Multi-hypothesis prediction technique, which exploits inter-frame correlation efficiently, is widely used in block-based distributed compressive video sensing. To solve the problem of inaccurate prediction in multi-hypothesis prediction technique at a low sampling rate and enhance the reconstruction quality of non-key frames, we present a resample-based hybrid multi-hypothesis scheme for block-based distributed compressive video sensing. The innovations in this paper include: (1) multi-hypothesis reconstruction based on measurements reorganization (MR-MH) which integrates side information into the original measurements; (2) hybrid multi-hypothesis (H-MH) reconstruction which mixes multiple multi-hypothesis reconstructions adaptively by resampling each reconstruction. Experimental results show that the proposed scheme outperforms the state-of-the-art technique at the same low sampling rate.

This paper presents a measurement-domain intra prediction coding framework that is compatible with compressive sensing (CS)-based image sensors. In this framework, we propose a low-complexity intra prediction algorithm that can be directly applied to measurements captured by the image sensor. We proposed a structural random 0/1 measurement matrix, embedding the block boundary information that can be extracted from the measurements for intra prediction. Furthermore, a low-cost Very Large Scale Integration (VLSI) architecture is implemented for the proposed framework, by substituting the matrix multiplication with shared adders and shifters. The experimental results show that our proposed framework can compress the measurements and increase coding efficiency, with 34.9% BD-rate reduction compared to the direct output of CS-based sensors. The VLSI architecture of the proposed framework is 9.1 Kin area, and achieves the 83% reduction in size of memory bandwidth and storage for the line buffer. This could significantly reduce both the energy consumption and bandwidth in communication of wireless camera systems, which are expected to be massively deployed in the Internet of Things (IoT) era.

In this letter, we propose a novel and effective haze removal method by using the structure-aware atmospheric veil. More specifically, the initial atmospheric veil is first estimated based on dark channel prior and morphological operator. Furthermore, an energy optimization function considering the structure feature of the input image is constructed to refine the initial atmospheric veil. At last, the haze-free image can be restored by inverting the atmospheric scattering model. Additionally, brightness adjustment is also performed for preventing the dehazing result too dark. Experimental results on hazy images reveal that the proposed method can effectively remove the haze and yield dehazing results with vivid color and high scene visibility.

Extreme learning machine (ELM) has recently attracted many researchers' interest due to its very fast learning speed, good generalization ability, and ease of implementation. However, it has a linear output layer which may limit the capability of exploring the available information, since higher-order statistics of the signals are not taken into account. To address this, we propose a novel ELM architecture in which the linear output layer is replaced by a Volterra filter structure. Additionally, the principal component analysis (PCA) technique is used to reduce the number of effective signals transmitted to the output layer. This idea not only improves the processing capability of the network, but also preserves the simplicity of the training process. Then we carry out performance evaluation and application analysis for the proposed architecture in the context of supervised classification and unsupervised equalization respectively, and the obtained results either on publicly available datasets or various channels, when compared to those produced by already proposed ELM versions and a state-of-the-art algorithm: support vector machine (SVM), highlight the adequacy and the advantages of the proposed architecture and characterize it as a promising tool to deal with signal processing tasks.

This paper presents convex filter networks that are obtained from extensions of morphological filters. The proposed filter network consists of a convex and concave filter that are extensions of the dilation and erosion of mathematical morphology with the maxout activation function. Maxout can approximate arbitrary convex functions as piecewise linear functions, including the max function. The class of the convex function hence includes the morphological dilation and can be trained for specific image processing tasks. In this paper, the closing filter is extended to a convex-concave filter network with maxout. The convex-concave filter is trained by the stochastic gradient method for noise and mask removal. The examples of noise and mask removal show that the convex-concave filter can obtain a recovered image, whose quality is comparable to inpainting by using the total variation minimization with reduced computational cost without mask information of the corrupted pixels.

In order to significantly reduce the time and space needed, compressive sensing builds upon the fundamental assumption of sparsity under a suitable discrete dictionary. However, in many signal processing applications there exists mismatch between the assumed and the true sparsity bases, so that the actual representative coefficients do not lie on the finite grid discretized by the assumed dictionary. Unlike previous work this paper introduces the unified compressive measurement operator into atomic norm denoising and investigates the problems of recovering the frequency support of a combination of multiple sinusoids from sub-Nyquist samples. We provide some useful properties to ensure the optimality of the unified framework via semidefinite programming (SDP). We also provide a sufficient condition to guarantee the uniqueness of the optimizer with high probability. Theoretical results demonstrate the proposed method can locate the nonzero coefficients on an infinitely dense grid over a wide range of SNR case.

As devices continue to shrink, the parameter shift due to process variation and aging effects has an increasing impact on the circuit yield and reliability. However, predicting how long a circuit can maintain its design yield above the design specification is difficult because the design yield changes during the aging process. Moreover, performing Monte Carlo (MC) simulation iteratively during aging analysis is infeasible. Therefore, most existing approaches ignore the continuity during simulations to obtain high speed, which may result in accumulation of extrapolation errors with time. In this paper, an incremental simulation technique is proposed for lifetime yield analysis to improve the simulation speed while maintaining the analysis accuracy. Because aging is often a gradual process, the proposed incremental technique is effective for reducing the simulation time. For yield analysis with degraded performance, this incremental technique also reduces the simulation time because each sample is the same circuit with small parameter changes in the MC analysis. When the proposed dynamic aging sampling technique is employed, 50× speedup can be obtained with almost no decline accuracy, which considerably improves the efficiency of lifetime yield analysis.

High-dynamic-range imaging (HDRI) technologies aim to extend the dynamic range of luminance against the limitation of camera sensors. Irradiance information of a scene can be reconstructed by fusing multiple low-dynamic-range (LDR) images with different exposures. The key issue is removing ghost artifacts caused by motion of moving objects and handheld cameras. This paper proposes a robust ghost-free HDRI algorithm by visual salience based bilateral motion detection and stack extension based exposure fusion. For ghost areas detection, visual salience is introduced to measure the differences between multiple images; bilateral motion detection is employed to improve the accuracy of labeling motion areas. For exposure fusion, the proposed algorithm reduces the discontinuity of brightness by stack extension and rejects the information of ghost areas to avoid artifacts via fusion masks. Experiment results show that the proposed algorithm can remove ghost artifacts accurately for both static and handheld cameras, remain robust to scenes with complex motion and keep low complexity over recent advances including rank minimization based method and patch based method by 63.6% and 20.4% time savings averagely.

In this study, expressions were compared with reference material using the coaxial feed-type open-ended cut-off circular waveguide reflection method to support simple and instantaneous evaluation of dielectric constants in small amounts of scarce liquids over a broad frequency range. S11 values were determined via electromagnetic analysis for individual jig structure conditions and dielectric property values without actual S11 measurement under the condition that the tip of the measurement jig with open and short-ended conditions and with the test material inserted. Next, information on the relationships linking jig structure, dielectric properties and S11 properties was stored on a database to simplify the procedure and improve accuracy in reference material evaluation. The accuracy of the estimation formula was first theoretically verified for cases in which values indicating the dielectric properties of the reference material and the actual material differed significantly to verify the effectiveness of the proposed method. The results indicated that dielectric property values for various liquids measured at 0.5 and 1.0GHz using the proposed method corresponded closely to those obtained using the method previously proposed by the authors. The effectiveness of the proposed method was evaluated by determining the dielectric properties of certain liquids at octave-range continuous frequencies between 0.5 and 1.0GHz based on interpolation from limited data of several frequencies. The results indicated that the approach enables quicker and easier measurement to establish the complex permittivity of liquids over a broad frequency range than the previous method.

This paper presents an approach to nonlinear impedance measurement exploiting an oscilloscope and Möbius transformation. Proposed system consists of a linear 4-port network and an oscilloscope. One of the port is excited by a high power source. The power is delivered to the second port, which is loaded with a DUT. Another set of two ports are used to observe a voltage set. This voltage set gives the impedance of the DUT through Möbius transformation. We formulated measurability M of the system, and derived the condition that M becomes constant for any DUT. To meet the condition, we propose a linear 4-port network consisting of a quarter-wavelength transmission line and resistors. We confirm the validity and utility of the proposed system by measuring the impedance of incandescent bulbs and an RF diode rectifier.

In this paper, we propose a novel design method of two channel critically sampled compactly supported biorthogonal graph wavelet filter banks with half-band kernels. First of all, we use the polynomial half-band kernels to construct a class of biorthogonal graph wavelet filter banks, which exactly satisfy the PR (perfect reconstruction) condition. We then present a design method of the polynomial half-band kernels with the specified degree of flatness. The proposed design method utilizes the PBP (Parametric Bernstein Polynomial), which ensures that the half-band kernels have the specified zeros at λ=2. Therefore the constraints of flatness are satisfied at both of λ=0 and λ=2, and then the resulting graph wavelet filters have the flat spectral responses in passband and stopband. Furthermore, we apply the Remez exchange algorithm to minimize the spectral error of lowpass (highpass) filter in the band of interest by using the remaining degree of freedom. Finally, several examples are designed to demonstrate the effectiveness of the proposed design method.

Autonomous Underwater Vehicle (AUV) can be utilized to directly measure the geomagnetic map in deep sea. The traditional map interpolation algorithms based on sampling continuation above the sea level yield low resolution and accuracy, which restricts the applications such as the deep sea geomagnetic positioning, navigation, searching and surveillance, etc. In this letter, we propose a Three-Dimensional (3D) Compressive Sensing (CS) algorithm in terms of the real trajectory of AUV which can be optimized with the required accuracy. The geomagnetic map recovered with the CS algorithm shows high precision compared with traditional interpolation schemes, by which the magnetic positioning accuracy can be greatly improved.

We describe SyncSync, a time-of-arrival (ToA)-based localization method for smartphones. In general, ToA measurements show better precision than time-difference-of-arrival (TDoA) measurements, although ToA systems require a synchronization mechanism between anchor and mobile nodes. For this synchronization, we employ modulated LED light with an acoustic wave for time-of-flight distance measurements. These are detected by the smartphone's video camera and microphone. The time resolution in consumer video cameras is typically only a few tenths of a second, but by utilizing a CMOS image sensor's rolling shutter effect we obtain synchronization resolutions of a few microseconds, which is sufficient for precise acoustic ToA measurements. We conducted experiments to confirm operation of the system, and obtained ToA localization errors within 10mm. The characteristics of the error distributions for both TDoA and ToA measurements were explained by dilution of precision.

As the role of wireless communication is becoming more important for realizing a future connected society for not only humans but also things, spectrum scarcity is becoming severe, because of the huge numbers of mobile terminals and many types of applications in use. In order to realize sustainable wireless connection under limited spectrum resources in a future wireless world, a new dynamic spectrum management scheme should be developed that considers the surrounding radio environment and user preferences. In this paper, we discuss a new spectrum utilization framework for a future wireless world called the “smart spectrum.” There are four main issues related to realizing the smart spectrum. First, in order to recognize the spectrum environment accurately, spectrum measurement is an important technology. Second, spectrum modeling for estimating the spectrum usage and the spectrum environment by using measurement results is required for designing wireless parameters for dynamic spectrum use in a shared spectrum environment. Third, in order to effectively gather the measurement results and provide the spectrum information to users, a measurement-based spectrum database can be used. Finally, smart spectrum management that operates in combination with a spectrum database is required for realizing efficient and organized dynamic spectrum utilization. In this paper, we discuss the concept of the smart spectrum, fundamental research studies of the smart spectrum, and the direction of development of the smart spectrum for targeting the future wireless world.

Array antenna technology for wireless systems is highly integrated for demands such as multi-functionality and high-performance. This paper details recent technologies in Japan in design techniques based on computational electromagnetics, antenna hardware techniques in the millimeter-wave band, array signal processing to add adaptive functions, and measurement methods to support design techniques, for array antennas for future wireless systems. Prospects of these four technologies are also described.

We consider fixed-to-variable length coding with a regular cost function by allowing the error probability up to any constantε. We first derive finite-length upper and lower bounds on the average codeword cost, which are used to derive general formulas of two kinds of minimum achievable rates. For a fixed-to-variable length code, we call the set of source sequences that can be decoded without error the dominant set of source sequences. For any two regular cost functions, it is revealed that the dominant set of source sequences for a code attaining the minimum achievable rate under a cost function is also the dominant set for a code attaining the minimum achievable rate under the other cost function. We also give general formulas of the second-order minimum achievable rates.

For network researchers and practitioners, active measurement, in which probe packets are injected into a network, is a powerful tool to measure end-to-end delay. It is, however, suffers the intrusiveness problem, where the load of the probe traffic itself affects the network QoS. In this paper, we first demonstrate that there exists a fundamental accuracy bound of the conventional active measurement of delay. Second, to transcend that bound, we propose INTrusiveness-aware ESTimation (INTEST), an approach that compensates for the delays produced by probe packets in wired networks. Simulations of M/M/1 and MMPP/M/1 show that INTEST enables a more accurate estimation of end-to-end delay than conventional methods. Furthermore, we extend INTEST for multi-hop networks by using timestamps or multi-flow probes.

This paper experimentally verifies the potential of higher order space division multiplexing in line-of-sight (LOS) channels for multiuser massive MIMO. We previously proposed an inter-user interference (IUI) cancellation scheme and a simplified user scheduling method for Massive Antenna Systems for Wireless Entrance (MAS-WE). In order to verify the effectiveness of the proposed techniques, channel state information (CSI) for a 1×32 SIMO channel is measured in a real propagation environment with simplified test equipment. Evaluations of the measured CSI data confirm the effectiveness of our proposals; they offer good equal gain transmission (EGT) performance, reduced spatial correlation with enlarged angular gap between users, and quite small channel state fluctuation. Link level simulations elucidate that the simple IUI cancellation method is stable in practical conditions. The degradation in symbol error rate with the measured CSI, relative to that yielded by the output of the theoretical LOS channel model, is insignificant.

The rapid development of information techniques has lead to more and more high-dimensional datasets, making classification more difficult. However, not all of the features are useful for classification, and some of these features may even cause low classification accuracy. Feature selection is a useful technique, which aims to reduce the dimensionality of datasets, for solving classification problems. In this paper, we propose a modified bat algorithm (BA) for feature selection, called MBAFS, using a SVM. Some mechanisms are designed for avoiding the premature convergence. On the one hand, in order to maintain the diversity of bats, they are guided by the combination of a random bat and the global best bat. On the other hand, to enhance the ability of escaping from local optimization, MBAFS employs one mutation mechanism while the algorithm trapped into local optima. Furthermore, the performance of MBAFS was tested on twelve benchmark datasets, and was compared with other BA based algorithms and some well-known BPSO based algorithms. Experimental results indicated that the proposed algorithm outperforms than other methods. Also, the comparison details showed that MBAFS is competitive in terms of computational time.

This work develops a third-order multibit switched-current (SI) delta-sigma modulator (DSM) with a four-bit switched-capacitor (SC) flash analog-to-digital converter (ADC) and an incremental data weighted averaging circuit (IDWA), which is fabricated using 0.18µm 1P6M CMOS technology. In the proposed DSM, a 4-bit SC flash ADC is used to improve its resolution, and an IDWA is used to reduce the nonlinearity of digital-to-analog converter (DAC) by moving the quantization noise out of the signal band by first-order noise shaping. Additionally, the proposed differential sample-and-hold circuit (SH) exhibits low input impedance with feedback and width-length adjustment in the SI feedback memory cell (FMC) to increase the conversion rate. A coupled differential replicate (CDR) common-mode feedforward circuit (CMFF) is used to compensate for the mirror error that is caused by the current mirror. Measurements indicate that the signal-to-noise ratio (SNR), dynamic range (DR), effective number of bits (ENOB), power consumption, and chip area are 64.1 dB, 64.4 dB, 10.36 bits, 18.82 mW, and 0.45 × 0.67 mm2 (without I/O pad), respectively, with a bandwidth of 20 kHz, an oversampling ratio (OSR) of 256, a sampling frequency of 10.24 MHz, and a supply voltage of 1.8 V.