Characterization of Speech Recognition Systems on GPU Architectures

This master thesis characterizes the performance and energy bottlenecks of speech recognition systems when running on modern GPU, with the aim of providing useful information for designing future GPU architectures, as well as proposing a GPU configuration more well-suited for speech recognition

拡張隠れセミマルコフモデルによる複数系列データモデリングとデータ収集・管理手法

In recent years, with the development of devices and the development of data aggregation methods, data to be analyzed and aggregating methods have been changed. Regarding the environment of Internet of Things (IoT), sensors or devices are connected to the communication terminal as access point or mobile phone and the terminal aggregate the sensing data and upload them to the cloud server. From the viewpoint of analysis, the aggregated data are sequential data and the grouped sequence is a meaningful set of sequences because the group represents the owner\u27s information. However, most of the researches for sequential data analysis are specialized for the target data, and not focusing on the "grouped" sequences. In addition from the viewpoint of aggregation, it needs to prepare the special terminals as an access point. The preparation of the equipment takes labor and cost. To analyze the "grouped" sequence and aggregate them without any preparation, this paper aims to realize the analysis method for grouped sequences and to realize the aggregation environment virtually. For analysis of grouped sequential data, we firstly analyze the grouped sequential data focusing on the event sequences and extract the requirements for their modeling. The requirements are (1) the order of events, (2) the duration of the event, (3) the interval between two events, and (4) the overlap of the event. To satisfy all requirements, this paper focuses on the Hidden Semi Markov Model (HSMM) as a base model because it can model the order of events and the duration of event. Then, we consider how to model these sequences with HSMM and propose its extensions. For the former consideration, we propose two models; duration and interval hidden semi-Markov model and interval state hidden-semi Markov model to satisfy both the duration of event and the interval between events simultaneously. For the latter consideration, we consider how to satisfy all requirements including the overlap of the events and propose a new modeling methodology, over-lapped state hidden semi-Markov model. The performance of each method are shown compared with HSMM from the view point of the training and recognition time, the decoding performance, and the recognition performance in the simulation experiment. In the evaluation, practical application data are also used in the simulation and it shows the effectiveness. For the data aggregation, most of conventional approaches for aggregating the grouped data are limited using pre-allocated access points or terminals. It can obtain the grouped data stably, but it needs to additional cost to allocate such terminals in order to aggregate a new group of sequences. Therefore, this paper focus on "area based information" as a target of the grouped sequences, and propose an extraordinary method to store such information using the storage of the terminals that exist in the area. It realize the temporary area based storage virtually by relaying the information with existing terminals in the area. In this approach, it is necessary to restrict the labor of terminals and also store the information as long as possible. To control optimally while the trade-off, we propose methods to control the relay timing and the size of the target storage area in ad hoc dynamically. Simulators are established as practical environment to evaluate the performance of both controlling method. The results show the effectiveness of our method compared with flooding based relay control. As a result of above proposal and evaluation, methods for the grouped sequential data modeling and its aggregation are appeared. Finally, we summarize the research with applicable examples.電気通信大学201

信頼性の高い大容量公共用移動通信システムを実現するためのソフトウェア無線およびコグニティブ無線に関する研究

Public safety mobile wireless communication systems (PMCSs) are widely used by public safety personnel, such as firefighters and police, as well as local governments. PMCSs are crucial to protect safety and security of communities. Conventional PMCSs effectively cover underpopulated areas as well as urban areas by employing long-zone scheme. Since the PMCSs can cover areas that are not covered by commercial cellule systems, they play the important role as the only communication tool. Moreover, the conventional PMCSs have enhanced robustness and reliability. The conventional PMCSs can keep their services even if backbone lines are cut off. In contrast, short-zone scheme systems cannot offer stable and wide service area without backbone line connection. For example, the Great East Japan Earthquake in Japan, police mobile communication systems had kept their functions while cellular phones became disabled. PMCSs are required to be quite high robustness and reliability in order to save human life. Recently, conventional PMCSs are required to realize further expansion of service areas and high speed transmission although they have stably provided users with wide service areas so far. Nowadays, in order to solve complicated public affair quickly, more stable service areas and broadband communication are required. Compared with conventional PMCSs in urban areas, commercial wireless mobile communication systems (CWMCSs) such as cellular systems supply stable service areas and broadband communication in times of peace. In accordance with development of wireless technology, PMCSs need to keep pace with CWMCSs. However, conventional PMCSs can hardly realize further stable service areas and high speed transmission because of large-zone scheme. In terms of realization of further stable service areas, no-service areas cannot be eliminated easily. This is because no-service areas are mostly attributed to shadowing; in large-zone scheme, a no-service area that must essentially be covered by a certain base station is seldom covered by other neighboring base stations. Although new allocation of base stations is fundamental answer to solve no-service area problem in PMCSs, building new base stations of PMCSs that are not used for a commercial purpose is restricted by national and local budget. Realization of high speed transmission of PMCSs is also difficult because of large-zone scheme. To realize high speed transmission, increase of transmit power or shrinking of service area coverage is required to compensate Signal to Noise Power Ratio (SNR) deterioration caused by expanding bandwidth. Increase of transmission power of mobile station used in large-zone scheme systems is almost impossible because transmission power of mobile station is originally high. Thus, shrinking of service areas is necessary for high speed communication. Currently, to realize high speed transmission, next generation broadband PMCSs (BPMCSs) employing short-or middle-zone scheme are being developed. In the 3GPP, it is considered that the Long Term Evolution (LTE) is used for communication of public safety. In Japan, National Institute of Information and Communications Technology (NICT) has researched and developed Public Broadband Wireless Communication System (PBWCS), which employs 200MHz as carrier frequency. The PBWCS has already been equipped in national police agency in Japan. However, we consider that the conventional narrowband PMCSs (NPM-CSs) are not replaced with the BPMCSs completely. This is because the BPMCSs cannot cover all the areas that the conventional NPM-CSs have covered. Moreover, there are problems of robustness and reliability when accidents happen. Hence, users of PMCSs will utilize both of NPMCSs and BPMCSs in accordance with the situation. In this case, users equipping several terminals feel inconvenient and also radio resources are not used effectively. The best solution to realize optimal PMCSs is employing heterogeneous cognitive radio (HCR) for PMCSs. By applying the HCR to PMCSs, service areas expansion and high speed transmission in PM-CSs will be realized effectively. We propose an integrated system combining NPMCSs with CWMCSs and BPMCSs to make communication quality of the PMCSs improve. The proposed HCR recognizes communication conditions of several systems and then provides PMCS\u27s users with optimal communication quality. Although software defined radio techniques (SDR) are ideal to operate cognitive radio more flexibly, we deal with HCR mainly to realize combined systems in this thesis. We study advantages, problems, and their solution to realize the HCR for PMCSs. Firstly, we research service area expansion of NPMCSs using HCR. The proposed HCR is utilized for stabilization of NPMCS\u27s service area. If communication quality of a NPMCS deteriorates owing to shadowing, the proposed HCR terminal obtains a part of NPMCS\u27s data called subsidiary information (SI) from CWMCSs or BPMCSs. The proposed HCR terminal can improve PMCS\u27s bit error rate (BER) performance by combining the SI with received signals of the NPMCS and then decoding the combined signals using forward error correction (FEC). Since convolutional codes are often used in FEC of NPMCSs, we consider BER improvement methods of the convolutional code. We derive modified Viterbi algorithm from maximum likelihood sequence estimation (MLSE) of the combined signals. Moreover, we introduce the distance spectrum to evaluate characteristics of the convolutional codes. The distance spectrum is used for estimating improvement of BER performances. Next, we consider synchronization methods to realize the proposed HCR. In the HCR, there are two types of synchronization method; one is the self-synchronization method to synchronize each system itself. The other is the co-synchronization method to combine different systems. In this thesis, we consider self-synchronization methods of NPMCSs mainly. This is because the HCR aims to improve communication quality of NPMCSs equipping conventional self-synchronization methods that are not probably available in low SNR environments. In this environment, since NPMCSs can hardly obtain their self-synchronization alone, powerful self-synchronization methods using HCR techniques are required. We propose two synchronization methods that are utilization of global portioning system (GPS) signals and utilization of the SI, respectively. The synchronization methods utilizing GPS signals can acquire timing synchronization. To obtain timing synchronization, the proposed HCR acquires accurate time and own location using the GPS signals. The HCR also gets the location of base stations and the frame timing by making the SI convey their information. Since the HCR can know accurate time and distance between the base station and the HCR, synchronization timing can be calculated. However, in GPS based method, preciseness of timing synchronization may be deteriorated by measurement error of GPS signals, diffraction caused by mountains, and propagation delay caused by reection. For this reason, we consider a mitigation method of the timing error and then evaluate BER performances using computer simulation. Moreover, we propose a SI based synchronization method that can obtain timing synchronization without GPS signals. The proposed method is employed when a NPMCS uses differential coded π/4 shift QPSK as the modulation scheme. The notable feature of the proposed method is to convey the phase rotation of the π/4 shift QPSK as the SI. The HCR can forecast PMCS\u27s envelopes from the obtained SI and then obtain the timing synchronization by correlating the forecasted envelopes with real received envelopes. Since the proposed method can also be used for co-synchronization and BER improvement, CWMCS\u27s resource consumption to convey the SI is suppressed. Finally, we consider HCRs combining several PMCSs. In this thesis, the combination of NPMCSs and the combination of a NPMCS and a BPMCS are researched. In the combination of NPMCSs, we consider that several PMCSs are integrated by SDR. In the combination of a NPMCS and a BPMCS, we propose site diversity based on HCR to improve uplink communication quality of the BPMCS. In this diversity, since uplink interference must be avoided, we employ combination of the adaptive array and HCR techniques. Moreover, we propose information compression methods for narrow band backbone lines so that received data can be conveyed to head office with little BER deterioration. PMCSs will have played an important role to ensure social safety. In the thesis, we consider the one of the next generation PMCSs employing SDR and HCR. Using this research, we can obtain a direction of optimal PMCSs. The next step that we need to perform is to apply our proposed method to actual radio systems. We must continue this research so that high reliable and compact PMCSs can be realized.電気通信大学201

Temporal Parallelization of Inference in Hidden Markov Models

This paper presents algorithms for parallelization of inference in hidden Markov models (HMMs). In particular, we propose parallel backward-forward type of filtering and smoothing algorithm as well as parallel Viterbi-type maximum-a-posteriori (MAP) algorithm. We define associative elements and operators to pose these inference problems as parallel-prefix-sum computations in sum-product and max-product algorithms and parallelize them using parallel-scan algorithms. The advantage of the proposed algorithms is that they are computationally efficient in HMM inference problems with long time horizons. We empirically compare the performance of the proposed methods to classical methods on a highly parallel graphical processing unit (GPU)

Unsupervised learning and recognition of physical activity plans

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2007.Includes bibliographical references (p. 125-129).This thesis desires to enable a new kind of interaction between humans and computational agents, such as robots or computers, by allowing the agent to anticipate and adapt to human intent. In the future, more robots may be deployed in situations that require collaboration with humans, such as scientific exploration, search and rescue, hospital assistance, and even domestic care. These situations require robots to work together with humans, as part of a team, rather than as a stand-alone tool. The intent recognition capability is necessary for computational agents to play a more collaborative role in human-robot interactions, moving beyond the standard master-slave relationship of humans and computers today. We provide an innovative capability for recognizing human intent, through statistical plan learning and online recognition. We approach the plan learning problem by employing unsupervised learning to automatically determine the activities in a plan based on training data. The plan activities are described by a mixture of multivariate probability densities. The number of distributions in the mixture used to describe the data is assumed to be given. The training data trajectories are fed again through the activities' density distributions to determine each possible sequence of activities that make up a plan. These activity sequences are then summarized with temporal information in a temporal plan network, which consists of a network of all possible plans. Our approach to plan recognition begins with formulating the temporal plan network as a hidden Markov model. Next, we determine the most likely path using the Viterbi algorithm. Finally, we refer back to the temporal plan network to obtain predicted future activities. Our research presents several innovations:(cont.) First, we introduce a modified representation of temporal plan networks that incorporates probabilistic information into the state space and temporal representations. Second, we learn plans from actual data, such that the notion of an activity is not arbitrarily or manually defined, but is determined by the characteristics of the data. Third, we develop a recognition algorithm that can perform recognition continuously by making probabilistic updates. Finally, our recognizer not only identifies previously executed activities, but also pre-dicts future activities based on the plan network. We demonstrate the capabilities of our algorithms on motion capture data. Our results show that the plan learning algorithm is able to generate reasonable temporal plan networks, depending on the dimensions of the training data and the recognition resolution used. The plan recognition algorithm is also successful in recognizing the correct activity sequences in the temporal plan network corresponding to the observed test data.by Shuonan Dong.S.M

An HMM-Based Framework for Video Semantic Analysis

Video semantic analysis is essential in video indexing and structuring. However, due to the lack of robust and generic algorithms, most of the existing works on semantic analysis are limited to specific domains. In this paper, we present a novel hidden Markove model (HMM)-based framework as a general solution to video semantic analysis. In the proposed framework, semantics in different granularities are mapped to a hierarchical model space, which is composed of detectors and connectors. In this manner, our model decomposes a complex analysis problem into simpler subproblems during the training process and automatically integrates those subproblems for recognition. The proposed framework is not only suitable for a broad range of applications, but also capable of modeling semantics in different semantic granularities. Additionally, we also present a new motion representation scheme, which is robust to different motion vector sources. The applications of the proposed framework in basketball event detection, soccer shot classification, and volleyball sequence analysis have demonstrated the effectiveness of the proposed framework on video semantic analysis

Reactive mission and motion planning with deadlock resolution avoiding dynamic obstacles

In the near future mobile robots, such as personal robots or mobile manipulators, will share the workspace with other robots and humans. We present a method for mission and motion planning that applies to small teams of robots performing a task in an environment with moving obstacles, such as humans. Given a mission specification written in linear temporal logic, such as patrolling a set of rooms, we synthesize an automaton from which the robots can extract valid strategies. This centralized automaton is executed by the robots in the team at runtime, and in conjunction with a distributed motion planner that guarantees avoidance of moving obstacles. Our contribution is a correct-by-construction synthesis approach to multi-robot mission planning that guarantees collision avoidance with respect to moving obstacles, guarantees satisfaction of the mission specification and resolves encountered deadlocks, where a moving obstacle blocks the robot temporally. Our method provides conditions under which deadlock will be avoided by identifying environment behaviors that, when encountered at runtime, may prevent the robot team from achieving its goals. In particular, (1) it identifies deadlock conditions; (2) it is able to check whether they can be resolved; and (3) the robots implement the deadlock resolution policy locally in a distributed manner. The approach is capable of synthesizing and executing plans even with a high density of dynamic obstacles. In contrast to many existing approaches to mission and motion planning, it is scalable with the number of moving obstacles. We demonstrate the approach in physical experiments with walking humanoids moving in 2D environments and in simulation with aerial vehicles (quadrotors) navigating in 2D and 3D environments.Boeing CompanyUnited States. Office of Naval Research. Multidisciplinary University Research Initiative. SMARTS (N00014-09-1051)United States. Office of Naval Research (N00014-12-1-1000)National Science Foundation (U.S.). Expeditions in Computer Augmented Program Engineerin

The Design and Implementation of an Over-the-top Cloud-based Vertical Handover Decision Service for Heterogeneous Wireless Networks

The widespread availability of heterogeneous wireless networks (hetnets) presents a resource allocation challenge to network operators and administrators. Overlapping network coverage should be utilized to its fullest extent, providing users with a fair share of bandwidth while maximizing the efficient use of the operator\u27s resources. Currently, network selection occurs locally at the mobile device and does not take into account factors such as the state of other networks that might be available in the device\u27s location. The local decision made by the device can often result in underutilization of network resources and a degraded user experience. This type of selfish network selection might not result in optimal bandwidth allocation when compared to approaches that make use of a centralized resource controller \cite{gpf}. The decision making process behind the selection of these networks continues to be an open area of research, and a variety of algorithms have been proposed to solve this problem. An over-the-top handover decision service treats each wireless access network in a hetnet as a black box, assuming detailed network topology and state information is unavailable to the handover decision algorithm. The algorithm then uses network data gathered empirically from users to provide them with a network selection service that considers the current conditions of available networks in a given location. This is a departure from past designs of vertical handover decision algorithms, which tend to approach the problem from the perspective of individual network operators. The wide range of radio access technologies operated by different network operators that are available to a device within a hetnet, coupled with the mobile data offload effort, is the primary motivator behind our novel choice in direction. This thesis documents the design and implementation of such an over-the-top vertical handover decision service