrate that the SNR is improved by more than one order of magnitude with the FFTS as compared to that of the commercial SFSA. We also highlight that with this DRS operating in the triggered data acquisition mode one can achieve spin noise (SN) signal with high SNR in a recording time window as low as 100
msec. We make use of this feature to perform time resolved high-resolution magnetometry. While the receiver was initially developed for SNS experiments,
it can be easily used for other atomic, molecular and optical (AMO) physics experiments as well.
... This is the non-normalized moving average filter, which can be implemented in a recursive running-sum structure, consisting of two construction blocks, the integrator having the transfer function H I (z) and the comb with a delay z −U having the transfer function H C U (z) [Lyo05]. Then by cascading N rectangular function filters, the B-spline interpolation of order N − 1 is implemented. ...
The number of wireless communication technologies and standards is constantly increasing to provide communication solutions for today’s technological needs. This is particularly relevant in the domain of the Internet of Things (IoT), where many standards are available, and many others are expected. To efficiently deploy the IoT network, the interoperability between the different solutions is critical. Interoperability on the physical level is achieved through multi-standard modems. These modems are made possible through the digital front-end (DFE), that offers a flexible radio front-end able of processing a wide range of signal types. This thesis first develops a generic architecture of both transmission and reception DFEs, which can be easily adapted to support different IoT standards. These architectures highlight the main role of sample rate conversion (SRC) in the DFE, and the importance of optimizing the SRC implementation. This optimization is then achieved through an in-depth study of the SRC functions, and the development of new structures of improved efficiency in terms of implementation complexity and power consumption, while offering equivalent or improved performance. The final part of the thesis addresses the optimization of the DFE hardware implementation, which is achieved through developing an optimal quantization method that minimizes the use of hardware resources while guaranteeing a given performance constraint. The obtained results are finally highlighted through implementing and comparing different implementation strategies on both field programmable gate array (FPGA) and application specific integrated circuit (ASIC) targets.
... A major drawback of this type of filter is the non-flat frequency response in the desired audio frequency range. To improve the flatness of the frequency response, a CIC filter with a lower decimation factor followed by compensation filters is usually a better choice, as proposed in [19], [34] and [35]. The CIC filter is followed by a couple of half-band filters of order N HB with a decimation factor of two. ...
Many applications rely on MEMS microphone arrays for locating sound sources prior to their execution. Those applications are not only executed under real-time constraints but are often embedded on low-power devices. These environments become challenging when increasing the number of microphones or requiring dynamic responses. Field-Programmable Gate Arrays (FPGAs) are usually chosen due to their flexibility and computational power. This work intends to guide the design of reconfigurable acoustic beamforming architectures, which are not only able to accurately determining the sound Direction-Of-Arrival (DoA) but are also capable to satisfy the most demanding applications in terms of power efficiency. Design considerations of the required operations performing the sound location are discussed and analysed in order to facilitate the elaboration of reconfigurable acoustic beamforming architectures. Performance strategies are proposed and evaluated based on the characteristics of the presented architecture. This power-efficient architecture is compared to a different architecture prioritizing performance in order to reveal the unavoidable design trade-offs.
... However, a major drawback of this type of filter is the nonflat frequency response in the desired audio frequency range. In order to improve the flatness of the frequency response, a CIC filter with a lower decimation factor followed by compensation filters is usually a better choice, as proposed in [74], [65] and [100]. ...
... However, a major disadvantage of this type of filter is the non-flat frequency response in the desired audio frequency range. In order to compensate the non-flat frequency response in the desired audio frequency range, the CIC filter with a lower decimation factor is followed by a compensation FIR filter, like in [74] and [100]. However, before the FIR filter, a moving average filter is used to cancel out the effects caused by the microphones' DC offset output. ...
Field-Programmable Gate Arrays (FPGAs) increasingly assume roles as hardware accelerators which significantly speed up computations in a wide range of streaming applications. For instance, specific streaming applications related to audio or image processing also demand high performance, runtime dynamism and power efficiency. Such applications demand a low latency while presenting a large amount of parallelism, both well-known features offered by FPGAs nowadays. Although the flexibility offered by FPGAs allows to implement customized architectures with higher computational performance and better power efficiency than multi-core CPUs and GPUs respectively, the design of such architectures is a very time-consuming task. Moreover, heterogeneous FPGA-based platforms and devices can only be fully exploited when modelling and analysing architectures combining the best of each technology.
The aim of this thesis is the acceleration of streaming applications by overcoming the challenges that the available FPGA-based systems present when mapping high-performance demanding streaming applications. On the one hand, performance analysis and techniques are proposed to exploit customized architectures for acoustic streaming applications demanding a real-time computation of the incoming signals from dense microphone arrays.
The proposed design-space exploration of reconfigurable architectures, including a complete analysis of the different trade-offs in terms of performance, power and frequency response, leads to designs providing the dynamic response, the high performance or the power efficiency demanded by highly constrained applications such as acoustic beamforming. On the other hand, heterogeneous FPGA-based systems performance models such as the roofline model are adapted for FPGAs to guide the design methodology to reach the highest performance. High-Level Synthesis tools are used not only as a complement of our roofline model but also for performance prediction. These models are applied to accelerate simple convolutional image filters and a more complex image algorithm for pedestrian detection.
... Step #1: For this step, zero-phase bandpass frequency filtering using Cascaded Integrator-Comb (CIC) filter is implemented [21]. The filter performs forward-backward filtering successively. ...
... Step #1: For this step, zero-phase bandpass frequency filtering using Cascaded Integrator-Comb (CIC) filter is implemented [21]. The filter performs forward-backward filtering successively. ...
The purpose of this document is to help individuals use the "Essential Motor Cortex Signal Processing MATLAB Toolbox". The toolbox implements various methods for three major aspects of investigating human motor cortex from Neuroscience view point: (1) ERP estimation and quantification, (2) Cortical Functional Connectivity analysis and (3) EMG quantification. The toolbox-which is distributed under the terms of the GNU GENERAL PUBLIC LICENSE as a set of MATLAB R routines-can be downloaded directly at the address: http://oset.ir/category.php?dir=Tools. or from the public repository on GitHub, at address below: https://github.com/EsiSeraj/ERP Connectivity EMG Analysis. The purpose of this toolbox is threefold: 1. Extract the event-related-potential (ERP) from preprocessed cerebral signals (i.e. EEG, MEG, etc.), identify and then quantify the event-related synchronization/desynchronization (ERS/ERD) events. Both time-course dynamics and time-frequency (TF) analyzes are included. 2. Measure, quantify and demonstrate the cortical functional connectivity (CFC) across scalp electrodes. These set of functions can also be applied to various types of cerebral signals (i.e. electric and magnetic). 3. Quantify electromyogram (EMG) recorded from active muscles during performing motor tasks.
