Mirror-image peptides composed of D-amino acids offer exceptional storage density, stability, and longevity, making them a promising medium for biological data storage. However, accurate de-novo sequencing remains challenging due to limited tandem mass spectrometry data and algorithmic constraints. To address this, we propose DBond, a deep neural network that predicts peptide bond cleavage by integrating sequence, precursor ion, and environmental features, enabling the design of mirror-image peptides that are easier to sequence. We further introduce MiPD513, a dataset of 513 mirror-image peptides, and a peptide bond cleavage labeling algorithm (PBCLA) that generates approximately 12.5 million labeled samples. Two prediction strategies, multi-label and single-label classification, are proposed, with the single-label approach found to perform better and providing a basis for sequence optimization. Code is available at https://github.com/LoserLus/DBond.

Meta-surfaces, also known as Reconfigurable Intelligent Surfaces (RIS), have emerged as a cost-effective, low power consumption, and flexible solution for enabling multiple applications in Internet of Things (IoT). However, in the context of meta-surface-assisted multi-pair IoT communications, significant interference issues often arise amount multiple channels. This issue is particularly pronounced in scenarios characterized by Line-of-Sight (LoS) conditions, where the channels exhibit low rank due to the significant correlation in propagation paths. These challenges pose a considerable threat to the quality of communication when multiplexing data streams. In this paper, we introduce a meta-surface-aided communication scheme for multi-pair interactions in IoT environments. Inspired by holographic technology, a novel compensation method on the whole meta-surface has been proposed, which allows for independent multi-pair direct data streams transmission with low interference. To further reduce correlation under LoS channel conditions, we propose a vortex beam-based solution that leverages the low correlation property between distinct topological modes. We use different vortex beams to carry distinct data streams, thereby enabling distinct receivers to capture their intended signal with low interference, aided by holographic meta-surfaces. Moreover, a prototype has been performed successfully to demonstrate two-pair multi-node communication scenario operating at 10 GHz with QPSK/16-QAM modulation. The experiment results demonstrate that, even under LoS conditions, the isolation between the two-pair channels exceeds 21 dB. This allows receiving users to undertake simultaneous, same-frequency multiplexed data transmission under extremely low interference conditions, with a real-time demodulation Bit Error Rate (BER) remaining below 3.8×10-3 at achievable Signal-to-Noise Ratio (SNR) conditions. Through the convergence of holographic meta-surfaces and vortex beams, we present a fresh perspective on achieving efficient, low-interference multi-pair IoT communications.

Direction-of-arrival (DOA) estimation is challenging in complex acoustic environments with background noise and interference. Utilizing spherical microphone arrays, closed-form estimators can be derived, which are attractive for practical applications due to their computational efficiency, eliminating the need for exhaustive extremum searching. However, current closed-form estimators are susceptible to interference. To address this issue, we propose an estimator that directly computes the DOA of the desired source using the covariance matrix of the observation signals. This approach effectively mitigates the impact of interference when the covariance matrix is accurately estimated. Simulation results demonstrate the superior performance of the proposed method compared to the subspace pseudo-intensity vector (SSPIV) and relative harmonic coefficients (RHC) methods.

The Intelligent Reflection Surface (IRS) is a crucial technology for the development of next-generation wireless mobile networks. However, its deployment is usually limited to Line-of-Sight (LoS) transmission paths of Base Stations (BSs) due to path-loss and beamforming restrictions. LoS wireless channels are known to have low-rank characteristics, which significantly reduce the capacity of IRS-assisted communication links. To overcome this issue, we propose a new IRS transmission scheme using vortex beams. These beams have low correlation properties between different modes, thus improving the channel capacity for served users. In this paper, we detail the channel capacity comparison of IRS systems using vortex beams and conventional plane waves. Additionally, a 2-bit phase quantized IRS prototype is designed and tested through full-wave Electro-Magnetic (EM) simulations and actual transmission experiments, allowing for the conversion of mode 1 vortex beams and plane waves at a pre-determined reflection angle (e.g., 30°). Our simulation results and prototype tests show that this design is effective in manipulating reflected vortex beams and plane waves in 3D space, meeting the demands of future intelligent wireless communications.

Convolutional neural networks often produce imperfect building masks that cannot be directly used to generate the regular polygons required in engineering applications. To address this issue, we propose a building mask regularization method that efficiently generates rectangular polygon building boundaries as post-processing. Our method represents the mask as a combination of rectangles by using a rectangular partition mechanism to separate the building mask into regular parts with boundaries close to bounding boxes. Each mask part is then rectified to align its longest edge with the horizontal line. Our proposed orthographic mean-shift clustering and segmentation groups rows and columns into refined building masks while removing unwanted noise such as seamless boundaries and burrs. Tests on two building datasets demonstrate that our method efficiently converts pixel-based segmentation masks into regularized building boundaries while preserving their shape. Furthermore, our method can produce better-regularized boundaries with higher intersection over union (IoU) scores for some low-quality building masks.

The non-local-based model for hyperspectral image (HSI) denoising first uses non-local self-similarity (NSS) prior to group similar full-band patches into three-dimensional non-local full-band groups (tensors) using a block matching (BM) operation, and then a low-rank (LR) penalty is typically applied to each non-local full-band group to reduce noise. While non-local-based methods have shown promising performance in HSI denoising, most existing methods have only considered the LR property of the non-local full-band group while ignoring the strong correlation between sparse coefficients. Moreover, such methods often result in unsatisfactory visual artifacts due to the noise sensitivity of BM operations, while requiring expensive computations. To address these limitations, this paper proposes a novel non-local structured sparsity regularization (NLSSR) approach for HSI denoising. First, to mitigate the noise sensitivity of the BM operation, we propose a graph-based domain distance scheme to index similar full-band patches to form the non-local full-band group. Second, we design an adaptive unidirectional low-rank (LR) dictionary with low complexity that takes into account the differences in intrinsic structure correlation among different modes of the non-local full-band tensor. Third, we utilize a global spectral LR prior to reduce spectral redundancy. Fourth, we develop a generalized soft-thresholding (GST) algorithm based on the alternating minimization framework to solve the NLSSR-based HSI denoising problem. We perform extensive experiments on both simulated and real data to show that the proposed NLSSR algorithm outperforms many popular or state-of-the-art HSI denoising methods in both quantitative and visual evaluations.

It is well known that the resolution of conventional radar is typically limited by the bandwidth of adopted waveform. However, we should not forget that the potential spatial resolution that radar can achieve could be much better, more precisely, in the order of sub-wavelength. Such radar operating °mode is called radar tomography, which exploits the spatial diversity, instead of large waveform bandwidth, to achieve super high spatial resolution. Although the theory of radar tomography had been developed for decades, very few experimental validations were reported in public literatures. In this paper, the investigations on radar tomographic imaging are conducted and validated with the measurements in microwave anechoic chamber. The spatial resolutions achieved in the experiments show good agreement with theoretical results.

Optical imagery is one of the most important sources for model remote sensing. Super resolution of the optical data could be very useful for industry and academia. Conventional solutions from computer vision only offers super resolution on small images describing relatively smaller scene and optimized towards human perception. In this paper, we proposed a new deep learning model for large remote sensing image super resolution. By using conditional coordinate and self-attention, the model could achieve arbitrary large image super resolution task with special focus on correctness of the details. Numerical evaluation of the model is carried out based on real satellite remote sensing data. Result shows a promising performance with PSNR $> 33\ \text{dB}$ and SSIM $> 0.93$ .

Land cover classification based on spaceborne synthetic aperture radar (SAR) imagery is an emerging technology with many applications. Machine learning and related ensemble models have attracted much attention to this technology. This letter presents a new object-oriented land cover classification based on Polarimetric SAR data that integrate the dual-channel (DC) Watershed region-merging segmentation algorithm and a Bagging-based ensemble extreme learning machine (Bagging-ELM) image object classifier. The advantage of the DC segmentation algorithm is verified by comparing it with the well-known simple linear iterative clustering (SLIC) superpixel algorithm. Following the DC segmentation, the Bagging-ELM is implemented to perform the classification of image objects. Besides, two other classification methods (single ELM and ensembled-tree algorithms) are used for performance comparison. The quantitative analysis with respect to ground-truth information available for the test sites shows that the proposed approach can achieve noticeable improvements in classification accuracy and outperforms the other two algorithms, showing the potential for spaceborne-SAR-based land cover classification task in real applications.

The recently developed event cameras can directly sense the motion in the scene by generating an asynchronous sequence of events, i.e., event streams, where each individual event (x, y, t) corresponds to the space-time location when a pixel sensor captures an intensity change. Compared with RGB cameras, event cameras are frameless but can capture much faster motion, therefore have great potential for recognizing gestures of fast motions. To deal with the unique output of event cameras, previous methods often treat event streams as time sequences, thus do not fully explore the space-time sparsity of the event stream data. In this work, we treat the event stream as a set of 3D points in space-time, i.e., space-time event clouds. To analyze event clouds and recognize gestures, we propose to leverage PointNet, a neural network architecture originally designed for matching and recognizing 3D point clouds. We further adapt PointNet to cater to event clouds for real-time gesture recognition. On the benchmark dataset of event camera based gesture recognition, i.e., IBM DVS128 Gesture dataset, our proposed method achieves a high accuracy of 97.08% and performs the best among existing methods.

In this letter, an autofocusing method for ultra-wideband ground-penetrating radar imaging with multiple-input multiple-output (MIMO) configuration is proposed. The proposed method is based on the minimization of the entropy of the images that have been migrated by frequency-wavenumber migration. A particular migration to zero-offset (MZO) operator is applied to reduce the processing complexity for MIMO data. The results on the simulation data show that the computation time of the autofocusing process is reduced significantly with the MZO operator, while some advantages of the MIMO configuration are successfully preserved.

Parameter estimation of internal solitary waves (ISWs) is an important application of oceanic synthetic aperture radar (SAR) images. Several methods have been widely applied for estimating the parameters of ISWs. Most of these methods assume that the ISW signals are corrupted by additive receiver noise. However, the ISW signals in SAR images suffer from both additive (thermal) and multiplicative (speckle) noises. Therefore, the estimation accuracy of previously proposed methods could not reach the Cramér-Rao bound (CRB). This paper proposes an optimal parameter estimation method of ISWs and derives the CRB for parameter estimation of ISWs. The variances of the estimated ISW parameters are shown to reach the CRB. This optimum estimator is validated using ISW SAR signals taken from simulation data and European Remote Sensing 2 (ERS-2) images. The results show that the proposed method is more accurate and effective than other methods. In addition, it should be noted that this optimality could also refer to parameter estimation of other SAR extended targets with an analytical expression.

This paper presents a study of a battlefield simulation platform with consideration of sensor network and optimal resources management. This development is mainly based on two public ally available software - GECCO and REPAST. The platform allows integration with a user developed optimization engine for optimal battlefield management. A simulation example was presented to show the application to a wireless sensor network composed of unattended ground sensors and unmanned aerial vehicles for target detection, classification and localization in the battlefield.

New method for designing sparse frequency waveform with sidelobe constraint is proposed in this paper. First, optimal power spectrum density (PSD) corresponding to minimum integrated sidelobe energy (ISE) is derived for sparse frequency waveform. Then, based on this optimal condition, the original problem dealing with both PSD requirement and sidelobe requirement is reformulated into a problem focusing only on PSD. In this way, the proposed method has a simpler optimization objective function and enjoys more efficiency than current methods focusing on both two requirements. Several optimization techniques are applied to this simpler design concept and numerical studies are provided to illustrate the effectiveness of it.

For a ground-based/airborne passive bistatic radar, its performance is dependent on the geometrical configuration and the passive transmit signal attributes. Theoretical power budget and ambiguity function analysis using a ground-based non-cooperative transmitter of opportunity with a passive bistatic radar being airborne but stationary (airship, etc.) had shown that target detection performance is limited by the strong direct path coupling signal. In comparison, the bistatic ground clutter power is significantly lower and even more so for noise power. For the passive radar to perform satisfactorily, sufficient attenuation must be provided for the direct path and strong ground clutter signals, corresponding to increasing the height of the target peak on the ambiguity function pedestal. In addition, performance could also be improved by increasing the time-bandwidth product (assuming no target migration issues), which lowers the pedestal of the ambiguity function of the strong direct path interfering signal.

This paper presents a study of using radial basis function neural network, that is trained by a finite number of off-line slow genetic algorithm solutions, for infinite number of fast approximate solutions. This approach makes powerful yet slow genetic algorithm solutions possible for real time problems.

Available conventional super resolution SAR imaging methods are based on complex images obtained by SAR imaging algorithms, and form super resolution SAR images via 2-D spectral estimation methods. A super resolution SAR imaging method combined with the chirp scaling algorithm (CSA) is presented in this paper. The new method forms a super resolution image by performing the range and the azimuth processing independently. This method can reduce the effects of phase errors on super resolution processing. The method is suitable for practical application since it does not require heavy computation. Experimental results show the effectiveness of the presented method.