In order to verify the correctness of functional requirements, we have been developing a verification method of the correctness of functional requirements specification using the Requirements Frame model. In this paper, we propose a verification method of non-functional requirements specification in terms of time-response requirements written with a natural language. We established a verification method by extending the Requirements Frame model. We have also developed a prototype system based on the method using Java. The extended Requirements Frame model and the verification method will be illustrated with examples.

Massive multiple-input multiple-output (MIMO) transmission, in which the number of antennas is considerably more than the number of user terminals, has attracted attention as a key technology in next-generation mobile communication systems, because it enables improvements in the service area and interference mitigation with simple signal processing. Multi-beam massive MIMO employing high-power beam selection in the analog part and a blind algorithm in the digital part, such as the constant modulus algorithm that does not need channel state information, has been proposed and shown to offer high transmission efficiency. In this paper, in order to realize higher transmission rates and communication efficiency, we propose a beam-selection method that uses multi-beam amplitude information only. Furthermore, this method can be realized through signal processing with a simple configuration and is highly suitable for hybrid analog-digital massive MIMO, which is advantageous in terms of cost and power consumption. Here, the effectiveness of the proposed method is verified by computer simulation.

This paper proposes a novel access control scheme with collision detection that utilizes multiple-input multiple-output (MIMO) technology. Carrier sense multiple access with collision detection (CSMA/CD) is used in Ethernet wired local area networks (LANs) for media access control (MAC). CSMA/CD can immediately abort a transmission if any collision is detected and is thus able to change to a retransmission state. In Ethernet, CSMA/CD results in a transmission efficiency of approximately 90% because the protocol makes the transmission band available for useful communication by this retransmission function. Conversely, in conventional wireless LANs (WLANs), the packet collisions due to interfering signals and the retransmission due to collisions are significant issues. Because conventional WLANs cannot detect packet collisions during signal transmission, the success of a transmission can only be determined by whether an acknowledgment (ACK) frame has been received. Consequently, the transmission efficiency is low — approximately 60%. The objective of our study is to increase the transmission efficiency of WLANs to make it at least equal to that of Ethernet. Thus, we propose a novel access control scheme with collision detection that utilizes MIMO technology. When preamble signals are transmitted before transmitting data packets from an antenna, the proposed scheme can detect packet collisions during signal transmission at another antenna; then, the affected packets are retransmitted immediately. Two fundamental technologies are utilized to realize our proposed scheme. The first technology is the access control protocol in the MAC layer in the form of the MIMO frame sequence protocol, which is used to detect signal interference. The other technology is signal processing in the physical (PHY) layer that actualizes collision detection. This paper primarily deals with the proposed MAC layer scheme, which is evaluated by theoretical analyses and computer simulations. Evaluation by computer simulations indicate that the proposed scheme in a transmission efficiency of over 90%.

We demonstrate heteroepitaxial growth of GaAs/Ge buffer layers for fabricating 1.3-µm range metamorphic InGaAs-based multiple quantum well (MQW) lasers in which the Ge buffer layer is grown using a metal-organic Ge precursor, iso-butyl germane, in a conventional metal-organic vapor phase epitaxy reactor. This enables us to grow Ge and GaAs buffer layers in the same reactor seamlessly. Transmission electron microscopy and X-ray diffraction analyses indicate that dislocations are well confined at the Ge/Si interface. Furthermore, thermal-cycle annealing significantly improves crystalline quality at the GaAs/Ge interface, resulting in higher photoluminescence intensity from the MQWs on the buffer layers.

We propose an efficient training method for memristor neural networks. The proposed method is suitable for the mini-batch-based training, which is a common technique for various neural networks. By integrating the two processes of gradient calculation in the backpropagation algorithm and weight update in the write operation to the memristors, the proposed method accelerates the training process and also eliminates the external computing resources required in the existing method, such as multipliers and memories. Through numerical experiments, we demonstrated that the proposed method achieves twice faster convergence of the training process than the existing method, while retaining the same level of the accuracy for the classification results.

We review the recent advances on strain and temperature sensing techniques based on multimodal interference in perfluorinated (PF) graded-index (GI) polymer optical fibers (POFs). First, we investigate their fundamental characteristics at 1300nm. When the core diameter is 62.5µm, we obtain strain and temperature sensitivities of -112pm/µε and +49.8nm/°C, the absolute values of which are, by simple calculation, approximately 13 and over 1800 times as large as those in silica GI multimode fibers, respectively. These ultra-high strain and temperature sensitivities probably originate from the unique PF polymer used as core material. Subsequently, we show that the temperature sensitivity (absolute value) is significantly enhanced with increasing temperature toward ∼70°C, which is close to the glass-transition temperature of the core polymer. When the core diameter is 62.5µm, the sensitivity at 72°C at 1300nm is 202nm/°C, which is approximately 26 times the value obtained at room temperature and >7000 times the highest value previously reported using a silica multimode fiber. Then, we develop a single-end-access configuration of this strain and temperature sensing system, which enhances the degree of freedom in embedding the sensors into structures. The light Fresnel-reflected at the distal open end of the POF is exploited. The obtained strain and temperature sensitivities are shown to be comparable to those in two-end-access configurations. Finally, we discuss the future prospects and give concluding remarks.

In this paper, we posit that, in future mobile network, network softwarization will be prevalent, and it becomes important to utilize deep machine learning within network to classify mobile traffic into fine grained slices, by identifying application types and devices so that we can apply Quality-of-Service (QoS) control, mobile edge/multi-access computing, and various network function per application and per device. This paper reports our initial attempt to apply deep machine learning for identifying application types from actual mobile network traffic captured from an MVNO, mobile virtual network operator and to design the system for classifying it to application specific slices.

The abnormal crowd behavior detection is an important research topic in computer vision to improve the response time of critical events. In this letter, we introduce a novel method to detect and localize the crowd gathering in surveillance videos. The proposed foreground stillness model is based on the foreground object mask and the dense optical flow to measure the instantaneous crowd stillness level. Further, we obtain the long-term crowd stillness level by the leaky bucket model, and the crowd gathering behavior can be detected by the threshold analysis. Experimental results indicate that our proposed approach can detect and locate crowd gathering events, and it is capable of distinguishing between standing and walking crowd. The experiments in realistic scenes with 88.65% accuracy for detection of gathering frames show that our method is effective for crowd gathering behavior detection.

Although Wolf Pack Algorithm (WPA) is a novel optimal algorithm with good performance, there is still room for improvement with respect to its convergence. In order to speed up its convergence and strengthen the search ability, we improve WPA with the Differential Evolution (DE) elite set strategy. The new proposed algorithm is called the WPADEES for short. WPADEES is faster than WPA in convergence, and it has a more feasible adaptability for various optimizations. Six standard benchmark functions are applied to verify the effects of these improvements. Our experiments show that the performance of WPADEES is superior to the standard WPA and other intelligence optimal algorithms, such as GA, DE, PSO, and ABC, in several situations.

Behaviors of an object-oriented system can be visualized as reverse-engineered sequence diagrams from execution traces. This approach is a valuable tool for program comprehension tasks. However, owing to the massiveness of information contained in an execution trace, a reverse-engineered sequence diagram is often afflicted by a scalability issue. To address this issue, many trace summarization techniques have been proposed. Most of the previous techniques focused on reducing the vertical size of the diagram. To cope with the scalability issue, decreasing the horizontal size of the diagram is also very important. Nonetheless, few studies have addressed this point; thus, there is a lot of needs for further development of horizontal summarization techniques. We present in this paper a method for identifying core objects for trace summarization by analyzing reference relations and dynamic properties. Visualizing only interactions related to core objects, we can obtain a horizontally compactified reverse-engineered sequence diagram that contains system's key behaviors. To identify core objects, first, we detect and eliminate temporary objects that are trivial for a system by analyzing reference relations and lifetimes of objects. Then, estimating the importance of each non-trivial object based on their dynamic properties, we identify highly important ones (i.e., core objects). We implemented our technique in our tool and evaluated it by using traces from various open-source software systems. The results showed that our technique was much more effective in terms of the horizontal reduction of a reverse-engineered sequence diagram, compared with the state-of-the-art trace summarization technique. The horizontal compression ratio of our technique was 134.6 on average, whereas that of the state-of-the-art technique was 11.5. The runtime overhead imposed by our technique was 167.6% on average. This overhead is relatively small compared with recent scalable dynamic analysis techniques, which shows the practicality of our technique. Overall, our technique can achieve a significant reduction of the horizontal size of a reverse-engineered sequence diagram with a small overhead and is expected to be a valuable tool for program comprehension.

This paper proposes a semi-supervised source separation method for stereophonic music signals containing multiple recorded or processed signals, where synthesized music is focused on the stereophonic music. As the synthesized music signals are often generated as linear combinations of many individual source signals and their respective mixing gains, phase or phase difference information between inter-channel signals, which represent spatial characteristics of recording environments, cannot be utilized as acoustic clues for source separation. Non-negative Tensor Factorization (NTF) is an effective technique which can be used to resolve this problem by decomposing amplitude spectrograms of stereo channel music signals into basis vectors and activations of individual music source signals, along with their corresponding mixing gains. However, it is difficult to achieve sufficient separation performance using this method alone, as the acoustic clues available for separation are limited. To address this issue, this paper proposes a Cepstral Distance Regularization (CDR) method for NTF-based stereo channel separation, which involves making the cepstrum of the separated source signals follow Gaussian Mixture Models (GMMs) of the corresponding the music source signal. These GMMs are trained in advance using available samples. Experimental evaluations separating three and four sound sources are conducted to investigate the effectiveness of the proposed method in both supervised and semi-supervised separation frameworks, and performance is also compared with that of a conventional NTF method. Experimental results demonstrate that the proposed method yields significant improvements within both separation frameworks, and that cepstral distance regularization provides better separation parameters.

A plasmonic black pole (PBP) consisting of a series of touching spherical metal surfaces is analyzed using the finite-difference time-domain (FDTD) method with the periodic boundary condition. First, the wavelength characteristics of the PBP are studied under the assumption that the PBP is omnidirectionally illuminated. It is found that partial truncation of each metal sphere reduces the reflectivity over a wide wavelength range. Next, we consider the case where the PBP is illuminated with a cylindrical wave from a specific direction. It is shown that an absorptivity of more than 80% is obtained over a wavelength range of λ=500 nm to 1000 nm. Calculation regarding the Poynting vector distribution also shows that the incident wave is bent and absorbed towards the center axis of the PBP.

Bug report summarization has been explored in past research to help developers comprehend important information for bug resolution process. As text mining technology advances, many summarization approaches have been proposed to provide substantial summaries on bug reports. In this paper, we propose an enhanced summarization approach called TSM by first extending a semantic model used in AUSUM with the anthropogenic and procedural information in bug reports and then integrating the extended semantic model with the shallow textual information used in BRC. We have conducted experiments with a dataset of realistic software projects. Compared with the baseline approaches BRC and AUSUM, TSM demonstrates the enhanced performance in achieving relative improvements of 34.3% and 7.4% in the F1 measure, respectively. The experimental results show that TSM can effectively improve the performance.

The growth mechanisms of three-dimensionally (3D) faceted InGaN quantum wells (QWs) on (=1=12=2) GaN substrates are discussed. The structure is composed of (=1=12=2), {=110=1}, and {=1100} planes, and the cross sectional shape is similar to that of 3D QWs on (0001). However, the 3D QWs on (=1=12=2) and (0001) show quite different inter-facet variation of In compositions. To clarify this observation, the local thicknesses of constituent InN and GaN on the 3D GaN are fitted with a formula derived from the diffusion equation. It is suggested that the difference in the In incorporation efficiency of each crystallographic plane strongly affects the surface In adatom migration.

Computing autocorrelation coefficient can effectively reduce the influence of additive white noise, thus estimation precision will be improved. In this paper, an autocorrelation-like function, different from the ordinary one, is defined, and is proven to own better linear predictive performance. Two algorithms for signal model are developed to achieve frequency estimates. We analyze the theoretical properties of the algorithms in the additive white Gaussian noise. The simulation results match with the theoretical values well in the sense of mean square error. The proposed algorithms compare with existing estimators, are closer to the Cramer-Rao bound (CRLB). In addition, computer simulations demonstrate that the proposed algorithms provide high accuracy and good anti-noise capability.

8B/10B is an encoding technique largely used in different communication protocols, with several advantages such as zero DC bias. In the last years transmission rates have grown rapidly, thus the need of encoders with better performance in terms of throughput, area and power consumption raised rapidly. In this article we will present and discuss the architecture of two symbols parallel encoder, comparing it with a classical pipelined solution.

This letter proposes a novel modulation classification method for overlapped sources named LRGP involving multinomial logistic regression (MLR) and multi-gene genetic programming (MGGP). MGGP based feature engineering is conducted to transform the cumulants of the received signals into highly discriminative features and a MLR based classifier is trained to identify the combination of the modulation formats of the overlapped sources instead of signal separation. Extensive simulations demonstrate that LRGP yields superior performance compared with existing methods.

In this letter, we study a wireless powered communication network (WPCN) with non-orthogonal multiple access (NOMA), where the user clustering scheme that groups each two users in a cluster is adopted to guarantee the system performance. The two users in a cluster transmit data simultaneously via NOMA, while time division multiple access (TDMA) is used among clusters. We aim to maximize the system throughput by finding the optimal cluster permutation and the optimal time allocation, which can be obtained by solving the optimization problems corresponding to all cluster permutations. The closed-form solution of each optimization problem is obtained by exploiting its constraint structures. However, the complexity of this exhaustive method is quite high, we further propose a sub-optimal clustering scheme with low complexity. The simulation results demonstrate the superiority of the proposed scheme.

This paper presents a 1024-QAM OFDM signal capable WLAN receiver in 65nm CMOS technology. Thermal noise-based IQ frequency-independent mismatch correction and IQ frequency-dependent mismatch correction with baseband loopback are proposed for the self-calibration in the receiver. The measured image rejection ratio of the self-calibration is -56.3dB. The receiver achieves the extremely low EVM of -37.1dB even with wide channel bandwidth of 80MHz and has the ability to receive the 1024-QAM signal. The result indicates that the receiver is extendable for the 802.11ax compliant receiver that supports a higher density modulation scheme of MIMO.

By focusing on the recent swing to the centralized approach by the software defined network (SDN), this paper presents a novel network architecture for refactoring the current distributed Internet protocol (IP) by not only utilizing the SDN itself but also implementing its cooperation with the optical transport layer. The first IP refactoring is for flexible network topology reconfiguration: the global routing and explicit routing functions are transferred from the distributed routers to the centralized SDN. The second IP refactoring is for cost-efficient maintenance migration: we introduce a resource portable IP router that can behave as a shared backup router by cooperating with the optical transport path switching. Extensive evaluations show that our architecture makes the current IP network easier to configure and more scalable. We also validate the feasibility of our proposal.