GaN solid state power amplifiers (SSPA) for wireless power transfer and microwave heating have been reviewed. For wireless power transfer, 9 W output power with 79% power added efficiency at 5.8 GHz has been achieved. For microwave heating, 450 W output power with 70% drain efficiency at 2.45 GHz has been achieved. Microwave power concentration and uniform microwave heating by phase control of multiple SSPAs are demonstrated.

This brief presents A 16/32Gb/s dual-mode transmitter including a linearity calibration loop to maintain amplitude linearity of the SST driver. Linearity detection and corresponding master-slave power supply circuits are designed to implement the proposed architecture. The proposed transmitter is manufactured in a 22nm FD-SOI process. The linearity calibration loop reduces the peak INL errors of the transmitter by 50%, and the RLM rises from 92.4% to 98.5% when the transmitter is in PAM4 mode. The chip area of the transmitter is 0.067mm2, while the proposed linearity enhanced part is 0.05×0.02mm2 and the total power consumption is 64.6mW with a 1.1V power supply. The linearity calibration loop can be detached from the circuit without consuming extra power.

The traditional RANSAC samples uniformly in the dataset which is not efficient in the task with rich prior information. This letter proposes GUISAC (Guided Sample Consensus), which samples with the guidance of various prior information. In image matching, GUISAC extracts seed points sets evenly on images based on various prior factors at first, then it incorporates seed points sets into the sampling subset with a growth function, and a new termination criterion is used to decide whether the current best hypothesis is good enough. Finally, experimental results show that the new method GUISAC has a great advantage in time-consuming than other similar RANSAC methods, and without loss of accuracy.

Matching circuits using LC elements are widely applied to high-frequency circuits such as power amplifier (PA) and low-noise amplifier (LNA). For determining matching condition of multi-stage matching circuits, this paper shows that any multi-stage LC-Ladder matching circuit with resistive termination can be decomposed to the extended L-type matching circuits with resistive termination containing negative elements where the analytical solution exists. The matching conditions of each extended L-type matching circuit are obtained easily from the termination resistances and the design frequency. By synthesizing these simple analysis solutions, it is possible to systematically determine the solution even in a large number of stages (high order) matching circuits.

High Efficiency Video Coding (HEVC) has a better coding efficiency comparing with H.264/AVC. However, performance enhancement results in increased computational complexity which is mainly brought by the quadtree based coding tree unit (CTU). In this paper, an early termination algorithm based on AdaBoost classifier for coding unit (CU) is proposed to accelerate the process of searching the best partition for CTU. Experiment results indicate that our method can save 39% computational complexity on average at the cost of increasing Bjontegaard-Delta rate (BD-rate) by 0.18.

We have previously introduced the static dependency pair method that proves termination by analyzing the static recursive structure of various extensions of term rewriting systems for handling higher-order functions. The key is to succeed with the formalization of recursive structures based on the notion of strong computability, which is introduced for the termination of typed λ-calculi. To bring the static dependency pair method close to existing functional programs, we also extend the method to term rewriting models in which functional abstractions with patterns are permitted. Since the static dependency pair method is not sound in general, we formulate a class; namely, accessibility, in which the method works well. The static dependency pair method is a very natural reasoning; therefore, our extension differs only slightly from previous results. On the other hand, a soundness proof is dramatically difficult.

By tracking the changing rate of hard decisions during every two consecutive iterations of the alternating direction method of multipliers (ADMM) penalized decoding, an efficient early termination (ET) criterion is proposed to improve the convergence rate of ADMM penalized decoder for low-density parity-check (LDPC) codes. Compared to the existing ET criterion for ADMM penalized decoding, the proposed method can reduce the average number of iterations significantly at low signal-to-noise ratios with negligible performance degradation.

In this letter, a novel on-orbit estimation and calibration method of GPS antenna geometry offsets for attitude determination of LEO satellites is proposed. Both baseline vectors in the NED coordinate system are achieved epoch-by-epoch firstly. Then multiple epochs' baseline vectors are united to compute all the offsets via an UKF for a certain long time. After on-orbit estimation and calibration, instantaneous and accurate attitude can be achieved. Numerical results show that the proposed method can obtain the offsets of each baseline in all directions with high accuracy estimation and small STDs, and effective attitudes can be achieved after antenna geometry calibration using the estimated offsets. The high accuracy give the proposed scheme a strong practical-oriented ability.

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.

In this paper, Epitaxial (Epi) Junction Termination Extension (JTE) technique for silicon carbide (SiC) power device is presented. Unlike conventional JTE, the Epi-JTE doesn't require high temperature (about 500°C) implantation process. Thus, it doesn't require high temperature (about 1700°C) process for implanted dose activation and surface defect curing. Therefore, the manufacturing cost will be decreased. Also, the fabrication process is very simple because the dose of the JTE is controlled by epitaxy growth. The blocking characteristic is analyzed through 2D-simulation for the proposed Epi-JTE. In addition, the effect was validated by experiment of fabricated SiC device with the Single-Zone-Epi-JTE. As a result, it has blocking capability of 79.4% compared to ideal parallel-plane junction breakdown.

In this paper, a novel scheme of the adaptive sampling of block compressive sensing is proposed for natural images. In view of the contents of images, the edge proportion in a block can be used to represent its sparsity. Furthermore, according to the edge proportion, the adaptive sampling rate can be adaptively allocated for better compressive sensing recovery. Given that there are too many blocks in an image, it may lead to a overhead cost for recording the ratio of measurement of each block. Therefore, K-means method is applied to classify the blocks into clusters and for each cluster a kind of ratio of measurement can be allocated. In addition, we design an iterative termination condition to reduce time-consuming in the iteration of compressive sensing recovery. The experimental results show that compared with the corresponding methods, the proposed scheme can acquire a better reconstructed image at the same sampling rate.

The recently proposed distributed adaptive direct position determination (D-ADPD) algorithm provides an efficient way to locating a radio emitter using a sensor network. However, this algorithm may be suboptimal in the situation of colored emitted signals. We propose an enhanced distributed adaptive direct position determination (EDA-DPD) algorithm. Simulations validate that the proposed EDA-DPD outperforms the D-ADPD in colored emitted signals scenarios and has the similar performance with the D-ADPD in white emitted signal scenarios.

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.

Automatic performance tuning of a practical application could be time-consuming and sometimes infeasible, because it often needs to evaluate the performances of a large number of code variants to find the best one. In this paper, hence, a light-weight rollback mechanism is proposed to evaluate each of code variants at a low cost. In the proposed mechanism, once one code variant of a target code block is executed, the execution state is rolled back to the previous state of not yet executing the block so as to repeatedly execute only the block to find the best code variant. It also has a feature of terminating a code variant whose execution time is longer than the shortest execution time so far. As a result, it can prevent executing the whole application many times and thus reduces the timing overhead of an auto-tuning process required for finding the best code variant.

Time redundancy is sometimes an only option for enhancing circuit reliability when the circuit area is severely restricted. In this paper, a time-redundant error-correction scheme, which is particularly suitable for coarse-grained reconfigurable arrays (CGRAs), is proposed. It judges the correctness of the executions by comparing the results of two identical runs. Once a mismatch is found, the second run is terminated immediately to start the third run, under the assumption that the errors tend to persist in many applications, for selecting the correct result in the three runs. The circuit area and reliability of the proposed method is compared with a straightforward implementation of time-redundancy and a selective triple modular redundancy (TMR). A case study on a CGRA revealed that the area of the proposed method is 1% larger than that of the implementation for the selective TMR. The study also shows the proposed scheme is up to 2.6x more reliable than the full-TMR when the persistent error is predominant.

With the successful adoption of link analysis techniques such as PageRank and web spam filtering, current web search engines well support “navigational search”. However, due to the use of a simple conjunctive Boolean filter in addition to the inappropriateness of user queries, such an engine does not necessarily well support “informational search”. Informational search would be better handled by a web search engine using an informational retrieval model combined with enhancement techniques such as query expansion and relevance feedback. Moreover, the realization of such an engine requires a method to prosess the model efficiently. In this paper we propose a novel extension of an existing top-k query processing technique to improve search efficiency. We add to it the technique utilizing a simple data structure called a “term-document binary matrix,” resulting in more efficient evaluation of top-k queries even when the queries have been expanded. We show on the basis of experimental evaluation using the TREC GOV2 data set and expanded versions of the evaluation queries attached to this data set that the proposed method can speed up evaluation considerably compared with existing techniques especially when the number of query terms gets larger.

For simply-typed term rewriting systems (STRSs) and higher-order rewrite systems (HRSs) a la Nipkow, we proposed a method for proving termination, namely the static dependency pair method. The method combines the dependency pair method introduced for first-order rewrite systems with the notion of strong computability introduced for typed λ-calculi. This method analyzes a static recursive structure based on definition dependency. By solving suitable constraints generated by the analysis, we can prove termination. In this paper, we extend the method to rewriting systems for functional programs (RFPs) with product, algebraic data, and ML-polymorphic types. Although the type system in STRSs contains only product and simple types and the type system in HRSs contains only simple types, our RFPs allow product types, type constructors (algebraic data types), and type variables (ML-polymorphic types). Hence, our RFPs are more representative of existing functional programs than STRSs and HRSs. Therefore, our result makes a large contribution to applying theoretical rewriting techniques to actual problems, that is, to proving the termination of existing functional programs.

A stopping criterion is an indispensable function to reduce unnecessary power consumption and decoding delay in turbo decoding. Until now, a common design philosophy in previous works has involved using the entire block of information from the MAP decoder and its input/output information to calculate the stopping index. It is an intuitive method but suffers from heavy memory requirements and high calculation complexity. In this paper, a low-complexity stopping criterion is proposed that avoids the aforementioned disadvantages. A general abstraction model is utilized to analyze the design bottleneck of stopping criteria. Instead of using an entire block of information, a compact representation derived from the internal information of the MAP decoder at a single time instant is used as a low-complexity stopping index. Theoretical explanation is provided to justify the feasibility of the proposed criterion. Simulation results show that the proposed criterion can reduce the complexity of stopping criterion dramatically while continuing to achieve the same level of performance as previous works.

The next generation video coding standard HEVC shows high coding performance compared with the H.264/AVC standard, but the computational complexity of the HEVC encoder (HM3.0) is significantly higher. In this letter, the early termination of the CU encoding algorithm is proposed to reduce the computational complexity in the HEVC encoder. The proposed method reduces the encoder complexity by 58.7%, while maintaining the same level of coding efficiency.

Nowadays, clustering is a popular tool for exploratory data analysis, with one technique being K-means clustering. Determining the appropriate number of clusters is a significant problem in K-means clustering because the results of the k-means technique depend on different numbers of clusters. Automatic determination of the appropriate number of clusters in a K-means clustering application is often needed in advance as an input parameter to the K-means algorithm. We propose a new method for automatic determination of the appropriate number of clusters using an extended co-occurrence matrix technique called a tri-co-occurrence matrix technique for multispectral imagery in the pre-clustering steps. The proposed method was tested using a dataset from a known number of clusters. The experimental results were compared with ground truth images and evaluated in terms of accuracy, with the numerical result of the tri-co-occurrence providing an accuracy of 84.86%. The results from the tests confirmed the effectiveness of the proposed method in finding the appropriate number of clusters and were compared with the original co-occurrence matrix technique and other algorithms.