Image representation by compressive sensing for visual sensor networks

This paper addresses the image representation problem in visual sensor networks. We propose a new image representation method for visual sensor networks based on compressive sensing (CS). CS is a new sampling method for sparse signals, which is able to compress the input data in the sampling process. Combining both signal sampling and data compression, CS is more capable of image representation for reducing the computation complexity in image/video encoder in visual sensor networks where computation resource is extremely limited. Since CS is more efficient for sparse signals, in our scheme, the input image is firstly decomposed into two components, i.e., dense and sparse components; then the dense component is encoded by the traditional approach (JPEG or JPEG 2000) while the sparse component is encoded by a CS technique. In order to improve the rate distortion performance, we leverage the strong correlation between dense and sparse components by using a piecewise autoregressive model to construct a prediction of the sparse component from the corresponding dense component. Given the measurements and the prediction of the sparse component as initial guess, we use projection onto convex set (POCS) to reconstruct the sparse component. Our method considerably reduces the number of random measurements needed for CS reconstruction and the decoding computational complexity, compared to the existing CS methods. In addition, our experimental results show that our method may achieves up to 2 dB gain in PSNR over the existing CS based schemes, for the same number of measurements.     

Video compressed sensing reconstruction based on structural group sparsity and successive approximation estimation model

The existing video compressed sensing (CS) algorithms for inconsistent sampling ignore the joint correlations of video signals in space and time, and their reconstruction quality and speed need further improvement. To balance reconstruction quality with computational complexity, we introduce a structural group sparsity model for use in the initial reconstruction phase and propose a weight-based group sparse optimization algorithm acting in joint domains. Then, a coarse-to-fine optical flow estimation model with successive approximation is introduced for use in the interframe prediction stage to recover non-key frames through alternating optical flow estimation and residual sparse reconstruction. Experimental results show that, compared with the existing algorithms, the proposed algorithm achieves a peak signal-to-noise ratio gain of 1–3 dB and a multi-scale structural similarity gain of 0.01–0.03 at a low time complexity, and the reconstructed frames not only have good edge contours but also retain textural details.     

Compressive image broadcasting in MIMO systems with receiver antenna heterogeneity

In this work we consider image delivery in MIMO broadcast networks with diverse channel quality and varying numbers of antennas across receivers. In such systems, performance is normally constrained by the weakest users with either a low channel SNR or only a single receive antenna. To address both dimensions of heterogeneity, we propose a new analog image delivery system that adapts seamlessly along both dimensions simultaneously. Our sender scales the DWT coefficients according to a power allocation strategy, and generates linear combinations of the coefficients using compressive sensing (CS), before transmitting them with amplitude modulation. On the receiving side, the received physical layer symbols are passed directly to the source decoder without conventional MIMO decoding, and the DWT coefficients are recovered using a CS decoder.There are two main contributions of our system. First, integrating CS into MIMO transmission ensures that the reconstructed image quality at the receivers is commensurate with both the channel SNR and the MIMO channel dimension. Second, we introduce a power allocation strategy to achieve a performance tradeoff between receivers with different antenna numbers. Experimental results show that the proposed system outperforms both the analog reference SoftCast and the conventional digital system known as HM-STBC. The average gain is 2.92 dB over SoftCast for single-antenna users and 1.53 dB over HM-STBC for two-antenna users.     

Hardware Implementation of Enhancement Embedded Reduced Runtime Recovery Algorithm for Compressively Sensed Images

Compressed sensing is based on the principle that, through optimization, the sparsity of a signal can be exploited to recover it from fewer samples than required by the criterion. In compressed sensing, data is compressed and converted into fewer measurements and transmitted through wireless channel which is reconstructed at the receiver. Since very few samples are used for reconstruction, there are possibilities of degradation in quality of reconstruction. Unlike traditional methods, enhancement can now be done using the recent technique of compressed sensing by embedding image enhancement techniques like edge detection, histogram, filtering and their combinations with CS recovery procedure. This work proposes such a method by binding the image enhancement techniques along with the compressed sensing process. Filter of Gaussian filter (FGF), a combinational filter proposed in this study enables an increase in PSNR of 1 dB when compared to other filtering techniques besides using least number of measurements and maintaining minimum time consumption. The runtime difference with and without the FGF is ~?3 s, which is affordable even in hardware with minimum specifications. Real time experimentation of embedded enhancement CS was carried out in WINGZ board to prove the feasibility of enhanced recovery process with lower end hardware.     

Efficient reconstruction of compressively sensed images and videos using non-iterative method

Compressed sensing is widely applied for compression and reconstruction of images and videos by projecting the pixel values to smaller dimensional measurements. These measurements are reconstructed at the receiver using various reconstruction procedures. Greedy algorithms are often used for such recovery. These solve the least squares problem to find the best match with minimum error. This is a time consuming and complex process, giving rise to a trade-off between reconstruction performance and algorithmic performance. This work proposes a non-iterative method, viz., non-iterative pseudo inverse based recovery algorithm (NIPIRA), for reconstruction of compressively sensed images and videos with small complexity and time consumption, provided the reconstruction quality is maintained. NIPIRA gives a minimum PSNR of 32 dB for very few measurements (M/N = 0.3125) and accuracy of above 97%. There is more than 92% of decrease in elapsed time compared with other iterative algorithms. NIPIRA is tested for its performance with respect to many other objective measures as well. The complexity of NIPIRA is s times less than existing recovery algorithms.     

Correlation estimation from compressed images

This paper addresses the problem of correlation estimation in sets of compressed images. We consider a framework where the images are represented under the form of linear measurements due to low complexity sensing or security requirements. We assume that the images are correlated through the displacement of visual objects due to motion or viewpoint change and the correlation is effectively represented by optical flow or motion field models. The correlation is estimated in the compressed domain by jointly processing the linear measurements. We first show that the correlated images can be efficiently related using a linear operator. Using this linear relationship we then describe the dependencies between images in the compressed domain. We further cast a regularized optimization problem where the correlation is estimated in order to satisfy both data consistency and motion smoothness objectives with a Graph Cut algorithm. We analyze in detail the correlation estimation performance and quantify the penalty due to image compression. Extensive experiments in stereo and video imaging applications show that our novel solution stays competitive with methods that implement complex image reconstruction steps prior to correlation estimation. We finally use the estimated correlation in a novel joint image reconstruction scheme that is based on an optimization problem with sparsity priors on the reconstructed images. Additional experiments show that our correlation estimation algorithm leads to an effective reconstruction of pairs of images in distributed image coding schemes that outperform independent reconstruction algorithms by 2–4 dB.     

Noise canceling LNA with gain enhancement by using double feedback

In this paper we present a balun low noise amplifier (LNA) in which the gain is boosted by using a double feedback structure. The circuit is based on a conventional balun LNA with noise and distortion cancelation. The LNA is based on the combination of a common-gate (CG) stage and common-source (CS) stage. We propose to replace the load resistors by active loads, which can be used to implement local feedback loops (in the CG and CS stages). This will boost the gain and reduce the noise figure (NF). Simulation results, with a 130 nm CMOS technology, show that the gain is 24 dB and the NF is less than 2.7 dB. The total power dissipation is only 5.4 mW (since no extra blocks are required), leading to a figure-of-merit (FOM) of 3.8 mW⁻¹ using a nominal 1.2 V supply. Measurement results are presented for the proposed DFB LNA included in a receiver front-end for biomedical applications (ISM and WMTS).     

压缩感知理论及其应用前景

压缩感知理论是近年来提出的一种基于信号稀疏性的新兴采样理论。与通常的数据采样定理不同,该理论提出可以用远远少于传统采样定理所需的采样点数或观测点数恢复出原信号或图像。本文主要阐述了压缩感知中信号的稀疏表示、测量矩阵的设计及信号的重构算法等基本理论,论述了该理论的广阔应用前景。     

Microstrip dual-band bandpass filter with independently tunable passbands using varactor-tuned stub loaded resonators

In this paper, a dualband bandpass filter with independently tunable passband is proposed. Two half-wavelength resonators with shunt stub have been placed side by side, fed with a common input-output microstrip line to achieve the individual tunability without affecting other passband. For tuning resonance frequency, varactor diodes are used at the ends of the half wavelength resonators and also at the end of the shunt stubs. Proper shunt stub length and width are derived numerically in such a way that only one control voltage is required in each passband. Measured results show that lower passband can be tuned in a frequency range from 1.78 to 1.96 GHz, whereas the upper passband varies from 2.27 to 2.39 GHz individually. H shaped DGS is integrated below the input-output feed lines to suppress higher order harmonics up to 21 GHz with more than 19 dB attenuation.     

Dictionary learning based reconstruction for distributed compressed video sensing

Distributed compressed video sensing (DCVS) is a framework that integrates both compressed sensing and distributed video coding characteristics to achieve a low-complexity video coding. However, how to design an efficient reconstruction by leveraging more realistic signal models that go beyond simple sparsity is still an open challenge. In this paper, we propose a novel “undersampled” correlation noise model to describe compressively sampled video signals, and present a maximum-likelihood dictionary learning based reconstruction algorithm for DCVS, in which both the correlation and sparsity constraints are included in a new probabilistic model. Moreover, the signal recovery in our algorithm is performed during the process of dictionary learning, instead of being employed as an independent task. Experimental results show that our proposal compares favorably with other existing methods, with 0.1–3.5 dB improvements in the average PSNR, and a 2–9 dB gain for non-key frames when key frames are subsampled at an increased rate.     

Fault detection technique for wavelength division multiplexing passive optical network using chaotic fiber laser

We propose a fault localization method for wavelength division multiplexing passive optical network (WDM-PON). A proof-of-concept experiment was demonstrated by utilizing the wavelength tunable chaotic laser generated from an erbium-doped fiber ring laser with a manual tunable fiber Bragg grating (TFBG) filter. The range of the chaotic lasing wavelength can cover the C-band. Basing on the TFBG filter, we can adjust the wavelength of the chaotic laser to match the WDM-PON channel with identical wavelength. We determined the fault location by calculating the cross-correlation between the reference and return signals. Analysis of the characteristics of the wavelength tunable chaotic laser showed that the breakpoint, the loose connector, and the mismatch connector could be precisely located. A dynamic range of approximately 23.8 dB and a spatial resolution of 4 cm, which was independent of the measuring range, were obtained.     

Stable and wavelength-tunable RSOA- and SOA-based fiber ring laser

In this work, we propose and experimentally investigate a wavelength-tunable fiber ring laser architecture by using the reflective semiconductor optical amplifier (RSOA) and semiconductor optical amplifier (SOA). Here, the wavelength tuning range from 1538.03 to 1561.91 nm can be obtained. The measured output power and optical signal to noise ratio (OSNRs) of the proposed fiber laser are between -0.8 and -2.5 dBm and 59.1 and 61.0 dB/0.06 nm, respectively. The power and wavelength stabilities of the proposed laser are also studied. In addition, the proposed laser can be directly modulated at 2.5 Gbit/s quadrature phase shift keying-orthogonal frequency-division multiplexing (QPSK-OFDM) signal and 20–50 km single-mode fiber (SMF) transmissions are achieved within the forward error correction (FEC) limit without dispersion compensation. It could be a cost-effective and promising candidate for the standard-reach and extended-reach wavelength division multiplexed passive optical network (WDM-PON).     

Compressively sampled light field reconstruction using orthogonal frequency selection and refinement

This paper considers the compressive sensing framework as a way of overcoming the spatio-angular trade-off inherent to light field acquisition devices. We present a novel method to reconstruct a full 4D light field from a sparse set of data samples or measurements. The approach relies on the assumption that sparse models in the 4D Fourier domain can efficiently represent light fields. The proposed algorithm reconstructs light fields by selecting the frequencies of the Fourier basis functions that best approximate the available samples in 4D hyper-blocks. The performance of the reconstruction algorithm is further improved by enforcing orthogonality of the approximation residue at each iteration, i.e. for each selected basis function. Since sparsity is better preserved in the continuous Fourier domain, we propose to refine the selected frequencies by searching for neighboring non-integer frequency values. Experiments show that the proposed algorithm yields performance improvements of more than 1 dB compared to state-of-the-art compressive light field reconstruction methods. The frequency refinement step also significantly enhances the visual quality of reconstruction results of our method by a 1.8 dB average.     

Stable and widely tunable wavelength-spacing single longitudinal mode dual-wavelength erbium-doped fiber laser

In this paper, a novel and simple widely tunable wavelength-spacing single longitudinal mode (SLM) dual-wavelength erbium-doped fiber laser (EDFL) based on the tunable filter group, a passive feedback fiber ring (FFR) and saturable absorber (SA), is proposed and demonstrated experimentally. Experiment results show that the wavelength spacing can tune from 0.8 nm up to 17 nm, which has potential to generate terahertz (THz) waves by photo-mixing the lasing wavelengths in a high-speed photo-detector, and the maximum fluctuation of peak power of EDFL is less than 0.37 dB within 75 min and the optical signal-to-noise ratio is more than 30 dB at room temperature. In the absence of high-speed photo-detector, THz beat-note is also successively observed with the help of an autocorrelator. Moreover, dual-wavelength fiber laser can selectively realize one wavelength lasing by simply tuning filters.     

Optimized estimator for real-time dynamic displacement measurement using accelerometers

This paper presents a method for optimizing the performance of a real-time, long term, and accurate accelerometer based displacement measurement technique, with no physical reference point. The technique was applied in a system for measuring machine frame displacement.The optimizer has three objectives with the aim to minimize phase delay, gain error and sensor noise. A multi-objective genetic algorithm was used to find Pareto optimal estimator parameters.The estimator is a combination of a high pass filter and a double integrator. In order to reduce the gain and phase errors two approaches have been used: zero placement and pole-zero placement. These approaches were analysed based on noise measurement at 0g-motion and compared. Only the pole-zero placement approach met the requirements for phase delay, gain error, and sensor noise.Two validation experiments were carried out with a Pareto optimal estimator. First, long term measurements at 0g-motion with the experimental setup were carried out, which showed displacement error of 27.6 ± 2.3 nm. Second, comparisons between the estimated and laser interferometer displacement measurements of the vibrating frame were conducted. The results showed a discrepancy lower than 2 dB at the required bandwidth.     

Photothermal study of RF-plasma polymerized hexamethyldisilazane thin films

Plasma polymerization by RF excited low pressure plasmas is a powerful technique for synthesizing thin films for a wide range of applications such in microelectronics, biomaterials and aeronautical industries. Among these applications plasma polymerized hexamethyldisilazane (HMDSN here in) is a promising material due its biocompatibility, optical and electrical properties. In the present paper, we reported the thermal diffusivity measurements of plasma polymerized HMDSN thin films using the probe beam deflection technique (PBD). The samples are thermally thin and optically transparent, with thicknesses ranging from 170 to 600 nm, difficulting their thermal characterization by other photo-thermal techniques (photoacoustic and pyroelectric). PBD measurements were carried out with a diode (20 mW, λ=640 nm) and a HeNe (0.9 mW, λ=632 nm) laser, used as pump source and probe beam, respectively. The probe beam was focused close to the sample surface and its direction was perpendicular to the pump beam. The transverse PBD in skimming configuration is employed and the deflection signal is analyzed using the phase method for the determination of thermal diffusivity. The results show the decreasing of thermal diffusivity with the decreasing of film thickness. HMDSN film׳s thermal diffusivity values are reported in this paper for the first time being in the range of typical polymeric samples.     

Efficient Spatially-Variant Single-Pixel Imaging Using Block-Based Compressed Sensing

Single-pixel imaging is an important alternative to conventional camera. Only a single-pixel detector is needed to capture image data by measuring the correlation of the target scene and a series of sensing patterns. Conventionally, Nyquist-Shannon theorem requires measurements not less than the image pixels for an error-free reconstruction. Compressed sensing (CS) enables image reconstructions with fewer measurements but the image quality and computational cost remain the primary concerns. This paper presents an efficient single-pixel imaging technique based on blocked-based CS in which the sensing matrices are designed based on spatially-variant resolution (SVR). The proposed method decreases the number of measurements as well as the image reconstruction time using the SVR sensing patterns. Furthermore, it takes advantage of block-based CS to reduce the expenses of computational resources. The proposed method is evaluated and compared to conventional uniform resolution (UR) image reconstruction in terms of image quality and reconstruction time. The results show that the proposed method consistently reduces the reconstruction time and able to give better image quality at lower sampling ratio (SR). This provides an efficient reconstruction for single-pixel imaging which is desirable in practical application and situations where low sampling rate is required.

     

A broadband Low Noise Amplifier with built-in linearizer in 0.13-µm CMOS process

A linearized ultra-wideband (UWB) CMOS Low Noise Amplifier (LNA) is presented in this paper. The linearity performance is enhanced by exploiting PMOS–NMOS common-gate (CG) inverter as a built-in linearizer which leads to cancel out both the second- and third-order distortions. Two inductors are placed at the drain terminals of CG transistors in the built-in linearizer to adjust the phase and magnitude of the third-order distortion. A second-order band-pass Chebyshev filter is utilized in the input port of common-source (CS) configuration to provide broadband input matching at 3.1–10.6 GHz frequency range to a 50-Ω antenna. Series and shunt peaking techniques are employed to extend the bandwidth (BW) and to flatten the gain response. Simulated in 0.13 µm CMOS technology, the CMOS LNA exhibits state of the art performance consuming 17.92 mW of dc power. The CMOS LNA features a maximum gain of 10.24 dB, 0.9–4.1 dB noise figure (NF), and a third-order input intercept point (IIP3) of 6.8 dBm at 6.3 GHz.     

Improved performance step impedance lowpass filter

Performances of the conventional Butterworth step impedance lowpass filters (LPF) are significantly improved by placing transmission zero either closer to the cut-off frequency (f_c) or away from it. It is achieved by using transverse resonance width of the capacitive line sections. We report method of designing transverse resonance type LPF (TR-LPF) for 5 to 11-pole filters. At f_c = 2.5 GHz, we obtained selectivity in the range 113.3–56.66 dB/GHz and 20–60 dB rejection BW in the range 9.61–7.29 GHz. The TR-LPF can suppress the stopband signal by 60 dB up to 5f_c. Insertion loss in passband is within 0.72 dB. Improved performance of TR-LPF can be designed for f_c up to 7.5 GHz.