Binary Hypothesis Testing Research Articles

Spread spectrum for covert communication in ultra-violet communication system.

In this paper, we propose a covert communication scheme based on a spread spectrum for ultra-violet communication. We first characterize the system model of the communication system, where Warden is adopted to monitor the legitimated user transmission based on the binary hypothesis test and Kolmogorov-Smirnov test. To avoid periodic transmission of the pilot sequence which potentially degrades covertness, we propose a blind synchronization approach, such that the pilot sequence does not need to be transmitted. It is observed from system-level simulation that the spread spectrum-based approach can achieve both low detection bit error rate and covertness under weak light intensity. Moreover, we experimentally evaluate the detection performance and covertness of the proposed approach.

A novel physical layer authentication mechanism for static and mobile 3D underwater acoustic communication networks

In this paper, we present an innovative physical layer authentication approach for underwater acoustic communication networks. Our method leverages the transmitter nodes’ positions to establish a robust authentication mechanism. We consider two scenarios based on the mobility of the nodes. In the first scenario, underwater nodes (both legitimate and malicious) are static, while the second scenario assumes that the underwater nodes are mobile moving at a certain velocity. For both scenarios, we estimate position by analyzing the signals received at reference nodes that are strategically placed within a predetermined underwater area. Once the estimates are available, we propose binary hypothesis testing based on the estimated position to determine the legitimacy of the transmitter node. Furthermore, when the nodes are mobile, we perform velocity estimation at a certain time by taking the difference of the estimated coordinates, for which we also find the uncertainty in the estimation. We use a linear Kalman filter operation to track the legitimate node’s mobility. We provide closed-form expressions of the false alarm rate and missed detection rate resulting from binary hypothesis testing. We validate our proposed mechanism through simulation, demonstrating error behavior against link quality, malicious node location, and receiver operating characteristic (ROC) curves.

ADHD classification with cross-dataset feature selection for biomarker consistency detection

Objective. Attention deficit hyperactivity disorder (ADHD) is a prevalent neurodevelopmental disorder in children. While numerous intelligent methods are applied for its subjective diagnosis, they seldom consider the consistency problem of ADHD biomarkers. In practice, these data-driven approaches lead to varying learned features for ADHD classification across diverse ADHD datasets. This phenomenon significantly undermines the reliability of identified biomarkers and hampers the interpretability of these methods. Approach. In this study, we propose a cross-dataset feature selection (FS) module using a grouped SVM-based recursive feature elimination approach (G-SVM-RFE) to enhance biomarker consistency across multiple datasets. Additionally, we employ connectome gradient data for ADHD classification. In details, we introduce the G-SVM-RFE method to effectively concentrate gradient components within a few brain regions, thereby increasing the likelihood of identifying these regions as ADHD biomarkers. The cross-dataset FS module is integrated into an existing binary hypothesis testing (BHT) framework. This module utilizes external datasets to identify global regions that yield stable biomarkers. Meanwhile, given a dataset which waits for implementing the classification task as local dataset, we learn its own specific regions to further improve the performance of accuracy on this dataset. Main results. By employing this module, our experiments achieve an average accuracy of 96.7% on diverse datasets. Importantly, the discriminative gradient components primarily originate from the global regions, providing evidence for the significance of these regions. We further identify regions with the high appearance frequencies as biomarkers, where all the used global regions and one local region are recognized. Significance. These biomarkers align with existing research on impaired brain regions in children with ADHD. Thus, our method demonstrates its validity by providing enhanced biological explanations derived from ADHD mechanisms.

COMIC: An unsupervised change detection method for heterogeneous remote sensing images based on copula mixtures and Cycle-Consistent Adversarial Networks

In this paper, we consider the problem of change detection (CD) with two heterogeneous remote sensing (RS) images. For this problem, an unsupervised change detection method has been proposed recently based on the image translation technique of Cycle-Consistent Adversarial Networks (CycleGANs), where one image is translated from its original modality to the modality of the other image so that the difference map can be obtained by performing arithmetical subtraction. However, the difference map derived from subtraction is susceptible to image translation errors, in which case the changed area and the unchanged area are less distinguishable. To overcome the above shortcoming, we propose a new unsupervised copula mixture and CycleGAN-based CD method (COMIC), which combines the advantages of copula mixtures on statistical modeling and the advantages of CycleGANs on data mining. In COMIC, the pre-event image is first translated from its original modality to the post-event image modality. After that, by constructing a copula mixture, the joint distribution of the features from the heterogeneous images can be learnt according to quantitive analysis of the dependence structure based on the translated image and the original pre-event image, which are of the same modality and contain totally the same objects. Then, we model the CD problem as a binary hypothesis testing problem and derive its test statistics based on the constructed copula mixture. Finally, the difference map can be obtained from the test statistics and the binary change map (BCM) is generated by K-means clustering. We perform experiments on real RS datasets, which demonstrate the superiority of COMIC over the state-of-the-art methods.

Generalized triple outcome decision-making in basket trials

ABSTRACT Making the go/no-go decision is critical in Phase II (or Ib) clinical trials. The conventional decision-making framework based on a binary hypothesis testing has been gradually replaced by the TODeM (Triple Outcome Decision-Making) which has three zones of outcomes: go, no-go, and consider. The TODeM provides more flexibility in decision-making with considering both of statistical significance and clinical relevance. However, Bayesian methods (e.g. EXNEX, MUCE, etc.) for the information borrowing are still based on the binary decision-making framework. We propose a new decision-making process G-TODeM (Generalized Triple Outcome Decision-Making) to apply those Bayesian methods with information borrowing across different cohorts to the TODeM framework. Essentially, the information borrowed from other cohorts can shrink the consider zone of the inference cohort.

Unveiling critical ADHD biomarkers in limbic system and cerebellum using a binary hypothesis testing approach.

Attention deficit hyperactivity disorder (ADHD) is a common childhood developmental disorder. In recent years, pattern recognition methods have been increasingly applied to neuroimaging studies of ADHD. However, these methods often suffer from limited accuracy and interpretability, impeding their contribution to the identification of ADHD-related biomarkers. To address these limitations, we applied the amplitude of low-frequency fluctuation (ALFF) results for the limbic system and cerebellar network as input data and conducted a binary hypothesis testing framework for ADHD biomarker detection. Our study on the ADHD-200 dataset at multiple sites resulted in an average classification accuracy of 93%, indicating strong discriminative power of the input brain regions between the ADHD and control groups. Moreover, our approach identified critical brain regions, including the thalamus, hippocampal gyrus, and cerebellum Crus 2, as biomarkers. Overall, this investigation uncovered potential ADHD biomarkers in the limbic system and cerebellar network through the use of ALFF realizing highly credible results, which can provide new insights for ADHD diagnosis and treatment.

Exploring Deep Learning for Adaptive Energy Detection Threshold Determination: A Multistage Approach

The concept of spectrum sensing has emerged as a fundamental solution to address the growing demand for accessing the limited resources of wireless communications networks. This paper introduces a straightforward yet efficient approach that incorporates multiple stages that are based on deep learning (DL) techniques to mitigate Radio Frequency (RF) impairments and estimate the transmitted signal using the time domain representation of received signal samples. The proposed method involves calculating the energies of the estimated transmitted signal samples and received signal samples and estimating the energy of the noise using these estimates. Subsequently, the received signal energy and the estimated noise energy, adjusted by a correction factor (k), are employed in binary hypothesis testing to determine the occupancy of the wireless channel under investigation. The proposed system demonstrates encouraging outcomes by effectively mitigating RF impairments, such as carrier frequency offset (CFO), phase offset, and additive white Gaussian noise (AWGN), to a considerable degree. As a result, it enables accurate estimation of the transmitted signal from the received signal, with 3.85% false alarm and 3.06% missed detection rates, underscoring the system’s capability to adaptively determine a decision threshold for energy detection.

A Covert Jamming Scheme Against an Intelligent Eavesdropper in Cooperative Cognitive Radio Networks

In this work, we design a covert jamming scheme against an intelligent Eve towards physical layer security in a cooperative cognitive radio networks. To protect the primary message from being decoded by an intelligent eavesdropper (Eve), a secondary user is picked as a friendly jammer that transmits artificial noise (AN) to the Eve. However, the intelligent Eve can detect the existence of AN, and thus the AN is inclined to be covertly transmitted at a lower detection probability, thereby mistaking the AN as background noise by the Eve. In this paper, firstly, by using a binary hypothesis testing, the detection error probability (DEP) is explicitly derived for detection performance analysis of the Eve in the perfect as well as statistical channel state information (CSI) scenario, separately. With the DEP, a covert jamming scheme is formulated to transmit the AN in both cases. Then, an optimization problem is formulated, and then recast into a two-part problem to maximize the secrecy performance with the covert jamming scheme in the perfect CSI case. Finally, the effective secrecy throughput is maximized under convert constraints in the statistical CSI case. Numerical results are provided to validate the DEP at the Eve and secrecy performance analysis.

Subtype classification of attention deficit hyperactivity disorder with hierarchical binary hypothesis testing framework

Objective. The diagnosis of attention deficit hyperactivity disorder (ADHD) subtypes is important for the refined treatment of ADHD children. Although automated diagnosis methods based on machine learning are performed with structural and functional magnetic resonance imaging (sMRI and fMRI) data which have full observation of brains, they are not satisfactory with the accuracy of less than for the ADHD subtype diagnosis. Approach. To improve the accuracy and obtain the biomarker of ADHD subtypes, we proposed a hierarchical binary hypothesis testing (H-BHT) framework by using brain functional connectivity (FC) as input bio-signals. The framework includes a two-stage procedure with a decision tree strategy and thus becomes suitable for the subtype classification. Also, typical FC is extracted in both two stages of identifying ADHD subtypes. That means the important FC is found out for the subtype recognition. Main results. We apply the proposed H-BHT framework to resting state fMRI datasets from ADHD-200 consortium. The results are achieved with the average accuracy and an average kappa score 0.947. Discriminative FC between ADHD subtypes is found by comparing the P-values of typical FC. Significance. The proposed framework not only is an effective structure for ADHD subtype classification, but also provides useful reference for multiclass classification of mental disease subtypes.

On the PMU Placement Optimization for the Detection of False Data Injection Attacks

The graph signal processing (GSP) approaches could facilitate the design of detectors for false data injection attacks (FDIAs) on smart grids. However, enhancing the performance of FDIA detection by optimizing the placement of phasor measurement units (PMUs) on smart grids remains an open problem. We formulate herein the detection of FDIAs as a binary hypothesis testing problem within the Grid-GSP framework and develop a subspace-based projection detection (SPD) algorithm, incorporating a quasi-optimal PMU placement strategy and two double thresholds. The proposed quasi-optimal PMU placement strategy enhances the performance of the SPD in FDIA detection. We further propose to enhance the SPD with the dynamic double threshold for high dynamic load profiles beyond a simple yet efficient static double threshold. Illustrative simulations based on the IEEE 118 bus test grid and the ERCOT dataset validate the superior performance of the proposed quasi-optimal PMU placement strategy along with the two double thresholds.

Sensing Travel Source–Sink Spatiotemporal Ranges Using Dockless Bicycle Trajectory via Density-Based Adaptive Clustering

The travel source–sink phenomenon is a typical urban traffic anomaly that reflects the imbalanced dissipation and aggregation of human mobility activities. It is useful for pertinently balancing urban facilities and optimizing urban structures to accurately sense the spatiotemporal ranges of travel source–sinks, such as for public transportation station optimization, sharing resource configurations, or stampede precautions among moving crowds. Unlike remote sensing using visual features, it is challenging to sense imbalanced and arbitrarily shaped source–sink areas using human mobility trajectories. This paper proposes a density-based adaptive clustering method to identify the spatiotemporal ranges of travel source–sink patterns. Firstly, a spatiotemporal field is utilized to construct a stable neighborhood of origin and destination points. Then, binary spatiotemporal statistical hypothesis tests are proposed to identify the source and sink core points. Finally, a density-based expansion strategy is employed to detect the spatial areas and temporal durations of sources and sinks. The experiments conducted using bicycle trajectory data in Shanghai show that the proposed method can accurately extract significantly imbalanced dissipation and aggregation events. The travel source–sink patterns detected by the proposed method have practical reference, meaning that they can provide useful insights into the redistribution of bike-sharing and station resources.

An Efficient Cross-Layer Authentication Scheme for Secure Communication in Vehicular Ad-Hoc Networks

Intelligent transportation systems contribute to improved traffic safety by facilitating real-time communication between vehicles and infrastructures. In this context, message authentication is crucial to safeguard vehicular ad-hoc networks (VANETs) from malicious attacks. The current state-of-the-art for authentication in VANETs relies on conventional cryptographic primitives, introducing significant computation and communication overheads. This paper presents a cross-layer authentication scheme for vehicular communication, incorporating the short-term reciprocal features of the wireless channel for re-authenticating the corresponding terminal, reducing the overall complexity and computation and communication overheads. The proposed scheme comprises four steps: S1. Upper-layer authentication is used to determine the legitimacy of the corresponding terminal at the first time slot; S2. Upon the verification result, a location-dependent shared key with a minimum number of mismatched bits is extracted between both terminals; S3. Using the extracted key and under binary hypothesis testing, a PHY challenge-response algorithm for multicarrier communication is proposed for re-authentication; S4. In the case of false detection, the key extraction step (S2) is re-executed after adapting the quantisation levels at different conditions of channel non-reciprocity based on the feedback from the re-authentication step (S3). Simulation results show the effectiveness of the proposed scheme even at small signal-to-noise ratios. In addition, the immunity of the proposed scheme is proved against active and passive attacks, including signatures' unforgeability against adaptive chosen message attacks in the random oracle model. Finally, a comprehensive comparison in terms of computation and communication overheads demonstrates the superiority of the proposed scheme over its best rivals.

Countering Active Attacks on RAFT-Based IoT Blockchain Networks

This paper considers an Internet of Things (IoT) blockchain wireless network consisting of a leader node and various follower nodes which together implement the RAFT consensus protocol to verify a blockchain transaction, as requested by a blockchain client. Further, two kinds of active attacks, i.e., jamming and impersonation, are considered on the IoT blockchain network due to the presence of multiple <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">active</i> malicious nodes in the close vicinity. When the IoT network is under a jamming attack, we utilize the stochastic geometry tool to derive the closed-form expressions for the coverage probabilities for both uplink and downlink IoT transmissions (which eventually translate to blockchain transaction success rate). On the other hand, when the IoT network is under an impersonation attack, we propose a novel method that enables a receive IoT node to exploit the pathloss of a transmit IoT node as its fingerprint to implement a binary hypothesis test for transmit node identification. To this end, we also provide the closed-form expressions for the probabilities of false alarm, missed detection, and miss-classification. Finally, we present detailed simulation results that indicate the following: i) the coverage probability (and hence blockchain transaction success rate) improves as the jammers’ locations move away from the IoT network, ii) the three error probabilities decrease (i.e., chances of corruption of the blockchain ledger data due to false data injection by malicious node decrease) as a function of the quality of the link between the transmit and receive IoT node.

Robust Target Localization for Multistatic Passive Radar Networks

Passive multistatic radar networks localize a non-cooperative target exploiting the bistatic measurements associated with signals emitted by several transmitters of opportunity and acquired by multiple receivers. However, in realistic scenarios, it might happen that one or more receivers are damaged and/or under malicious attacks with a consequent degradation of the system performance. This paper proposes a procedure to reveal sensors that return anomalous measurements due to an attack or a failure. Specifically, we first detect such measurements by solving a binary hypothesis test, then we cancel them and estimate the final delays as well as the target position. The performance of the overall architecture is assessed using both synthetic data and real-recorded data.

Sensitive enhancement of cat state quantum illumination.

Quantum illumination is a binary hypothesis testing to detect a possible low-reflective object. Theoretically, both cat state illumination and Gaussian-state illumination possess the upper bound of 3dB sensitivity gain, over the usual coherent state illumination, for the significantly low illuminating intensity. Here, we investigate further how to enhance the quantum advantage of quantum illumination by optimizing the illuminating cat states for larger illuminating intensity. By comparing the quantum Fisher information or error exponent, we show that the sensitivity of the quantum illumination with generic cat states proposed here can be optimized further, and the 10.3% sensitive enhancement over the previous cat state illumination can be obtained.

Optimal Information Usage in Binary Sequential Hypothesis Testing

An interesting question is whether an information theoretic interpretation can be given of optimal algorithms in sequential hypothesis testing. We prove that for the binary sequential probability ratio test of a continuous observation process, the mutual information between the observation process up to the decision time and the actual hypothesis conditioned on the decision variable is equal to zero. This result can be interpreted as an optimal usage of the information on the hypothesis available in the observations by the sequential probability ratio test. As a consequence, the mutual information between the random decision time of the sequential probability ratio test and the actual hypothesis conditioned on the decision variable is also equal to zero.

ERP Detection Based on Smoothness Priors.

Detection of event-related potentials (ERPs) in electroencephalography (EEG) is of great interest in the study of brain responses to various stimuli. This is challenging due to the low signal-to-noise ratio of these deflections. To address this problem, a new scheme to detect the ERPs based on smoothness priors is proposed. The problem is considered as a binary hypothesis test and solved using a smooth version of the generalized likelihood ratio test (SGLRT). First, we estimate the parameters of probability density functions from the training data under the Gaussian assumption. Then, these parameters are treated as known values and the unknown ERPs are estimated under the smoothness constraint. The performance of the proposed SGLRT is assessed for ERP detection in post-stimuli EEG recordings of two oddball settings. We compared our method with several powerful methods regarding ERP detection. The presented method performs better than the competing algorithms and improves the classification accuracy. SGLRT can be employed as a powerful means for different ERP detection schemes. The proposed scheme is opening a new direction in ERP identification which provides better classification results compared to several popular ERP detection methods.

ADHD classification combining biomarker detection with attention auto-encoding neural network

Attention Deficit Hyperactivity Disorder (ADHD) is one of most prevalent neurodevelopmental disorders in children. In decades, various neurobiological diagnosis methods have been well developed, yielding ADHD classification accuracy significantly improved. To our knowledge, deep-learning-based approaches attract more interest nowadays, since they effectively learn the potential features indicating ADHD disease. However, these learned features cannot directly project to certain biomarkers that have neurobiological meanings, which greatly hinders the neurobiological findings on ADHD. To ameliorate this problem, we proposed an attention auto-encoding network (Att-AENet) under an existing binary hypothesis testing (BHT) framework. By using brain functional connectivities (FCs) as input materials, an attention mechanism was employed to calculate the weights of FCs and measure the FC significance on ADHD. Therefore, the FC biomarkers of ADHD were conveniently identified from these weights. In detail, we built two attention subnetworks with dense and two-dimension convolution structures, respectively, and then evaluated the effect of these subnetworks from the viewpoint of ADHD classification. Sequentially, we took the biomarker detection analysis on the proposed dense-based attention subnetwork for its higher classification accuracy. On the test of ADHD-200 database, it shows the Att-AENet achieves a remarkable classification result with the average accuracy of 98.9%, especially 99.9% on some large-size datasets. More importantly, the FC biomarkers, corresponding to the discriminative weights learned by our attention subnetwork, are in accord with existing reported results. It demonstrates the reliability of these biomarkers. As a result, the proposed Att-AENet well fulfills the task of ADHD biomarker detection.

Minimum probability of error of list M-ary hypothesis testing

Abstract We study a variation of Bayesian $M$-ary hypothesis testing in which the test outputs a list of $L$ candidates out of the $M$ possible upon processing the observation. We study the minimum error probability of list hypothesis testing, where an error is defined as the event where the true hypothesis is not in the list output by the test. We derive two exact expressions of the minimum probability or error. The first is expressed as the error probability of a certain non-Bayesian binary hypothesis test and is reminiscent of the meta-converse bound by Polyanskiy, Poor and Verdú (2010). The second, is expressed as the tail probability of the likelihood ratio between the two distributions involved in the aforementioned non-Bayesian binary hypothesis test. Hypothesis testing, error probability, information theory.

Distributed Hypothesis Testing over a Noisy Channel: Error-Exponents Trade-Off.

A two-terminal distributed binary hypothesis testing problem over a noisy channel is studied. The two terminals, called the observer and the decision maker, each has access to n independent and identically distributed samples, denoted by U and V, respectively. The observer communicates to the decision maker over a discrete memoryless channel, and the decision maker performs a binary hypothesis test on the joint probability distribution of (U,V) based on V and the noisy information received from the observer. The trade-off between the exponents of the type I and type II error probabilities is investigated. Two inner bounds are obtained, one using a separation-based scheme that involves type-based compression and unequal error-protection channel coding, and the other using a joint scheme that incorporates type-based hybrid coding. The separation-based scheme is shown to recover the inner bound obtained by Han and Kobayashi for the special case of a rate-limited noiseless channel, and also the one obtained by the authors previously for a corner point of the trade-off. Finally, we show via an example that the joint scheme achieves a strictly tighter bound than the separation-based scheme for some points of the error-exponents trade-off.