Add this article to private library Remove from private library Submit corrections to this record View record in the new ADS The book presents an introductory treatment of digital and analog communication systems with emphasis on digital systems. Attention is given to the following topics: systems and signal analysis, random signal theory, information and channel capacity, baseband data transmission, analog signal transmission, noise in analog communication systems, digital carrier modulation schemes, error control coding, and the digital transmission of analog signals. Bibtex entry for this abstract Preferred format for this abstract

Digital and analog communication systems

The random equivalent sampling (RES) is a well-known sampling technique that can be used to capture a high-speed repetitive waveform with low sampling rate. In this paper, the feasibility of spectrum-blind multiband signal reconstruction for data sampled from RES is investigated. We propose a RES sampling pattern and its corresponding mathematical model that guarantees well-conditioned reconstruction of multiband signal with unknown spectral support. We give the minimum number of RES acquisitions that hold overwhelming probability to successfully reconstruct original signal. We demonstrate that for signal with specific spectral occupation, the number of RES acquisitions and the minimum sampling rate could be approached. The signal reconstruction is studied in the framework of compressive sampling (CS) theory. The eigen-decomposition and minimum description length (MDL) criteria are adopted to adaptively estimate the dimension of signal, and the number of unknowns of reconstruction problem is reduced. Experimental results are reported to indicate that, for a spectrum-blind sparse multiband signal, the proposed reconstruction algorithm for RES is feasible and robust.

Minimum Rate Sampling and Spectrum Blind Reconstruction in Random Equivalent Sampling

In wireless sensor networks, collected data usually have a certain degree of loss and are unable to meet actual application needs due to node failures or energy limitation, etc. The current data recovery methods in wireless sensor networks focus on the usage of spatial–temporal correlation between perceptual data but seldom exploit the correlation between different attributes. This paper proposes a data recovery algorithm based on the Attribute Correlation and Extremely randomized Trees (ACET). Firstly, the Spearman’s correlation coefficient is adopted to construct the correlation model between different attributes. In case that a given attribute is lost, the correlation model is used to select other attributes that have a strong correlation with this attribute, and then take advantage of them to train the extremely randomized trees. Finally, the lost data can be recovered by the trained model. Experimental results show that the correlation between attributes can improve the effectiveness of data recovery compared with other methods.

Data recovery in wireless sensor networks based on attribute correlation and extremely randomized trees

Spectrum sensing is an important task in cognitive radio. However, currently available Analog-to-Digital Converters (ADC) can hardly satisfy the sampling rate requirement for wideband signals. Even with such an ADC, the cost is extremely high in terms of price and power consumption. In this paper, we propose a spectrum-sensing method based on single-channel sub-Nyquist sampling. Firstly, a serial Multi-Coset Sampling (MCS) structure is designed to avoid mismatches among sub-ADCs in the traditional parallel MCS. Clocks of the sample/hold and ADC are provided by two non-uniform sampling clocks. The cooperation between these two non-uniform sampling clocks shifts the high sampling rate burden from the ADC to the sample/hold. Secondly, a power spectrum estimation method using sub-Nyquist samples is introduced, and an efficient spectrum-sensing algorithm is proposed. By exploiting the frequency-smoothing property, the proposed efficient spectrum-sensing algorithm only needs to estimate power spectrum at partial frequency bins to conduct spectrum sensing, which will save a large amount of computational cost. Finally, the sampling pattern design of the proposed serial MCS is given, and it is proved to be a minimal circular sparse ruler with an additional constraint. Simulations show that mismatches in traditional parallel MCS have a serious impact on spectrum-sensing performance, while the proposed serial MCS combined with the efficient spectrum-sensing algorithm exhibits outstanding spectrum-sensing performance at much lower computational cost.

Wideband Spectrum Sensing Based on Single-Channel Sub-Nyquist Sampling for Cognitive Radio.

Raw data simulation is the front-end work of synthetic aperture radar (SAR), which is of great significance. For high-squint spotlight SAR, the frequency domain simulation algorithm is invalid because of the range-azimuth coupling effect. In order to realize high-squint spotlight SAR raw data simulation in the frequency domain, an algorithm based on coordinate transformation and non-uniform fast Fourier transform (NUFFT) is proposed. This algorithm generates broadside raw data using a two-dimensional (2-D) frequency simulation algorithm; then, coordinate transformation is used by analyzing the characteristics of broadside and high-squint spotlight SAR. After coordinate transformation, NUFFT is carried out to realize the coupling relation in the 2-D frequency domain. Since the coordinate transformation ignores the influence of range walk, the range walk is compensated after NUFFT. As a result, compared with the traditional squint spotlight SAR frequency domain simulation algorithm, the proposed algorithm can improve the accuracy of point and distributed target imaging results, and the efficiency of the proposed algorithm can be significantly improved in contrast the traditional time domain algorithm.

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A Novel High-Squint Spotlight SAR Raw Data Simulation Scheme in 2-D Frequency Domain

Sub-Nyquist sampling technology can ease the conflict between high resolution and wide swath in a synthetic aperture radar (SAR) system. However, the existing sub-Nyquist SAR imposes a constraint on the type of the observed scene and can only reconstruct the scene with small sparsity (i.e., number of significant coefficients). The information channel model of microwave imaging radar based on information theory, in which scene, echo, and the mapping relation between the two correspond to information source, sink, and channel, is built, and noisy-channel coding theorem explains the reason for the aforementioned under this model. To allow the wider application of sub-Nyquist SAR, this paper proposes sub-Nyquist SAR based on pseudo-random space-time modulation. This modulation is the spatial and temporal phase modulation to the traditional SAR raw data and can increase the mutual information of information source and sink so that the scenes with large sparsity can be reconstructed. Simulations of scenes with different sparsity, e.g., an ocean with several ships and urban scenes, were run to verify the validity of our proposed method, and the results show that the scenes with large sparsity can be successfully reconstructed.

Sub-Nyquist SAR Based on Pseudo-Random Time-Space Modulation

Because of the unprediction of the scene scattering characteristic and the finite quantization bits, saturated data always exists. Saturation phenomenon leads to a non-linear distortion and interferes to the recognition of the target so that it affects the image quality. Especially when the scene scattering characteristic largely varies, it can generate false targets and degrade signal-to-noise ratio (SNR). Compressed sensing (CS), a non-linear reconstructed algorithm, is that samples in sub-Nyquist rate is used to recover the sparse signal with few non-zero elements. This paper proposes the recovery method based on the nonlinear characteristic of CS to recover the saturated part of the raw data to the unsaturation state and ensure the unsaturated parts maintain the original state. Simulation results validate the proposed method.

The Recovery Algorithm of Saturated Sar Raw Data Based on Compressed Sensing

High resolution and wide swath, which are related to imaging quality and observation efficiency, are the key specifications for spaceborne synthetic aperture radar (SAR). Owing to the restrictions of the Nyquist sampling theorem, it is difficult to improve both specifications simultaneously. The increase of the swath width often leads to the decrease of the spatial resolution, e.g., in scanning SAR and terrain observation with progressive scan SAR. For a sparse scene, an image containing only a few targets has massive data but little useful information. This paper proposes a novel SAR observation mode, AgileSAR, which is based on the time–space sampling method and can overcome the limitations of the Nyquist theorem. It also increases the swath width while preserving the resolution of the sparse scene. AgileSAR steers the antenna beam towards a different sub-swath, generally after one or two pulse intervals, and the average pulse repetition rate corresponding to every sub-swath is much lower than that determined by the Nyquist theorem. Compared with Sentinel-1, which can achieve 5-m resolution and 80-km swath, a single azimuth-channel AgileSAR system can achieve 5-m resolution and 300-km swath for a sparse scene, once the corresponding system parameters are designed. The $\text{l}_{1}$ relaxation method is used to reconstruct sparse SAR images, and the reconstruction performance is quantitatively analyzed based on the estimation error. The simulation results validating the proposed method with sub-Nyquist samples can achieve approximately similar performance as conventional SAR with Nyquist samples.

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AgileSAR: Achieving Wide-Swath Spaceborne SAR Based on Time-Space Sampling

Digital storage and transmission are common processes in modern synthetic aperture radar systems; thus, analog-to-digital converters are indispensable. Such processes can lead to two types of error: quantization (or granular) error and saturation (or clipping) error, which cause sampling noise, and radiometric and harmonic distortions in final images. Traditionally, reasonable choices of the gain and the number of quantization bits by the analog-to-digital converter based on the echo distribution can effectively reduce these errors. However, establishing the gain control repository of a synthetic aperture radar mission is a long process. In addition, if the dynamic range of the backscattering coefficient is extremely large or if unexpected strong targets appear in a scene, then harmonics occur in the echo, which turns the variable gain amplifier into chaos based on statistic and, inevitably, results in saturation in the raw data. Once raw data saturation occurs, the SAR system can conventionally adjust only the analog-to-digital converter in the next observation, thus reducing timeliness. Power loss compensation based on a statistical model and saturation (clipping) factor on a large-scale could compensate for the energy loss in images; however, detail interference, such as harmonic distortion, cannot be effectively suppressed, which will lead to false targets in the focused data. To address this particular problem, a novel anti-saturation method for large dynamic range scenes is proposed in this paper. The log-normal distribution is used in this article to describe dynamic range scenes with strong isolated targets, which mainly cause receiver saturation. Using the statistical distribution of complex scenes as a priori information, a maximum a posteriori estimation algorithm is proposed to simultaneously compensate for the saturated values in the raw data and retain the non-saturated values. Thus, the details of the weak background are well preserved, and the isolated strong targets with sparsity are reconstructed perfectly. With Monte Carlo simulation, the proposed method can improve the radiometric accuracy by 5 to 10 dB and effectively suppress the energy of false targets. Based on TerraSAR-X, ALOS-2, and Radarsat-1 synthetic aperture radar data, the effectiveness and robustness of the proposed method are also verified by simulations.

Reconstruction of Synthetic Aperture Radar Raw Data under Analog-To-Digital Converter Saturation Distortion for Large Dynamic Range Scenes

With the rise of high-calorie diets and the aging of populations, the incidence of diabetes was increased dramatically in the world and the number of people with diabetes was predicted to rise to 600 million by 2045. Numerous studies have confirmed that several organ systems, including the skeletal system, are seriously affected by diabetes. In that study, the bone regeneration and the biomechanics of the newly regenerated bone were investigated in diabetic rats, which may provide a supplement for previous studies.A total of 40 SD rats were randomly divided into the type 2 diabetes mellitus (T2DM) group (n = 20) and the control group (n = 20). Beyond that high fat diet and streptozotocin (STZ) were jointly used in the T2DM group, there were no differences between the two groups in terms of treatment conditions. Distraction osteogenesis was used in all animals for the next experimental observation. The evaluation criterion of the regenerated bone was based on radioscopy (once a week), micro-computed tomography (CT), general morphology, biomechanics (including ultimate load, modulus of elasticity, energy to failure, and stiffness), histomorphometry (including von Kossa, Masson trichrome, Goldner trichrome, and safranin O staining), and immunohistochemistry.All rats in the T2DM group with fasting glucose levels (FGL, > 16.7 mmol/L) were allowed to complete the following experiments. The results showed that rats with T2DM have a higher body weight (549.01 g ± 31.34 g) than rats in the control group (488.60 g ± 33.60 g) at the end of observation. Additionally, compared to the control group, slower bone regeneration in the distracted segments was observed in the T2DM group according to radiography, micro-CT, general morphology, and histomorphometry. Furthermore, a biomechanical test showed that there was a worse ultimate load (31.01 ± 3.39%), modulus of elasticity (34.44 ± 5.06%), energy to failure (27.42 ± 5.87%), and stiffness (34.55 ± 7.66%) than the control group (45.85 ± 7.61%, 54.38 ± 9.33%, 59.41 ± 10.96%, and 54.07 ± 9.30%, respectively). Furthermore, the decreased expressions of hypoxia-inducible factor 1α (HIF-1α) and vascular endothelial growth factor (VEGF) were presented in T2DM group by immunohistochemistry.The present study demonstrated that diabetes mellitus impairs bone regeneration and biomechanics in newly regenerated bone, a phenomenon that might be related to oxidative stress and poor angiogenesis brought on by the disease. 

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Wenjiao Chen

Papers

Sub-Nyquist SAR Based on Pseudo-Random Time-Space Modulation

The Recovery Algorithm of Saturated Sar Raw Data Based on Compressed Sensing

AgileSAR: Achieving Wide-Swath Spaceborne SAR Based on Time-Space Sampling

Reconstruction of Synthetic Aperture Radar Raw Data under Analog-To-Digital Converter Saturation Distortion for Large Dynamic Range Scenes

Diabetes mellitus impairs bone regeneration and biomechanics