Many studies have revealed that the performance of software-based Virtual Network Functions (VNFs) is insufficient for mission-critical networks. Scaling-out approaches, such as auto-scaling of VNFs, could handle a huge amount of traffic; however, the exponential traffic growth confronts us the limitations of both expandability of physical resources and complexity of their management. In this paper, we propose a fast datapath processing method called Packet Aggregation Flow (PA-Flow) that is based on hop-by-hop packet aggregation for more efficient Service Function Chaining (SFC). PA-Flow extends a notion of existing intra-node packet aggregation toward network-wide packet aggregation, and we introduce following three novel features. First, packet I/O overheads at intermediate network devices including NFV-nodes are mitigated by reduction of packet amount. Second, aggregated packets are further aggregated as going through the service chain in a hop-by-hop manner. Finally, next-hop aware packet aggregation is realized using OpenFlow-based flow tables. PA-Flow is designed to be available with various VNF forms (e.g. VM/container/baremetal-based) and virtual I/O technologies (e.g. vhost-user/SR-IOV), and its implementation does not bring noticeable delay for aggregation. We conducted two evaluations: (i) a baseline evaluation for understanding fundamental performance characteristics of PA-Flow (ii) a simulation-based SFC evaluation for proving PA-Flow's effect in a realistic environment. The results showed that throughput of short packet forwarding was improved by 4 times. Moreover, the total number of packets was reduced by 93% in a large-scale SFC.

The capacity of optical transport networks has been increasing steadily and the networks are becoming more dynamic, complex, and transparent. Though it is common to use worst case assumptions for estimating the quality of transmission (QoT) in the physical layer, over provisioning results in high margin requirements. Accurate estimation on the QoT for to-be-established lightpaths is crucial for reducing provisioning margins. Machine learning (ML) is regarded as one of the most powerful methodological approaches to perform network data analysis and enable automated network self-configuration. In this paper, an artificial neural network (ANN) framework, a branch of ML, to estimate the optical signal-to-noise ratio (OSNR) of to-be-established lightpaths is proposed. It takes account of both nonlinear interference between spectrum neighboring channels and optical monitoring uncertainties. The link information vector of the lightpath is used as input and the OSNR of the lightpath is the target for output of the ANN. The nonlinear interference impact of the number of neighboring channels on the estimation accuracy is considered. Extensive simulation results show that the proposed OSNR estimation scheme can work with any RWA algorithm. High estimation accuracy of over 98% with estimation errors of less than 0.5dB can be achieved given enough training data. ANN model with R=4 neighboring channels should be used to achieve more accurate OSNR estimates. Based on the results, it is expected that the proposed ANN-based OSNR estimation for new lightpath provisioning can be a promising tool for margin reduction and low-cost operation of future optical transport networks.

Clinical researches show that the morbidity of coronary artery disease (CAD) is gradually increasing in many countries every year, and it causes hundreds of thousands of people all over the world dying for each year. As the optical coherence tomography with high resolution and better contrast applied to the lesion tissue investigation of human vessel, many more micro-structures of the vessel could be easily and clearly visible to doctors, which help to improve the CAD treatment effect. Manual qualitative analysis and classification of vessel lesion tissue are time-consuming to doctors because a single-time intravascular optical coherence (IVOCT) data set of a patient usually contains hundreds of in-vivo vessel images. To overcome this problem, we focus on the investigation of the superficial layer of the lesion region and propose a model based on local multi-layer region for vessel lesion components (lipid, fibrous and calcified plaque) features characterization and extraction. At the pre-processing stage, we applied two novel automatic methods to remove the catheter and guide-wire respectively. Based on the detected lumen boundary, the multi-layer model in the proximity lumen boundary region (PLBR) was built. In the multi-layer model, features extracted from the A-line sub-region (ALSR) of each layer was employed to characterize the type of the tissue existing in the ALSR. We used 7 human datasets containing total 490 OCT images to assess our tissue classification method. Validation was obtained by comparing the manual assessment with the automatic results derived by our method. The proposed automatic tissue classification method achieved an average accuracy of 89.53%, 93.81% and 91.78% for fibrous, calcified and lipid plaque respectively.

With the rapid increase in IoT (Internet of Things) applications, more sensor devices, generating periodic data flows whose packets are transmitted at regular intervals, are being incorporated into WSNs (Wireless Sensor Networks). However, packet collision caused by the hidden node problem is becoming serious, particularly in large-scale multi-hop WSNs. Moreover, focusing on periodic data flows, continuous packet collisions among periodic data flows occur if the periodic packet transmission phases become synchronized. In this paper, we tackle the compounded negative effect of the hidden node problem and the continuous collision problem among periodic data flows. As this is a complex variant of the hidden node problem, there is no simple and well-studied solution. To solve this problem, we propose a new MAC layer mechanism. The proposed method predicts a future risky duration during which a collision can be caused by hidden nodes by taking into account the periodic characteristics of data packet generation. In the risky duration, each sensor node stops transmitting data packets in order to avoid collisions. To the best of our knowledge, this is the first paper that considers the compounded effect of hidden nodes and continuous collisions among periodic data flows. Other advantages of the proposed method include eliminating the need for any new control packets and it can be implemented in widely-diffused IEEE 802.11 and IEEE 802.15.4 devices.

To efficiently collect sensor readings in cluster-based wireless sensor networks, we propose a structural compressed network coding (SCNC) scheme that jointly considers structural compressed sensing (SCS) and network coding (NC). The proposed scheme exploits the structural compressibility of sensor readings for data compression and reconstruction. Random linear network coding (RLNC) is used to re-project the measurements and thus enhance network reliability. Furthermore, we calculate the energy consumption of intra- and inter-cluster transmission and analyze the effect of the cluster size on the total transmission energy consumption. To that end, we introduce an iterative reweighed sparsity recovery algorithm to address the all-or-nothing effect of RLNC and decrease the recovery error. Experiments show that the SCNC scheme can decrease the number of measurements required for decoding and improve the network's robustness, particularly when the loss rate is high. Moreover, the proposed recovery algorithm has better reconstruction performance than several other state-of-the-art recovery algorithms.

We consider the problem of placing arrows, which indicate the direction of each edge in directed graph drawings, without making them overlap other arrows, vertices and edges as much as possible. The following two methods have been proposed for this problem. One is an exact algorithm for the case in which the position of each arrow is restricted to some discrete points. The other is a heuristic algorithm for the case in which the arrow is allowed to move continuously on each edge. In this paper, we assume that the arrow positions are not restricted to discrete points and propose an exact algorithm for the problem of finding an arrow placement such that (a) the weighted sum of the numbers of overlaps with edges, vertices and other arrows is minimized and (b) the sum of the distances between the arrows and their edges' terminal vertices is minimized as a secondary objective. The proposed method solves this problem by reducing it to a mixed integer linear programming problem. Since this is an exponential time algorithm, we add a simple procedure as preprocessing to reduce the running time. Experimental results show that the proposed method can find a better arrow placement than the previous methods and the procedure for reducing the running time is effective.

In recent decade, many state-of-the-art algorithms on image classification as well as audio classification have achieved noticeable successes with the development of deep convolutional neural network (CNN). However, most of the works only exploit single type of training data. In this paper, we present a study on classifying bird species by exploiting the combination of both visual (images) and audio (sounds) data using CNN, which has been sparsely treated so far. Specifically, we propose CNN-based multimodal learning models in three types of fusion strategies (early, middle, late) to settle the issues of combining training data cross domains. The advantage of our proposed method lies on the fact that we can utilize CNN not only to extract features from image and audio data (spectrogram) but also to combine the features across modalities. In the experiment, we train and evaluate the network structure on a comprehensive CUB-200-2011 standard data set combing our originally collected audio data set with respect to the data species. We observe that a model which utilizes the combination of both data outperforms models trained with only an either type of data. We also show that transfer learning can significantly increase the classification performance.

A retrodirective system utilizing harmonic reradiation from a rectenna is developed and verified for long-range wireless power transfer applications, such as low-power or battery-less devices and lightweight aerial vehicles. The second harmonic generated by the rectifying circuit is used instead of a pilot signal, and thus an oscillator for creating the pilot signal is not required. The proposed retrodirective system consists of a 2.45 GHz transmitter with a two-element phased array antenna, a 4.9 GHz direction-of-arrival (DoA) estimation system, a phase control system, and a rectenna. The rectenna, consisting of a half-wave dipole antenna, receives microwave power from the 2.45 GHz transmitter and reradiates the harmonic toward the 4.9 GHz DoA estimation system. The rectenna characteristics and experimental demonstrations of the proposed retrodirective system are described. From measurement results, the dc output power pattern for the developed retrodirective system is in good agreement with that obtained using manual beam steering. The measured DoA estimation errors are within the range of -2.4° to 4.8°.

Unmanned aerial vehicle mounted base stations (UAV-BSs) can provide wireless cellular service to ground users in a variety of scenarios. The efficient deployment of such UAV-BSs while optimizing the coverage area is one of the key challenges. We investigate the deployment of UAV-BS to maximize the coverage of ground users, and further analyzes the impact of the deployment of UAV-BS on the fairness of ground users. In this paper, we first calculated the location of the UAV-BS according to the QoS requirements of the ground users, and then the fairness of ground users is taken into account by calculating three different fairness indexes. The performance of two genetic algorithms, namely Standard Genetic Algorithm (SGA) and Multi-Population Genetic Algorithm (MPGA) are compared to solve the optimization problem of UAV-BS deployment. The simulations are presented showing that the performance of the two algorithms, and the fairness performance of the ground users is also given.

To the best of our knowledge, there are a few researches on air-handwriting character-level writer identification only employing acceleration and angular velocity data. In this paper, we propose a deep learning approach to writer identification only using inertial sensor data of air-handwriting. In particular, we separate different representations of degree of freedom (DoF) of air-handwriting to extract local dependency and interrelationship in different CNNs separately. Experiments on a public dataset achieve an average good performance without any extra hand-designed feature extractions.

In this paper, a new transceiver system for the in-vehicle communication system is proposed to enhance data transmission rate and timing accuracy in TDM-based application. The proposed system utilizes point-to-point (P2P) channel, a closed-loop clock forwarding path, and a transceiver with a repeater and clock delay adjuster. The proposed system with 4 ECU (Electronic Computing Unit) nodes is implemented in 180nm CMOS technology and, when compared with conventional bus-based system, achieved more than 125 times faster data transmission. The maximum data rate was 2.5Gbps at 1.8V power supply and the worst peak-to-peak jitter for the data and clock signals over 5000 data symbols were about 49.6ps and 9.8ps respectively.

In the beam-type microwave power transmission system, it is required to minimize the interference with communication and the influence on the human body. Retrodirective system that re-radiates a beam in the direction of arrival of a signal is well known as a beam control technique for accurate microwave power transmission. In this paper, we newly propose to apply the retrodirective system to both transmitting and receiving antennas. The leakage to the outside of the system is expected to minimize self-convergently while following the atmospheric fluctuation and the antenna movement by repeating the retrodirective between the transmitting and receiving antenna in this system. We considered this phenomenon theoretically using an infinite array antenna model. Finally, it has been shown by the equivalent circuit simulation that stable transmission can be realized by oscillating the system.

We design a new oblivious routing algorithm for two-dimensional mesh-based Networks-on-Chip (NoCs) called LEF (Long Edge First) which offers high throughput with low design complexity. LEF's basic idea comes from conventional wisdom in choosing the appropriate dimension-order routing (DOR) algorithm for supercomputers with asymmetric mesh or torus interconnects: routing longest dimensions first provides better performance than other strategies. In LEF, we combine the XY DOR and the YX DOR. When routing a packet, which DOR algorithm is chosen depends on the relative position between the source node and the destination node. Decisions of selecting the appropriate DOR algorithm are not fixed to the network shape but instead made on a per-packet basis. We also propose an efficient deadlock avoidance method for LEF in which the use of virtual channels is more flexible than in the conventional method. We evaluate LEF against O1TURN, another effective oblivious routing algorithm, and a minimal adaptive routing algorithm based on the odd-even turn model. The evaluation results show that LEF is particularly effective when the communication is within an asymmetric mesh. In a 16×8 NoC, LEF even outperforms the adaptive routing algorithm in some cases and delivers from around 4% up to around 64.5% higher throughput than O1TURN. Our results also show that the proposed deadlock avoidance method helps to improve LEF's performance significantly and can be used to improve O1TURN's performance. We also examine LEF in large-scale NoCs with thousands of nodes. Our results show that, as the NoC size increases, the performance of the routing algorithms becomes more strongly influenced by the resource allocation policy in the network and the effect is different for each algorithm. This is evident in that results of middle-scale NoCs with around 100 nodes cannot be applied directly to large-scale NoCs.

Envelope tracking (ET) technology provides the potential for achieving high efficiency in power amplifiers (PAs) with high peak-to-average ratio (PAR) signals. Envelope amplifiers with high fidelity, high efficiency, and wide bandwidth are critical components for the widespread application of envelope tracking. This paper presents the design of a linear-assisted switching buck converter for use in an envelope amplifier. To effectively leverage the high efficiency of buck converters and the wide bandwidth capabilities of linear amplifiers, a parallel combination of these two devices is employed in this work. A novel current-sense constant-on-time (COT) controller is proposed to coordinate this hybrid power supply. The combination mainly enables the switching converter to provide the average power required by the PA with high efficiency, while the wideband linear amplifier provides a wide range of dynamic voltages. The technique improves the efficiency of the envelope amplifier, especially for applications requiring high PAR with wider bandwidth signals. Measurement of the envelope amplifier showed an efficiency of approximately 77% with 10 W output power using LTE downlink signals. The overall ET system was demonstrated by using a GaN PA. The measured average power-added efficiency of the amplifier reached above 45% for an LTE modulated signal with 20 MHz bandwidth and PAR of 8.0 dB, at an average output power of 5 W and gain of 10.1 dB. The measured normalized RMS error is below 2.1% with adjacent channel leakage ratio of -48 dBc at an offset frequency of 20 MHz.

Phase interferometer using baseline composed by uniform linear array (ULA) with stable phase center for estimating the angle of arrival (AOA) is always employed in the direction finding (DF) system. However, the phase center of antenna element could vary with the incident angle, frequency, multipath and so on. To deal with these problems, a novel method is proposed in this paper to calibrate the phase center over ultra-wideband (UWB). Meanwhile, the restrictions of this method are discussed. Numerical simulations reveal that higher accuracy and larger unambiguous angle range can be obtained by the proposed method.

An underdetermined direction of arrival estimation method based on signal sparsity is proposed when independent and coherent signals coexist. Firstly, the estimate of the mixing matrix of the impinging signals is obtained by clustering the single source points which are detected by the ratio of time-frequency transforms of the received signals. Then, each column vector of the mixing matrix is processed by exploiting the forward and backward vectors in turn to obtain the directions of arrival of all signals. The number of independent signals and coherent signal groups that can be estimated by the proposed method can be greater than the number of sensors. The validity of the method is demonstrated by simulations.

In this letter, a polarization filtering based transmission (PFBT) scheme is proposed to enhance the spectrum efficiency in wireless communications. In such scheme, the information is divided into several parts and each is conveyed by a polarized signal with a unique polarization state (PS). Then, the polarized signals are added up and transmitted by the dual-polarized antenna. At the receiver side, the oblique projection polarization filters (OPPFs) are adopted to separate each polarized signal. Thus, they can be demodulated separately. We mainly focus on the construction methods of the OPPF matrix when the number of the separate parts is 2 and 3 and evaluate the performance in terms of the capacity and the bit error rate. In addition, we also discuss the probability of the signal separation when the number of the separate parts is equal or greater than 4. Theoretical results and simulation results demonstrate the performance of the proposed scheme.

Using linear programming (LP) decoding based on alternating direction method of multipliers (ADMM) for low-density parity-check (LDPC) codes shows lower complexity than the original LP decoding. However, the development of the ADMM-LP decoding algorithm could still be limited by the computational complexity of Euclidean projections onto parity check polytope. In this paper, we proposed a bisection method iterative algorithm (BMIA) for projection onto parity check polytope avoiding sorting operation and the complexity is linear. In addition, the convergence of the proposed algorithm is more than three times as fast as the existing algorithm, which can even be 10 times in the case of high input dimension.

The packet classification problem to determine the behavior of incoming packets at the network devices. The processing latency of packet classification by linear search is proportional to the number of classification rules. To limit the latency caused by classification to a certain level, we should develop a classification algorithm that classifies packets in a time independent of the number of classification rules. Arbitrary (including noncontiguous) bitmask rules are efficiently expressive for controlling higher layer communication, achiving access control lists, Quality of Service and so on. In this paper, we propose a classification algorithm based on run-based trie [1] according to arbitrary bitmask rules. The space complexity of proposed algorithm is in linear in the size of a rule list. The time complexity except for construction of that can be regarded as constant which is independent the number of rules. Experimental results using a packet classification algorithm benchmark [2] show that our method classifies packets in constant time independent of the number of rules.

Network function virtualization (NFV) achieves the flexibility of network service provisioning by using virtualization technology. However, NFV is exposed to a serious security threat known as cross-VM cache timing attacks. In this letter, we look into real security impacts on network virtualization. Specifically, we present two kinds of practical cache timing attacks on virtualized firewalls and routers. We also propose some countermeasures to mitigate such attacks on virtualized network functions.