For improving the fifth-generation mobile communication system, a highly efficient power amplifier must be designed for the base station. An outphasing amplifier is expected to be a solution for achieving high efficiency. We designed a combiner, one of the key components of the outphasing amplifier, using a serial Chireix combiner and fabricated an amplifier with a GaN HEMT, achieving 70% or more high efficiency up to 9 dB back-off power in an 800 MHz band. We also fabricated a 2 GHz-band outphasing amplifier with the same design. We applied digital predistortion (DPD) to control the balance of amplifying units in this amplifier and achieved an average efficiency of 65% under a 20 MHz modulation bandwidth.

This letter proposes a power amplifier (PA) with compact matching network. This structure is a parallel dual radial microstrip line in the output matching network branch. The input impedance expression based on the structure is deduced through theoretical analysis, and the load impedance that satisfies the class EFJ PA is obtained through the impedance expression. Compared with the traditional design method, this design method is simple and novel, and the structure is more compact. In order to further improve efficiency and expand bandwidth, the input matching network adopts a stepped impedance matching method. In order to verify the correctness of the design, a broadband high-efficiency PA was designed using GaN HEMT CGH40010F. The test results show that the drain efficiency is 61%-71% in the frequency band 1.4-3.8GHz, the saturated output power is 40.3-41.8dBm, and the size is 53×47mm2.

In the context of Web 2.0, the interaction between users and resources is more and more frequent in the process of resource sharing and consumption. However, the current research on resource pricing mainly focuses on the attributes of the resource itself, and does not weigh the interests of the resource sharing participants. In order to deal with these problems, the pricing mechanism of resource-user interaction evaluation based on multi-agent game theory is established in this paper. Moreover, the user similarity, the evaluation bias based on link analysis and punishment of academic group cheating are also included in the model. Based on the data of 181 scholars and 509 articles from the Wanfang database, this paper conducts 5483 pricing experiments for 13 months, and the results show that this model is more effective than other pricing models - the pricing accuracy of resource resources is 94.2%, and the accuracy of user value evaluation is 96.4%. Besides, this model can intuitively show the relationship within users and within resources. The case study also exhibits that the user's knowledge level is not positively correlated with his or her authority. Discovering and punishing academic group cheating is conducive to objectively evaluating researchers and resources. The pricing mechanism of scientific and technological resources and the users proposed in this paper is the premise of fair trade of scientific and technological resources.

The unattended malicious nodes pose great security threats to the integrity of the IoT sensor networks. However, preventions such as cryptography and authentication are difficult to be deployed in resource constrained IoT sensor nodes with low processing capabilities and short power supply. To tackle these malicious sensor nodes, in this study, the trust computing method is applied into the IoT sensor networks as a light weight security mechanism, and based on the theory of Chebyshev Polynomials for the approximation of time series, the trust data sequence generated by each sensor node is linearized and treated as a time series for malicious node detection. The proposed method is evaluated against existing schemes using several simulations and the results demonstrate that our method can better deal with malicious nodes resulting in higher correct packet delivery rate.

Many scientific and technological resources (STR) cannot meet the needs of real demand-based industrial services. To address this issue, the characteristics of scientific and technological resource services (STRS) are analyzed, and a method of the optimal combination of demand-based STR based on multi-community collaborative search is then put forward. An optimal combined evaluative system that includes various indexes, namely response time, innovation, composability, and correlation, is developed for multi-services of STR, and a hybrid optimal combined model for STR is constructed. An evaluative algorithm of multi-community collaborative search is used to study the interactions between general communities and model communities, thereby improving the adaptive ability of the algorithm to random dynamic resource services. The average convergence value CMCCSA=0.00274 is obtained by the convergence measurement function, which exceeds other comparison algorithms. The findings of this study indicate that the proposed methods can preferably reach the maximum efficiency of demand-based STR, and new ideas and methods for implementing demand-based real industrial services for STR are provided.

As an important type of science and technology service resource, energy consumption data play a vital role in the process of value chain integration between home appliance manufacturers and the state grid. Accurate electricity consumption prediction is essential for demand response programs in smart grid planning. The vast majority of existing prediction algorithms only exploit data belonging to a single domain, i.e., historical electricity load data. However, dependencies and correlations may exist among different domains, such as the regional weather condition and local residential/industrial energy consumption profiles. To take advantage of cross-domain resources, a hybrid energy consumption prediction framework is presented in this paper. This framework combines the long short-term memory model with an encoder-decoder unit (ED-LSTM) to perform sequence-to-sequence forecasting. Extensive experiments are conducted with several of the most commonly used algorithms over integrated cross-domain datasets. The results indicate that the proposed multistep forecasting framework outperforms most of the existing approaches.

A feedforward (FF) network using ΔΣ modulators is investigated to implement a non-binary analog-to-digital (A/D) converter. Weighting coefficients in the network are determined to suppress the generation of quantization noise. A moving average is adopted to prevent the analog signal amplitude from increasing beyond the allowable input range of the modulators. The noise transfer function is derived and used to estimate the signal-to-noise ratio (SNR). The FF network output is a non-uniformly distributed multi-level signal, which results in a better SNR than a uniformly distributed one. Also, the effect of the characteristic mismatch in analog components on the SNR is analyzed. Our behavioral simulations show that the SNR is improved by more than 30 dB, or equivalently a bit resolution of 5 bits, compared with a conventional first-order ΔΣ modulator.

Recent trends in designing filters involve development of sparse filters with coefficients that not only have real but also zero values. These sparse filters can achieve a high performance through optimizing the selection of the zero coefficients and computing the real (non-zero) coefficients. Designing an infinite impulse response (IIR) sparse filter is more challenging than designing a finite impulse response (FIR) sparse filter. Therefore, studies on the design of IIR sparse filters have been rare. In this study, we consider IIR filters whose coefficients involve zero value, called sparse IIR filter. First, we formulate the design problem as a linear programing problem without imposing any stability condition. Subsequently, we reformulate the design problem by altering the error function and prepare several possible denominator polynomials with stable poles. Finally, by incorporating these methods into successive thinning algorithms, we develop a new design algorithm for the filters. To demonstrate the effectiveness of the proposed method, its performance is compared with that of other existing methods.

On the Internet, there are lots of unused domain names that are not used for any actual services. Domain parking is a monetization mechanism for displaying online advertisements in such unused domain names. Some domain names used in cyber attacks are known to leverage domain parking services after the attack. However, the temporal relationships between domain parking services and malicious domain names have not been studied well. In this study, we investigated how malicious domain names using domain parking services change over time. We conducted a large-scale measurement study of more than 66.8 million domain names that have used domain parking services in the past 19 months. We reveal the existence of 3,964 domain names that have been malicious after using domain parking. We further identify what types of malicious activities (e.g., phishing and malware) such malicious domain names tend to be used for. We also reveal the existence of 3.02 million domain names that utilized multiple parking services simultaneously or while switching between them. Our study can contribute to the efficient analysis of malicious domain names using domain parking services.

The purpose of this paper is to find an automated pricing algorithm to calculate the real cost of each product by considering the associate costs of the business. The methodology consists of two main stages. A brief semi-structured survey and a mathematical calculation the expenses and adding them to the original cost of the offered products and services. The output of this process obtains the minimum recommended selling price (MRSP) that the business should not go below, to increase the likelihood of generating profit and avoiding the unexpected loss. The contribution of this study appears in filling the gap by calculating the minimum recommended price automatically and assisting businesses to foresee future budgets. This contribution has a certain limitation, where it is unable to calculate the MRSP of the in-house created products from raw materials. It calculates the MRSP only for the products bought from the wholesaler to be sold by the retailer.

It is found that the electrical resistance-length characteristic in an electroactive supercoiled polymer artificial muscle strongly depends on the temperature. This may come from the thermal expansion of coils in the artificial muscle, which increases the contact area of neighboring coils and results in a lower electrical resistance at a higher temperature. On the other hand, the electrical resistance-length characteristic collected during electrical driving seriously deviates from those collected at constant temperatures. Inhomogeneous heating during electrical driving seems to be a key for the deviation.

We address analysis and design problems of aggregate demand response systems composed of various consumers based on controllability to facilitate to design automated demand response machines that are installed into consumers to automatically respond to electricity price changes. To this end, we introduce a controllability index that expresses the worst-case error between the expected total electricity consumption and the electricity supply when the best electricity price is chosen. The analysis problem using the index considers how to maximize the controllability of the whole consumer group when the consumption characteristic of each consumer is not fixed. In contrast, the design problem considers the whole consumer group when the consumption characteristics of a part of the group are fixed. By solving the analysis problem, we first clarify how the controllability, average consumption characteristics of all consumers, and the number of selectable electricity prices are related. In particular, the minimum value of the controllability index is determined by the number of selectable electricity prices. Next, we prove that the design problem can be solved by a simple linear optimization. Numerical experiments demonstrate that our results are able to increase the controllability of the overall consumer group.

Because solar energy is intermittent and a ship's power-system load fluctuates and changes abruptly, in this work, the solar radiation parameters were adjusted according to the latitude and longitude of the ship and the change of the sea environment. An objective function was constructed that accounted for the cost and service life simultaneously to optimize the configuration of the marine diesel engine hybrid energy system. Finally, the improved artificial bee colony algorithm was used to optimize and obtain the optimal system configuration. The feasibility of the method was verified by ship navigation tests. This method exhibited better configuration performance optimization than the traditional methods.

In this paper, we used the data set of domain names Global Top 1M provided by Alexa to analyze the effectiveness of Fallback in ECN. For the same test server, we first negotiate a connection with Not-ECN-Capable, and then negotiate a connection with ECN-Capable, if the sender does not receive the response to ECN-Capable negotiation from the receiver by the end of retransmission timeout, it will enter the Fallback state, and switch to negotiating a connection with Not-ECN-Capable. By extracting the header fields of the TCP/IP packets, we confirmed that in most regions, connectivity will be slightly improved after Fallback is enabled and Fallback has a positive effect on the total time of the whole access process. Meanwhile, we provided the updated information about the characteristics related to ECN with Fallback in different regions by considering the geographical region distribution of all targeted servers.

This letter proposes a downlink multiple-input multiple-output (MIMO) non-orthogonal multiple access technique that mitigates multi-cell interference (MCI) at cell-edge users, regardless of the number of interfering cells, thereby improving the spectral efficiency. This technique employs specific receive beamforming vectors at the cell-edge users in clusters to minimize the MCI. Based on the receive beamforming vectors adopted by the cell-edge users, the transmit beamforming vectors for a base station (BS) and the receive beamforming vectors for cell-center users are designed to eliminate the inter-cluster interference and maximize the spectral efficiency. As each user can directly obtain its own receive beamforming vector, this technique does not require channel feedback from the users to a BS to design the receive beamforming vectors, thereby reducing the system overhead. We also derive the upper bound of the average sum rate achievable using the proposed technique. Finally, we demonstrate through simulations that the proposed technique achieves a better sum rate performance than the existing schemes and that the derived upper bound is valid.

Surveillance through aerial systems is in place for years. Such systems are expensive, and a large fleet is in operation around the world without upgrades. These systems have low resolution and multiple analog cameras on-board, with Digital Video Recorders (DVRs) at the control station. Generated digital videos have multi-scenes from multi-feeds embedded in a single video stream and lack video stabilization. Replacing on-board analog cameras with the latest digital counterparts requires huge investment. These videos require stabilization and other automated video analysis prepossessing steps before passing it to the mosaicing algorithm. Available mosaicing software are not tailored to segregate feeds from different cameras and scenes, automate image enhancements, and stabilize before mosaicing (image stitching). We present "AirMatch", a new automated system that first separates camera feeds and scenes, then stabilize and enhance the video feed of each camera; generates a mosaic of each scene of every feed and produce a super quality mosaic by stitching mosaics of all feeds. In our proposed solution, state-of-the-art video analytics techniques are tailored to work on videos from vintage cameras in aerial applications. Our new framework is independent of specialized hardware requirements and generates effective mosaics. Affine motion transform with smoothing Gaussian filter is selected for the stabilization of videos. A histogram-based method is performed for scene change detection and image contrast enhancement. Oriented FAST and rotated BRIEF (ORB) is selected for feature detection and descriptors in video stitching. Several experiments on a number of video streams are performed and the analysis shows that our system can efficiently generate mosaics of videos with high distortion and artifacts, compared with other commercially available mosaicing software.

In this paper, we advocate applying the concept of content-based wake-up to distributed estimation in wireless sensor networks employing wake-up receivers. With distributed estimation, where sensing data of multiple nodes are used for estimating a target observation, the energy consumption can be reduced by ensuring that only a subset of nodes in the network transmit their data, such that the collected data can guarantee the required estimation accuracy. In this case, a sink needs to selectively wake up those sensor nodes whose data can contribute to the improvement of estimation accuracy. In this paper, we propose wake-up signaling called estimative sampling (ES) that can selectively activate the desired nodes by using content-based wake-up control. The ES method includes a mechanism that dynamically searches for the desired nodes over a distribution of sensing data. With numerical results obtained by computer simulations, we show that the distributed estimation with ES method achieves lower energy consumption than conventional identity-based wake-up while satisfying the required accuracy. We also show that the proposed dynamic mechanism finely controls the trade-off between delay and energy consumption to complete the distributed estimation.

Delay Tolerant Networks (DTNs) are vulnerable to message flooding attacks in which a very large number of malicious messages are sent so that network resources are depleted. To address this problem, previous studies mainly focused on constraining the number of messages that nodes can generate per time slot by allowing nodes to monitor the other nodes' communication history. Since the adversaries may hide their attacks by claiming a false history, nodes exchange their communication histories and detect an attacker who has presented an inconsistent communication history. However, this approach increases node energy consumption since the number of communication histories increases every time a node communicates with another node. To deal with this problem, in this paper, we propose an energy-efficient defense against such message flooding attacks. The main idea of the proposed scheme is to time limit the communication history exchange so as to reduce the volume while ensuring the effective detection of inconsistencies. The advantage of this approach is that, by removing communication histories after they have revealed such inconsistencies, the energy consumption is reduced. To estimate such expiration time, analytical expressions based upon a Markov chain based message propagation model, are derived for the probability that a communication history reveals such inconsistency in an arbitrary time. Extensive performance evaluation results obtained by means of computer simulations and several performance criteria verify that the proposed scheme successfully improves the overall energy efficiency. For example, these performance results have shown that, as compared to other previously known defenses against message flooding attacks, the proposed scheme extends by at least 22% the battery lifetime of DTN nodes, while maintaining the same levels of protection.

Spectral graph theory provides an algebraic approach to investigate the characteristics of weighted networks using the eigenvalues and eigenvectors of a matrix (e.g., normalized Laplacian matrix) that represents the structure of the network. However, it is difficult to accurately represent the structures of large-scale and complex networks (e.g., social network) as a matrix. This difficulty can be avoided if there is a universality, such that the eigenvalues are independent of the detailed structure in large-scale and complex network. In this paper, we clarify Wigner's Semicircle Law for weighted networks as such a universality. The law indicates that the eigenvalues of the normalized Laplacian matrix of weighted networks can be calculated from a few network statistics (the average degree, average link weight, and square average link weight) when the weighted networks satisfy a sufficient condition of the node degrees and the link weights.

The need of telecommunications operators to reduce Capital and Operational Expenditures in networks which traffic is continuously growing has made them search for new alternatives to simplify and automate their procedures. Because of the different transport network segments and multiple layers, the deployment of end-to-end services is a complex task. Also, because of the multiple vendor existence, the control plane has not been fully homogenized, making end-to-end connectivity services a manual and slow process, and the allocation of computing resources across the entire network a difficult task. The new massive capacity requested by Data Centers and the new 5G connectivity services will urge for a better solution to orchestrate the transport network and the distributed computing resources. This article presents and demonstrates a Network Slicing solution together with an end-to-end service orchestration for transport networks. The Network Slicing solution permits the co-existence of virtual networks (one per service) over the same physical network to ensure the specific service requirements. The network orchestrator allows automated end-to-end services across multi-layer multi-domain network segments making use of the standard Transport API (TAPI) data model for both l0 and l2 layers. Both solutions will allow to keep up with beyond 5G services and the higher and faster demand of network and computing resources.