This paper presents a method combining array signal processing and adaptive noise cancellation to suppress unwanted ambient interferences in in situ measurement of radiated emissions of equipment. First, the signals received by the antenna array are processed to form a main data channel and an auxiliary data channel. The main channel contains the radiated emissions of the equipment under test and the attenuated ambient interferences. The auxiliary channel only contains the attenuated ambient interferences. Then, the adaptive noise cancellation technique is used to suppress the ambient interferences based on the correlation of the interferences in the main and auxiliary channels. The proposed method overcomes the problem that the ambient interferences in the two channels of the virtual chamber method are not correlated, and realizes the suppression of multi-source ambient noises in the use of fewer array elements. The results of simulation and experiment show that the proposed method can effectively extract radiated emissions of the equipment under test in complex electromagnetic environment. Finally, discussions on the effect of the beam width of the main channel and the generalization of the proposed method to three dimensionally distributed signals are addressed.

There are different types of social ties among people, and recognizing specialized types of relationship, such as family or friend, has important significance. It can be applied to personal credit, criminal investigation, anti-terrorism and many other business scenarios. So far, some machine learning algorithms have been used to establish social relationship inferencing models, such as Decision Tree, Support Vector Machine, Naive Bayesian and so on. Although these algorithms discover family members in some context, they still suffer from low accuracy, parameter sensitive, and weak robustness. In this work, we develop a Novel Family Relationship Recognition (NFRR) algorithm on telecom dataset for identifying one's family members from its contact list. In telecom dataset, all attributes are divided into three series, temporal, spatial and behavioral. First, we discover the most probable places of residence and workplace by statistical models, then we aggregate data and select the top-ranked contacts as the user's intimate contacts. Next, we establish Relational Spectrum Matrix (RSM) of each user and its intimate contacts to form communication feature. Then we search the user's nearest neighbors in labelled training set and generate its Specialized Family Spectrum (SFS). Finally, we decide family relationship by comparing the similarity between RSM of intimate contacts and the SFS. We conduct complete experiments to exhibit effectiveness of the proposed algorithm, and experimental results also show that it has a lower complexity.

In this paper, we propose a quality-level selection method for adaptive video streaming with scalable video coding (SVC). The proposed method works on the client with the dynamic adaptive streaming over HTTP (DASH) with SVC. The proposed method consists of two components: introducing segment group and a buffer-aware layer selection algorithm. In general, quality of experience (QoE) performance degrades due to stalling (playback buffer underflow), low playback quality, frequent quality-level switching, and extreme-down quality switching. The proposed algorithm focuses on reducing the frequent quality-level switching, and extreme-down quality switching without increasing stalling and degrading playback quality. In the proposed method, a SVC-DASH client selects a layer every G segments, called a segment group to prevent frequent quality-level switching. In addition, the proposed method selects the quality of a layer based on a playback buffer in a layer selection algorithm for preventing extreme-down switching. We implement the proposed method on a real SVC-DASH system and evaluate its performance by subjective evaluations of multiple users. As a result, we confirm that the proposed algorithm can obtain better mean opinion score (MOS) value than a conventional SVC-DASH, and confirm that the proposed algorithm is effective to improve QoE performance in SVC-DASH.

A novel algorithm is proposed for estimating the direction of arrival (DOA) of linear frequency modulated (LFM) signals for the uniform circular array (UCA). Firstly, the UCA is transformed into an equivalent virtual uniform linear array (ULA) using the mode-space algorithm. Then, the short time Fourier transform (STFT) of each element's output is worked out. We can obtain the spatial time-frequency distribution matrix of the virtual ULA by selecting the single-source time-frequency (t-f) points in the t-f plane and then get the signal subspace of the array. The characteristics nature of the Bessel function allow us to obtain the multiple invariance (MI) of the virtual ULA. So the multiple rotational invariant equation of the array can be obtained and its closed-form solution can be worked out using the multi-least-squares (MLS) criterion. Finally, the two dimensional (2-D) DOA estimation of LFM signals for UCA can be obtained. Numerical simulation results illustrate that the UCA-STFT-MI-ESPRIT algorithm proposed in this paper can improve the estimation precision greatly compared with the traditional ESPRIT-like algorithms and has much lower computational complexity than the MUSIC-like algorithms.

This paper presents a high power efficient pulse VCO with tail-filter for the chip-scale atomic clock (CSAC) application. The stringent power and clock stability specifications of next-generation CSAC demand a VCO with ultra-low power consumption and low phase noise. The proposed VCO architecture aims for the high power efficiency, while further reducing the phase noise using tail filtering technique. The VCO has been implemented in a standard 45nm SOI technology for validation. At an oscillation frequency of 5.0GHz, the proposed VCO achieves a phase noise of -120dBc/Hz at 1MHz offset, while consuming 1.35mW. This translates into an FoM of -191dBc/Hz.

In the current era of data science, data quality has a significant and critical impact on business operations. This is no different for the meteorological data encountered in the field of meteorology. However, the conventional methods of meteorological data quality control mainly focus on error detection and null-value detection; that is, they only consider the results of the data output but ignore the quality problems that may also arise in the workflow. To rectify this issue, this paper proposes the Total Meteorological Data Quality (TMDQ) framework based on the Total Quality Management (TQM) perspective, especially considering the systematic nature of data warehousing and process focus needs. In practical applications, this paper uses the proposed framework as the basis for the development of a system to help meteorological observers improve and maintain the quality of meteorological data in a timely and efficient manner. To verify the feasibility of the proposed framework and demonstrate its capabilities and usage, it was implemented in the Tamsui Meteorological Observatory (TMO) in Taiwan. The four quality dimension indicators established through the proposed framework will help meteorological observers grasp the various characteristics of meteorological data from different aspects. The application and research limitations of the proposed framework are discussed and possible directions for future research are presented.

The shift-and-add property (SAP) of a p-ary m-sequence {ak} with period N=pn-1 means that this sequence satisfies the equation {ak+η}+{ak+τ}={ak+λ} for some integers η, τ and λ. For an arbitrarily-given p-ary m-sequence {ak}, we develop an algebraic approach to determine the integer λ for the arbitrarily-given integers η and τ. And all trinomials can be given. Our calculation only depends on the reciprocal polynomial of the primitive polynomial which produces the given m-sequence {ak}, and the cyclotomic cosets mod pn-1.

5G infrastructures will heavily rely on novel paradigms such as Network Function Virtualization and Service Function Chaining to build complex business chains involving multiple parties. Although virtualization of security middleboxes looks a common practice today, we argue that this approach is inefficient and does not fit the peculiar characteristics of virtualized environments. In this paper, we outline a new paradigm towards autonomous security assurance in 5G infrastructures, leveraging service orchestration for semi-autonomous management and reaction, yet decoupling security management from service graph design. Our work is expected to improve the design and deployment of complex business chains, as well as the application of artificial intelligence and machine learning techniques over large and intertwined security datasets. We describe the overall concept and architecture, and discuss in details the three architectural layers. We also report preliminary work on implementation of the system, by introducing relevant technologies.

For any positive integer n, define an iterated function $f(n)=left{ egin{array}{ll} n/2, & mbox{ $n$ even, } 3n+1, & mbox{ $n$ odd. } end{array} ight.$ Suppose k (if it exists) is the lowest number such that fk(n)

The optical-transition edge sensors are single-photon detectors that can determine photon energies at visible to telecommunication wavelengths. They offer a high detection efficiency and negligible dark count, which are very attractive qualities for applications in quantum optics or bioimaging. This study reviews the operating principles of such detectors and the current status of their development.

Packet losses significantly degrade TCP performance in high-latency environments. This is because TCP needs at least one round-trip time (RTT) to recover lost packets. The recovery time will grow longer, especially in high-latency environments. TCP keeps transmission rate low while lost packets are recovered, thereby degrading throughput. To prevent this performance degradation, the number of retransmissions must be kept as low as possible. Therefore, we propose a scheme to apply a technology called “forward error correction” (FEC) to the entire TCP operation in order to improve throughput. Since simply applying FEC might not work effectively, three function, namely, controlling redundancy level and transmission rate, suppressing the return of duplicate ACKs, interleaving redundant packets, were devised. The effectiveness of the proposed scheme was demonstrated by simulation evaluations in high-latency environments.

Order preserving matching refers to the problem of reporting substrings in the text which are order-isomorphic to the pattern. In this paper, we show a simple heuristic which runs in linear time on average, based on finding the largest elements in each substring and checking their locations against that of the pattern. It is easy to implement and experimental results showed that the running time grows linearly.

In this paper, we investigate a cooperative communication system comprised of a source, a destination, and multiple decode-and-forward (DF) relays in the presence of a potential malicious eavesdropper is within or without the coverage area of the source. Based on the more general Nakagami-m fading channels, we analyze the security performance of the single-relay selection and multi-relay selection schemes for protecting the source against eavesdropping. In the single-relay selection scheme, only the best relay is chosen to assist in the source transmission. Differing from the single-relay selection, multi-relay selection scheme allows multiple relays to forward the source to the destination. We also consider the classic direct transmission as a benchmark scheme to compare with the two relay selection schemes. We derive the exact closed-form expressions of outage probability (OP) and intercept probability (IP) for the direct transmission, the single-relay selection as well as the multi-relay selection scheme over Nakagami-m fading channel when the eavesdropper is within and without the coverage area of the source. Moreover, the security-reliability tradeoff (SRT) of these three schemes are also analyzed. It is verified that the SRT of the multi-relay selection consistently outperforms the single-relay selection, which of both the single-relay and multi-relay selection schemes outperform the direct transmission when the number of relays is large, no matter the eavesdropper is within or without the coverage of the source. In addition, as the number of DF relays increases, the SRT of relay selection schemes improve notably. However, the SRT of both two relay selection approaches become worse when the eavesdropper is within the coverage area of the source.

If a shared IP network is to deliver large-volume streaming media content, such as real-time videos, we need a technique for explicitly setting and dynamically changing the transmission paths used to respond to the congestion situation of the network, including multi-path transmission of a single-flow, to maximize network bandwidth utilization and stabilize transmission quality. However, current technologies cannot realize flexible multi-path transmission because they require complicated algorithms for route searching and the control load for route changing is excessive. This paper proposes a scheme that realizes routing control for multi-path transmission by combining multiple virtual networks on the same physical network. The proposed scheme lowers the control load incurred in creating a detour route because routing control is performed by combining existing routing planes. In addition, our scheme simplifies route searching procedure because congestion avoidance control of multi-path transmission can be realized by the control of a single path. An experiment on the JGN-X network virtualization platform finds that while the time taken to build an inter-slice link must be improved, the time required to inspect whether each slice has virtual nodes that can be connected to the original slice and be used as a detour destination can be as short as 40 microseconds per slice even with large slices having more than 100 virtual nodes.

Increasing hardware resources, such as multi-core and multi-socket CPUs, memory capacity and high-speed NICs, impose significant challenges on Network Function Virtualization (NFV) backends. They increase the potential numbers of per-server NFs or tenants, which requires a packet switching architecture that is not only scalable to large number of virtual ports, but also robust to attacks on the data plane. This is a real problem; a recent study has reported that Open vSwitch, a widely used software switch, had a buffer-overflow bug in its data plane that results the entire SDN domain to be hijacked by worms propagated in the network. In order to address this problem, we propose REdge. It scales to thousands of virtual ports or NFs (as opposed to hundreds in the current state-of-the art), and protect modular, flexible packet switching logic against various bugs, such as buffer overflow and other unexpected operations using static program checking. When 2048 NFs are active and packets are distributed to them based on the MAC or IP addresses, REdge achieves 3.16 Mpps or higher packet forwarding rates for 60 byte packets and achieves the wire rate for 1500 byte packets in the 25 Gbps link.

We investigate the uplink channel selection problem of unmanned aerial vehicle (UAV)-aided data collection system in delay-sensitive sensor networks. In the studied model, the fixed-wing UAV is dispatched to gather sensing information from terrestrial sensor nodes (SNs) and they contend for uplink channels for transmission. With the goal of minimizing the system-wide delay, we formulate a resource allocation problem. Encountered with the challenge that the flight trajectory of UAV is unknown to SNs and the wireless channel is time-varying, we solve the problem by stochastic game approach and further propose a fully distributed channel selection algorithm which is proved to converge to a pure strategy Nash Equilibrium (NE). Simulation results are presented to show that our proposed algorithm has good performance.

A novel Nyquist Folding Receiver (NYFR) based passive localization algorithm with Sparse Bayesian Learning (SBL) is proposed to estimate the position of a spaceborne Synthetic Aperture Radar (SAR).Taking the geometry and kinematics of a satellite into consideration, this paper presents a surveillance geometry model, which formulates the localization problem into a sparse vector recovery problem. A NYFR technology is utilized to intercept the SAR signal. Then, a convergence algorithm with SBL is introduced to recover the sparse vector. Furthermore, simulation results demonstrate the availability and performance of our algorithm.

Business process similarity measure is required by many applications, such as business process query, improvement, redesign, and etc. Many process behavior similarity measures have been proposed in the past two decades. However, to the best of our knowledge, most existing work only focuses on the direct causality transition relations and totally neglect the concurrent and transitive transition relations that are proved to be equally important when measuring process behavior similarity. In this paper, we take the weakness of existing process behavior similarity measures as a starting point, and propose a comprehensive approach to measure the business process behavior similarity based on the so-called Extended Transition Relation set, ETR-set for short. Essentially, the ETR-set is an ex-tended transition relation set containing direct causal transition relations, minimum concurrent transition relations and transitive causal transition relations. Based on the ETR-set, a novel process behavior similarity measure is defined. By constructing a concurrent reachability graph, our approach finds an effective technique to obtain the ETR-set. Finally, we evaluate our proposed approach in terms of its property analysis as well as conducting a group of control experiments.

It is well-known that chaos synchronization in coupled chaotic systems arises from conditions with specific coupling, such as complete, phase, and generalized synchronization. Recently, several methods for controlling this chaos synchronization using a nonlinear feedback controller have been proposed. In this study, we applied a proposed reducing range of orbit feedback method to coupled cubic maps in order to control synchronization of chaos-chaos intermittency. By evaluating the system's behavior and its dependence on the feedback and coupling strength, we confirmed that synchronization of chaos-chaos intermittency could be induced using this nonlinear feedback controller, despite the fact that the asynchronous state within a unilateral attractor is maintained. In particular, the degree of synchronization is high at the edge between the chaos-chaos intermittency parameter region for feedback strength and the non-chaos-chaos intermittency region. These characteristics are largely maintained on large-scale coupled cubic maps.

This paper describes the advantages and disadvantages observed when describing complex parameterizable Artificial Neural Networks (ANNs) at the behavioral level using SystemC and at the Register Transfer Level (RTL) using VHDL. ANNs are complex to parameterize because they have a configurable number of layers, and each one of them has a unique configuration. This kind of structure makes ANNs, a priori, challenging to parameterize using Hardware Description Languages (HDL). Thus, it seems intuitively that ANNs would benefit from the raise in level of abstraction from RTL to behavioral level. This paper presents the results of implementing an ANN using both levels of abstractions. Results surprisingly show that VHDL leads to better results and allows a much higher degree of parameterization than SystemC. The implementation of these parameterizable ANNs are made open source and are freely available online. Finally, at the end of the paper we make some recommendation for future HLS tools to improve their parameterization capabilities.