A Computationally Efficient Fano-Based Sequential Detection Algorithm for V-BLAST Systems

We discuss recent advances towards matter-wave interference experiments with free beams of metallic and dielectric nanoparticles. They require a brilliant source, an efficient detection scheme and a coherent method to divide the de Broglie waves associated with these clusters: We describe an approach based on a magnetron sputtering source which ejects an intense cluster beam with a wide mass dispersion but a small velocity spread of Δv/v < 10%. The source is universal as it can be used with all conducting and many semiconducting or even insulating materials. Here we focus on metals and dielectrics with a low work function of the bulk and thus a low cluster ionization energy. This allows us to realize photoionization gratings as coherent matter-wave beam splitters and also to realize an efficient ionization detection scheme. These new methods are now combined in an upgraded Talbot-Lau interferometer with three 266 nm depletion gratings. We here describe the experimental boundary conditions and how to realize them in the lab. This next generation of near-field interferometers shall allow us to soon push the limits of matter-wave interference to masses up to 10⁶ amu.

The decoding order has a deep impact in the complexity of sphere detection. A new ordering scheme for sphere detection is presented in this paper, which is based on SIC (serial interference canceling) and the gradient defined by the accumulated probability of the absolute difference between symbol element and the zero-forcing solution. The simulation results show that the proposed ordering scheme can achieve a significant complexity reduction, especially for high numbers of antennas and large constellation sizes in the low SNR region. Compared with sphere detection complexity under BSQR (balanced sorted QR) decomposition and GB (gradient-based) orderings atSNR=5 dB, the average number of visited nodes under our proposed ordering is reduced by almost 30% and 33% in4×416QAM system and by almost 30% and 50% reduction in6×616QAM system, respectively. For4×464QAM system, almost 75% and 80% reduction atSNR=0 dBand more than 40% and 50% reduction atSNR=5 dBcan be achieved, respectively.

In this paper, we propose a sequential type-based detection scheme for wireless sensor networks in the case of spatially and temporally identically and independently distributed observations. First, we investigate the optimal sequential detection rule of the proposed scheme, and then with the motivation of reducing the computational complexity of the optimal detection rule, we consider an approximation scheme and derive a suboptimal detection rule. We also compare the performances of the type-based sequential detection scheme with those of the non-sequential type-based detection scheme in terms of both average number of observations and total energy consumption, and determine the region of individual node power where the proposed scheme outperforms the non-sequential scheme. In addition, we show that the approximated detection rule provides the similar results as the optimal detection rule with a significant reduction of the computational complexity, which makes the approximated detection rule useful for real-time applications.

A sequential two-dimensional (2D) partial response maximum likelihood (PRML) detection scheme for holographic data storage (HDS) systems is proposed. We use two complexity reduction schemes, a reduced-state trellis and a constant-weight (CW) constraint. In the reduced-state trellis, the limited candidate bits surrounding the target bit are considered for the 2D PRML detector. In the CW constraint, the trellis transitions that violate the CW condition that each code-word block has only one white bit are eliminated. However, the 2D PRML detector using the complexity reduction schemes, which operates on 47 states and 169 branches, has performance degradation. To overcome performance degradation, a sequential detection algorithm uses the estimated a priori probability. By the sequential procedure, we mitigate 2D intersymbol interference with an enhanced reliability of the branch metric. Simulation results show that the proposed 2D PRML detection scheme yields about 3 dB gains over the one-dimensional PRML detection scheme.

Branch Retinal Artery Occlusion (BRAO) has become a serious disorder causing permanent loss of vision. It is mainly caused due to the rupture of blood vessels which is too small in size and thereby increasing the risk for efficient detection. Therefore an efficient detection scheme is necessary to evaluate appropriately and pave the way for systemic therapy to preserve or recover vision in the affected eye. In this paper, a fully automatic detection scheme has been developed for accurate segmentation followed by classification of BRAO volumes using the fundus images. Coming to the point this is the first automatic classification framework using an optimized neural network for classifying BRAO. It's mainly a three-step process where the first step concentrates on removing noise and thereby mproving the quality of fundus image. According to which an boosted anisotropic diffusionbased enhancement filter is used here, which effectively removes the noise. This is justified using image quality metrics. Second step here involves adaptive cluster with super pixel segmentation which clearly segments the affected area with high contrast. For the segmented image IR Analyzer is used for extracting region properties. In the final step gray wolf optimization-based convolution neural network (GWO-CNN) classifier is used. Here GWO-CNN classifier is used to differentiate the normal and abnormal images. The effectiveness of the above method is evaluated and obtained an accuracy of 98.57% when it is implemented in MATLAB 2018 software.

In implementations of distributed detection based on underwater wireless sensor networks (UWSN), a few technique challenges are faced. Among them is the strict limited communication ability of local sensors due to constrained bandwidth as well as energy, and the severe synchronization error among sensors which makes data fusion in the fusion center (FC) difficult. To solve the above problems to some extent, a sequential distributed detection (SDD) scheme based on level-triggered sampling (LTS) mechanism is proposed. In the LTS-SDD scheme, sequential test method is applied in both sensors and the FC, then the distributed detection system works with asynchronous sampling among sensors and low-speed communication between sensors and the FC. Though potential, the proposed scheme faced some unaddressed challenges when used in practical underwater distributed detection system, which are also discussed in the paper. To validate the suggested scheme, distributed detection of weak single frequency signal via UWSN is studied under some assumptions. Corresponding simulation results show that the LTS-SDD scheme works well and its error performance as well as average decision delay performance is superior to sequential detection (SD) scheme with single sensor.

This paper proposes a MIMO-OFDM scheme reducing the computational complexity and providing comparable performance to QRD-M detection scheme in IEEE 802.11n. The proposed scheme checks the channel condition per subcarrier and then selects either ordered DFE scheme or adaptive QRD-M detection scheme according to that information. The post-detection SNR which judges channel condition depends on Moore-Penrose pseudo-inverse matrix G. The proposed scheme selects the ordered DFE detection scheme in case of good channel condition and the adaptive QRD-M detection scheme in case of bad channel condition per subcarrier. The adaptive QRDM detection scheme selects the number of variable survival symbols M in accordance with the channel condition at each layer. Compared with the conventional QRD-M (M=16) detection scheme, the proposed detection scheme reduces the computational complexity about 67 percents and achieves similar performance.

Recommender systems are widely used in electronic commerce, social media and online streaming services to provide personalized recommendations to the users by exploiting past ratings and interactions. This paper considers the security aspect with quick and accurate detection of attacks by observing the newly created profiles sequentially to prevent the damage which may be incurred by the injection of new profiles with dishonest ratings. The proposed framework consists of a latent variable model, which is trained by a variational EM algorithm, followed by a sequential detection algorithm. The latent variable model generates homogeneous representations of the users given their rating history and mixed data-type attributes such as age and gender. The representations are then exploited to generate univariate statistics to be efficiently used in a CUSUM-like sequential detection algorithm that can quickly detect persistent attacks while maintaining low false alarm rates. We apply our proposed framework to three different real-world datasets and exhibit superior performance in comparison to the existing baseline algorithms for both attack profile and sequential detection. Furthermore, we demonstrate robustness to different attack strategies and configurations.

A linear detection scheme such as minimum mean squared error (MMSE) algorithm is considered to be an effective means for a large scale antenna system with hundreds of antennas. Even with a linear detection scheme, we suffer from the computational complexity problem especially in computing matrix inversion processes with dimensions greater than a hundred. If this MMSE detection scheme is combined with soft iterative detection, the complexity is further increased. In this paper, we propose an efficient complexity reduction method for MMSE based soft iterative detection scheme with parallel interference cancelation, by utilizing the characteristics of the filtering matrix. The simulation results demonstrate that the detection algorithm with the proposed approach achieves approximating performance to the conventional full complexity MMSE method.

An energy efficient distributed Change Detection scheme based on Page's CUSUM algorithm was presented in [2]. In this paper we consider a nonparametric version of this algorithm. In the algorithm in [2], each sensor runs CUSUM and transmits only when the CUSUM is above some threshold. The transmissions from the sensors are fused at the physical layer. The channel is modeled as a Multiple Access Channel (MAC) corrupted with noise. The fusion center performs another CUSUM to detect the change. In this paper, we generalize the algorithm to also include nonparametric CUSUM and provide a unified analysis.

An efficient frequency detection scheme for GMSK signal reception is described. In this detection scheme, a three-level eye opening of the frequency detector output at the decision instant offset by a half-bit is used to improve bit error rate (BER) performance. Experimental results for 16 kbit/s differentially encoded GMSK transmission with a premodulation filter bandwidth-bit duration product (Bb T) = 0–25 (typical for mobile radio applications) show that the half-bit offset decision frequency detection scheme can significantly improve BER performance, in comparison with conventional detection schemes.

The false data injection attacks (FDIA) pose significant threats to the security of the power distribution systems, underscoring the critical need for efficient FDIA detection and localization schemes. In this article, a joint FDIA detection and localization scheme based on an adversarial anomaly transformer is proposed, and the experimental analysis is performed using a developed cyber-physical power distribution simulation system testbed. To address the prevalent overgeneralization issue in reconstruction-based unsupervised methods, an anomaly attention mechanism is first introduced into the FDIA detection and localization scheme to amplify the distinguishability between normal and attacked data. Subsequently, a generative adversarial network framework is used to capture the distribution of data accurately. In addition, the drift streaming peaks over threshold dynamic threshold method is used to adapt to fluctuant measurements and enhance online detection applications. Finally, simulations and experiments are conducted on the developed cyber-physical testbed to demonstrate the superiority of proposed scheme.

Recently, the turbo-detected sphere packing (SP) aided space-time block-coding (STBC) STBC-SP scheme was demonstrated to provide useful performance improvements over conventionally-modulated orthogonal design-based STBC schemes in the context of single-user multiple-input multiple-output (SU-MIMO) systems. Hence, in this letter, we extend its employment to the multiuser MIMO (MU-MIMO) scenario. For the sake of achieving a near maximum a posteriori (MAP) performance while imposing a moderate complexity, we specifically design the K -best sphere detection (SD) scheme for supporting the operation of the SP-modulated system, since the conventional SD cannot be directly applied to such a system. Consequently, with the aid of our modified SD, a significant performance gain can be achieved by the SP-modulated system over its conventionally-modulated counterpart in the context of MU-MIMO systems.

An energy-efficient event detection and dissemination (EEDD) scheme for event-driven wireless sensor networks applications is introduced. Based on clustering-based architecture, EEDD includes sensor and aggregation management, sleep and wakeup scheduling, and a customized multi-channel MAC/routing protocol with power control. Bit map based MAC approach is adapted to use not only in intra-cluster communication, but also in inter-cluster communication. Lifetime is prolonged by integrated design of role-based sleep and medium access, while urgent events can be transmitted immediately.

This work addresses the problem of robust distributed estimation in the presence of sensor faults when the fusion center sequentially receives quantized messages from local sensors. The mean square error (MSE) of distributed estimation schemes increases dramatically if the information received from the faulty sensors within the network is not excluded from the estimation process. Accordingly, an efficient collaborative sensor-fault detection (CSFD) scheme is proposed in which the results of a homogeneity test are used to identify the faulty nodes within the network such that their quantized messages can be filtered out when estimating the parameter of interest. Utilizing an asymptotic analytical technique, a lower bound is derived for the MSE of the proposed distributed estimation scheme. A good agreement is observed between the simulated MSE results and the lower bound values, and thus it is inferred that the lower bound provides a convenient and reliable means of predicting the performance of the proposed estimation scheme in real-world sensor networks. In addition, a low-complexity CSFD (LC-CSFD) scheme is proposed to identify faulty sensors in WSNs with a very large number of nodes. The simulation results confirm that the accuracy of the estimates obtained from the CSFD and LCCSFD schemes is significantly better than that obtained from a conventional estimation scheme when applied in sensor networks characterized by an unknown number of sensor faults of various types.