This paper proposes a partial transmit sequences (PTS)-based PAPR reduction method and a phase factor estimation method without side information for OFDM systems with QPSK and 16QAM modulation. In the transmitter, an iterative algorithm that minimizes the p-norm of a transmitted signal determines phase factors to reduce PAPR. Unlike conventional methods, the phase factors are allowed to take continuous values in a limited range. In the receiver, the phase factor is blindly estimated by evaluating the phase differences between the equalizer's output and its closest constellation points. Simulation results show that the proposed PAPR reduction method is more computationally efficient than the conventional PTS. Moreover, the combined use of the two proposed methods achieves a satisfactory tradeoff between PAPR and BER by limiting the phase factors properly.

Evolvable hardware (EHW) based on field-programmable gate arrays (FPGAs) opens up new possibilities towards building efficient adaptive system. State of the art EHW systems based on virtual reconfiguration and dynamic partial reconfiguration (DPR) both have their limitations. The former has a huge area overhead and circuit delay, and the later has slow configuration speed and low flexibility. Therefore a general low-cost fast hybrid reconfiguration architecture is proposed in this paper, which merges the high flexibility of virtual reconfiguration and the low resource cost of DPR. Moreover, the bitstream relocation technology is introduced to save the bitstream storage space, and the discrepancy configuration technology is adopted to reduce reconfiguration time. And an embedded RAM core is adopted to store bitstreams which accelerate the reconfiguration speed further. The proposed architecture is evaluated by the online evolution of digital image filter implemented on the Xilinx Virtex-6 FPGA development board ML605. And the experimental results show that our system has lower resource overhead, higher operating frequency, faster reconfiguration speed and less bitstream storage space in comparison with the previous works.

Aiming at solving the performance degradation caused by the covariance matrix mismatch in wideband beamforming for conformal arrays, a novel adaptive beamforming algorithm is proposed in this paper. In this algorithm, the interference-plus-noise covariance matrix is firstly reconstructed to solve the desired signal contamination problem. Then, a sparse reconstruction method is utilized to reduce the high computational cost and the requirement of sampling data. A novel cost function is formulated by the focusing matrix and singular value decomposition. Finally, the optimization problem is efficiently solved in a second-order cone programming framework. Simulation results using a cylindrical array demonstrate the effectiveness and robustness of the proposed algorithm and prove that this algorithm can achieve superior performance over the existing wideband beamforming methods for conformal arrays.

The combination of GPS measurements with the dynamic model via a Kalman filter or an extended Kalman filter, also known as GPS based reduced dynamic orbit determination (RDOD) techniques, have been widely used for accurate and real time navigation of satellites in low earth orbit (LEO). In previous studies, the GPS measurement noise variance is empirically taken as a constant, which is not reasonable because of insufficient prior information or dynamic environment. An improper estimate of the measurement noise may lead to poor performance or even divergence of the filter. In this letter, an adaptive extended Kalman filter (AEKF)-based approach using GPS dual-frequency pseudo-range measurements is presented, where the GPS pseudo-range measurement noise variance is adaptively estimated by the Carrier to Noise Ratio (C/N0) from the tracking loop of GPS receiver. The simulation results show that the proposed AEKF approach can achieve apparent improvements of the position accuracy and almost brings no extra computational burdens for satellite borne processor.

A Tikhonov regularized RLS algorithm with an exponential weighting factor, i.e., a leaky RLS (LRLS) algorithm was proposed by the author. A quadratic version of the LRLS algorithm also exists in the literature of adaptive filters. In this letter, a cubic version of the LRLS filter which is computationally efficient is proposed when the length of the adaptive filter is short. The proposed LRLS filter includes only a divide per iteration although its multiplications and additions increase in number. Simulation results show that the proposed LRLS filter is faster for its short length than the existing quadratic version of the LRLS filter.

This paper proposes a novel wavelet de-noising scheme regarding the existing burst noises that consist of background and impulsive noises in power-line communications. The proposed de-noising scheme employs multi-level threshold functions to efficiently and adaptively reduce the given burst noises. The experiment results show that the proposed de-noising scheme significantly outperformed the conventional schemes.

Scaling the supply voltage (Vdd) and threshold voltage (Vth) for minimizing the energy consumption of processors dynamically is highly desired for applications such as wireless sensor network and Internet of Things (IoT). In this paper, we refer to the pair of Vdd and Vth, which minimizes the energy consumption of the processor under a given operating condition, as a minimum energy point (MEP in short). Since the MEP is heavily dependent on an operating condition determined by a chip temperature, an activity factor, a process variation, and a performance required for the processor, it is not very easy to closely track the MEP at runtime. This paper proposes a simple but effective algorithm for dynamically tracking the MEP of a processor under a wide range of operating conditions. Gate-level simulation of a 32-bit RISC processor in a 65nm process demonstrates that the proposed algorithm tracks the MEP under a situation that operating condition widely vary.

This paper considers a design method of a discrete-time adaptive output feedback control system with a feedforward input based on almost strict positive realness (ASPR-ness). The proposed scheme utilizes the property of ASPR of the controlled plant, and the reference signal is used as feedforward input. The parallel feedforward compensator (PFC) which renders an ASPR augmented controlled plant is also investigated. Besides, it is shown that the output of original plant can track reference signal perfectly without any steady state error. The effectiveness of the proposed scheme is confirmed through a pilot-scale temperature control system.

Scaling supply voltage (VDD) and threshold voltage (Vth) dynamically has a strong impact on energy efficiency of CMOS LSI circuits. Techniques for optimizing VDD and Vth simultaneously under dynamic workloads are thus widely investigated over the past 15 years. In this paper, we refer to the optimum pair of VDD and Vth, which minimizes the energy consumption of a circuit under a specific performance constraint, as a minimum energy point (MEP). Based on the simple transregional models of a CMOS circuit, this paper derives a simple necessary and sufficient condition for the MEP operation. The simple condition helps find the MEP of CMOS circuits. Measurement results using standard-cell based memories (SCMs) fabricated in a 65-nm process technology also validate the condition derived in this paper.

Recent Network on Chip (NoC) design must take the thermal issue into consideration due to its great impact on the network performance and reliability, especially for 3D NoC. In this work, we design a virtual channel based fully adaptive routing algorithm for the runtime 3D NoC thermal-aware management. To improve the network throughput and latency, we use two virtual channels for each horizontal direction and design a routing function which can not only avoid deadlock and livelock, but also ensure high adaptivity and routability in the throttled network. For path selection, we design a strategy that takes priority to the distance, but also considers path diversity and traffic state. For throttling information collection, instead of transmitting the topology information of the whole network, we use a 12 bits register to reserve the router state for one hop away, which saves the hardware cost largely and decreases the network latency. In the experiments, we test our proposed routing algorithm in different states with different sizes, and the proposed algorithm shows better network latency and throughput with low power compared with traditional algorithms.

We describe a field experiment of flexible modulation format adaptation on a real-time 400Gbit/s/ch DSP-LSI. This real-time DSP-LSI features OSNR estimation, practical simplified back propagation, and high gain soft-decision forward error correction. With these techniques, we have successfully demonstrated modulation format allocation and transmission of 56-channel 400Gbit/s-2SC-PDM-16QAM and 200Gbit/s-2SC-PDM-QPSK signals in 216km and 3246km standard single mode fiber, respectively.

Improving energy efficiency is critical for embedded systems in our rapidly evolving information society. Near real-time data processing tasks, such as multimedia streaming applications, exhibit a common fact that their deadline periods are longer than their input intervals due to buffering. In general, executing tasks at lower performance is more energy efficient. On the other hand, higher performance is necessary for huge tasks to meet their deadlines. To minimize the energy consumption while meeting deadlines strictly, adaptive task scheduling including dynamic performance mode selection is very important. In this work, we propose an energy efficient slack-based task scheduling algorithm for such tasks by adapting to task size variations and applying DVFS with the help of statistical analysis. We confirmed that our proposal can further reduce the energy consumption when compared to oracle frame-based scheduling.

In current video encoding systems, the acquisition process is independent from the video encoding process. In order to compensate for the independence, pre-filters prior to the encoder are used. However, conventional pre-filters are designed under constraints on the temporal resolution, so they are not optimized enough in terms of coding efficiency. By relaxing the restriction on the temporal resolution of current video encoding systems, there is a good possibility to generate a video signal suitable for the video encoding process. This paper proposes a video generation method with an adaptive temporal filter that utilizes a temporally over-sampled signal. The filter is designed based on dynamic-programming. Experimental results show that the proposed method can reduce encoding rate on average by 3.01 [%] compared to the constant mean filter.

Improved fractional variable tap-length adaptive algorithm that contains Sigmoid limited fluctuation function and adaptive variable step-size of tap-length based on fragment-full error is presented. The proposed algorithm can solve many deficiencies in previous algorithm, comprising small convergence rate and weak anti-interference ability. The parameters are able to modify reasonably on the basis of different situations. The Sigmoid constrained function can decrease the fluctuant amplitude of the instantaneous errors effectively and improves the ability of anti-noise interference. Simulations demonstrate that the proposed algorithm equips better performance.

Many basic tasks in Wireless Sensor Networks (WSNs) rely heavily on the availability and accuracy of target locations. Since the number of targets is usually limited, localization benefits from Compressed Sensing (CS) in the sense that measurements can be greatly reduced. Though some CS-based localization schemes have been proposed, all of these solutions make an assumption that all targets are located on a pre-sampled and fixed grid, and perform poorly when some targets are located off the grid. To address this problem, we develop an adaptive dictionary algorithm where the grid is adaptively adjusted. To achieve this, we formulate localization as a joint parameter estimation and sparse signal recovery problem. Additionally, we transform the problem into a tractable convex optimization problem by using Taylor approximation. Finally, the block coordinate descent method is leveraged to iteratively optimize over the parameters and sparse signal. After iterations, the measurements can be linearly represented by a sparse signal which indicates the number and locations of targets. Extensive simulation results show that the proposed adaptive dictionary algorithm provides better performance than state-of-the-art fixed dictionary algorithms.

In the field of adaptive notch filtering, Monotonically Increasing Gradient (MIG) algorithm has recently been proposed by Sugiura and Shimamura [1], where it is claimed that the MIG algorithm shows monotonically increasing gradient characteristics. However, our analysis has found that the underlying theory in [1] includes crucial errors. This letter shows that the formulation of the gradient characteristics in [1] is incorrect, and reveals that the MIG algorithm fails to realize monotonically increasing gradient characteristics when the input signal includes white noise.

Based on the reconstruction of the augmented interference-plus-noise (IPN) covariance matrix (CM) and the estimation of the desired signal's extended steering vector (SV), we propose a novel robust widely linear (WL) beamforming algorithm. Firstly, an extension of the iterative adaptive approach (IAA) algorithm is employed to acquire the spatial spectrum. Secondly, the IAA spatial spectrum is adopted to reconstruct the augmented signal-plus-noise (SPN) CM and the augmented IPNCM. Thirdly, the extended SV of the desired signal is estimated by using the iterative robust Capon beamformer with adaptive uncertainty level (AU-IRCB). Compared with several representative robust WL beamforming algorithms, simulation results are provided to confirm that the proposed method can achieve a better performance and has a much lower complexity.

To improve the quality of waveforms and achieve a high input power factor (IPF) for matrix rectifier, a novel quasi sliding mode control (SMC) with adaptive compensation is proposed in this letter. Applying quasi-SMC can effective obviate the disturbances of time delay and spatial lag, and SMC based on continuous function is better than discontinuous function to eliminate the chattering. Furthermore, compared with conventional compensation, an adaptive quasi-SMC compensation without any accurate detection for internal parameters is easier to be implementated, which has shown a superior advance. Theoretical analysis and experiments are carried out to validate the correctness of the novel control scheme.

HTTP Adaptive Streaming (HAS) has become a popular solution for media delivery over the mobile Internet. However, existing HAS systems are based on the pull-based HTTP/1.1 protocol, leading to high overheads (e.g., in terms of energy, processing, bandwidth) for clients, servers, as well as network nodes. The new HTTP/2 protocol provides a server push feature, which allows the client to receive more than one video segment for each request in order to reduce request-related overheads. In this study, we propose an adaptation method to leverage the push feature of HTTP/2. Our method takes into account not only the request-related overhead but also buffer stability and gradual transitions. The experimental results show that our proposed method performs well under strong throughput variations of mobile networks.

This letter presents a Preferable Mad-y (PMad-y) turn model and Low-cost Adaptive and Fault-tolerant Routing (LAFR) method that use one and two virtual channels along the X and Y dimensions for 2D mesh Network-on-Chip (NoC). Applying PMad-y rules and using the link status of neighbor routers within 2-hops, LAFR can tolerate multiple faulty links and routers in more complicated faulty situations and impose the reliability of network without losing the performance of network. Simulation results show that LAFR achieves better saturation throughput (0.98% on average) than those of other fault-tolerant routing methods and maintains high reliability of more than 99.56% on average. For achieving 100% reliability of network, a Preferable LAFR (PLAFR) is proposed.