Number Of Frequency Channels Research Articles

Processing of acoustically degraded emotional prosody in Alzheimer’s disease and primary progressive aphasia

AbstractBackgroundAccurately interpreting a speaker’s nonverbal emotional vocal signals (their prosody) is often essential for successful communication. In the noisy conditions of daily life, this presents the brain with a difficult computational challenge that may be vulnerable to neurodegenerative pathologies. However, processing of acoustically degraded prosody has not been studied in these diseases.MethodHere we addressed this issue in patients representing all major syndromes of primary progressive aphasia (N = 33) and Alzheimer’s disease (N = 18) versus healthy age‐matched controls (N = 24). As a model paradigm for the degraded ‘noisy’ speech signals of everyday life, we used noise‐vocoding: digital division of the speech signal into a variable number of frequency channels constituted from amplitude‐modulated white noise, such that fewer channels convey less spectrotemporal detail and reduce intelligibility. We assessed the impact of noise‐vocoding on recognition of three canonical prosodic emotions (anger, surprise, sadness) at three noise‐vocoding levels. Correlations were also conducted with perception of degraded emotional prosody and measures of social cognition.ResultCompared with health controls, all patient groups were impaired in the identification of ‘clear’ emotional prosody. This impairment in all groups was sustained when the emotional prosody stimuli were vocoded, with differences seen between 18 and six channels, and 12 and six channels. Cost analyses conducted showed a significantly larger cost of noise‐vocoding in lvPPA than in healthy controls. Accurate identification of degraded emotional prosody in patients was significantly correlated with measures of social cognition.ConclusionOur findings open a window on a dimension of real‐world emotional communication that has often been relatively overlooked in dementia, and suggest a novel candidate paradigm for investigating and quantifying this systematically.

The pairwise and cross-pairwise y-type polarised kinetic Sunyaev Zeldovich effect from transverse velocity of galaxy clusters

We develop a new theoretical framework for studying the pairwise and cross-pairwise polarised kinetic Sunyaev Zeldovich (pkSZ) effect arising from the transverse peculiar velocity of galaxy clusters. The pkSZ effect is second order in peculiar velocities and has a spectrum that can be decomposed into y-type and blackbody components, whereas the unpolarised linear kSZ effect has only the blackbody component. Thus, the detectability of the pkSZ effect depends only on the sensitivity and the number of frequency channels of the survey and not on the other primary and secondary CMB anisotropies. We consider pairing of clusters with other clusters as well as cross-pairing of clusters with galaxies from spectroscopic galaxy surveys. The pairwise pkSZ signal is a function of intra-pair spatial separation. We develop and compare estimators of the pairwise pkSZ effect and study the detectability of the pairwise signal with cluster catalogs consisting of a few hundred thousand clusters expected from surveys such as eROSITA and CMB-S4. We find that cross-pairing clusters with galaxies from a large overlapping spectroscopic survey having a few billion galaxies will enable us to detect the pairwise pkSZ effect with CMB-S4. The pairwise pkSZ effect will thus open up a new window into the large-scale structure of the Universe in the coming decades.

Processing of acoustically degraded emotional prosody in primary progressive aphasia and Alzheimer’s disease

AbstractBackgroundAccurately interpreting a speaker’s nonverbal emotional vocal signals (their prosody) is often essential for successful communication. In the noisy conditions of daily life, this presents the brain with a difficult computational challenge that may be vulnerable to neurodegenerative pathologies. However, processing of acoustically degraded prosody has not been studied in these diseases.MethodHere we addressed this issue in patients representing all major syndromes of primary progressive aphasia (N = 33) and Alzheimer’s disease (N = 18) versus healthy age‐matched controls (N = 24). As a model paradigm for the degraded ‘noisy’ speech signals of everyday life, we used noise‐vocoding: digital division of the speech signal into a variable number of frequency channels constituted from amplitude‐modulated white noise, such that fewer channels convey less spectrotemporal detail and reduce intelligibility. We assessed the impact of noise‐vocoding on recognition of three canonical prosodic emotions (anger, surprise, sadness) at three noise‐vocoding levels.ResultCompared with healthy controls, all patient groups were impaired recognising prosodic emotions (particularly anger) in clear speech, while patients with Alzheimer’s disease showed a disproportionate cost for recognition of the noise‐vocoded emotion of sadness.ConclusionOur findings open a window on a dimension of emotional communication that has been relatively overlooked in dementia, and suggest a novel candidate paradigm for investigating and quantifying this systematically.

Processing of acoustically degraded speech in primary progressive aphasia and Alzheimer’s disease

AbstractBackgroundIn daily life, spoken messages are often “degraded” by competing sounds and vocal idiosyncrasies. Comprehension of degraded speech demands intense computations across distributed neural networks; it is likely to present a particular challenge for patients with neurodegenerative pathologies. However, this issue has not been studied systematically.MethodHere, we studied the processing of degraded speech signals in a cohort of patients representing all major variant syndromes of primary progressive aphasia (N = 32), in relation to patients with typical Alzheimer’s disease (N = 19) and healthy age‐matched controls (N = 25). As a model paradigm for the degraded ‘noisy’ speech signals of daily life, we used noise‐vocoding: digital division of the speech signal into a variable number of frequency channels constituted from amplitude‐modulated white noise, such that fewer channels convey less spectrotemporal detail and reduce intelligibility. We assessed the impact of noise‐vocoding on the recognition of spoken three‐digit numbers (e.g., ‘five‐hundred‐and‐eighty‐seven’) and used psychometric modelling to ascertain the threshold number of noise‐vocoding ‘channels’ required for intelligibility by each participant.ResultCompared with healthy controls, all patient groups showed normal comprehension of natural speech; for vocoded speech, patients with semantic variant primary progressive aphasia performed normally whereas other patient groups had a raised noise‐vocoding threshold and this was particularly marked in logopenic aphasia.ConclusionOur findings suggest that degraded speech is a promising paradigm for probing real‐world communication difficulties in people with dementia, and for stratifying syndromes and pathologies. Future work should assess the potential of this paradigm for developing new biomarkers and interventions to assess and improve communication function in these diseases.

Air-to-Ground Cellular Communications for Airplane Maintenance Data Offloading

Airplane sensors and on-board equipment collect an increasingly large amount of maintenance data during flights that are used for airplane maintenance. We propose to download part of the data during airplane’s descent via a cellular base station (BS) located at the airport. We formulate and solve an offline optimization problem to quantify how much data can be offloaded in a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">non-dedicated band</i> while ensuring that the interference power at the terrestrial BSs in the vicinity of the airport remains below a maximum allowable threshold. Our problem allows for adaptive tuning of transmit power, number of frequency channels to be used, and beamforming according to the position of the plane on the descent path. Our results show that, when the BS at the airport and the plane are equipped with uniform planar arrays, during the last 5 minutes of descent, in the microwave band the plane can offload up to 5GB of maintenance data in a 20 MHz band with a transmit power of 1 W or 40 W. In the mmWave band, the plane can offload up to 24 times more data in a 1 GHz band, with a transmit power of 40 W (using most of the bandwidth) and below 4 times with a transmit power of 1 W (effectively using a maximum of 202 MHz due to bandwidth tuning). Beamforming, power and bandwidth tuning are all crucial in maintaining a good performance in the mmWave band while in the microwave band, dynamic tuning of bandwidth does not improve the performance noticeably.

A Real-time, Pipelined Incoherent Dedispersion Method and Implementation in FPGA

In pulsar observation, dispersion occurs due to the interstellar medium. The dispersion significantly affects the detection of pulsar signals. To overcome the dispersion effect, incoherent dedispersion methods are often applied. The tranditional inchoherent dedispersion methods are computationally expensive and troublesome. To deal with this problem, in this paper, we developed a Real-Time, Pipelined Incoherent Dedispersion Method (RT-PIDM). RT-PIMD only caches the summed-up time series, instead of all the frequency spectra, so the memory consumption is determined by the number of DM trails, whereas the traditional method’s memory consumption is determined by the number of frequency channels. In most of the situations, the number of frequency channels is several times more than that of DM trails, which means the memory consumption of traditional methods is more than that of RT-PIDM. With RT-PIDM, we designed a 1.2 GHz bandwidth prototype digital backend, and we finished pulsar observation with the 40 m radio telescope at Yunnan Observatory. The results demonstrate that the RT-PIDM can be implemented inside a single FPGA chip with less Block RAM, and the proposed RT-PIDM dedisperses the pulsar signal in real time and achieves the same result as compared to traditional incoherent dedispersion.

Diurnal Cycle of Passive Microwave Brightness Temperatures over Land at a Global Scale

Satellite-borne passive microwave radiometers provide brightness temperature (TB) measurements in a large spectral range which includes a number of frequency channels and generally two polarizations: horizontal and vertical. These TBs are widely used to retrieve several atmospheric and surface variables and parameters such as precipitation, soil moisture, water vapor, air temperature profile, and land surface emissivity. Since TBs are measured at different microwave frequencies with various instruments and at various incidence angles, spatial resolutions, and radiometric characteristics, a mere direct integration of them from different microwave sensors would not necessarily provide consistency. However, when appropriately harmonized, they can provide a complete dataset to estimate the diurnal cycle. This study first constructs the diurnal cycle of land TBs using the non-sun-synchronous Global Precipitation Measurement (GPM) Microwave Imager (GMI) observations by utilizing a cubic spline fit. The acquisition times of GMI vary from day to day and, therefore, the shape (amplitude and phase) of the diurnal cycle for each month is obtained by merging several days of measurements. This diurnal pattern is used as a point of reference when intercalibrated TBs from other passive microwave sensors with daily fixed acquisition times (e.g., Special Sensor Microwave Imager/Sounder, and Advanced Microwave Scanning Radiometer 2) are used to modify and tune the monthly diurnal cycle to daily diurnal cycle at a global scale. Since the GMI does not cover polar regions, the proposed method estimates a consistent diurnal cycle of land TBs at global scale. Results show that the shape and peak of the constructed TB diurnal cycle is approximately similar to the diurnal cycle of land surface temperature. The diurnal brightness temperature range for different land cover types has also been explored using the derived diurnal cycle of TBs. In general, a large diurnal TB range of more than 15 K has been observed for the grassland, shrubland, and tundra land cover types, whereas it is less than 5K over forests. Furthermore, seasonal variations in the diurnal TB range for different land cover types show a more consistent result over the Southern Hemisphere than over the Northern Hemisphere. The calibrated TB diurnal cycle may then be used to consistently estimate the diurnal cycle of land surface emissivity. Moreover, since changes in land surface emissivity are related to moisture change and freeze–thaw (FT) transitions in high-latitude regions, the results of this study enhance temporal detection of FT state, particularly during the transition times when multiple FT changes may occur within a day.

Trio-Connectivity for Efficient Uplink Performance in Future Mobile HetNets

The technical challenges, e.g. the mobility management and the offloading process, hinder the conventional cellular systems to meet the huge data traffic requirements of the next generation mobile communications. The traditional system (e.g dual-connectivity (DC)) has been proposed to improve the mobility management, however, it will inherit the big trade-off in the offloading process between the energy consumption for the small cell (SC) discovery (SCD) process and the efficiency of utilizing the system resources (e.g. frequency and signaling). In this paper, we present a framework to model the potential offloading opportunities as well as the offloading loss when a typical user equipment (UE) performs the inter-frequency (IRF) scan periodically. The proposed framework also studies the impact of the SCD on the energy efficiency. To improve the system performance and reduce the power consumption at the UEs, a new scheme, trio-connectivity (TC), is proposed in this paper to tackle the aforementioned challenges. The TC includes three planes: control-plane (C-plane), user-plane (U-plane) and indication-plane (I-plane). The I-plane works as an indicator to help the UE to identify and discover the SCs in the system prior to offloading. The role of the I-plane is to keep the SCD on one frequency channel regardless of the number of frequency channels in the system. In the proposed offloading mechanism, some of the energy consumption is transferred from the UE to the network. By using the proposed framework, UE energy efficiency and system energy efficiency as well as the total energy consumption are derived as performance metrics to compare between the TC and the DC. The results show that the TC can outperform the DC in dense cellular systems.

Downlink Coverage and Rate Analysis of Low Earth Orbit Satellite Constellations Using Stochastic Geometry

As low Earth orbit (LEO) satellite communication systems are gaining increasing popularity, new theoretical methodologies are required to investigate such networks' performance at large. This is because deterministic and location-based models that have previously been applied to analyze satellite systems are typically restricted to support simulations only. In this paper, we derive analytical expressions for the downlink coverage probability and average data rate of generic LEO networks, regardless of the actual satellites' locality and their service area geometry. Our solution stems from stochastic geometry, which abstracts the generic networks into uniform binomial point processes. Applying the proposed model, we then study the performance of the networks as a function of key constellation design parameters. Finally, to fit the theoretical modeling more precisely to real deterministic constellations, we introduce the effective number of satellites as a parameter to compensate for the practical uneven distribution of satellites on different latitudes. In addition to deriving exact network performance metrics, the study reveals several guidelines for selecting the design parameters for future massive LEO constellations, e.g., the number of frequency channels and altitude.

Cutting Through the Emissions: Feature Selection from Electromagnetic Side-Channel Data for Activity Detection

Electromagnetic side-channel analysis (EM-SCA) has been used as a window to eavesdrop on computing devices for information security purposes. It has recently been proposed to use as a digital evidence acquisition method in forensic investigation scenarios as well. The massive amount of data produced by EM signal acquisition devices makes it difficult to process in real-time making on-site EM-SCA infeasible. Uncertainty surrounds the precise information leaking frequency channel demanding the acquisition of signals over a wide bandwidth. As a consequence, investigators are left with a large number of potential frequency channels to be inspected; with many not containing any useful information leakages. The identification of a small subset of frequency channels that leak a sufficient amount of information can significantly boost the performance enabling real-time analysis. This work presents a systematic methodology to identify information leaking frequency channels from high dimensional EM data with the help of multiple filtering techniques and machine learning algorithms. The evaluations show that it is possible to narrow down the number of frequency channels from over 20,000 to less than a hundred (81 channels). The experiments presented show an accuracy of 0.9315 when all the 20,000 channels are used, an accuracy of 0.9395 with the highest 500 channels after calculating the variance between the average value of each class, and an accuracy of 0.9047 when the best 81 channels according to Recursive Feature Elimination are considered.

Human Frequency Following Responses to Vocoded Speech: Amplitude Modulation Versus Amplitude Plus Frequency Modulation.

The most commonly employed speech processing strategies in cochlear implants (CIs) only extract and encode amplitude modulation (AM) in a limited number of frequency channels. proposed a novel speech processing strategy that encodes both frequency modulation (FM) and AM to improve CI performance. Using behavioral tests, they reported better speech, speaker, and tone recognition with this novel strategy than with the AM-alone strategy. Here, we used the scalp-recorded human frequency following responses (FFRs) to examine the differences in the neural representation of vocoded speech sounds with AM alone and AM + FM as the spectral and temporal cues were varied. Specifically, we were interested in determining whether the addition of FM to AM improved the neural representation of envelope periodicity (FFRENV) and temporal fine structure (FFRTFS), as reflected in the temporal pattern of the phase-locked neural activity generating the FFR. FFRs were recorded from 13 normal-hearing, adult listeners in response to the original unprocessed stimulus (a synthetic diphthong /au/ with a 110-Hz fundamental frequency or F0 and a 250-msec duration) and the 2-, 4-, 8- and 16-channel sine vocoded versions of /au/ with AM alone and AM + FM. Temporal waveforms, autocorrelation analyses, fast Fourier Transform, and stimulus-response spectral correlations were used to analyze both the strength and fidelity of the neural representation of envelope periodicity (F0) and TFS (formant structure). The periodicity strength in the FFRENV decreased more for the AM stimuli than for the relatively resilient AM + FM stimuli as the number of channels was increased. Regardless of the number of channels, a clear spectral peak of FFRENV was consistently observed at the stimulus F0 for all the AM + FM stimuli but not for the AM stimuli. Neural representation as revealed by the spectral correlation of FFRTFS was better for the AM + FM stimuli when compared to the AM stimuli. Neural representation of the time-varying formant-related harmonics as revealed by the spectral correlation was also better for the AM + FM stimuli as compared to the AM stimuli. These results are consistent with previously reported behavioral results and suggest that the AM + FM processing strategy elicited brainstem neural activity that better preserved periodicity, temporal fine structure, and time-varying spectral information than the AM processing strategy. The relatively more robust neural representation of AM + FM stimuli observed here likely contributes to the superior performance on speech, speaker, and tone recognition with the AM + FM processing strategy. Taken together, these results suggest that neural information preserved in the FFR may be used to evaluate signal processing strategies considered for CIs.

METHOD OF SIGNAL PARAMETERS ESTIMATION OF RADIOSTATIONS WITH FAST FREQUENCY HOPPING SPREAD SPECTRUM

Modern special-purpose radios utilize ultra-short burst mode with frequency hopping spread spectrum. Such digital radios operate, as a rule, in the fast frequency hopping spread spectrum mode and use a considerable number of frequency channels with frequency separation between adjacent channels, which is significantly less than the spectrum width of the frequency element. The main modulation in such radios is frequency manipulation. The identification of signals with fast frequency hopping spread spectrum and frequency manipulation is performed by estimating the speed of frequency tuning, the speed of information transmission, the number of frequency channels and the step of the frequency grid and comparing the obtained values with the corresponding characteristics of known radio stations with fast frequency hopping spread spectrum. The time limits and the duration of the frequency elements are calculated by the envelope of the received signal. For this purpose, a suitable method has been developed, which consists in filtering the envelope by means of a moving average window for suppression of the noise component and threshold processing. The threshold value is selected by the given probability of its exceeding by noise sample. The proposed approach to determining the time limits of frequency elements does not require any a priori signal information. To determine the frequencies of the frequency elements periodogram estimates are used. An exponential extrapolation of the discrete power spectrum was used to increase the frequency resolution at a fixed window length of the fast Fourier transform. It is shown that in order to uniquely determine the step of the frequency grid and the diversity of frequency manipulation frequencies, it is necessary to analyze the number of frequency elements not less than the number of frequency channels. By analyzing the histogram of the differences of sorted frequency vector of the frequency elements, determine the frequency grid, frequency diversity and the multiplicity of frequency manipulation. An approximate estimate of the number of frequency channels is calculated as the ratio of the magnitude of the frequency vector of the frequency elements to the step of the frequency grid. As a rule, symbol transmission at frequency manipulation is carried out at orthogonal frequencies, so it can be roughly assumed that the symbolic speed is equal to the frequency diversity of the frequency manipulation. The baud rate can be set only after signal demodulation and bit stream analysis, which can determine the frame structure and the number of service and information bits. The proposed method provides the estimation of the values of the parameters necessary for the identification of radio stations of signal parameters with a relative error not exceeding 0.3%, with values of signal to noise ratio not lower than 5 dB.

Spectral and Temporal Envelope Cues for Human and Automatic Speech Recognition in Noise.

Acoustic features of speech include various spectral and temporal cues. It is known that temporal envelope plays a critical role for speech recognition by human listeners, while automated speech recognition (ASR) heavily relies on spectral analysis. This study compared sentence-recognition scores of humans and an ASR software, Dragon, when spectral and temporal-envelope cues were manipulated in background noise. Temporal fine structure of meaningful sentences was reduced by noise or tone vocoders. Three types of background noise were introduced: a white noise, a time-reversed multi-talker noise, and a fake-formant noise. Spectral information was manipulated by changing the number of frequency channels. With a 20-dB signal-to-noise ratio (SNR) and four vocoding channels, white noise had a stronger disruptive effect than the fake-formant noise. The same observation with 22 channels was made when SNR was lowered to 0 dB. In contrast, ASR was unable to function with four vocoding channels even with a 20-dB SNR. Its performance was least affected by white noise and most affected by the fake-formant noise. Increasing the number of channels, which improved the spectral resolution, generated non-monotonic behaviors for the ASR with white noise but not with colored noise. The ASR also showed highly improved performance with tone vocoders. It is possible that fake-formant noise affected the software's performance by disrupting spectral cues, whereas white noise affected performance by compromising speech segmentation. Overall, these results suggest that human listeners and ASR utilize different listening strategies in noise.

Simulating speech processing with cochlear implants: How does channel interaction affect learning in neural networks?

We introduce a novel machine learning approach for investigating speech processing with cochlear implants (CIs)—prostheses used to replace a damaged inner ear. Concretely, we use a simple perceptron and a deep convolutional network to classify speech spectrograms that are modified to approximate CI-delivered speech. Implant-delivered signals suffer from reduced spectral resolution, chiefly due to a small number of frequency channels and a phenomenon called channel interaction. The latter involves the spread of information from neighboring channels to similar populations of neurons and can be modeled by linearly combining adjacent channels. We find that early during training, this input modification degrades performance if the networks are first pre-trained on high-resolution speech—with a larger number of channels, and without added channel interaction. This suggests that the spectral degradation caused by channel interaction alters the signal to conflict with perceptual expectations acquired from high-resolution speech. We thus predict that a reduction of channel interaction will accelerate learning in CI users who are implanted after having adapted to high-resolution speech during normal hearing. (The code for replicating our experiments is available online: https://github.com/clips/SimulatingCochlearImplants).

The impact of noise power estimation on speech intelligibility in cochlear-implant speech coding strategies

The advanced combination encoder (ACE™) is an established speech-coding strategy in cochlear-implant processing that selects a number of frequency channels based on amplitudes. However, speech intelligibility outcomes with this strategy are limited in noisy conditions. To improve speech intelligibility, either noise-dominant channels can be attenuated prior to ACE™ with noise reduction or, alternatively, channels can be selected based on estimated signal-to-noise ratios. A noise power estimation stage is, therefore, required. This study investigated the impact of noise power estimation in noise-reduction and channel-selection strategies. Results imply that estimation with improved noise-tracking capabilities does not necessarily translate into increased speech intelligibility.

Efficient Analysis and Synthesis Using a New Factorization of the Gabor Frame Matrix

In this paper, we consider the case in which one needs to carry out Gabor analysis and synthesis on large signals using a short support analysis window and its corresponding, possibly longer canonical dual window, respectively. In this asymmetric context, we propose a novel factorization of the Gabor frame operator that exploits its strong and well-known structure and leads to a computational cost for synthesis, which is comparable to the one needed for short support analysis. The proposed factorization applies to any Gabor system with very mild conditions and leads to a potentially promising alternative to current synthesis algorithms in the case of short analysis windows whose support is slightly above the number of frequency channels.

Autonomous allocation of wireless body-sensor networks to frequency channels: modeling with repeated “balls-in-bins” experiments

In this paper we model a situation where several wireless body sensor networks (WBSN) compete for occupation of a number of frequency channels. Each channel can host at most one WBSN with satisfactory performance and WBSNs have the ability to change their operating channel, subject to the constraint that they can only monitor the performance or occupancy of their current channel but not of any other channel. We consider a number of randomized schemes for changing the frequency channels and present and evaluate Markov chain models for these, building on a “balls-in-bins” approach.

Model comparison for GLONASS RTK with low-cost receivers

GLONASS ambiguity resolution in differential real-time kinematic (RTK) processing is affected by inter-frequency phase biases (IFPBs). Previous studies empirically determined that IFPBs are linearly dependent on the frequency channel number and calibration values have been derived to mitigate these biases for geodetic receivers. The corresponding IFPB-constrained model is currently the de facto approach in RTK, but the growing market of GNSS receivers, and especially low-cost receivers, makes calibration and proper handling of metadata a complex endeavor. Since IFPBs originate from timing offsets occurring between the carrier phase and the code measurements, we confirm other studies that show that IFPBs are not exactly linearly dependent on the frequency channel number, but rather linearly dependent on the channel wavelength, which calls for a modification in the GLONASS functional model. As an alternative to calibration, we revisit a calibration-free method for GLONASS ambiguity resolution and provide new insights into its applicability. A practical experiment illustrates that the calibration-free approach can offer better ambiguity fixing performance when the uncertainty on the IFPB parameter is large, unless partial ambiguity resolution is performed.

Radio-frequency interference mitigating hyperspectral L-band radiometer

Abstract. Radio-frequency interference (RFI) can significantly contaminate the measured radiometric signal of current spaceborne L-band passive microwave radiometers. These spaceborne radiometers operate within the protected passive remote sensing and radio-astronomy frequency allocation of 1400–1427 MHz but nonetheless are still subjected to frequent RFI intrusions. We present a unique surface-based and airborne hyperspectral 385 channel, dual polarization, L-band Fourier transform, RFI-detecting radiometer designed with a frequency range from 1400 through ≈ 1550 MHz. The extended frequency range was intended to increase the likelihood of detecting adjacent RFI-free channels to increase the signal, and therefore the thermal resolution, of the radiometer instrument. The external instrument calibration uses three targets (sky, ambient, and warm), and validation from independent stability measurements shows a mean absolute error (MAE) of 1.0 K for ambient and warm targets and 1.5 K for sky. A simple but effective RFI removal method which exploits the large number of frequency channels is also described. This method separates the desired thermal emission from RFI intrusions and was evaluated with synthetic microwave spectra generated using a Monte Carlo approach and validated with surface-based and airborne experimental measurements.

Realistic interference-free channel assignment for dynamic wireless mesh networks using beamforming

To make the most efficient use of scarce bandwidth, channel assignment methods for wireless mesh networks (WMNs) should try to minimize the number of frequency channels used while achieving maximum network throughput. Beamforming is a well-known technique that improves spatial reuse in wireless networks. However, there are no channel assignment methods for WMNs that use beamforming to reduce the number of frequency channels. We develop the first channel assignment method for dynamic WMNs that incorporates beamforming in the conflict graph and matrix. This reduces co-channel interference significantly, thereby reducing the number of frequency channels required (NCR) to ensure interference-free communication among the mesh nodes while achieving maximum network throughput. Our novel Linear Array Beamforming-based Channel Assignment (LAB-CA) method significantly increases the spectrum utilization efficiency of WMNs at the expense of increased hardware complexity. It outperforms classical omni-directional antenna pattern-based channel assignment (OAP-CA) in terms of NCR. In a heterogeneous WMN where mesh nodes have differing numbers of radio interfaces, LAB-CA also outperforms OAP-CA in terms of NCR in both sparse and dense scenarios. A further significant reduction in NCR is achieved when the number of antennas in the linear antenna arrays of mesh nodes is increased.