When a mathematical model is not available for a dynamical system, it is reasonable to use a data-driven approach for analysis and control of the system. With this motivation, the authors have recently developed a data-driven solution to Lyapunov equations, which uses not the model but the data of several state trajectories of the system. However, the number of state trajectories to uniquely determine the solution is O(n2) for the dimension n of the system. This prevents us from applying the method to a case with a large n. Thus, this paper proposes a novel class of data-driven Lyapunov equations, which requires a smaller amount of data. Although the previous method constructs one scalar equation from one state trajectory, the proposed method constructs three scalar equations from any combination of two state trajectories. Based on this idea, we derive data-driven Lyapunov equations such that the number of state trajectories to uniquely determine the solution is O(n).

This paper addresses an observer-design method only using data. Usually, the observer requires a mathematical model of a system for state prediction and observer gain calculation. As an alternative to the model-based prediction, the proposed predictor calculates the states using a linear combination of the given data. To design the observer gain, the data which represent dual systems are derived from the data which represent the original system. Linear matrix inequalities that depend on data of the dual system provides the observer gains.

This research develops a new automatic path following control method for a car model based on just-in-time modeling. The purpose is that a lot of basic driving data for various situations are accumulated into a database, and we realize automatic path following for unknown roads by using only data in the database. Especially, just-in-time modeling is repeatedly utilized in order to follow the desired points on the given road. From the results of a numerical simulation, it turns out that the proposed new method can make the car follow the desired points on the given road with small error, and it shows high computational efficiency.

This study develops a new automatic hovering control method based on just-in-time modeling for a multicopter. Especially, the main aim is to compute gains of a feedback control law such that the multicopter hovers at a desired height and at a desired time without overshoot/undershoot. First, a database that contains various hovering data is constructed, and then the proposed method computes gains for a query input from the database. From simulation results, it turns out that the multicopter achieves control purposes, and hence the new method is effective.

Soil moisture sensor calibration based on the Multivariate Adaptive Regression Splines (MARSplines) model is studied in this paper. Different from the generic polynomial fitting methods, the MARSplines model is a non-parametric model, and it is able to model the complex relationship between the actual and measured soil moisture. Rao-1 algorithm is employed to tune the hyper-parameters of the calibration model and thus the performance of the proposed method is further improved. Data collected from four commercial soil moisture sensors is utilized to verify the effectiveness of the proposed method. To assess the calibration performance, the proposed model is compared with the model without using the temperature information. The numeric studies prove that it is promising to apply the proposed model for real applications.

This paper proposes two high-fidelity and computationally efficient neural voice conversion (VC) methods based on a direct waveform modification using spectral differentials. The conventional spectral-differential VC method with a minimum-phase filter achieves high-quality conversion for narrow-band (16 kHz-sampled) VC but requires heavy computational cost in filtering. This is because the minimum phase obtained using a fixed lifter of the Hilbert transform often results in a long-tap filter. Furthermore, when we extend the method to full-band (48 kHz-sampled) VC, the computational cost is heavy due to increased sampling points, and the converted-speech quality degrades due to large fluctuations in the high-frequency band. To construct a short-tap filter, we propose a lifter-training method for data-driven phase reconstruction that trains a lifter of the Hilbert transform by taking into account filter truncation. We also propose a frequency-band-wise modeling method based on sub-band multi-rate signal processing (sub-band modeling method) for full-band VC. It enhances the computational efficiency by reducing sampling points of signals converted with filtering and improves converted-speech quality by modeling only the low-frequency band. We conducted several objective and subjective evaluations to investigate the effectiveness of the proposed methods through implementation of the real-time, online, full-band VC system we developed, which is based on the proposed methods. The results indicate that 1) the proposed lifter-training method for narrow-band VC can shorten the tap length to 1/16 without degrading the converted-speech quality, and 2) the proposed sub-band modeling method for full-band VC can improve the converted-speech quality while reducing the computational cost, and 3) our real-time, online, full-band VC system can convert 48 kHz-sampled speech in real time attaining the converted speech with a 3.6 out of 5.0 mean opinion score of naturalness.

The Cyber-Physical Integrated Society (CPIS) is being formed with the fusion of cyber-space and the real-world. In this paper, we will discuss Data-Driven Decision-Making (DDDM) support systems to solve social problems in the CPIS. First, we introduce a Web of Resources (WoR) that uses Web booking log data for destination data management. Next, we introduce an Internet of Persons (IoP) system to visualize individual and group flows of people by analyzing collected Wi-Fi usage log data. Specifically, we present examples of how WoR and IoP visualize flows of groups of people that can be shared across different industries, including telecommunications carriers and railway operators, and policy decision support for local, short-term events. Finally, the importance of data-driven training of human resources to support DDDM in the future CPIS is discussed.

Data-driven untying of a recursive autoencoder (RAE) is proposed for utterance intent classification for spoken dialogue systems. Although an RAE expresses a nonlinear operation on two neighboring child nodes in a parse tree in the application of spoken language understanding (SLU) of spoken dialogue systems, the nonlinear operation is considered to be intrinsically different depending on the types of child nodes. To reduce the gap between the single nonlinear operation of an RAE and intrinsically different operations depending on the node types, a data-driven untying of autoencoders using part-of-speech (PoS) tags at leaf nodes is proposed. When using the proposed method, the experimental results on two corpora: ATIS English data set and Japanese data set of a smartphone-based spoken dialogue system showed improved accuracies compared to when using the tied RAE, as well as a reasonable difference in untying between two languages.

Crowd modeling and simulation is an active research field that has drawn increasing attention from industry, academia and government recently. In this paper, we present a generic data-driven approach to generate crowd behaviors that can match the video data. The proposed approach is a bi-layer model to simulate crowd behaviors in pedestrian traffic in terms of exclusion statistics, parallel dynamics and social psychology. The bottom layer models the microscopic collision avoidance behaviors, while the top one focuses on the macroscopic pedestrian behaviors. To validate its effectiveness, the approach is applied to generate collective behaviors and re-create scenarios in the Informatics Forum, the main building of the School of Informatics at the University of Edinburgh. The simulation results demonstrate that the proposed approach is able to generate desirable crowd behaviors and offer promising prediction performance.

In this letter, we propose a novel speech enhancement algorithm based on data-driven residual gain estimation. The entire system consists of two stages. At the first stage, a conventional speech enhancement algorithm enhances the input signal while estimating several signal-to-noise ratio (SNR)-related parameters. The residual gain, which is estimated by a data-driven method, is applied to further enhance the signal at the second stage. A number of experimental results show that the proposed speech enhancement algorithm outperforms the conventional speech enhancement technique based on soft decision and the data-driven approach using SNR grid look-up table.

To realize a secure networking infrastructure, the author is carrying out CUE (Coordinating Users' requirements and Engineering constraints) project with a network carrier and a VLSI manufacture. Since CUE-series data-driven processors developed in the project were specifically designed to be an embedded programmable component as well as a multi-processor element, particular design considerations were taken to achieve real-time multiprocessing capabilities essentially needed in multi-media communication environment. A novel data-driven paradigm is first introduced with special emphasis on VLSI-oriented parallel processing architectures. Data-driven protocol handlings on CUE-p and CUE-v1 are then discussed for their real-time multiprocessing capability without any runtime overheads. The emulation facility RESCUE (Real-time Execution System for CUE-series data-driven processors) was also built to develop scalable chip multi-processors in self-evolutional manner. Based on emulation results, the latest version named CUE-v2 was realized as a hybrid processor enabling simultaneous processing of data-driven and control-driven threads to achieve higher performance for inline processing and to avoid any bottlenecks in sequential parts of real-time programs frequently encountered in actual time-sensitive applications. Effectiveness of the data-driven chip multi-processor architecture will finally be addressed for lower power consumption and scalability to realize future VLSI processors in the sub-100 nm era.

This paper discusses and clarifies effectiveness of data-driven implementation of protocol handling system to access TINA (Telecommunications Information Networking Architecture) network and internet. TINA is a networking architecture that achieves networking services and management ubiquitously for users and networks. Many TINA related ACTS (Advanced Communication Technologies and Services) projects have been organized in Europe. In Japan, The TINA Trial (TTT) to achieve ATM network management and services based on TINA architectures was done by NTT and several manufactures from April 1997 to April 1999. In these studies and trials, much effort is devoted to development of software based on service architecture and network architecture being standardized in TINA-C (TINA Consortium). In order to achieve TINA environment universally in customers and network sides, we have to consider how to deploy TINA environment onto user side and how to use access transmission capacity as efficiently as possible. Recent technology can easily achieve application and environment downloading from the network side to user side by use of e. g. , JAVA. In accessing the network, there are several possible bottlenecks in information exchange in customer side such as PC processing capability, access protocol handling capability, intra-house wiring bandwidth. Authors, in parallel with TINA software architecture study, have been studying versatile requirements for hardware platform of TINA network. In those studies, we have clarified that the stream-oriented data-driven processor authors have been studying and developing have high reliability, high multiprocessing and multimedia information processing capability. Based on these studies, this paper first shows Von Neumann-based protocol handler is ineffective in case of multiprocessing through mathematical and emulation studies. Then, we show our data-driven protocol handling can effectively realize access protocol handling by emulation study. Then, we describe a result of first step of implementation of data-driven TCP/IP protocol handling. This result proves our TCP/IP hub based on data-driven processor is applicable not only for TINA/CORBA network but normal internet access. Finally, we show a possible customer premises network configuration which resolves bottleneck to access TINA network through ATM access.

A sparse memory access architecture which is proposed to achieve a high-computational-speed neural-network LSI is described in detail. This architecture uses two key techniques, compressible synapse-weight neuron calculation and differential neuron operation, to reduce the number of accesses to synapse weight memories and the number of neuron calculations without incurring an accuracy penalty. The test chip based on this architecture has 96 parallel data-driven processing units and enough memory for 12,288 synapse weights. In a pattern recognition example, the number of memory accesses and neuron calculations was reduced to 0.87% that needed in the conventional method and the practical performance was 18 GCPS. The sparse memory access architecture is also effective when the synapse weights are stored in off-chip memory.

This paper describes the effectiveness of stream-oriented data-driven scheme for achieving autonomous fault management of hyper-distributed systems such as networks based on the Telecommunications Information Networking Architecture (TINA). TINA, whose specifications are in the finalizing phase within TINA-Consortium, is aiming at achieving interoperability and reusability of telecom applications software and independent of underlying technologies. However, to actually implement TINA network, it is essential to consider the technology constraints. Especially autonomous fault management at run-time is crucial for distributed network environment because centralized control using global information is very difficult. So far many works have been done on so-called off-line management but runtime management of service failure seems immature. This paper proposes introduction of stream-oriented data-driven processors to the autonomous fault management at runtime in TINA based distributed network environment. It examines the features of distributed network applications and technology requirements to achieve fault management of those distributed applications such as effective multiprocessing of surveillance, testing, reconfiguration in addition to ordinary processing.

The data-driven model of computation is well suited for flexible and highly parallel simulation of neural networks. First, the operational semantics of data-driven languages preserve the locality and functionality of neural networks, and naturally describe their inherent parallelism. Second, the asynchronous data-driven execution facilitates the implementation of large and scalable multiprocessor systems, which are necessary to obtain considerable degrees of simulation sppedups. In this paper, we present a dynamic data-driven multiprocessor system, and demonstrate its suitability for the paralel simulation of back propagation neural networks. Two parallel implementations are described and evaluated using an image data compression network. The system is scalable, and as a result, the performance improved proportionally with the increase in number of processors.

Automotive electronics technology has become extremely advanced in the regions of automotive engine control, anti-skid brake control, and others. These control systems require highly advanced control performance and high speed microprocessors which can rapidly execute interrupt processing. Automotive engine control systems are now widely utilized in cars with high speed, high power engines. At present, it is generally acknowledged that such high performance engine control for the 10,000 rpm, 12 cylinder engines requires three or more conventional microprocessors. We fabricated an engine control system prototype incorporating the data-driven processor under development, which was installed in an actual automobile. In this paper, the characteristics of the engine control program and simulation results are firstly discussed. Secondly, the structure of the engine control system prototype and the control performance applied to the actual automobile are shown. Finally, from the results of software simulation and the installation of the engine control system prototype with the data-driven processor, we conclude that a single chip data-driven microprocessor can control a high speed, high power, 10,000 rpm, 12 cylinder automobile engine.

High speed simulation of neural networks can be achieved through parallel implementations capable of exploiting their massive inherent parallelism. In this paper, we show how this inherent parallelism can be effectively exploited on parallel data-driven systems. By using these systems, the asynchronous parallelism of neural networks can be naturally specified by the functional data-driven programs, and maximally exploited by pipelined and scalable data-driven processors. We shall demonstrate the suitability of data-driven systems for the parallel simulation of neural networks through a parallel implementation of the widely used back propagation networks. The implementation is based on the exploitation of the network and training set parallelisms inherent in these networks, and is evaluated using an image data compression network.

This paper describes a new method for designing switching software called DDL (Data Driven Logic). The new design method adopts the dataflow concept and graphical programming using a dataflow diagram. A dataflow diagram is used for software representation, and a dataflow mechanism is emulated on a conventional von Neumann processor. The DDL method has the following advantages; (1) general advantages of dataflow software; i.e. easily understandable programs using graphical representations, and easy description of parallelism, (2) modular design using reusable software components, (3) easy design and programming with a graphical user interface. This paper presents the general concepts and structure of DDL. It also discusses the dataflow emulation mechanism, the DDL software development process, the DDL programming environment, an evaluation of the DDL call processing program applied to a commercial PABX, and some unsolved problems of DDL.