A hubness-score based normalization of the pairwise similarity is proposed for the sequence-alignment based cover song retrieval. The hubness, which is the tendency of some data points in high-dimensional data sets to link more frequently to other points than the rest of the points from the set, is widely-known to deteriorate the information retrieval accuracy. This paper tries to relieve the performance degradation due to the hubness by normalizing the pairwise similarity with a hubness score. Experiments on two cover song datasets confirm that the proposed similarity normalization improves the cover song retrieval accuracy.

Goal refinement is a crucial step in goal-oriented requirements analysis to create a goal model of high quality. Poor goal refinement leads to missing requirements and eliciting incorrect requirements as well as less comprehensiveness of produced goal models. This paper proposes a technique to automate detecting bad smells of goal refinement, symptoms of poor goal refinement. At first, to clarify bad smells, we asked subjects to discover poor goal refinement concretely. Based on the classification of the specified poor refinement, we defined four types of bad smells of goal refinement: Low Semantic Relation, Many Siblings, Few Siblings, and Coarse Grained Leaf, and developed two types of measures to detect them: measures on the graph structure of a goal model and semantic similarity of goal descriptions. We have implemented a supporting tool to detect bad smells and assessed its usefulness by an experiment.

With the high development of computation requirements in Internet of Things, resource-limited edge servers usually require to cooperate to perform the tasks. Most related studies usually assume a static cooperation approach which might not suit the dynamic environment of edge computing. In this paper, we consider a dynamic cooperation approach by guiding edge servers to form coalitions dynamically. It raises two issues: 1) how to guide them to optimally form coalitions and 2) how to cope with the dynamic feature where server statuses dynamically change as the tasks are performed. The coalitional Markov decision process (CMDP) model proposed in our previous work can handle these issues well. However, its basic solution, coalitional Q-learning, cannot handle the large scale problem when the task number is large in edge computing. Our response is to propose a novel algorithm called deep coalitional Q-learning (DCQL) to solve it. To sum up, we first formulate the dynamic cooperation problem of edge servers as a CMDP: each edge server is regarded as an agent and the dynamic process is modeled as a MDP where the agents observe the current state to formulate several coalitions. Each coalition takes an action to impact the environment which correspondingly transfers to the next state to repeat the above process. Then, we propose DCQL which includes a deep neural network and so can well cope with large scale problem. DCQL can guide the edge servers to form coalitions dynamically with the target of optimizing some goal. Furthermore, we run experiments to verify our proposed algorithm's effectiveness in different settings.

In this letter, the almost binary sequence (sequence with a single zero element) is considered as a special class of binary sequence. Four new bounds on the cross-correlation of balanced (almost) binary sequences with period Q ≡ 1(mod 4) under the precondition of out-of-phase autocorrelation values {-1} or {1, -3} are firstly presented. Then, seven new pairs of balanced (almost) binary sequences of period Q with ideal or optimal autocorrelation values and meeting the lower cross-correlation bounds are proposed by using cyclotomic classes of order 4. These new bounds of (almost) binary sequences with period Q achieve smaller maximum out-of-phase autocorrelation values and cross-correlation values.

In the fifth-generation mobile communications system (5G), it is critical to ensure wireless security as well as large-capacity and high-speed communication. To achieve this, a chaos modulation method as an encrypted and channel-coded modulation method in the physical layer is proposed. However, in the conventional chaos modulation method, the decoding complexity increases exponentially with respect to the modulation order. To solve this problem, in this study, a hybrid modulation method that applies quadrature amplitude modulation (QAM) and chaos to reduce the amount of decoding complexity, in which some transmission bits are allocated to QAM while maintaining the encryption for all bits is proposed. In the proposed method, a low-complexity decoding method is constructed by ordering chaos and QAM symbols based on the theory of index modulation. Numerical results show that the proposed method maintains good error-rate performance with reduced decoding complexity and ensures wireless security.

In this paper, event-triggered control over a sensor network is studied as one of the control methods of cyber-physical systems. Event-triggered control is a method that communications occur only when the measured value is widely changed. In the proposed method, by solving an LMI (Linear Matrix Inequality) feasibility problem, an event-triggered output feedback controller such that the closed-loop system is asymptotically stable is derived. First, the problem formulation is given. Next, the control problem is reduced to an LMI feasibility problem. Finally, the proposed method is demonstrated by a numerical example.

AlphaSeq is a new paradigm to design sequencess with desired properties based on deep reinforcement learning (DRL). In this work, we propose a new metric function and a new reward function, to design an improved version of AlphaSeq. We show analytically and also through numerical simulations that the proposed algorithm can discover sequence sets with preferable properties faster than that of the previous algorithm.

This paper presents a physical layer wireless device identification method that uses a convolutional neural network (CNN) operating on a quadrant IQ transition image. This work introduces classification and detection tasks in one process. The proposed method can identify IoT wireless devices by exploiting their RF fingerprints, a technology to identify wireless devices by using unique variations in analog signals. We propose a quadrant IQ image technique to reduce the size of CNN while maintaining accuracy. The CNN utilizes the IQ transition image, which image processing cut out into four-part. An over-the-air experiment is performed on six Zigbee wireless devices to confirm the proposed identification method's validity. The measurement results demonstrate that the proposed method can achieve 99% accuracy with the light-weight CNN model with 36,500 weight parameters in serial use and 146,000 in parallel use. Furthermore, the proposed threshold algorithm can verify the authenticity using one classifier and achieved 80% accuracy for further secured wireless communication. This work also introduces the identification of expanded signals with SNR between 10 to 30dB. As a result, at SNR values above 20dB, the proposals achieve classification and detection accuracies of 87% and 80%, respectively.

Parallel processing is a typical approach to answer analytical queries on large database. As the size of the database increases, we often try to increase the parallelism by incorporating more processing nodes. However, this approach increases the possibility of node failure as well. According to the conventional practice, if a failure occurs during query processing, the database system restarts the query processing from the beginning. Such temporal cost may be unacceptable to the user. This paper proposes a fault-tolerant query processing mechanism, named PhoeniQ, for analytical parallel database systems. PhoeniQ continuously takes a checkpoint for every operator pipeline and replicates the output of each stateful operator among different processing nodes. If a single processing node fails during query processing, another can promptly take over the processing. Hence, PhoneniQ allows the database system to efficiently resume query processing after a partial failure event. This paper presents a key design of PhoeniQ and prototype-based experiments to demonstrate that PhoeniQ imposes negligible performance overhead and efficiently continues query processing in the face of node failure.

This paper proposes a novel maximum Doppler frequency detection technique for user moving velocity estimation. The maximum Doppler frequency is estimated in the proposed detection technique by making use of the fact that user moving velocity is not distributed continuously. The fluctuation of the channel state information during a packet is applied for the proposed detection, in which likelihood estimation is performed by comparing the fluctuation with the thresholds. The thresholds are theoretically derived on the assumption that the fluctuation is distributed with an exponential function. An approximated detection technique is proposed to simplify the theoretical threshold derivation. The performance of the proposed detection is evaluated by computer simulation. The proposed detection accomplishes better detection performance as the fluctuation values are summed over more packets. The proposed detection achieves about 90% correct detection performance in a fading channel with the Eb/N0 = 35dB, when the fluctuation values are summed over only three packets. Furthermore, the approximated detection also achieves the same detection performance.

Speech separation is the task of extracting target speech while suppressing background interference components. In applications like video telephones, visual information about the target speaker is available, which can be leveraged for multi-speaker speech separation. Most previous multi-speaker separation methods are mainly based on convolutional or recurrent neural networks. Recently, Transformer-based Seq2Seq models have achieved state-of-the-art performance in various tasks, such as neural machine translation (NMT), automatic speech recognition (ASR), etc. Transformer has showed an advantage in modeling audio-visual temporal context by multi-head attention blocks through explicitly assigning attention weights. Besides, Transformer doesn't have any recurrent sub-networks, thus supporting parallelization of sequence computation. In this paper, we propose a novel speaker-independent audio-visual speech separation method based on Transformer, which can be flexibly applied to unknown number and identity of speakers. The model receives both audio-visual streams, including noisy spectrogram and speaker lip embeddings, and predicts a complex time-frequency mask for the corresponding target speaker. The model is made up by three main components: audio encoder, visual encoder and Transformer-based mask generator. Two different structures of encoders are investigated and compared, including ResNet-based and Transformer-based. The performance of the proposed method is evaluated in terms of source separation and speech quality metrics. The experimental results on the benchmark GRID dataset show the effectiveness of the method on speaker-independent separation task in multi-talker environments. The model generalizes well to unseen identities of speakers and noise types. Though only trained on 2-speaker mixtures, the model achieves reasonable performance when tested on 2-speaker and 3-speaker mixtures. Besides, the model still shows an advantage compared with previous audio-visual speech separation works.

In this letter, we discuss the ambiguity function of interleaved sequences. Furthermore, using the Guassian sum and choosing binary m-sequences as column sequences, we investigate the property of a binary sequence set given by Zhou, Tang, Gong (IEEE Trans. Inf. Theory, 54(9), 2008), which has low ambiguity property in a large region. Those sequences could be used in radar systems.

Reducing the power consumption of capsule endoscopy is essential for its further development. We introduce K-SVD dictionary learning to design a dictionary for sparse coding, and improve reconstruction accuracy of capsule endoscopic images captured using compressed sensing. At a compression ratio of 20%, the proposed method improves image quality by approximately 4.4 dB for the peak signal-to-noise ratio.

To sustain and expand the agricultural economy even as its workforce shrinks, the efficiency of farm operations must be improved. One key to efficiency improvement is completely unmanned driving of farm machines, which requires stable monitoring and control of machines from remote sites, a safety system to ensure safe autonomous driving even without manual operations, and precise positioning in not only small farm fields but also wider areas. As possible solutions for those issues, we have developed technologies of wireless network quality prediction, an end-to-end overlay network, machine vision for safety and positioning, network cooperated vehicle control and autonomous tractor control and conducted experiments in actual field environments. Experimental results show that: 1) remote monitoring and control can be seamlessly continued even when connection between the tractor and the remote site needs to be switched across different wireless networks during autonomous driving; 2) the safety of the autonomous driving can automatically be ensured by detecting both the existence of people in front of the unmanned tractor and disturbance of network quality affecting remote monitoring operation; and 3) the unmanned tractor can continue precise autonomous driving even when precise positioning by satellite systems cannot be performed.

Computer networks tend to be subjected to the proliferation of mobile demands, therefore it poses a great challenge to guarantee the quality of network service. For real-time systems, the QoS performance bound analysis for the complex network topology and background traffic in modern networks is often difficult. Network calculus, nevertheless, converts a complex non-linear network system into an analyzable linear system to accomplish more accurate delay bound analysis. The existing network environment contains complex network resource allocation schemes, and delay bound analysis is generally pessimistic, hence it is essential to modify the analysis model to improve the bound accuracy. In this paper, the main research approach is to obtain the measurement results of an actual network by building a measurement environment and the corresponding theoretical results by network calculus. A comparison between measurement data and theoretical results is made for the purpose of clarifying the scheme of bandwidth scheduling. The measurement results and theoretical analysis results are verified and corrected, in order to propose an accurate per-flow end-to-end delay bound analytic model for a large-scale scheduling network. On this basis, the instructional significance of the analysis results for the engineering construction is discussed.

Through the concept of network slicing, a single physical network infrastructure can be split into multiple logically-independent Network Slices (NS), each of which is customized for the needs of its respective individual user or industrial vertical. In the beyond 5G (B5G) system, this customization can be done for many targeted services, including, but not limited to, 5G use cases and beyond 5G. The network slices should be optimized and customized to stitch a suitable environment for targeted industrial services and verticals. This paper proposes a novel Quality of Service (QoS) framework that optimizes and customizes the network slices to ensure the service level agreement (SLA) in terms of end-to-end reliability, delay, and bandwidth communication. The proposed framework makes use of network softwarization technologies, including software-defined networking (SDN) and network function virtualization (NFV), to preserve the SLA and ensure elasticity in managing the NS. This paper also mathematically models the end-to-end network by considering three parts: radio access network (RAN), transport network (TN), and core network (CN). The network is modeled in an abstract manner based on these three parts. Finally, we develop a prototype system to implement these algorithms using the open network operating system (ONOS) as a SDN controller. Simulations are conducted using the Mininet simulator. The results show that our QoS framework and the proposed resource allocation algorithms can effectively schedule network resources for various NS types and provide reliable E2E QoS services to end-users.

A periodic array of InSb spheres on a substrate is numerically analyzed at terahertz frequencies. The incident field is shown to be coupled to the substrate due to the guided-mode resonance. The effect of the background refractive index on the transmission characteristics is investigated for sensor applications.

Volume integral equations combined with orthogonality of guided mode and non-guided field are proposed for the TE incidence of two-dimensional optical slab waveguide. The slab waveguide is assumed to satisfy the single mode condition. The formulation of the integral equations are described in detail. The matrix equation obtained by applying the method of moments to the integral equations is shown. Numerical results for step, gap, and grating waveguides are given. They are compared to published papers to validate the proposed method.

The spectral envelope parameter is a significant speech parameter in the vocoder's quality. Recently, the Vector Quantized Variational AutoEncoder (VQ-VAE) is a state-of-the-art end-to-end quantization method based on the deep learning model. This paper proposed a new technique for improving the embedding space learning of VQ-VAE with the Generative Adversarial Network for quantizing the spectral envelope parameter, called VQ-VAE-EMGAN. In experiments, we designed the quantizer for the spectral envelope parameters of the WORLD vocoder extracted from the 16kHz speech waveform. As the results shown, the proposed technique reduced the Log Spectral Distortion (LSD) around 0.5dB and increased the PESQ by around 0.17 on average for four target bit operations compared to the conventional VQ-VAE.

In this paper, an automatic retransmission request (ARQ) scheme for IEEE 802.11ac is presented, which can solve the problem of severe packet loss and greatly improve the performance in error-prone environments. The proposed solution only requires to be deployed on the sender and is compatible with the 802.11 protocol. The algorithm utilizes the basic strategy of sliding retransmission and then adds the method of copying frames. The media access control (MAC) protocol data unit (MPDU) lost in the transmission and the newly added data frame brought by the sliding window change are replicated. The scheme retransmits the duplicated aggregated packet and further improves the throughput by increasing the probability of successful transmission of sub-frames. Besides, we also establish a mathematical model to analyze the performance of the proposed scheme. We introduce the concept of average aggregated sub-frames and express the sliding retransmission strategy as the aggregated transmission of average aggregated sub-frames, thereby simplifying the model and effectively analyzing the theoretical throughput of the proposed algorithm. The simulation results of Network simulator 3 (NS-3) simulation results demonstrate that the performance of the proposed algorithm is better than the traditional sliding retransmission ARQ algorithm in error-prone channels with a higher physical layer rate.