High efficiency video coding (HEVC) is a video compression standard that outperforms the predecessor H.264/AVC by doubling the compression efficiency. To enhance the coding accuracy, HEVC adopts sample adaptive offset (SAO), which reduces the distortion of reconstructed pixels using classification based non-linear filtering. In the traditional coding tree unit (CTU) grain based VLSI encoder implementation, during the pixel classification stage, SAO cannot use the raw samples in the boundary of the current CTU because these pixels have not been processed by deblocking filter (DF). This paper proposes a hardware-oriented category determination algorithm based on estimating the deblocking strengths on CTU boundaries and selectively adopting the promising samples in these areas during SAO classification. Compared with HEVC test mode (HM11.0), experimental results indicate that the proposed method achieves an average 0.13%, 0.14%, and 0.12% BD-bitrate reduction (equivalent to 0.0055dB, 0.0058dB, and 0.0097dB increases in PSNR) in CTU sizes of 64 × 64, 32 × 32, and 16 × 16, respectively.

In this paper we propose an adaptive bitrate (ABR) algorithm that selects the video rates by observing and controlling the playback buffer. In a Hypertext Transfer Protocol (HTTP) adaptive streaming session, buffer dynamics largely depend on the chunk sizes. First, we present the algorithm that selects the next video rates based on the current buffer level, while considering the upcoming chunk sizes. We aim to exploit the variation of chunk sizes of a variable bitrate (VBR) encoded video to optimize the tradeoff between the video rate and buffer underflow events while keeping a low frequency of video rate changes. To evaluate the performance of the proposed algorithm, we consider three scenarios: (i) the video flow does not compete with any cross traffic, (ii) the video flow shares the bottleneck link with competing TCP traffic, and (iii) two HTTP clients share the bottleneck. We show that the proposed algorithm selects a high playback video rate and avoids unnecessary rebuffering while keeping a low frequency of video rate changes. Furthermore, we show that the proposed algorithm changes the video quality gradually to guarantee the user's viewing experience.

Noise and area consumption has been a trade-off in circuit design. Especially for switched-capacitor filters (SCF), kT/C noise gives a limitation to the minimum value of unit capacitance. In case of SCFs with a large capacitance spread, this limitation will result in a large area consumption due to large capacitors. This paper introduces a technique to reduce capacitance spread using charge scaling. It will be shown that this technique can reduce total capacitance of SCFs without deteriorating their noise performances. A design method to reduce the output noise of SC low-pass filters (LPF) based on the combination of cut-set scaling, charge scaling and adaptive configuration is proposed. The proposed technique can reduce the output noise voltage by 30% for small input signals.

Bag of Visual Words (BoVW) is an effective framework for image retrieval. Query expansion (QE) further boosts retrieval performance by refining a query with relevant visual words found from the geometric consistency check between the query image and highly ranked retrieved images obtained from the first round of retrieval. Since QE checks the pairwise consistency between query and highly ranked images, its performance may deteriorate when there are slight degradations in the query image. We propose Query Bootstrapping as a variant of QE to circumvent this problem by using the consistency of highly ranked images instead of pairwise consistency. In so doing, we regard frequently co-occurring visual words in highly ranked images as relevant visual words. Frequent itemset mining (FIM) is used to find such visual words efficiently. However, the FIM-based approach requires sensitive parameters to be fine-tuned, namely, support (min/max-support) and the number of top ranked images (top-k). Here, we propose an adaptive support algorithm that adaptively determines both the minimum support and maximum support by referring to the first round's retrieval list. Selecting relevant images by using a geometric consistency check further boosts retrieval performance by reducing outlier images from a mining process. An important parameter for the LO-RANSAC algorithm that is used for the geometric consistency check, namely, inlier threshold, is automatically determined by our algorithm. We further introduce tf-fi-idf on top of tf-idf in order to take into account the frequency of inliers (fi) in the retrieved images. We evaluated the performance of QB in terms of mean average precision (mAP) on three benchmark datasets and found that it gave significant performance boosts of 5.37%, 9.65%, and 8.52% over that of state-of-the-art QE on Oxford 5k, Oxford 105k, and Paris 6k, respectively.

The inertia weight is the control parameter that tunes the balance between the exploration and exploitation movements in particle swarm optimization searches. Since the introduction of inertia weight, various strategies have been proposed for determining the appropriate inertia weight value. This paper presents a brief review of the various types of inertia weight strategies which are classified and discussed in four categories: static, time varying, dynamic, and adaptive. Furthermore, a novel entropy-based gain regulator (EGR) is proposed to detect the evolutionary state of particle swarm optimization in terms of the distances from particles to the current global best. And then apply proper inertia weights with respect to the corresponding distinct states. Experimental results on five widely applied benchmark functions show that the EGR produced significant improvements of particle swarm optimization.

Recent development in network technology can realize the control of a remote plant by a digital controller. However, there is a delay caused by data transmission of control inputs and outputs. The delay degrades the control performance without taking it into consideration. In general, it is a difficult problem to identify the delay beforehand. We also assume that the plant's parameters have uncertainty. To solve the problem, we use reinforcement learning to achieve optimal digital control. First, we consider state feedback control. Next, we consider the case where the plant's outputs are observed, and apply reinforcement learning to output feedback control. Finally, we demonstrate by simulation that the proposed control method can search for the optimal gain and that it can adapt to the change of the delay.

The MIMO technique can improve system performance of not only communication system but also of radar systems. In this paper, we apply the MIMO radar with enhanced angular resolution to the indoor location estimation of humans. The Khatri-Rao (KR) matrix product is also adopted for further angular resolution enhancement. We show that the MIMO radar with the KR matrix product processing can increase the number of virtual elements effectively with suitable element arrangement, hence higher angular resolution can be realized. In general, the KR matrix product processing is not suitable for coherent radar because of signal correlation. However, when targets signals have enough Doppler frequency differential against each other, this approach works well because the signals are decorrelated. In addition, Doppler filtering is introduced to remove unwanted responses of stationary objects which make human detection difficult with conventional methods. Computer simulation and experimental results are provided to show performance of the proposed method.

Since digital hearing aids are sensitive to time delay and power consumption, the computational complexity of noise reduction must be reduced as much as possible. Therefore, some complicated algorithms based on the analysis of the time-frequency domain are very difficult to implement in digital hearing aids. This paper presents a new approach that yields an improved noise reduction algorithm with greatly reduce computational complexity for multi-channel digital hearing aids. First, the sub-band sound pressure level (SPL) is calculated in real time. Then, based on the calculated sub-band SPL, the noise in the sub-band is estimated and the possibility of speech is computed. Finally, a posteriori and a priori signal-to-noise ratios are estimated and the gain function is acquired to reduce the noise adaptively. By replacing the FFT and IFFT transforms by the known SPL, the proposed algorithm greatly reduces the computation loads. Experiments on a prototype digital hearing aid show that the time delay is decreased to nearly half that of the traditional adaptive Wiener filtering and spectral subtraction algorithms, but the SNR improvement and PESQ score are rather satisfied. Compared with modulation frequency-based noise reduction algorithm, which is used in many commercial digital hearing aids, the proposed algorithm achieves not only more than 5dB SNR improvement but also less time delay and power consumption.

The characteristic basis function method using improved primary characteristic basis functions (IP-CBFM) has been proposed as a technique for high-precision analysis of monostatic radar cross section (RCS) of a scattering field in a specific coordinate plane. IP-CBFM is a method which reduces the number of CBF necessary to express a current distribution by combining secondary CBF calculated for each block of the scatterer with the primary CBF to form a single improved primary CBF (IP-CBF). When the proposed technique was evaluated by calculating the monostatic RCS of a perfect electric conductor plate and cylinder, it was found that solutions corresponding well with analysis results from conventional CBFM can be obtained from small-scale matrix equations.

Microwave systems have a number of promising applications in surveillance and monitoring systems. The main advantage of microwave systems is their ability to detect targets at distance under adverse conditions such as dim, smoky, and humid environments. Specifically, the wide bandwidth of ultra-wideband radar enables high range resolution. In a previous study, we proposed an accurate shape estimation algorithm for multiple targets using multiple ultra-wideband Doppler interferometers. However, this algorithm produces false image artifacts under conditions with severe interference. The present paper proposes a technique to suppress such false images by detecting inconsistent combinations of the radial velocity and time derivative of image positions. We study the performance of the proposed method through numerical simulations of a two-dimensional section of a moving human body, and demonstrate the remarkable performance of the proposed method in suppressing false image artifacts in many scenarios.

With extensive use of automotive radars, mutual interference between radars has become a crucial issue, since it increases the noise floor in the frequency domain triggering frequent false alarms and unsafe decision. This paper introduces a mathematical model for a frequency-modulated continuous-wave (FMCW) radar in interfering environments. In addition, this paper proposes a time-domain interference suppression method to provide anti-interference capability regardless of the signal-to-interference ratio. Numerical results are presented to verify the performance of a 77GHz FMCW radar systme with the proposed method in interference-rich environments.

The Lyapunov stability theory-based adaptive filter (LST-AF) is a robust filtering algorithm which the tracking error quickly converges to zero asymptotically. Recently, the software module of the LST-AF algorithm is effectively used in engineering applications such as tracking, prediction, noise cancellation and system identification problems. Therefore, hardware implementation becomes necessary in many cases where real time procedure is needed. In this paper, an implementation of the LST-AF algorithm on Field Programmable Gate Arrays (FPGA) is realized for the first time to our knowledge. The proposed hardware implementation on FPGA is performed for two main benchmark problems; i) tracking of an artificial signal and a Henon chaotic signal, ii) estimation of filter parameters using a system identification model. Experimental results are comparatively presented to test accuracy, performance and logic occupation. The results show that our proposed hardware implementation not only conserves the capabilities of software versions of the LST-AF algorithm but also achieves a better performance than them.

Ultra-wideband radar exhibits high range resolution, and excellent capability for penetrating dielectric media, especially when using lower frequency microwaves. Thus, it has a great potential for innovative non-destructive testing of aging roads or bridges or for non-invasive medical imaging applications. In this context, we have already proposed an accurate dielectric constant estimation method for a homogeneous dielectric medium, based on a geometrical optics (GO) approximation, where the dielectric boundary points and their normal vectors are directly reproduced using the range point migration (RPM) method. In addition, to compensate for the estimation error incurred by the GO approximation, a waveform compensation scheme employing the finite-difference time domain (FDTD) method was incorporated. This paper shows the experimental validation of this method, where a new approach for suppressing the creeping wave along the dielectric boundary is also introduced. The results from real observation data validate the effectiveness of the proposed method in terms of highly accurate dielectric constant estimation and embedded object boundary reconstruction.

A regulated charge pump (CP) with an extended range of load current is presented. A power-efficient adaptive feedback controller is adopted. Verified by a 0.18µm CMOS technology with a power supply of 3.3V, the measured output voltage of the CP is regulated above 5V when the load current is varied from 2.5mA to 50mA. The measured power efficiency spans from 81.7% at lighter load to 75.2% when load current is 50mA. The measured output ripples are small and below 24mV.

This paper proposes a method of watermarking for digital audio signals based on adaptive phase modulation. Audio signals are usually non-stationary, i.e., their own characteristics are time-variant. The features for watermarking are usually not selected by combining the principle of variability, which affects the performance of the whole watermarking system. The proposed method embeds a watermark into an audio signal by adaptively modulating its phase with the watermark using IIR all-pass filters. The frequency location of the pole-zero of an IIR all-pass filter that characterizes the transfer function of the filter is adapted on the basis of signal power distribution on sub-bands in a magnitude spectrum domain. The pole-zero locations are adapted so that the phase modulation produces slight distortion in watermarked signals to achieve the best sound quality. The experimental results show that the proposed method could embed inaudible watermarks into various kinds of audio signals and correctly detect watermarks without the aid of original signals. A reasonable trade-off between inaudibility and robustness could be obtained by balancing the phase modulation scheme. The proposed method can embed a watermark into audio signals up to 100 bits per second with 99% accuracy and 6 bits per second with 94.3% accuracy in the cases of no attack and attacks, respectively.

We propose an effective technique for estimation of targets by ground penetrating radar (GPR) using model-based compressive sensing (CS). We demonstrate the technique's performance by applying it to detection of buried landmines. The conventional CS algorithm enables the reconstruction of sparse subsurface images using much reduced measurement by exploiting its sparsity. However, for landmine detection purposes, CS faces some challenges because the landmine is not exactly a point target and also faces high level clutter from the propagation in the medium. By exploiting the physical characteristics of the landmine using model-based CS, the probability of landmine detection can be increased. Using a small pixel size, the landmine reflection in the image is represented by several pixels grouped in a three dimensional plane. This block structure can be used in the model based CS processing for imaging the buried landmine. The evaluation using laboratory data and datasets obtained from an actual mine field in Cambodia shows that the model-based CS gives better reconstruction of landmine images than conventional CS.

This letter proposes a situation-adaptive detection algorithm for the improved efficiency of the detection performance and complexity in the MIMO-OFDM system. The proposed algorithm adaptively uses the QRD-M, DFE, and iterative detection scheme in according to the detection environment. Especially, the proposed algorithm effectively reduces the occurrence probability of error in the successive interference cancellation procedure by the unit of the spatial stream. The simulations demonstrate that the adaptive detection method using the proposed algorithm provides a better trade-off between detection performance and complexity in the MIMO-OFDM system.

The RCS of a radar target is an important factor related with the radar performance such as detection, tracking and classification. When dealing with the design of 26/79GHz automotive surveillance radar system, it is essential to know individual RCS of typical vehicles and pedestrian. However, there are few papers related to the RCS measurement at 26 and 79GHz. In this letter, the RCS measurements of typical vehicles and pedestrian were performed in a large-scale anechoic chamber room and the characteristics are discussed.

In this letter, an effective acoustic feedback cancellation algorithm is proposed based on the normalized sub-band adaptive filter (NSAF). To improve the confliction between fast convergence rate and low misalignment in the NSAF algorithm, a variable step size is designed to automatically vary according to the update state of the filter. The update state of the filter is adaptively detected via the normalized distance between the long term average and the short term average of the tap-weight vector. Simulation results demonstrate that the proposed algorithm has superior performance in terms of convergence rate and misalignment.

Analogy-based software effort estimation has gained a considerable amount of attention in current research and practice. Its excellent estimation accuracy relies on its solution adaptation stage, where an effort estimate is produced from similar past projects. This study proposes a solution adaptation technique named LSA-X that introduces an approach to exploit the potential of productivity factors, i.e., project variables with a high correlation with software productivity, in the solution adaptation stage. The LSA-X technique tailors the exploitation of the productivity factors with a procedure based on the Linear Size Adaptation (LSA) technique. The results, based on 19 datasets show that in circumstances where a dataset exhibits a high correlation coefficient between productivity and a related factor (r≥0.30), the proposed LSA-X technique statistically outperformed (95% confidence) the other 8 commonly used techniques compared in this study. In other circumstances, our results suggest using any linear adaptation technique based on software size to compensate for the limitations of the LSA-X technique.