Traffic collision is an extremely serious issue in the world today. The World Health Organization (WHO) reported the number of road traffic deaths globally has plateaued at 1.25 million a year. In an attempt to decrease the occurrence of such traffic collisions, various driving systems for detecting pedestrians and vehicles have been proposed, but they are inadequate as they cannot detect vehicles and pedestrians in blind places such as sharp bends and blind intersections. Therefore, mobile networks such as long term evolution (LTE), LTE-Advanced, and 5G networks are attracting a great deal of attention as platforms for connected car services. Such platforms enable individual devices such as vehicles, drones, and sensors to exchange real-time information (e.g., location information) with each other. To guarantee effective connected car services, it is important to deliver a data block within a certain maximum tolerable delay (called a deadline in this work). The Third Generation Partnership Project (3GPP) stipulates that this deadline be 100 ms and that the arrival ratio within the deadline be 0.95. We investigated an intersection at which vehicle collisions often occur to evaluate a realistic environment and found that schedulers such as proportional fairness (PF) and payload-size and deadline-aware (PayDA) cannot satisfy the deadline and arrival ratio within the deadline, especially as network loads increase. They fail because they do not consider three key elements — radio quality, chunk size, and the deadline — when radio resources are allocated. In this paper, we propose a deadline-aware scheduling scheme that considers chunk size and the deadline in addition to radio quality and uses them to prioritize users in order to meet the deadline. The results of a simulation on ns-3 showed that the proposed method can achieve approximately four times the number of vehicles satisfying network requirements compared to PayDA.

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.

Communications industry will see dramatic changes with the arrival of 5G. 5G is not only about high capacity and ultra-low latency, but also about accommodating Verticals, providing newer flexibility in business development and agility. Network slicing has become an enabler for on-demand accommodation of such Verticals in a mobile network. To support such new features, 3GPP is continuing standardization of a 5G system with all necessary requirements in mind. This paper provides a detailed view of the standards and the technologies that'll make 5G a reality. Specifically, this paper focuses on the new 5G Radio Access Network (RAN), network slicing enabled new 5G Core (5GC) Network, and new management system capable of handling network slicing related management aspect of a mobile network.

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.

The Machine-to-Machine (M2M) service network platform accommodates M2M communications traffic efficiently by using tree-structured networks and the computation resources deployed on network nodes. In the M2M service network platform, program files required for controlling devices are placed on network nodes, which have different amounts of computation resources according to their position in the hierarchy. The program files must be dynamically repositioned in response to service quality requests from each device, such as computation power, link bandwidth, and latency. This paper proposes a Program File Placement (PFP) method for the M2M service network platform. First, the PFP problem is formulated in the Mixed-Integer Linear Programming (MILP) approach. We prove that the decision version of the PFP problem is NP-complete. Next, we present heuristic algorithms that attain sub-optimal but attractive solutions. Evaluations show that the heuristic algorithm based on the number of devices that share a program file reduces the total number of placed program files compared to the algorithm that moves program files based on their position.

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.

A formal graph system (FGS) is a logic programming system that directly manipulates graphs by dealing with graph patterns instead of terms of first-order predicate logic. In this paper, based on an FGS, we introduce a primitive formal ordered tree system (pFOTS) as a formal system defining labeled ordered tree languages. A pFOTS program is a finite set of graph rewriting rules. A logic program is well-known to be suitable to represent background knowledge. The query learning model is an established mathematical model of learning via queries in computational learning theory. In this learning model, we show the exact learnability of a pFOTS program consisting of one graph rewriting rule and background knowledge defined by a pFOTS program using a polynomial number of queries.

Given two feasible solutions A and B, a reconfiguration problem asks whether there exists a reconfiguration sequence (A0=A, A1,...,Aℓ=B) such that (i) A0,...,Aℓ are feasible solutions and (ii) we can obtain Ai from Ai-1 under the prescribed rule (the reconfiguration rule) for each i ∈ {1,...,ℓ}. In this paper, we address the reconfiguration problem for induced trees, where an induced tree is a connected and acyclic induced subgraph of an input graph. We consider the following two rules as the prescribed rules: Token Jumping: removing u from an induced tree and adding v to the tree, and Token Sliding: removing u from an induced tree and adding v adjacent to u to the tree, where u and v are vertices of an input graph. As the main results, we show that (I) the reconfiguration problemis PSPACE-complete even if the input graph is of bounded maximum degree, (II) the reconfiguration problem is W[1]-hard when parameterized by both the size of induced trees and the length of the reconfiguration sequence, and (III) there exists an FPT algorithm when the problem is parameterized by both the size of induced trees and the maximum degree of an input graph under Token Jumping and Token Sliding.

This paper introduces a new noise generation algorithm for vocoder-based speech waveform generation. White noise is generally used for generating an aperiodic component. Since short-term white noise includes a zero-frequency component (ZFC) and inaudible components below 20 Hz, they are reduced in advance when synthesizing. We propose a new noise generation algorithm based on that for velvet noise to overcome the problem. The objective evaluation demonstrated that the proposed algorithm can reduce the unwanted components.

Language models are a key technology in various tasks, such as, speech recognition and machine translation. They are usually used on texts covering various domains and as a result domain adaptation has been a long ongoing challenge in language model research. With the rising popularity of neural network based language models, many methods have been proposed in recent years. These methods can be separated into two categories: model based and feature based adaptation methods. Feature based domain adaptation has compared to model based domain adaptation the advantage that it does not require domain labels in the corpus. Most existing feature based adaptation methods are based on bias adaptation. We propose a novel feature based domain adaptation technique using hidden layer factorisation. This method is fundamentally different from existing methods because we use the domain features to calculate a linear combination of linear layers. These linear layers can capture domain specific information and information common to different domains. In the experiments, we compare our proposed method with existing adaptation methods. The compared adaptation techniques are based on two different ideas, that is, bias based adaptation and gating of hidden units. All language models in our comparison use state-of-the-art long short-term memory based recurrent neural networks. We demonstrate the effectiveness of the proposed method with perplexity results for the well-known Penn Treebank and speech recognition results for a corpus of TED talks.

This paper presents a 6th-order quadrature bandpass delta sigma AD modulator (QBPDSM) with 2nd-order image rejection using dynamic amplifier and noise coupling (NC) SAR quantizer embedded by passive adder for the application of wireless communication system. A novel complex integrator using dynamic amplifier is proposed to improve the energy efficiency of the QBPDSM. The NC SAR quantizer can realize an additional 2nd-order noise shaping and 2nd-order image rejection by the digital domain noise coupling technique. As a result, the 6th-order QBPDSM with 2nd-order image rejection is realized by two complex integrators using dynamic amplifier and the NC SAR quantizer. The SPICE simulation results demonstrate the feasibility of the proposed QBPDSM in 90nm CMOS technology. Simulated SNDR of 76.30dB is realized while a sinusoid -3.25dBFS input is sampled at 33.3MS/s and the bandwidth of 2.083MHz (OSR=8) is achieved. The total power consumption in the modulator is 6.74mW while the supply voltage is 1.2V.

This paper evaluates the throughput of a distributed antenna network (DAN) with multiple mobile terminal scheduling and the usage of joint maximum-likelihood detection (MLD). Mobile terminals are closer to the desired antennas in the DAN which leads to higher throughput and better frequency utilization efficiency. However, when multiple mobile terminal scheduling is applied to the DAN, interference can occur between transmitted signals from antennas. Therefore, in this research, mobile terminal scheduling along with joint MLD is applied to reduce the effects of interference. A system level simulation shows that the usage of joint MLD in a densely packed DAN provides better system throughput regardless of the numbers of mobile terminals and fading channels.

In this paper, we analyze the impact of channel estimation errors in an amplify-and-forward (AF)-based two-way relaying network (TWRN) where adaptive modulation (AM) is employed in individual relaying path. In particular, the performance degradation caused by channel estimation error is investigated over Nakagami-m fading channels. We first derive an end-to-end signal-to-noise ratio (SNR), a cumulative distribution function, and a probability density function in the presence of channel estimation error for the AF-based TWRN with adaptive modulation (TWRN-AM). By utilizing the derived SNR statistics, we present accurate expressions of the average spectral efficiency and bit error rates with an outage-constraint in which transmission does not take place during outage events of bidirectional communications. Based on our derived analytical results, an optimal power allocation scheme for TWRN-AM is proposed to improve the average spectral efficiency by minimizing system outages.

This paper studies power allocation for Chase combining (CC) hybrid ARQ (HARQ) in block-fading channels, with causal channel state information (CSI) available both at the receiver and transmitter. A best-effort power allocation scheme is proposed to improve the average throughput of CC HARQ. The scheme is formulated as an optimization problem that, for each round, allocating the transmit power to maximize the average incremental information according to the HARQ retransmission status and CSI. By convex optimization, the solution is derived in simple analytical form. At the same time, the HARQ performance metrics including throughput and outage probability are computed by recursive numerical integral. With at most 4 transmission rounds, this best-effort method achieves about 75% of ergodic capacity in independent Rayleigh block fading channels.

Softwarized networks are expected to be utilized as a core network for the 5th Generation (5G) mobile services. For the mobile core network architecture, service chaining is expected to be utilized for dynamically steering traffic across multiple network functions. In this paper, for dynamic multipath service chaining, we propose a greedy-based VNF placement algorithm. This method can provide multipath service chaining so as to utilize the node resources such as CPU effectively while decreasing the cost about bandwidth and transmission delay. The proposed algorithm consists of four difference algorithms, and VNFs are placed appropriately with those algorithm. Our proposed algorithm obtains near optimal solution for the formulated optimization problem with a greedy algorithm, and hence multipath service chains can be provided dynamically. We evaluate the performance of our proposed method with simulation and compare its performance with the performances of other methods. In numerical examples, it is shown that our proposed algorithm can provide multipath service chains appropriately so as to utilize the limited amount of node resources effectively. Moreover, it is shown that our proposed algorithm is effective for providing service chaining dynamically in large-scale network.

Superconducting nanowire single-photon detector(SNSPD) has been one of the important ingredients for photonic quantum information processing (QIP). In order to see the potential of SNSPDs, I briefly review recent progresses of the photonic QIP with SNSPDs implemented for various purposes and present a possible direction for the development of SNSPDs.

Network embedding has attracted an increasing amount of attention in recent years due to its wide-ranging applications in graph mining tasks such as vertex classification, community detection, and network visualization. Network embedding is an important method to learn low-dimensional representations of vertices in networks, aiming to capture and preserve the network structure. Almost all the existing network embedding methods adopt the so-called Skip-gram model in Word2vec. However, as a bag-of-words model, the skip-gram model mainly utilized the local structure information. The lack of information metrics for vertices in global network leads to the mix of vertices with different labels in the new embedding space. To solve this problem, in this paper we propose a Network Representation Learning method with Deep Metric Learning, namely DML-NRL. By setting the initialized anchor vertices and adding the similarity measure in the training progress, the distance information between different labels of vertices in the network is integrated into the vertex representation, which improves the accuracy of network embedding algorithm effectively. We compare our method with baselines by applying them to the tasks of multi-label classification and data visualization of vertices. The experimental results show that our method outperforms the baselines in all three datasets, and the method has proved to be effective and robust.

Multiuser massive multi-input multi-output (MU massive MIMO) is considered as a promising technology for the fifth generation (5G) of the wireless communication system. In this paper, we propose a low-complexity joint antenna and user selection scheme with block diagonalization (BD) precoding for MU massive MIMO downlink channel in the time division duplex (TDD) system. The base station (BS) is equipped with a large-scale transmit antenna array while each user is using the single receive antenna in the system. To reduce the hardware cost, BS will be implemented by limited number of radio frequency (RF) chains and BS must activate some selected transmit antennas in the BS side for data transmitting and some users' receive antennas in user side for data receiving. To achieve the reduction in the computation complexity in the antenna and user selection while maintaining the same or higher sum-rate in the system, the proposed scheme relies on three complexity reduction key factors. The first key factor is that finding the average channel gains for the transmit antenna in the BS side and the receive antenna in the user side to select the best channel gain antennas and users. The second key factor called the complexity control factor ξ(Xi) for the antenna set and the user set limitation is used to control the complexity of the brute force search. The third one is that using the assumption of the point-to-point deterministic MIMO channel model to avoid the singular value decomposition (SVD) computation in the brute force search. We show that the proposed scheme offers enormous reduction in the computation complexity while ensuring the acceptable performance in terms of total system sum-rate compared with optimal and other conventional schemes.

Massive multiple-input multiple-output (MIMO) systems are a key promising technology for future broadband cellular networks. The propagation paths within massive MIMO radio channels are often sparse, both in the sub-6GHz frequency band and at millimeter wave frequencies. Herein, we propose a two-layer beamforming scheme for downlink transmission over massive multiuser MIMO sparse beam-space channels. The first layer employs a bipartite graph to dynamically group users in the beam-space domain; the aim is to minimize inter-user interference while significantly reducing the effective channel dimensionality. The second layer performs baseband linear MIMO precoding to maximize spatial multiplexing gain and system throughput. Simulation results demonstrate the proposed two-layer beamforming scheme outperforms other, more conventional algorithms.

Blind nonlinear compensation for RF receivers is an important research topic in 5G mobile communication, in which higher level modulation schemes are employed more often to achieve high capacity and ultra-broadband services. Since nonlinear compensation circuits must handle intermodulation bandwidths that are more than three times the signal bandwidth, reducing the sampling frequency is essential for saving power consumption. This paper proposes a novel blind nonlinear compensation technique that employs sub-Nyquist sampling analog-to-digital conversion. Although outband distortion spectrum is folded in the proposed sub-Nyquist sampling technique, determination of compensator coefficients is still possible by using the distortion power. Proposed technique achieves almost same compensation performance in EVM as the conventional compensation scheme, while reducing sampling speed of analog to digital convertor (ADC) to less than half the normal sampling frequency. The proposed technique can be applied in concurrent dual-band communication systems and adapt to flat Rayleigh fading environments.