Energy-based Detector Research Articles

Low-Power Energy-Based Spike Detector ASIC for Implantable Multichannel BMIs

Advances in microtechnology have enabled an exponential increase in the number of neurons that can be simultaneously recorded. To meet high-channel count and implantability demands, emerging applications require new methods for local real-time processing to reduce the data to transmit. Nonlinear energy operators are widely used to distinguish neural spikes from background noise featuring a good tradeoff between hardware resources and accuracy. However, they require an additional smoothing filter, which affects both area occupation and power dissipation. In this paper, we investigate a spike detector, based on a series of two nonlinear energy operators, and a simple and adaptive threshold, based on a three-point median operator. We show that our proposal provides good accuracy compared to other energy-based detectors on a synthetic dataset at different noise levels. Based on the proposed technique, a 1024-channel neural signal processor was designed in a 28 nm TSMC CMOS process by using latch-based static random-access memory (SRAM), demonstrating a total power consumption of 1.4 μW/ch and a silicon area occupation of 230 μm2/ch. These features, together with a comparison with the state of the art, demonstrate that our proposal constitutes an alternative for the development of next-generation multichannel neural interfaces.

Detect Pilot Spoofing Attack for Intelligent Reflecting Surface Assisted Systems

Pilot spoofing attack (PSA) is a kind of active eavesdropping, which launches the identical training sequence to manipulate the channel estimation outcome during the pilot training phase. The newly proposed intelligent reflecting surface (IRS) system is vulnerable to the PSA because it relies on the pilot-assisted channel estimation methods to obtain channel statement information (CSI). To combat PSA in IRS-assisted systems, we consider two significant challenges: 1) channel distribution information (CDI) is uncertain, which will make the PSA detection methods based on the statistic feature (SF) invalid; 2) the noise is uncertain when detecting PSA in practical scenarios, which would impair the popular energy-based detectors. In this paper, we design a three-step training (TST) scheme. The effective detector could achieve reliable detection performance by examining the transmitter's received signal power levels when PSA occurs. The proposed method does not require the prior knowledge of noise variance and could obtain the CSI of both legitimate and illegitimate dyadic backscatter channels. Theoretical analysis and numerical simulations are presented further to demonstrate the efficiency of our proposed TST scheme.

Gaussianity Testing as an Effective Technique for Detecting Discontinuous Transmission in 5G Networks

The paper investigates statistical distribution testing-based detection methods in an intermittent signal detection scenario. The relevance of the research is driven by 5G networks based on packet transmission, incorporating the concept of cognitive radio and adapting spectrum detection methods from Long Term Evolution (LTE) Licensed-Assisted Access (LAA). The conducted study refers to the recently proposed methods based on testing goodness-of-fit (GoF) of statistical distributions, which are compared with a conventional energy detector. The authors examine the applicability of well-known GoF methods in intermittent transmission, as they require reconsideration in 5G communication systems, and investigate the behavior of the innovative energy-based GoF. The experiments are carried out for different transmitter activity factors, i.e., channel occupancy and signal-to-noise ratio (SNR), demonstrating the superiority of the GoF-based methods in general and particularly the invented GoF test over other energy-based detectors for discontinuous signals detection.

Ecoacoustics: acoustic sensing for biodiversity monitoring at scale

Ecoacoustics: acoustic sensing for biodiversity monitoring at scale

A New Enhanced Energy-Detector-Based FM-DCSK UWB System for Tactile Internet

Frequency-modulated differential chaos shift keying ultrawideband (FM-DCSK UWB) system has attracted more and more attention in recent years because of its low-complexity and low-power advantages. Nevertheless, its system performance is severely degraded in the presence of narrowband interference (NBI), which further limits its practical applications. In this paper, an enhanced energy-based detector (EED) is proposed for the FM-DCSK UWB system to tackle this problem. Compared with the conventional energy detector (ED), the proposed EED significantly enhances the anti-NBI capability while maintaining the low-power and low-complexity feature. As a further insight, the analytical bit-error-rate (BER) expression of the proposed EED-based FM-DCSK UWB system is derived over additive white Gaussian noise as well as IEEE 802.15.4a multipath fading channels. Moreover, simulation results are carried out to demonstrate the accuracy of the theoretical analysis and the merit of the proposed EED. It is illustrated that the proposed EED achieves better performance than the conventional ED in various transmission scenarios. Thanks to the above-mentioned advantages, the proposed EED-based FM-DCSK UWB system appears to be an excellent candidate for low-power and low-complexity tactile Internet-of-Things applications, e.g., wireless sensor networks and wireless body area networks.

Adaptive Technique with Cross Correlation for Lowering Signal-to-Noise Ratio Wall in Sensor Networks

Energy detection is considered as most popular spectrum sensing technique. However, the impact of noise uncertainty in wireless environment limits the sensitivity of energy-based detector. It generally suffers from Signal-to-Noise Ratio (SNR) wall, which is defined as the minimum SNR in which robust detection is not possible. This paper presents an adaptive technique to improve SNR wall in sensor networks that combines cross-correlation scheme and dynamic threshold method to improve performance. An analytical expression of SNR wall is derived to show improved performance over traditional energy detector. Theoretical analyses and simulations validate effectiveness of the proposed method under noise uncertainty environment.

Performance Analysis of Energy-Based RFI Detector

As radio frequency interference (RFI) affects many systems operating radio frequencies, RFI detection is essential for excising such RFI efficiently. For this reason, here we investigate an energy-based RFI detector for wireless communication systems suffering from RFI. For this detector, its average probability of RFI detection is studied and approximated, and asymptotic closed-form expressions are derived. Besides, an exact closed-form expression for its average probability of false alarm is derived. Monte-Carlo simulations validate the derived analytical expressions and corroborate that the investigated energy detector (ED) outperforms a kurtosis detector (KD)—even under the scenario that KD intercepts the received signal for a longer interval—and a generalized likelihood ratio test detector (GLRT). At last, the performance of ED is also assessed using real-world RFI contaminated data.

Likelihood-Based Automatic Modulation Classification in OFDM With Index Modulation

In orthogonal frequency division multiplexing (OFDM) with index modulation, the modulation parameters to be classified include both the signal constellation and the number of active subcarriers. This is different from conventional OFDM schemes where only the signal constellation needs to be classified. In this paper, to solve this challenging problem, the likelihood-based automatic modulation classification (AMC) is studied. First, in the scenario with known channel state information (CSI), two classifiers based on average likelihood ratio test (ALRT) and hybrid likelihood ratio test (HLRT), respectively, are derived. Concretely, in HLRT-based classifier, energy-based detector, and log-likelihood ratio-based detector are employed to identify the active subcarriers. Second, a HLRT-based blind AMC is proposed in the scenario with unknown CSI, where the efficient implementation of expectation maximization algorithm is presented to estimate channel fading coefficients and noise variance. Finally, the effectiveness of the proposed AMC algorithms is confirmed by computer simulations.

IR-UWB Sensor Network Using Massive MIMO Decision Fusion: Design and Performance Analysis

This paper proposes and analyzes impulse radio ultra-wideband (IR-UWB) system for wireless sensor networks (WSNs) using massive antenna arrays (MAAs) at fusion center (FC) for distributed detection. The coherent- and energy-based fusion rules are analyzed for the proposed WSN over multiple access channels. The tradeoff between the performance and the implementation complexity of the coherent- and energy-based fusion is studied. Furthermore, we show that MAAs at FC with various levels of channel knowledge can enhance the performance of energy-based detector in a UWB sensor network with simple system implementation and signal processing. Performance of the proposed UWB sensor network with reference to probability of detection, false alarm, and error are analyzed over the standard IEEE 802.15.4a multipath channels and results are validated using simulations. We have also analyzed the impact of various design parameters, such as the number of sensors, receiver antennas, sensor quality, and integration interval, on the system performance.

Energy-based anomaly detection for mixed data

Anomalies are those deviating significantly from the norm. Thus, anomaly detection amounts to finding data points located far away from their neighbors, i.e., those lying in low-density regions. Classic anomaly detection methods are largely designed for single data type such as continuous or discrete. However, real-world data is increasingly heterogeneous, where a data point can have both discrete and continuous attributes. Mixed data poses multiple challenges including (a) capturing the inter-type correlation structures and (b) measuring deviation from the norm under multiple types. These challenges are exaggerated under (c) high-dimensional regimes. In this paper, we propose a new scalable unsupervised anomaly detection method for mixed data based on Mixed-variate Restricted Boltzmann Machine (Mv.RBM). The Mv.RBM is a principled probabilistic method that estimates density of mixed data. We propose to use free energy derived from Mv.RBM as anomaly score as it is identical to data negative log-density up to an additive constant. We then extend this method to detect anomalies across multiple levels of data abstraction, an effective approach to deal with high-dimensional settings. The extension is dubbed \(\mathtt {MIXMAD}\), which stands for MIXed data Multilevel Anomaly Detection. In \(\mathtt {MIXMAD}\), we sequentially construct an ensemble of mixed-data Deep Belief Nets (DBNs) with varying depths. Each DBN is an energy-based detector at a predefined abstraction level. Predictions across the ensemble are finally combined via a simple rank aggregation method. The proposed methods are evaluated on a comprehensive suit of synthetic and real high-dimensional datasets. The results demonstrate that for anomaly detection, (a) a proper handling of mixed types is necessary, (b) free energy is a powerful anomaly scoring method, (c) multilevel abstraction of data is important for high-dimensional data, and (d) empirically Mv.RBM and \(\mathtt {MIXMAD}\) are superior to popular unsupervised detection methods for both homogeneous and mixed data.

RF-Spectrum Opportunities for Cognitive Radio Networks Operating Over GSM Channels

In this paper, we characterize the radio frequency spectrum opportunities available in a common global system for mobile communications (GSM) channel to support the operation of a cognitive radio network (CRN). In a first step, we describe the technical details involved to sample the channel using a software defined radio device. Adopting a simple energy-based detector, we identify the two energy regions where the GSM system is active or inactive and evaluate the spectrum sensing accuracy. Based on the output of the detector, we show that the distribution of the durations of busy and idle periods are approximated by geometric distributions. Finally, we validate a theoretical model for the distribution of the service time. The validation results indicate that the service time can be successfully represented by a discrete generalized Pareto distribution, which is confirmed by the Kolmogorov–Smirnov test. Because the throughput of the CRN is represented by the inverse of the service time, the proposed analysis provides an upper bound for the networks’ throughput, indicating the maximum throughput that can be attained when a single secondary user transmits over a GSM cellular channel. The results presented in this paper are validated with real data, confirming the accuracy of the proposed service time model.

Soft Combination for Cooperative Spectrum Sensing in Fading Channels

In this paper, we study the distributed energy-based detectors for spectrum sensing in cognitive radio networks. We assume that the sensing channel includes both small-scale and large-scale fading. The small-scale fading is modeled as Nakagami- $m$ and independent for different cooperating cognitive users, while the large-scale fading is assumed to be known (or can be estimated) by the cognitive users, due to their slowly changing nature. Furthermore, we assume that the channel gains are constant in one observation interval and vary independently in different intervals. Based on the Bayesian rule, we derive the optimal energy combining rule, i.e., the average likelihood ratio (ALR) detector. We also suggest two solutions: 1) mixture of gamma (MoG)-based ALR detector and 2) generalized Gauss–Laguerre formula (GLF)-based ALR detector, to overcome the problem of the intractable integrals in the optimal rule, and we propose two novel suboptimal but practical combining rules: 1) GLF-based linear combining detector, which can be implemented by linear functions and a comparator with negligible performance degradation and 2) GLF-based weighted-energy detector applicable for the low SNR regime. The simulation results reveal that with MoG and GLF detectors, the ALR detector can be implemented almost precisely with lower complexity. Moreover, all the proposed detectors outperform the conventional ones, especially when large-scale channel gains differ for different cognitive users.

Joint classification of whistles and echolocation clicks from odontocetes

We propose to use acoustic features of both clicks and whistles to classify odontocete sounds to species. The species studied are Cuvier’s beaked whales (Ziphius cavirostris), bottlenose dolphin (Tursiops truncatus), melon-headed whale (Peponocephala electra), and short- and long-beaked common dolphin (Delphinus delphis and D. capensis). An energy-based detector is used for echolocation click detection, and Roch’s Silbido algorithm is used for whistle detection. Detected whistles are characterized by maximum and minimum frequencies, duration, slope, spectral maxima, spectral gaps, number and frequency of inflection points, number of “loop” repetitions, and other acoustic characteristics. Detected clicks are characterized by cepstral characteristics, as well as by a set of noise-resistant statistics. Clicks that occur within a certain time neighborhood of a whistle have the corresponding feature vectors merged to produce the input to the classification system. Random forest and Gaussian mixture model classifiers are tested on the resulting features and performance is characterized. [Funding from ONR.]

GLRT-Based Combining Schemes for PPM IR-UWB Systems with Multiple Receive Antennas

This paper deals with impulse-radio ultra wide-band systems with multiple receive antennas. For binary PPM modulations, we propose various combining/detection schemes derived by the generalized likelihood ratio criterion. These schemes first combine delayed versions of the waveforms received at the different antennas and then compute the energies of the resulting signal over intervals smaller than half the symbol period. The decision statistic is finally obtained by a linear combination of these energies. The proposed algorithms can be implemented using currently available technology. They outperform a detection scheme that simply combines the decision statistics obtained (at each antenna) by means of a conventional energy-based detector. Their advantage increases with the number of antennas.

Quantifying bat call detection performance of humans and machines

Methods for detecting echolocation calls in field recordings of bats vary in performance and influence the effective range of a recording system. In experiments using synthetic calls from five species, human detection accuracy was 89.7+/-0.6%, compared to 76.3+/-0.8% for a model-based detector, 72.2+/-0.8% for an energy-based detector, and 98.4+/-0.2% for an optimal linear detector. The energy-based detector was 11 times faster than the model-based detector and 110 times faster than humans. Human accuracy was positively correlated with test duration (R(2)=0.43, P<0.05), meaning that higher accuracy was achieved at the expense of slower performance. Species was a significant factor determining accuracy for all detectors (P<0.001) because of call bandwidth: Narrowband calls concentrated energy in a narrower frequency band and were easier to detect. For a hypothetical recording system, range at 90% human detection accuracy varied from 10 to 35 m among species, while range dropped by approximately 20% using the automated detectors. The optimal detector outperformed humans by 5 dB and the automated methods by 9 dB. The results quantify the tradeoff between detector speed and accuracy and are useful for designing field studies of bats.