Deterioration Of Detection Performance Research Articles

Ship Detection in Low-Quality SAR Images via an Unsupervised Domain Adaption Method

Ship detection in low-quality Synthetic Aperture Radar (SAR) images poses a persistent challenge. Noise signals in complex environments disrupt imaging conditions, hindering SAR systems from acquiring precise target information, thereby significantly compromising the performance of detectors. Some methods mitigate interference via denoising techniques, while others introduce noise using classical methods to learn target features in the presence of noise. This conundrum is regarded as a cross-domain problem in this paper; a ship detection method in low-quality images is proposed to learn features of targets and shrink serious deterioration of detection performance by utilizing Generative Adversarial Networks (GANs). First, an image-to-image translation task is implemented using CycleGAN to generate low-quality SAR images with complex interference from the source domain to the target domain. Second, with the annotation inheritance, these generated SAR images participate in a training process to improve the detection accuracy and model robustness. Multiple experiments indicate that the proposed method conspicuously improves the detection performance and efficaciously reduces the missed detection rate in the SAR ship detection task. This cross-domain approach achieved outstanding improvements in the form of 11.0% mAP and 3.22% mAP on the GaoFen-3 ship dataset and SRSSD-V1.0, respectively. In addition, the characteristics and potentials of near-shore and off-shore SAR image reconstruction with style transfer based on Generative Adversarial Networks were explored and analyzed in this work.

A classification method to detect faults in a rotating machinery based on kernelled support tensor machine and multilinear principal component analysis

Rotatingmachinery is the main component of mechanical equipment. Nevertheless, due to variation of operating condition results in important detection performance deterioration. Therefore, fault detection and diagnosis of rotating machines is very critical for the reliable operation. In this paper, a novel classification technique is employed for fault detection of rotating machines based on kernelled support tensor machine (KSTM) and multilinear principal component analysis (MPCA). The vibration signal is firstly formulated as a 3-way tensor using trial, condition and channel. In order to process the rotating machines faults and identify the information classes in tensor space, the KSTM is then introduced from sets of binary support tensor machine classifiers by the one-against-one parallel strategy. The MPCA is utilized for reduction dimensionality of the high-dimensional signature space and reservation the tensorial structure information. The performance of the developed technique in classification faults of rotating machinery has been thoroughly evaluated through collecting signals on bearing and gear test-rigs. Experimental results showed that the proposed method can achieve the highest classification results among the six classification techniques investigated in this study.

Cross-domain intelligent fault classification of bearings based on tensor-aligned invariant subspace learning and two-dimensional convolutional neural networks

Rolling element bearings faults are one of the main causes of breakdown of rotating machines. Aside from this, due to variation of operating condition, domain shift phenomenon results in important detection performance deterioration. Therefore, cross-domain intelligent fault detection and diagnosis of bearings is very critical for the reliable operation. In this paper, a new intelligent fault diagnosis approach based on tensor-aligned invariant subspace learning and two-dimensional convolutional neural networks (TAISL–2DCNN) is proposed for cross-domain intelligent fault diagnosis of bearings. The vibration signals of bearings fault are first formulated as a third-order tensor via trial, condition and channel. For adapting the source domain and the target domain tensor representations directly, without vectorization, the domain adaptation (DA) approach named TAISL is first proposed for tensor representation in bearing intelligent fault diagnosis field. Then the 2DCNN is utilized to recognize different faults. The performance of the presented algorithm has been thoroughly evaluated through extensive cross-domain fault diagnosis experiments. The verification results confirm that the developed approach is able to reliably and accurately identify different fault categories and severities of bearings when testing and training data are drawn from different distribution.

Thermoplastic Membranes Incorporating Semiconductive Metal–Organic Frameworks: An Advance on Flexible X‐ray Detectors

AbstractSemiconductive metal–organic frameworks (MOFs) have emerged in applications such as chemical sensors, electrocatalysts, energy storage materials, and electronic devices. However, examples of semiconductive MOFs within flexible electronics have not been reported. We present flexible X‐ray detectors prepared by thermoplastic dispersal of a semiconductive MOF (SCU‐13) through a commercially available polymer, poly(vinylidene fluoride). The flexible detectors exhibit efficient X‐ray‐to‐electric current conversion with enhanced charge‐carrier mobility and low trap density compared to pelleted devices. A high X‐ray detection sensitivity of 65.86 μCGyair−1 cm−2 was achieved, which outperforms other pelleted devices and commercial flexible X‐ray detectors. We demonstrate that the MOF‐based flexible detectors can be operated at multiple bending angles without a deterioration in detection performance. As a proof‐of‐concept, an X‐ray phase contrast under bending conditions was constructed using a 5×5 pixelated MOF‐based imager.

Thermoplastic Membranes Incorporating Semiconductive Metal-Organic Frameworks: An Advance on Flexible X-ray Detectors.

Semiconductive metal-organic frameworks (MOFs) have emerged in applications such as chemical sensors, electrocatalysts, energy storage materials, and electronic devices. However, examples of semiconductive MOFs within flexible electronics have not been reported. We present flexible X-ray detectors prepared by thermoplastic dispersal of a semiconductive MOF (SCU-13) through a commercially available polymer, poly(vinylidene fluoride). The flexible detectors exhibit efficient X-ray-to-electric current conversion with enhanced charge-carrier mobility and low trap density compared to pelleted devices. A high X-ray detection sensitivity of 65.86 μCGyair -1 cm-2 was achieved, which outperforms other pelleted devices and commercial flexible X-ray detectors. We demonstrate that the MOF-based flexible detectors can be operated at multiple bending angles without a deterioration in detection performance. As a proof-of-concept, an X-ray phase contrast under bending conditions was constructed using a 5×5 pixelated MOF-based imager.

Ultrasensitive and stable X-ray detection using zero-dimensional lead-free perovskites

Abstract Sensitive and reliable X-ray detectors are essential for medical radiography, industrial inspection and security screening. Lowering the radiation dose allows reduced health risks and increased frequency and fidelity of diagnostic technologies for earlier detection of disease and its recurrence. Three-dimensional (3D) organic–inorganic hybrid lead halide perovskites are promising for direct X-ray detection – they show improved sensitivity compared to conventional X-ray detectors. However, their high and unstable dark current, caused by ion migration and high dark carrier concentration in the 3D hybrid perovskites, limits their performance and long-term operation stability. Here we report ultrasensitive, stable X-ray detectors made using zero-dimensional (0D) methylammonium bismuth iodide perovskite (MA3Bi2I9) single crystals. The 0D crystal structure leads to a high activation energy (Ea) for ion migration (0.46 eV) and is also accompanied by a low dark carrier concentration (~ 106 cm−3). The X-ray detectors exhibit sensitivity of 10,620 µC Gyair−1 cm−2, a limit of detection (LoD) of 0.62 nGyair s−1, and stable operation even under high applied biases; no deterioration in detection performance was observed following sensing of an integrated X-ray irradiation dose of ~23,800 mGyair, equivalent to > 200,000 times the dose required for a single commercial X-ray chest radiograph. Regulating the ion migration channels and decreasing the dark carrier concentration in perovskites provide routes for stable and ultrasensitive X-ray detectors.

Learning and Analysis of Damping Factor in Massive MIMO Detection Using BP Algorithm With Node Selection

In a massive multiple-input multiple-output (MIMO) system, belief propagation (BP) detection is known as a method to separate and detect received signals. In BP detection, a MIMO channel is represented by a factor graph and the transmitted symbols are estimated by message passing. However, the convergence property of BP deteriorates due to multiple loops included in the MIMO channel. As a method to improve the convergence property and the detection performance, the damped BP that averages two successive messages with a weighing factor (called damping factor) is known. To train the damping factors off-line for each antenna configuration, deep neural network-based damped BP (DNN-dBP) has been reported. The problem with DNN-dBP is that the detection performance deteriorates when there is a difference of the channel correlation between training and test. This is because the optimal damping factors vary with the channel correlation. In this paper, to solve this issue, we derive the damping factors of BP with the node selection (NS) method that selects nodes to be updated to lower spatial correlation using DNN-dBP. By applying the NS method, the channel correlation among the selected nodes in BP detection is lowered. Therefore, the proposed method can improve the detection performance deterioration due to the mismatches of the channel correlations between training and test in DNN-dBP. In addition, the convergence property of BP is improved by applying the NS method. Therefore, the proposed method has the same detection performance with low computational complexity as the conventional DNN-dBP. By computer simulation, it is shown that the proposed method significantly improves the bit error rate (BER) performance deterioration due to the mismatches of the channel correlations between training and test in DNN-dBP. The results also show that the proposed method can show the same BER performance with low computational complexity as the conventional DNN-dBP. We also investigate the distribution of the trained damping factors and evaluate the tendency of that.

Adaptive Data-Driven Wideband Compressive Spectrum Sensing for Cognitive Radio Networks

This paper presents a novel adaptive wideband compressed spectrum sensing scheme for cognitive radio (CR) networks. Compared to the traditional CSSbased CR scenarios, the proposed approach reconstructs neither the received signal nor its spectrum during the compressed sensing procedure. On the contrary, a precise estimation of wide spectrum support is recovered with a fewer number of compressed measurements. Then, the spectrum occupancy is determined directly from the reconstructed support vector. To carry out this process, a data-driven methodology is utilized to obtain the minimum number of necessary samples required for support reconstruction, and a closed-form expression is obtained that optimally estimates the number of desired samples as a function of the sparsity level and number of channels. Following this phase, an adjustable sequential framework is developed where the first step predicts the optimal number of compressed measurements and the second step recovers the sparse support and makes sensing decision. Theoretical analysis and numerical simulations demonstrate the improvement achieved with the proposed algorithm to significantly reduce both sampling costs and average sensing time without any deterioration in detection performance. Furthermore, the remainder of the sensing time can be employed by secondary users for data transmission, thus leading to the enhancement of the total throughput of the CR network.

Detection and Parameter Estimation for Jitterbug Covert Channel Based on Coefficient of Variation

Jitterbug is a passive network covert timing channel supplying reliable stealthy transmission. It is also the basic manner of some improved covert timing channels designed for higher undetectability. The existing entropy-based detection scheme based on training sample binning may suffer from model mismatching, which results in detection performance deterioration. In this paper, a new detection method based on the feature of Jitterbug covert channel traffic is proposed. A fixed binning strategy without training samples is used to obtain bins distribution feature. Coefficient of variation (CV) is calculated for several sets of selected bins and the weighted mean is used to calculate the final CV value to distinguish Jitterbug from normal traffic. Furthermore, the timing window parameter of Jitterbug is estimated based on the detected traffic. Experimental results show that the proposed detection method can achieve high detection performance even with interference of network jitter, and the parameter estimation method can provide accurate values after accumulating plenty of detected samples.

Adaptive waveform design based on Morlet wavelet for ultra-wideband MIMO radar

For the issue of deterioration in detection performance caused by dynamically changing environment in ultra- wideband (UWB) multiple input multiple output (MIMO) radar, this paper proposes a novel adaptive waveform design which is aimed to improve the ability of discriminating target and clutter from the radar scene. Firstly, a sequence of Morlet wavelet pulses with frequency hopping and pulse position modulation by Welch-Costas array is designed. Then a waveform optimization solution is proposed which is achieved by applying the minimization mutual-information (MI) strategy. After that, with subsequent iterations of the algorithm, simulation results demonstrate that the optimal waveform design method brings an improvement in the target detection ability in the presence of noise and clutter.

Novel Compressed Sensing Algorithm Based on Modulation Classification and Symbol Rate Recognition

In the complex and changing radio environment, how to achieve the fast and accurate spectrum sensing over an ultra-wide bandwidth is a big challenge. A novel compressed sensing algorithm based on modulation classification and symbol rate recognition is proposed by using the minimal sampling rate to detect spectrum holes. It is more efficient than the Nyquist sampling rate and traditional compressed sampling rate, which requires the reconstruction of the original signal. Simulation results show that it can further decrease the compressed sampling rate depending on the relation between compression ratio with modulation scheme and symbol rate. Furthermore, in order to improve the accuracy of modulation classification and symbol rate recognition, a compressed sensing and wavelet transform (CS-WT) feature detector is proposed to perform wideband detection in low SNR condition. Simulation results show that CS-WT feature detector can effectively reduce the noise introduced by CS process. Given the false alarm of 0.05 and the detection probability of 0.9, the detection probability of proposed CS-WT feature detection algorithm can be improved 4 dB compared to traditional cyclostationary detection. Therefore, the overall sampling rate can be dramatically reduced without spectrum detection performance deterioration compared to the conventional static sampling algorithm.

P 64. Cartography of causal contributions of human frontal cortex to visual attention

It is widely known that the human frontal cortex is involved in the allocation of visual attention, however, the exact causal functional contributions of individual subregions are not well understood. In the present study, we used a simple visual localization task and applied rTMS pulses to map frontal cortical subregions likely to generate significant visuo-spatial biases during the spatial deployment of attention prior to perception. A group of nine adult healthy subjects executed a task based on the localization of small rectangular black dots. Targets were displayed unilaterally (left or right) or bilaterally (left and right) for a brief period (40 ms). In a systematic mapping approach, a stimulation grid of 9 (3 × 3) sites was anchored 2 cm rostral to the motor hand area. Three pulses of real or sham 10 Hz rTMS were delivered in a counterbalanced manner, at each of the grid locations 50 ms post target onset to interfere with the ongoing neural processing. As a main finding, significant deterioration of detection performance, as measured by effective reaction time, for stimuli in the contralateral hemifield and increased performance for ipsilateral targets were observed for two grid regions anatomically associated with the right FEF and right middle frontal gyrus. We conclude that the disruptive effects of TMS on a simple spatial localization task for which a well-balanced deployment of attention is required are exquisitely spatially selective, and are found in specific frontal cortical subregions.

IMT-Advanced 시스템의 시변 채널에서의 MMSE 기반 등화 성능 분석

초고속 무선 인터넷 서비스에 대한 사용자의 요구가 증대되면서 차세대 이동통신의 표준화 및 연구 개발이 계속되고 있다. 4세대 이동통신인 IMT-Advanced 시스템은 정지 상태에서 1 Gbps, 이동 상태에서 100 Mbps의 전송 속도를 제공해야 하며 최대 350 km/h의 이동 중에도 안정적인 수신 성능을 보장해야만 한다. 한편, 이동 속도가 증가함에 따라 도플러 편이 역시 증가하게 되고 이로 인해 부반송파간 간섭이 증대되어 궁극적으로 수신 성능의 저하를 야기하게 된다. 이에, 이 논문에서는 IMT-Advanced 시스템의 무선접속규격을 준용하는 채널 환경에서 수신기의 등화 기법에 따른 수신 성능을 평가하고 분석한다. 이동속도와 부호율을 변경하면서 수행한 컴퓨터 모의실험에 따르면 MMSE 기반의 등화 기법을 적용하여 수신할 경우 단일 탭 등화 기법을 적용할 경우보다 시변 채널에 의한 성능 저하를 대폭 완화시킬 수 있음을 고찰할 수 있다. As the user's demand for ultra high-speed wireless internet has increased, the standardization, research and development of future mobile communication systems have been done for several years. IMT-Advanced system which is called fourth generation mobile communication should support the data rate of 1 Gbps for nomadic users and 100 Mbps for mobile users. Also, the system should hold call connection at the mobile speed of 350 km/h. Meanwhile, since Doppler spread is linearly proportional to mobile speed, high mobility leads to the increase of interference between subcarriers and the deterioration of detection performance consequently. In this paper, we evaluate and analyze detection probability with respect to equalization methods in time-varying channels under system parameters complying with IMT-Advanced requirements. According to computer simulation conducted by varying mobile speed and code rate, MMSE based equalization can mitigate performance degradation of IMT-Advanced system considerably in time-varying channels.

Ship wake detection in synthetic aperture radar images using a combination of a wavelet correlator and Radon transform

A moving ship in a synthetic aperture radar (SAR) image produces a multiscale wake with a characteristic linear V-shaped pattern. Detection of the wake can provide substantial information about the ship, such as its size,direction, and speed of movement. In general, ship-generated wakes in SAR images are associated with high sea clutter, which causes the deterioration of detection performance. We present a hybrid method that combines the wavelet technique and the Radon transform technique to detect the ship wake. The wavelet technique is first applied to generate a set of multiscale images. An orthogonal basis function is adopted so that three high-pass images in the horizontal, vertical, and diagonal directions are generated for each resolution scale. Then a spatial correlator is applied among the moduli of different scale, where modulus images are formed from three high-pass images. The output of the correlation process is shown to be highly representative at ship wake edges. Comparisons with other methods indicate the superior performance of this approach in that not only are the wakes detected, but the V-shaped pattern is well preserved. The second stage of the method involves the application of the Radon transform technique to estimate the V opening angle from the detected ship wakes. Compared with a direct Radon transform, the greater effectiveness of the proposed scheme is demonstrated in terms of efficiency as well as reliability for ship wake detection in noisy backgrounds.