Backscatter communications, an emerging low-cost and low-consumption green communication paradigm, has received significant attention in both industry and academia. In recent years, multiple-input multiple-output (MIMO) technology has also been integrated into backscatter communications to meet the requirements of transmission performance in IoT scenarios. However, due to the hardware limitations of backscatter tags, one of the main challenges for multi-antenna backscatter tags is to reduce the circuit complexity of space-time block codes (STBC) while maintaining transmission performance. Although low-complexity STBC for dual-antenna tags has been explored, to the best of our knowledge, research on high-performance, low-complexity STBC for four-antenna tags remains in its infancy. In this paper, we propose a 2×4 extended Alamouti code (EAC) with full-rate capability for MIMO backscatter communications. The results show that the required impedance of the proposed EAC on the backscatter tag is less than half of that of the conventional orthogonal STBC (OSTBC), which considerably reduces the complexity of the tag circuit. Additionally, we also derive the asymptotic closed form expression of the symbol error rate (SER) of the proposed EAC to obtain the system insights. The derivation results show that the achievable diversity order of the proposed EAC in 1×4×N backscatter channel is 2×min(2,N), which indicates that the proposed EAC can achieve the same diversity performance as the conventional OSTBC by setting an appropriate number of receiver antennas. Finally, numerical simulations are performed to validate the accuracy of our analysis and the superiority of the proposed EAC in SER.

Adverse weather conditions, low-light environments, and bumpy road surfaces pose significant challenges to SLAM in robotic navigation and autonomous driving. Existing datasets in this field predominantly rely on single sensors or combinations of LiDAR, cameras, and IMUs. However, 4D millimeter-wave radar demonstrates robustness in adverse weather, infrared cameras excel in capturing details under low-light conditions, and depth images provide richer spatial information. Multi-sensor fusion methods also show potential for better adaptation to bumpy roads. Despite some SLAM studies incorporating these sensors and conditions, there remains a lack of comprehensive datasets addressing low-light environments and bumpy road conditions, or featuring a sufficiently diverse range of sensor data. In this study, we introduce a multi-sensor dataset covering challenging scenarios such as snowy weather, rainy weather, nighttime conditions, speed bumps, and rough terrains. The dataset includes rarely utilized sensors for extreme conditions, such as 4D millimeter-wave radar, infrared cameras, and depth cameras, alongside 3D LiDAR, RGB cameras, GPS, and IMU. It supports both autonomous driving and ground robot applications and provides reliable GPS/INS ground truth data, covering structured and semi-structured terrains. We evaluated various SLAM algorithms using this dataset, including RGB images, infrared images, depth images, LiDAR, and 4D millimeter-wave radar. The dataset spans a total of 18.5 km, 69 minutes, and approximately 660 GB, offering a valuable resource for advancing SLAM research under complex and extreme conditions. Our dataset is available athttps://gongweisheng.github.io/DIDLM.github.io/

In the rapidly advancing realm of wireless communication, device-to-device (D2D) technology, an emerging approach for data exchange and connectivity, has been attracting increasing attention. Unmanned Aerial Vehicles (UAVs) can act as air relays or base stations, and integrate isolated D2D clusters into a cohesive network fabric in outdoor environments. However, in complex terrain, the communication signals are subject to irregular attenuation, and the signal propagation attenuation of different frequency bands in the same terrain is inconsistent. It is challenging to utilize UAVs to coverage D2D terrestrial users in complex terrain. In this paper, we propose the UAVs relaying for bridging the terrestrial D2D networks assisted by multi-frequency radio maps. From the real-world topographical data, we generate multi-frequency radio maps, which represent the distortion of different frequency band signals by rich information about land layouts. Next, we focus on the air-to-ground D2D network topology and formulate it into an optimization problem. Then, we decompose it into two subproblems. The first subproblem pertains to the design of the ground network structure. We employ the D2D frequency band radio map to assess the communication quality between user pairs, and propose a measure of D2D closeness centrality to select ‘cellular users’ that can communicate directly to a UAV. The second subproblem involves the UAVs’ deployment and the frequency selection. We present a multi-frequency radio map improved k-means method, which has lower algorithm complexity than the traversal method by reducing the utilization of the radio maps. Simulations validate the proposed scheme, demonstrating that: 1. Multi-frequency radio maps can provide efficient gains with real-world complex topography; 2. The proposed network structure and algorithm outperform other existing approaches.

Backscatter communications is receiving increasing attention in Internet of Things (IoT), while one major challenge for backscatter communications is that the circuit complexity for high performance tag is also high. In this article, for dual-antenna backscatter tag, we propose a zero-padding space-time block code (ZPSTBC) that can simultaneously improve the tag performance and reduce the complexity of the tag circuit. An interesting thing is that padding zero into space-time block code (STBC) is not beneficial in conventional multi-input-multi-output (MIMO) channels, but may lead to performance improvement in MIMO backscatter channels, from perspectives of error rate and energy harvesting, and also reduce the circuit complexity of the tag. This is due to characteristic of the MIMO structures and tag circuit of backscatter communications. We provide rigorous mathematical analysis and numerical simulations to illustrate the performance improvement and the tag complexity reduction caused by the proposed ZPSTBC.

Millimeter wave (mmWave)-based uncrewed aerial vehicle (UAV) communication is a promising candidate for future communications. However, hovering UAVs are susceptible to inevitable position fluctuations, while mmWave are highly sensitive to obstacles. Both factors contribute to variations in the system’s quality of service (QoS). Existing studies addressing either mmWave blockages or UAV fluctuations fail to capture their combined effects on QoS. This paper presents a tractable analytical model that establishes a theoretical relationship between UAV fluctuations and mmWave blockages (static, dynamic, and self-blockages). Closed-form expressions for reliable service probability and coverage probability are derived, providing insights into the impact of these combined factors on QoS. Monte Carlo simulations validate the theoretical analysis, showing that small fluctuations (e.g., less than 0.1 m in the studied scenario) have minimal impact on QoS, while larger fluctuations significantly degrade QoS, with various blockages further exacerbating this degradation. While the results may seem intuitive, the derived formulas reveal non-linearities and subtle dependencies, such as varying QoS sensitivity across different ranges of UAV fluctuations and mmWave blockages. Additionally, the analysis identifies feasible UAV placements to enhance the QoS.

Acting as dynamic base stations, unmanned aerial vehicles have a significant advantage over conventional base stations for dynamic object localization and tracking. In practice, however, the localization and tracking performance highly depends on the observation sequence of time-variant received signal strength (RSS) on UAVs from the dynamic object, which can be severely distorted by complex land layouts. In this paper, we propose dynamic object tracking by UAV with radio map, which contains abundant time-variant RSS knowledge. We generate time-varying radio maps from complex real-world topographic data. Then, we derive the grid-based method for dynamic object position estimation with the non-memoryless observations from the dynamic object. Numerical results show that the proposed algorithm can considerably improve tracking precision and efficiency for real-world topographical data.

This paper investigates the coverage performance of millimeter wave (mmWave) unmanned aerial vehicle (UAV) networks. Due to the sensitivity of mmWave to both static and moving obstacles, we aim to improve generality and gain more in-depth insights by simultaneously considering the effects of static and dynamic blockages of mmWave. Hence, we derive a tractable expression for the coverage probability of a target user at an arbitrary location rather than the center of a region as commonly assumed. Analytical result is then used to evaluate the impact of various system parameters on establishing reliable mmWave UAV communications in terms of achieving maximum coverage probability. Simulation results indicate that mmWave blockage, especially the dynamic blockage caused by moving blockers and the position of the target user significantly impact the coverage performance.

Backscatter communication, a passive transmission technique, is widely employed in Internet of Things (IoT). For localization purpose, Ultra-Wideband (UWB) technology offers large bandwidth and high time domain resolution, making it ideal for various positioning applications in IoT. In this letter, we show that the time indices of the backscatter UWB pulses are topologically correlated in multipath propagation environments. Based on this, we propose a novel TOA estimation method which can dramatically improve the estimation accuracy in practical SNR regimes. To our best knowledge, this is the first time that a TOA estimator for backscatter UWB is proposed by taking the advantage of the topological structure of the time indices of the pulses.

Millimeter wave (mmWave)-based unmanned aerial vehicle (UAV) communication is susceptible to blockages, even from humans. Previous studies that primarily focused only on static blockage may not accurately characterize the system performance. This paper investigates the coverage performance of mmWave UAV networks by jointly considering multiple types of blockages under finite homogeneous Poisson point process and Binomial point process, which are commonly employed in finite area scenarios with random and fixed number of UAVs, respectively. Particularly, we derive the average line-of-sight probability and coverage probability under static, dynamic, and self blockages. Simulations verify our theoretical results, demonstrating that: the above system performance predominantly depends on self-blockage if UAVs are at high altitudes. Conversely, at relatively low altitudes, all three types of blockages impact them, with static blockage being the dominant factor. To avoid self-blockage, UAV height should satisfy$h\!\gt \!h_{R}\!+\!\frac {r_{i}}{\tan \varphi _{b}}$, where$h_{R}$is the height of the user equipment (UE),$r_{i}$is the two-dimensional distance of the UAV-UE link,$\varphi _{b}$is the elevation angle between UE and UAV. The required height is proportional to$r_{i}$and increases as distance d between the user and UE decreases, as$\varphi _{b}$is proportional to d. The findings help on designing the network parameters. To our best knowledge, this is the first work to analyze the coverage of mmWave UAV networks under multiple types of blockages.

As aerial mobile receivers, unmanned aerial vehicles (UAVs) possess the capability to localize and track dynamically moving objects that broadcast signals. In practice, the performance of localization and tracking is heavily reliant on the observation sequence of time-variant received signal strength (RSS) from the dynamic object on UAVs. However, this sequence can be significantly distorted by complex land layouts. In this paper, we propose dynamic object tracking by muti-UAV with time-variant radio maps, which contain rich information on the impact of land layouts on RSS. We generate the time-variant radio maps from real-world topographic data, which can be highly complex, from the multi-UAV perspectives. Then, a grid-based method and a particle filter based method, both tailored to the observed RSS from the dynamic object and the generated time-variant radio maps, are derived for the case where both the UAVs and the object are dynamic. Simulations validate the two proposed algorithms and show that time-variant radio maps can significantly improve tracking precision and efficiency compared to other benchmarks for both the mountainous area and the urban area with highly complex layouts. In addition, the particle filter based method can reduce the computational complexity via Monte Carlo sampling of the time-variant radio maps with little sacrifice in tracking precision.

In this paper, we aim to implement outdoor-to-indoor communication with the aid of an active reconfigurable intelligent surface (active-RIS), where the active-RIS allows the incoming signal from an outdoor base station (BS) to pass through the surface and be received by indoor users (UEs) after shifting phase and magnifying amplitude. Then, the problem of joint beamforming optimization of the BS and active-ITS is investigated to maximize the system capacity. A computationally efficient algorithm is developed to solve it with suboptimal solutions. Simulations indicate the superiority of the active-RIS and the effectiveness of the proposed algorithm.

Acting as the dynamic base stations, the unmanned aerial vehicles has a significant advantage over the conventional base station for target localization. When localizing a target, UAV can move toward the estimated target location after each estimation, making the received signals stronger after each iteration, and hence yield more localization precision for each iteration. In this paper, we propose target localization by UAV with radio maps as prior knowledge, which are generated from real topographic maps. We derived the minimum mean square error (MMSE) estimator for target localization by UAV with the radio map. Numerical results show that with radio map knowledge and the MMSE estimator, localization precision can be considerably improved.

When acting as dynamic receivers, unmanned aerial vehicles (UAVs) have several advantages over the conventional static base stations for received signal strength (RSS) based target searching. It is highly likely that the RSS is becoming stronger as the UAV is moving towards the estimated coordinate, and this leads to a higher estimation precision for each localization update. On the other hand, despite its low cost, low energy consumption and simple hardware requirement, RSS can be severely influenced by land layouts, therefore the assumption of a simple free space propagation model may lead to poor estimation precision and even failure, especially for an area where the topography is highly complex. In this paper, we propose multi-UAV target searching with the assistance of radio maps, which contain abundant information of the topography influence on RSS. We generate radio maps from real topographic data, and derive the minimum mean square error estimator for multi-UAV target searching with the radio map knowledge for memoryless observations. Simulations for areas with complex topography, show that the localization precision and searching efficiency can be considerably improved with the assistance of radio maps.

Outdoor-to-indoor communications in millimeter-wave (mmWave) cellular networks have been one challenging research problem due to the severe attenuation and the high penetration loss caused by propagation characteristics of mmWave signals. We propose a viable solution to implement the outdoor-to-indoor mmWave communication with the aid of an active intelligent transmitting surface (active-ITS), where the active-ITS allows the incoming signal from an outdoor base station (BS) to pass through the surface and be received by indoor users (UEs) after shifting its phase and magnifying its amplitude. Then, the problem of joint precoding of the BS and active-ITS is investigated to maximize the weighted sum-rate (WSR) of the system. An efficient block coordinate descent (BCD) based algorithm is developed to solve it with the suboptimal solutions in nearly closed-forms. In addition, to reduce the size and hardware cost of active-ITSs, we provide a block-amplifying architecture to partially remove the circuit components for power-amplifying, where multiple transmissive-type elements (TEs) in each block share the same power amplifier. Simulations indicate that active-ITS has the potential of achieving a given performance with much fewer TEs compared to the passive-ITS under the same total system power consumption, which makes it suitable for application to the space-limited and aesthetic-needed scenario, and the performance degradation caused by the block-amplifying architecture is negligible.

Energy self-sustainability is critically important for future Internet of Things (IoT) networks to support an ever-growing massive number of wireless devices with low maintenance cost and high spectrum/energy efficiency. Power-splitting (PS)-based simultaneous wireless information and power transfer (PS-SWIPT) is a promising solution to realize it. However, the performance of PS-SWIPT is severely influenced by the channel attenuation caused by the detrimental radio propagation environment. Intelligent reflecting surface (IRS) is an emerging technology that can reconfigure the incident signal with considerable array gain so as to improve the PS-SWIPT performance. Thus, in this article, we investigate the weighted sumrate (WSR) maximization problem of the IRS-assisted multi-input–multioutput (MIMO) PS-SWIPT IoT network with multiple low-power IoT PS-based devices (PSDs). The formulated problem is nonconvex and arduous to tackle due to the presence of the intricately coupled variables and the mutually exclusive constraints. To the best of our knowledge, the problem is not addressed yet and cannot be solved by employing the existing methods directly. To cope with the problem, we develop a joint optimization framework that decomposes the original problem into several subproblems that can be solved alternately. Simulation results confirm the effectiveness of IRS to improve the WSR of the PS-SWIPT energy self-sustainable IoT networks and demonstrate that the proposed algorithm outperforms benchmark methods considerably.

The communications quality of unmanned aerial vehicle (UAV) has been brought into focus increasingly, which can be severely affected by other users, as well as complex topography. In this letter, we propose a path planning algorithm for UAV anti-jamming communications with the joint assistance of two kinds of radio maps—signal power map and signal to interference plus noise ratio (SINR) map, which contain the abundant information of path loss caused by the topography and interference from jammers. We show how to generate these maps from real topographic data, and provide the optimal UAV path planning by adopting the power and the SNR maps to the heuristic and graphic based A* algorithm. Simulations with various settings for areas with complex topography in real world, show that the proposed algorithm can considerably improve the efficiency by minimizing the UAV flying distance for anti-jamming communications.

Backscatter communications have received increasingly attention in Internet of Things (IoT) due to its advantages of requiring no internal battery and almost zero maintenance. However, the drawback of backscatter communications is that, its channel fades deeper than conventional channel. To improve the performance, multiple-input multiple-output (MIMO) techniques have been investigated for backscatter communications. By considering duty cycle, a particular behavior of backscatter communications, as a new performance criterion, we propose a$2 \times 2$orthogonal space-time block code for MIMO backscatter communications. The new code is a rotation of the classical Alamouti code, and in MIMO backscatter channels, this simple rotation will incur an improved duty cycle for both linear and nonlinear energy harvester models, as well as a same, or even better symbol error rate performance. We provide both rigorous mathematical proofs and numerical simulations to show the above results. An interesting thing is that, this new code does not outperform Alamouti code in conventional MIMO channels, which indicates that the space-time code design criteria for conventional channels is not the optimal one for backscatter channels, and existing space-time codes should be reconsidered and/or redesigned to achieve a higher performance in MIMO backscatter communications.

In radio-frequency identification (RFID)-based sensors networks, each sensor is integrated with a tag and sensors may co-exist in an area of interest. Before sending data packets, the sensors send their IDs for channel reservations. However, ID collisions happen frequently at the reservation stage which leads to significant time delays, especially for massive and dense networks. In this article, by employing group theory, we show that for B = 2k, where k is a positive integer, the set (-1, +1)Band the Hadamard product 0 forms a group ((-1, +1)B, 01 that can be divided into (2B/B) disjoint subsets, each of which has B binary vectors that are mutually orthogonal. Based on this finding, we propose orthogonal coset identification (OCSID) and its generalization, query tree (QT)-OCSID, that can recover ID information from collisions, and thus considerably improve the efficiency at the reservation stage, particularly when the network is large and/or dense. In an ideal case, it can recover/decode B tags for each query. The fundamental difference between the proposed OCSID schemes and code-division multiple accessbased schemes is that, OCSID achieves orthogonal design for ID information recovery by exploiting the inherent orthogonal structure of the binary vector set, instead of spreading the spectrum. Hence, it requires a narrower frequency band, lower circuit complexity, and lower synchronization precision, which are much more preferred by hardware limited devices.

In this article, we propose a 2 × 2 space-time code (STC) for backscatter communications. The interesting thing is that although the proposed code performs much worse than the classical Alamouti code in conventional channels, it achieves almost the same performance as the Alamouti code in backscatter channels. More importantly, it has a considerably lower tag circuit complexity, which is more preferred in hardware-limited backscatter devices. This article indicates that the sets of “good STCs” for conventional channels and backscatter channels are not heavily overlapped, and there may exist many simpler STCs, which were previously ignored and not reported in conventional channels, have high performance, particularly, in backscatter channels.

Backscatter communications have two major antenna configurations: the bistatic configuration, in which the reader employs two different sets of antennas to transmit and receive, and the monostatic configuration, in which the reader employs one set of antennas for both transmitting and receiving. In this paper, we provide a comprehensive study on the MIMO techniques for the M × L monostatic channel. Particularly we study on the joint design of the query and the coding matrices. We show that the maximum achievable diversity order of the monostatic channel is the diversity order achieved by the block-lever unitary query and orthogonal space-time block code (BUTQ-OSTBC) design pair, which is ML 2 , exactly the half of the diversity order of the conventional MIMO channel. Then we show that uniform query, the simplest query approach, cannot achieve the maximum achievable diversity order in the monostatic channel. We generalize BUTQ-OSTBC to the general augmenting approach, and show that unitary matrix is optimal in terms of SER performance among all possible query matrices, when OSTBC is employed. The above results further indicate that in the backscatter channel, additional diversity can be obtained by varying the query signals over time slots within the channel coherent time, which is quite different from the results from the conventional MIMO channels. We verify our results by Monte Carlo simulations.

In this paper, we analyze the achievable rate for ambient backscatter communications under three different channels: the binary input and binary output (BIBO) channel, the binary input and signal output (BISO) channel, and the binary input and energy output (BIEO) channel. Instead of assuming Gaussian input distribution, the proposed study matches the practical ambient backscatter scenarios, where the input of the tag can only be binary. We derive the closed-form capacity expression as well as the capacity-achieving input distribution for the BIBO channel. To show the influence of the signal-to-noise ratio (SNR) on the capacity, a closed-form tight ceiling is also derived when SNR turns relatively large. For BISO and BIEO channel, we obtain the closed-form mutual information, while the semi-closed-form capacity value can be obtained via one dimensional searching. Simulations are provided to corroborate the theoretical studies. Interestingly, the simulations show that: (i) the detection threshold maximizing the capacity of BIBO channel is the same as the one from the maximum likelihood signal detection; (ii) the maximal of the mutual information of all channels is achieved almost by a uniform input distribution; and (iii) the mutual information of the BIEO channel is larger than that of the BIBO channel, but is smaller than that of the BISO channel.

Recent advances on remote sensing techniques allow easier access to imaging spectrometer data. Manually labeling and processing of such collected hyperspectral images (HSIs) with a vast quantities of samples and a large number of bands is labor and time consuming. To relieve these manual processes, machine learning based HSI processing methods have attracted increasing research attention. A major assumption in many machine learning problems is that the training and testing data are in the same feature space and follow the same distribution. However, this assumption doesn’t always hold true in many real world problems, especially in certain HSI processing problems with extremely insufficient or even without training samples. In this letter, we present a transfer learning framework to address this unsupervised challenge (i.e., without training samples in the target domain), by making the following three main contributions: 1) to the best of our knowledge, this is the first time for transfer learning framework to be used for the classification of totally unknown target HSI data with no training samples; 2) the characteristics of HSI are learned on dual spaces to exploit its structure knowledge to better label HSI samples; and 3) two specific new scenarios suitable for transfer learning are investigated. Experimental results on several real world HSIs support the superiority of the proposed work.

Tag anticollision is critical to the performance of many radio frequency identification systems in industrial applications. Tree-based schemes are popular arbitration algorithms for tag collision, as they are scalable and easy to implement. In these schemes, collided tags are recursively divided into two groups until each group contains only one tag. However, we have determined that previous pure tree-based schemes do not fully utilize the information that can be collected; they allow only the responding tags to be involved in the splitting process. In this paper, we propose a novel pure tree-based method that we named parallel splitting with retrieve (PSR), which allows both the nonresponding and unidentified tags to also be involved in the splitting process. PSR consists of two mechanisms: parallel splitting (PS) and retrieve. The PS mechanism can reduce the total number of tag collisions and the retrieve mechanism can reduce the extra idle slots caused by PS. In comparison with conventional pure tree-based schemes, the proposed PSR algorithm achieves higher efficiency without the need to estimate the number of tags; simulation results indicate an efficiency level of 0.41. The PSR algorithm also has similar performance to state-of-the-art algorithms that do not need to estimate the number of tags, but it is simpler to implement and is thus preferred for resource constrained systems.

Future backscatter RFID systems are required to be more reliable and data intensive for emerging fields such as Internet of Things (IoT). Motivated by this, orthogonal space-time block code (OSTBC), which is very successful in mobile communications for its low complexity and high performance, has already been investigated for MIMO backscatter RFID. Also a recently proposed scheme called unitary query was shown to be able to considerably improve the reliability of MIMO backscatter RFID by exploiting query diversity. Therefore, incorporating the classical OSTBC (at the tag end) with the unitary query (at the query end) seems promising. However, in this paper, we first show that simple direct employment of OSTBC together with unitary query incurs a linear decoding problem and eventually leads to severe performance degradation. As a redesign of the unitary query idea and the classical OSTBC specifically for MIMO backscatter RFID, we present a BUTQ-mOSTBC design pair idea by proposing the block-level unitary query (BUTQ) at the query end and the corresponding modified OSTBC (mOSTBC) at the tag end. The proposed BUTQ-mOSTBC can resolve the linear decoding problem, keep the simplicity and high performance properties of the classical OSTBC, and achieve the query diversity for the MIMO backscatter RFID.

A multiple-input multiple-output backscatter radio frequency identification (RFID) system consists of three operational ends: the query end (with$M$reader transmitting antennas), the tag end (with$L$tag antennas), and the receiving end (with$N$reader receiving antennas). Such an$M\times L\times N$setting in RFID can bring spatial diversity and has been studied with the use of space-time code (STC) at the tag end. Current research generally has ignored query signaling as a means to improve performance. Here we propose a novelunitary queryscheme, which creates time diversitywithin the channel coherent timeand can yield significant performance improvements. To overcome the difficulty of evaluating the performance when unitary query is employed at the query end and STC is employed at the tag end, we derive a new measure based on the ranks of certain carefully constructed matrices to show that unitary query has superior performance. Simulations show that unitary query can bring 5–10 dB gain in mid signal-to-noise ratio regimes. In addition, different from the conventional uniform query case, unitary query can also improve the performance of single-antenna tags significantly, enabling single-antenna tags with low complexity and small size to be used for high performance.

The channel of radio frequency identification (RFID) can be modeled as a cascaded channel consisting of a forward link and a backscatter link. The correlation between the forward and backscatter links strongly affects the bit-error-rate (BER) performance of RFID systems. In this paper, we analytically study how the link correlation affects the BER performance of a single-input, multiple-output (SIMO) RFID system. We de rive exact forms and asymptotic forms of the BER for SIMO RFID systems with maximal ratio combining (MRC) at the reader side. The asymptotic forms provides an insight into the relationship between the link correlation and the BER perfor mance. It is shown that the BER is an increasing function of the magnitude of link correlation in the high SNR regime and is proportional to (1 + ρ2)L/(1 - ρ2)L in the extremely high SNR regime for an L branch channel.

Electroencephalogram (EEG) recordings of brain waves have been shown to have unique pattern for each individual and thus have potential for biometric applications. In this paper, we propose an EEG feature extraction and hashing approach for person authentication. Multi-variate autoregressive (mAR) coefficients are extracted as features from multiple EEG channels and then hashed by using our recently proposed fast Johnson-Lindenstrauss transform (FJLT)-based hashing algorithm to obtain compact hash vectors. Based on the EEG hash vectors, a naive Bayes probabilistic model is employed for person authentication. Our EEG hashing approach presents a fundamental departure from existing methods in EEG-biometry study. The promising results suggest that hashing may open new research directions and applications in the emerging EEG-based biometry area.