Fountain codes are erasure correcting codes realizing reliable communication systems for the multicast on the Internet. The zigzag decodable fountain (ZDF) codes are one of generalization of the Raptor codes, i.e., applying shift operation to generate the output packets. The ZDF codes are decoded by a two-stage iterative decoding algorithm, which combines the packet-wise peeling algorithm and the bit-wise peeling algorithm. By the bit-wise peeling algorithm and shift operation, ZDF codes outperform Raptor codes under iterative decoding in terms of decoding erasure rates and overheads. However, the bit-wise peeling algorithm spends long decoding time. This paper proposes fast bit-wise decoding algorithms for the ZDF codes. Simulation results show that the proposed algorithm drastically reduces the decoding time compared with the previous algorithm.

A random access scheme is a fundamental scenario in which the users transmit through a shared channel and cannot coordinate with each other. Recently, successive interference cancellation (SIC) is introduced into the random access scheme. The SIC decodes the transmitted packets using collided packets. The coded slotted ALOHA (CSA) is a random access scheme using the SIC. The CSA encodes each packet by a local code prior to transmission. It is known that the CSA achieves excellent throughput. On the other hand, it is reported that shift operation improves the decoding performance for packet-oriented erasure correcting coding systems. In this paper, we propose a protocol which applies the shift operation to the CSA. Numerical simulations show that the proposed protocol achieves better throughput and packet loss rate than the CSA. Moreover, we analyze the asymptotic behavior of the throughput and the decoding erasure rate for the proposed protocol by the density evolution.

Personalized trip planning is a challenging problem given that places of interest should be selected according to user preferences and sequentially arranged while satisfying various constraints. In this study, we aimed to model various uncertain aspects that should be considered during trip planning and efficiently generate personalized plans that maximize user satisfaction based on preferences and constraints. Specifically, we propose a probabilistic itinerary evaluation model based on a hybrid temporal Bayesian network that determines suitable itineraries considering preferences, constraints, and uncertain environmental variables. The model retrieves the sum of time-weighted user satisfaction, and ant colony optimization generates the trip plan that maximizes the objective function. First, the optimization algorithm generates candidate itineraries and evaluates them using the proposed model. Then, we improve candidate itineraries based on the evaluation results of previous itineraries. To validate the proposed trip planning approach, we conducted an extensive user study by asking participants to choose their preferred trip plans from options created by a human planner and our approach. The results show that our approach provides human-like trip plans, as participants selected our generated plans in 57% of the pairs. We also evaluated the efficiency of the employed ant colony optimization algorithm for trip planning by performance comparisons with other optimization methods.

Liquid crystal director distributions between strong and weak polar anchoring surfaces in hybrid aligned cells are numerically analyzed. When the anchoring is a critical one, homogeneously or homeotropicly liquid crystal alignment can be obtained. Such cells have no threshold voltage and a driving voltage can be reduced less than 0.5 volt.

The estimation of the matrix rank of harmonic components of a music spectrogram provides some useful information, e.g., the determination of the number of basis vectors of the matrix-factorization-based algorithms, which is required for the automatic music transcription or in post-processing. In this work, we develop an algorithm based on Stein's unbiased risk estimator (SURE) algorithm with the matrix factorization model. The noise variance required for the SURE algorithm is estimated by suppressing the harmonic component via median filtering. An evaluation performed using the MIDI-aligned piano sounds (MAPS) database revealed an average estimation error of -0.26 (standard deviation: 4.4) for the proposed algorithm.

Multi-Mouse Quiz (MMQ) is a quiz application based on the Single Display Groupware (SDG)[1] concept through which several users can answer quizzes by sharing a computer to take the quiz in a classroom or any other learning environment. We conducted a practice, where we used the MMQ to support collaborative learning, which was combined with a museum visit. In the previous research, we found that the 3rd-grade children were able to operate the MMQ without any special assistance from the researchers, and that their use of the MMQ was characterized by high engagement[2]. In this study, we also conducted qualitative evaluation in the form of observation data and a free description of the questionnaire; we found that, compared to previous studies, which used MMQ with 6th-grade children, the 3rd-grade were more willing to use body language to express their emotions, and this tendency made the whole class more active. Furthermore, MMQ quiz learning inspired children with reflection perspectives to participate in the museum activity and activities in the computer room.

Most existing truth discovery approaches are designed for structured data, and cannot meet the strong need to extract trustworthy information from raw text data for its unique characteristics such as multifactorial property of text answers (i.e., an answer may contain multiple key factors) and the diversity of word usages (i.e., different words may have the same semantic meaning). As for text answers, there are no absolute correctness or errors, most answers may be partially correct, which is quite different from the situation of traditional truth discovery. To solve these challenges, we propose an optimization-based text truth discovery model which jointly groups keywords extracted from the answers of the specific question into a set of multiple factors. Then, we select the subset of multiple factors as identified truth set for each question by parallel ant colony synchronization optimization algorithm. After that, the answers to each question can be ranked based on the similarities between factors answer provided and identified truth factors. The experiment results on real dataset show that though text data structures are complex, our model can still find reliable answers compared with retrieval-based and state-of-the-art approaches.

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.

Fill-a-Pix is a pencil-and-paper puzzle, which is popular worldwide since announced by Conceptis in 2003. It provides a rectangular grid of squares that must be filled in to create a picture. Precisely, we are given a rectangular grid of squares some of which has an integer from 0 to 9 in it, and our task is to paint some squares black so that every square with an integer has the same number of painted squares around it including the square itself. Despite its popularity, computational complexity of Fill-a-Pix has not been known. We in this paper show that the puzzle is NP-complete, ASP-complete, and #P-complete via a parsimonious reduction from the Boolean satisfiability problem. We also consider the fewest clues problem of Fill-a-Pix, where the fewest clues problem is recently introduced by Demaine et al. for analyzing computational complexity of designing “good” puzzles. We show that the fewest clues problem of Fill-a-Pix is Σ2P-complete.

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.

Unlike Radio Frequency Identification (RFID), emerging Computational RFID (CRFID) integrates the RF front-end and MCU with multiple sensors. CRFIDs need to transmit data within the interrogator range, so when the tags moved rapidly or the contact duration with interrogator is limited, the sensor data collected by CRFID must be transferred to interrogator quickly. In this paper, we focus on throughput optimization for backscatter link, take physical and medium access control (MAC) layers both into consideration, put forward our scheme called ORRIS. On physical layer, we propose Cluster Gather Degree (CGD) indicator, which is the clustering degree of signal in IQ domain. Then CGD is regarded as the criterion to adaptively adjust the rate encoding mode and link frequency, accordingly achieve adaptive rate transmission. On MAC layer, based on the idea of asynchronous transfer, we utilize the the number of clusters in IQ domain to select the optimal Q value as much as possible. So that achieve burst transmission or bulk data transmission. Experiments and analyses on the static and mobile scenarios show that our proposal has significantly better mean throughput than BLINK or CARA, which demonstrate the effectiveness of our scheme.

Optical orthogonal signature pattern codes (OOSPCs) have attracted great attention due to their important application in the spatial code-division multiple-access network for image transmission. In this paper, we give a construction for OOSPCs based on cyclic codes over Fp. Applying this construction with the Reed-Solomon codes and the generalized Berlekamp-Justesen codes, we obtain two classes of asymptotically optimal OOSPCs.

This paper proposes a deep neural network (DNN) based framework to address the problem of vector quantization (VQ) for high-dimensional data. The main challenge of applying DNN to VQ is how to reduce the binary coding error of the auto-encoder when the distribution of the coding units is far from binary. To address this problem, three fine-tuning methods have been adopted: 1) adding Gaussian noise to the input of the coding layer, 2) forcing the output of the coding layer to be binary, 3) adding a non-binary penalty term to the loss function. These fine-tuning methods have been extensively evaluated on quantizing speech magnitude spectra. The results demonstrated that each of the methods is useful for improving the coding performance. When implemented for quantizing 968-dimensional speech spectra using only 18-bit, the DNN-based VQ framework achieved an averaged PESQ of about 2.09, which is far beyond the capability of conventional VQ methods.

Due to its simplicity and efficiency, differential evolution (DE) has gained the interest of researchers from various fields for solving global optimization problems. However, it is prone to premature convergence at local minima. To overcome this drawback, a novel hybrid dragonfly algorithm with differential evolution (Hybrid DA-DE) for solving global optimization problems is proposed. Firstly, a novel mutation operator is introduced based on the dragonfly algorithm (DA). Secondly, the scaling factor (F) is adjusted in a self-adaptive and individual-dependent way without extra parameters. The proposed algorithm combines the exploitation capability of DE and exploration capability of DA to achieve optimal global solutions. The effectiveness of this algorithm is evaluated using 30 classical benchmark functions with sixteen state-of-the-art meta-heuristic algorithms. A series of experimental results show that Hybrid DA-DE outperforms other algorithms significantly. Meanwhile, Hybrid DA-DE has the best adaptability to high-dimensional problems.

This paper presents a hybrid architecture for a neural network (NN) trained by a particle swarm optimization (PSO) algorithm. The NN is implemented on the hardware side while the PSO is executed by a processor on the software side. In addition, principal component analysis (PCA) is also applied to reduce correlated information. The PCA module is implemented in hardware by the SystemVerilog programming language to increase operating speed. Experimental results showed that the proposed architecture had been successfully implemented. In addition, the hardware-based NN trained by PSO (NN-PSO) program was faster than the software-based NN trained by the PSO program. The proposed NN-PSO with PCA also obtained better recognition rates than the NN-PSO without-PCA.

As a generalization of perfect nonlinear (PN) functions, zero-difference balanced (ZDB) functions play an important role in coding theory, cryptography and communications engineering. Inspired by a foregoing work of Liu et al. [1], we present a class of ZDB functions with new parameters based on the cyclotomy in finite fields. Employing these ZDB functions, we obtain simultaneously optimal constant composition codes and perfect difference systems of sets.

An adaptive bit allocation scheme for zero-forcing (ZF) Tomlinson-Harashima precoding (THP) is proposed. The ZF-THP enables us to achieve feasible bit error rate (BER) performance when appropriate substream permutations are installed at the transmitter. In this study, the number of bits in each substream is adaptively allocated to minimize the average BER in fading environments. Numerical examples are provided to compare the proposed method with eigenbeam space division multiplexing (E-SDM) method.

We research device-free (DF) multi-target tracking scheme in this paper. The existing localization and tracking algorithms are always pay attention to the single target and need to collect a large amount of localization information. In this paper, we exploit the sparse property of multiple target locations to achieve target trace accurately with much less sampling both in the wireless links and the time slots. The proposed approach mainly includes the target localization part and target trace recovery part. In target localization part, by exploiting the inherent sparsity of the target number, Compressive Sensing (CS) is utilized to reduce the wireless links distributed. In the target trace recovery part, we exploit the compressive property of target trace, as well as designing the measurement matrix and the sparse matrix, to reduce the samplings in time domain. Additionally, Kronecker Compressive Sensing (KCS) theory is used to simultaneously recover the multiple traces both of the X label and the Y Label. Finally, simulations show that the proposed approach holds an effective recovery performance.

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.