Curvature continuous path generation for autonomous vehicle using B-spline curves

In this paper we introduce a roadmap algorithm for generating collision-free paths in terms of cubic B-spline curves for unmanned vehicles used in mining operations. The algorithm automatically generates collision-free paths that are curvature continuous with an upper bounded curvature and a small slope discontinuity of curvature at knots, when we are given the locations of the obstacles, the boundary geometry of the working area, positions and directions of the vehicle at the start, loading, and the goal points. Our algorithm also allows us to find a switch back point where the vehicle reverses its direction to enter the loading area. Examples are provided to demonstrate the effectiveness of the proposed algorithms.     

Recurrent infomax generates cell assemblies, neuronal avalanches, and simple cell-like selectivity

Recently multineuronal recording has allowed us to observe patterned firings, synchronization, oscillation, and global state transitions in the recurrent networks of central nervous systems. We propose a learning algorithm based on the process of information maximization in a recurrent network, which we call recurrent infomax (RI). RI maximizes information retention and thereby minimizes information loss through time in a network. We find that feeding in external inputs consisting of information obtained from photographs of natural scenes into an RI-based model of a recurrent network results in the appearance of Gabor-like selectivity quite similar to that existing in simple cells of the primary visual cortex. We find that without external input, this network exhibits cell assembly-like and synfire chain-like spontaneous activity as well as a critical neuronal avalanche. In addition, we find that RI embeds externally input temporal firing patterns to the network so that it spontaneously reproduces these patterns after learning. RI provides a simple framework to explain a wide range of phenomena observed in in vivo and in vitro neuronal networks, and it will provide a novel understanding of experimental results for multineuronal activity and plasticity from an information-theoretic point of view.     

Multi-contact vertical ladder climbing with an HRP-2 humanoid

We describe the research and the integration methods we developed to make the HRP-2 humanoid robot climb vertical industrial-norm ladders. We use our multi-contact planner and multi-objective closed-loop control formulated as a QP (quadratic program). First, a set of contacts to climb the ladder is planned off-line (automatically or by the user). These contacts are provided as an input for a finite state machine. The latter builds supplementary tasks that account for geometric uncertainties and specific grasps procedures to be added to the QP controller. The latter provides instant desired states in terms of joint accelerations and contact forces to be tracked by the embedded low-level motor controllers. Our trials revealed that hardware changes are necessary, and parts of software must be made more robust. Yet, we confirmed that HRP-2 has the kinematic and power capabilities to climb real industrial ladders, such as those found in nuclear power plants and large scale manufacturing factories (e.g. aircraft, shipyard) and construction sites.     

A design of high optical efficiency wedge type light guide plate adopting twin inclined cylindrical surface

We propose wedge type light guide plate (LGP) adopting twin inclined cylindrical surfaces (TICS) at the incident part of a LGP. It has higher optical efficiency than conventional wedge type LGP. In dimension of LGP which the thickness of incident part is 1.5 mm and that of luminance part is 0.7 mm, the optical efficiency of wedge type LGP adopting TICS is expected to be 96% in comparison with a normal flat LGP. In addition, there is an advantage to reduce the non‐uniformity of vicinity of light emitted diodes (LEDs) by TICS reflecting light laterally. However, it is necessary that the LED centers are aligned with the intersections of TICS. The misalignment of LED causes non‐uniformity of optical profile that is slightly larger than a normal LGP.     

A preliminary study on learning effect in a contact positioning task in a virtual environment

How the human central nervous system adjusts sensorimotor coordination to haptic stimuli is a major topic in several fields including robotics. There are studies focusing on contact positioning tasks such as catching a ball or a volley in tennis, where position control of a human hand is required only at the moment of contact for successful completion of the tasks. A previous study dealt with a task of catching a ball in a virtual environment, and reported an interesting observation of the learning process in the relationship between operational performance and energy consumption of an operator; however, the operational condition given to the operators in the previous study seemed to be relatively simple. The interest in this paper is similar to that of the previous study; however, this paper considers application of a slightly more complex operational condition to a task of catching a ball. For corresponding to increase of difficulty in operation in the task, two movements of a paddle for catching a ball are given to operators, and difference between the movements is observed. Although the final goal of the study in this paper is similar to that in the previous study, this paper considers how learning effect can be observed in operational performance by comparing the performance of novice operators with that of experienced operators. The comparison shows differences in the performances between the operators from three analytic aspects. This paper also considers how many operations are required for novice operators to exhibit the learning effect in operational performance.     

Object detection based on a robust and accurate statistical multi-point-pair model

In this paper, we propose a robust and accurate background model, called grayscale arranging pairs (GAP). The model is based on the statistical reach feature (SRF), which is defined as a set of statistical pair-wise features. Using the GAP model, moving objects are successfully detected under a variety of complex environmental conditions. The main concept of the proposed method is the use of multiple point pairs that exhibit a stable statistical intensity relationship as a background model. The intensity difference between pixels of the pair is much more stable than the intensity of a single pixel, especially in varying environments. Our proposed method focuses more on the history of global spatial correlations between pixels than on the history of any given pixel or local spatial correlations. Furthermore, we clarify how to reduce the GAP modeling time and present experimental results comparing GAP with existing object detection methods, demonstrating that superior object detection with higher precision and recall rates is achieved by GAP.     

A Network Architecture with High Availability for Real-time Premium Traffic over the Internet

Real-time traffic will be a predominant traffic type in the next generation networks, and networks with 100% reliability and availability will be required by real-time premium traffic. It is believed that QoS guarantees could be better provided by connection oriented networks such as Multi Protocol Label Switching (MPLS). These connection oriented networks are more vulnerable to network failure. Conventional path protection methods perform re-routing to cope with this. However, re-routing always causes packet losses and results in service outage. These losses are bursty in nature and highly degrade the QoS of the real-time premium traffic. Thus, 100% availability cannot be achieved by conventional methods. The novel path protection proposed in this paper recovers the bursty packet losses due to re-routing by using forward error correction (FEC) path. Therefore, it can provide network architecture with no service outage for such traffic. The numerical results show that the proposed method can achieve a very high availability for real-time premium traffic in future IP/MPLS networks.

Japanese/English Cross-Language Information Retrieval: Exploration of Query Translation and Transliteration

Cross-language information retrieval (CLIR), where queriesand documents are in different languages, has of late become one ofthe major topics within the information retrieval community. Thispaper proposes a Japanese/English CLIR system, where we combine aquery translation and retrieval modules. We currently target theretrieval of technical documents, and therefore the performance of oursystem is highly dependent on the quality of the translation oftechnical terms. However, the technical term translation is stillproblematic in that technical terms are often compound words, and thusnew terms are progressively created by combining existing basewords. In addition, Japanese often represents loanwords based on itsspecial phonogram. Consequently, existing dictionaries find itdifficult to achieve sufficient coverage. To counter the firstproblem, we produce a Japanese/English dictionary for base words, andtranslate compound words on a word-by-word basis. We also use aprobabilistic method to resolve translation ambiguity. For the secondproblem, we use a transliteration method, which corresponds wordsunlisted in the base word dictionary to their phonetic equivalents inthe target language. We evaluate our system using a test collectionfor CLIR, and show that both the compound word translation andtransliteration methods improve the system performance.     

Efficient blind dereverberation and echo cancellation based on independent component analysis for actual acoustic signals

This letter presents a new algorithm for blind dereverberation and echo cancellation based on independent component analysis (ICA) for actual acoustic signals. We focus on frequency domain ICA (FD-ICA) because its computational cost and speed of learning convergence are sufficiently reasonable for practical applications such as hands-free speech recognition. In applying conventional FD-ICA as a preprocessing of automatic speech recognition in noisy environments, one of the most critical problems is how to cope with reverberations. To extract a clean signal from the reverberant observation, we model the separation process in the short-time Fourier transform domain and apply the multiple input/output inverse-filtering theorem (MINT) to the FD-ICA separation model. A naive implementation of this method is computationally expensive, because its time complexity is the second order of reverberation time. Therefore, the main issue in dereverberation is to reduce the high computational cost of ICA. In this letter, we reduce the computational complexity to the linear order of the reverberation time by using two techniques: (1) a separation model based on the independence of delayed observed signals with MINT and (2) spatial sphering for preprocessing. Experiments show that the computational cost grows in proportion to the linear order of the reverberation time and that our method improves the word correctness of automatic speech recognition by 10 to 20 points in a RT??= 670 ms reverberant environment.