Novel architecture to facilitate disassembly and reuse of electronic components and sub-assemblies

This paper advances the proposition that in many electronic products, the partitioning scheme adopted and the interconnection system used to interconnect the sub-assemblies or components are intimately related to the economic benefits, and hence the attractiveness, of reuse of these items. An architecture has been developed in which the residual values of the connectors, components and sub-assemblies are maximized, and opportunities for take-back and reuse of redundant items are greatly enhanced. The system described also offers significant manufacturing cost benefits in terms of ease of assembly, compactness and robustness.

Defining the orientation of the tandem fusions that occurred during the evolution of Indian muntjac chromosomes by BAC mapping

The Indian muntjac (Muntiacus muntjak vaginalis) has a karyotype of 2n=6 in the female and 7 in the male, the karyotypic evolution of which through extensive tandem fusions and several centric fusions has been well-documented by recent molecular cytogenetic studies. In an attempt to define the fusion orientations of conserved chromosomal segments and the molecular mechanisms underlying the tandem fusions, we have constructed a highly redundant (more than six times of whole genome coverage) bacterial artificial chromosome (BAC) library of Indian muntjac. The BAC library contains 124,800 clones with no chromosome bias and has an average insert DNA size of 120 kb. A total of 223 clones have been mapped by fluorescent in situ hybridization onto the chromosomes of both Indian muntjac and Chinese muntjac and a high-resolution comparative map has been established. Our mapping results demonstrate that all tandem fusions that occurred during the evolution of Indian muntjac karyotype from the acrocentric 2n=70 hypothetical ancestral karyotype are centromere-telomere (head-tail) fusions.

Impedance control balances stability with metabolically costly muscle activation.

Humans are able to stabilize their movements in environments with unstable dynamics by selectively modifying arm impedance independently of force and torque. We further investigated adaptation to unstable dynamics to determine whether the CNS maintains a constant overall level of stability as the instability of the environmental dynamics is varied. Subjects performed reaching movements in unstable force fields of varying strength, generated by a robotic manipulator. Although the force fields disrupted the initial movements, subjects were able to adapt to the novel dynamics and learned to produce straight trajectories. After adaptation, the endpoint stiffness of the arm was measured at the midpoint of the movement. The stiffness had been selectively modified in the direction of the instability. The stiffness in the stable direction was relatively unchanged from that measured during movements in a null force field prior to exposure to the unstable force field. This impedance modification was achieved without changes in force and torque. The overall stiffness of the arm and environment in the direction of instability was adapted to the force field strength such that it remained equivalent to that of the null force field. This suggests that the CNS attempts both to maintain a minimum level of stability and minimize energy expenditure.

Biologically-inspired object recognition system with features from complex wavelets

In this paper, a novel cortex-inspired feed-forward hierarchical object recognition system based on complex wavelets is proposed and tested. Complex wavelets contain three key properties for object representation: shift invariance, which enables the extraction of stable local features; good directional selectivity, which simplifies the determination of image orientations; and limited redundancy, which allows for efficient signal analysis using the multi-resolution decomposition offered by complex wavelets. In this paper, we propose a complete cortex-inspired object recognition system based on complex wavelets. We find that the implementation of the HMAX model for object recognition in [1, 2] is rather over-complete and includes too much redundant information and processing. We have optimized the structure of the model to make it more efficient. Specifically, we have used the Caltech 5 standard dataset to compare with Serre's model in [2] (which employs Gabor filter bands). Results demonstrate that the complex wavelet model achieves a speed improvement of about 4 times over the Serre model and gives comparable recognition performance. © 2011 IEEE.

Sparsity-Inducing Optimization Algorithm for the Extraction of Planar Structures in Noisy Point-Cloud Data

Most of the manual labor needed to create the geometric building information model (BIM) of an existing facility is spent converting raw point cloud data (PCD) to a BIM description. Automating this process would drastically reduce the modeling cost. Surface extraction from PCD is a fundamental step in this process. Compact modeling of redundant points in PCD as a set of planes leads to smaller file size and fast interactive visualization on cheap hardware. Traditional approaches for smooth surface reconstruction do not explicitly model the sparse scene structure or significantly exploit the redundancy. This paper proposes a method based on sparsity-inducing optimization to address the planar surface extraction problem. Through sparse optimization, points in PCD are segmented according to their embedded linear subspaces. Within each segmented part, plane models can be estimated. Experimental results on a typical noisy PCD demonstrate the effectiveness of the algorithm.

Optimized selection of key frames for monocular videogrammetric surveying of civil infrastructure

Videogrammetry is an inexpensive and easy-to-use technology for spatial 3D scene recovery. When applied to large scale civil infrastructure scenes, only a small percentage of the collected video frames are required to achieve robust results. However, choosing the right frames requires careful consideration. Videotaping a built infrastructure scene results in large video files filled with blurry, noisy, or redundant frames. This is due to frame rate to camera speed ratios that are often higher than necessary; camera and lens imperfections and limitations that result in imaging noise; and occasional jerky motions of the camera that result in motion blur; all of which can significantly affect the performance of the videogrammetric pipeline. To tackle these issues, this paper proposes a novel method for automating the selection of an optimized number of informative, high quality frames. According to this method, as the first step, blurred frames are removed using the thresholds determined based on a minimum level of frame quality required to obtain robust results. Then, an optimum number of key frames are selected from the remaining frames using the selection criteria devised by the authors. Experimental results show that the proposed method outperforms existing methods in terms of improved 3D reconstruction results, while maintaining the optimum number of extracted frames needed to generate high quality 3D point clouds.© 2012 Elsevier Ltd. All rights reserved.

Integrated high pressure microhydraulic actuation and control for surgical instruments

To reduce the surgical trauma to the patient, minimally invasive surgery is gaining considerable importance since the eighties. More recently, robot assisted minimally invasive surgery was introduced to enhance the surgeon's performance in these procedures. This resulted in an intensive research on the design, fabrication and control of surgical robots over the last decades. A new development in the field of surgical tool manipulators is presented in this article: a flexible manipulator with distributed degrees of freedom powered by microhydraulic actuators. The tool consists of successive flexible segments, each with two bending degrees of freedom. To actuate these compliant segments, dedicated fluidic actuators are incorporated, together with compact hydraulic valves which control the actuator motion. Especially the development of microvalves for this application was challenging, and are the main focus of this paper. The valves distribute the hydraulic power from one common high pressure supply to a series of artificial muscle actuators. Tests show that the angular stroke of the each segment of this medical instrument is 90°. © 2012 Springer Science+Business Media, LLC.

An example of the use of neural computing techniques in materials science - The modelling of fatigue thresholds in Ni-base superalloys

Two adaptive numerical modelling techniques have been applied to prediction of fatigue thresholds in Ni-base superalloys. A Bayesian neural network and a neurofuzzy network have been compared, both of which have the ability to automatically adjust the network's complexity to the current dataset. In both cases, despite inevitable data restrictions, threshold values have been modelled with some degree of success. However, it is argued in this paper that the neurofuzzy modelling approach offers real benefits over the use of a classical neural network as the mathematical complexity of the relationships can be restricted to allow for the paucity of data, and the linguistic fuzzy rules produced allow assessment of the model without extensive interrogation and examination using a hypothetical dataset. The additive neurofuzzy network structure means that redundant inputs can be excluded from the model and simple sub-networks produced which represent global output trends. Both of these aspects are important for final verification and validation of the information extracted from the numerical data. In some situations neurofuzzy networks may require less data to produce a stable solution, and may be easier to verify in the light of existing physical understanding because of the production of transparent linguistic rules. © 1999 Elsevier Science S.A.

Improved bounds for subband-adaptive iterative shrinkage/thresholding algorithms.

This paper presents new methods for computing the step sizes of the subband-adaptive iterative shrinkage-thresholding algorithms proposed by Bayram & Selesnick and Vonesch & Unser. The method yields tighter wavelet-domain bounds of the system matrix, thus leading to improved convergence speeds. It is directly applicable to non-redundant wavelet bases, and we also adapt it for cases of redundant frames. It turns out that the simplest and most intuitive setting for the step sizes that ignores subband aliasing is often satisfactory in practice. We show that our methods can be used to advantage with reweighted least squares penalty functions as well as L1 penalties. We emphasize that the algorithms presented here are suitable for performing inverse filtering on very large datasets, including 3D data, since inversions are applied only to diagonal matrices and fast transforms are used to achieve all matrix-vector products.

Towards soft self-reconfigurable robots

Modular self-reconfigurable robots have previously demonstrated that automatic control of their own body shapes enriches their behavioural functions. However, having predefined rigid modules technically limits real-world systems from being hyper-redundant and compliant. Encouraged by recent progress using elastically deformable material for robots, we propose the concept of soft self-reconfigurable robots which may become hyper-flexible during interaction with the environment. As the first attempt towards this goal, the paper proposes a novel approach using viscoelastic material Hot-Melt Adhesives (HMAs): for physical connection and disconnection control between bodies that are not necessarily predefined rigid modules. We present a model that characterizes the temperature dependency of the strength of HMA bonds, which is then validated and used in a feedback controller for automatic connection and disconnection. Using a minimalistic robot platform that is equipped with two devices handling HMAs, the performance of this method is evaluated in a pick-and-place experiment with aluminium and wooden parts. © 2012 IEEE.

Robotic body extension based on hot melt adhesives

The capability of extending body structures is one of the most significant challenges in the robotics research and it has been partially explored in self-reconfigurable robotics. By using such a capability, a robot is able to adaptively change its structure from, for example, a wheel like body shape to a legged one to deal with complexity in the environment. Despite their expectations, the existing mechanisms for extending body structures are still highly complex and the flexibility in self-reconfiguration is still very limited. In order to account for the problems, this paper investigates a novel approach to robotic body extension by employing an unconventional material called Hot Melt Adhesives (HMAs). Because of its thermo-plastic and thermo-adhesive characteristics, this material can be used for additive fabrication based on a simple robotic manipulator while the established structures can be integrated into the robot's own body to accomplish a task which could not have been achieved otherwise. This paper first investigates the HMA material properties and its handling techniques, then evaluates performances of the proposed robotic body extension approach through a case study of a "water scooping" task. © 2012 IEEE.

Compact non-binary fast adders using single-electron devices

This paper proposes compact adders that are based on non-binary redundant number systems and single-electron (SE) devices. The adders use the number of single electrons to represent discrete multiple-valued logic state and manipulate single electrons to perform arithmetic operations. These adders have fast speed and are referred as fast adders. We develop a family of SE transfer circuits based on MOSFET-based SE turnstile. The fast adder circuit can be easily designed by directly mapping the graphical counter tree diagram (CTD) representation of the addition algorithm to SE devices and circuits. We propose two design approaches to implement fast adders using SE transfer circuits the threshold approach and the periodic approach. The periodic approach uses the voltage-controlled single-electron transfer characteristics to efficiently achieve periodic arithmetic functions. We use HSPICE simulator to verify fast adders operations. The speeds of the proposed adders are fast. The numbers of transistors of the adders are much smaller than conventional approaches. The power dissipations are much lower than CMOS and multiple-valued current-mode fast adders. (C) 2009 Elsevier Ltd. All rights reserved.

Digits speech recognition based on geometrical learning

We investigate the use of independent component analysis (ICA) for speech feature extraction in digits speech recognition systems.We observe that this may be true for a recognition tasks based on geometrical learning with little training data. In contrast to image processing, phase information is not essential for digits speech recognition. We therefore propose a new scheme that shows how the phase sensitivity can be removed by using an analytical description of the ICA-adapted basis functions via the Hilbert transform. Furthermore, since the basis functions are not shift invariant, we extend the method to include a frequency-based ICA stage that removes redundant time shift information. The digits speech recognition results show promising accuracy, Experiments show method based on ICA and geometrical learning outperforms HMM in different number of train samples.

Continuous speech recognition based on ICA and geometrical learning

We investigate the use of independent component analysis (ICA) for speech feature extraction in digits speech recognition systems. We observe that this may be true for recognition tasks based on Geometrical Learning with little training data. In contrast to image processing, phase information is not essential for digits speech recognition. We therefore propose a new scheme that shows how the phase sensitivity can be removed by using an analytical description of the ICA-adapted basis functions. Furthermore, since the basis functions are not shift invariant, we extend the method to include a frequency-based ICA stage that removes redundant time shift information. The digits speech recognition results show promising accuracy. Experiments show that the method based on ICA and Geometrical Learning outperforms HMM in a different number of training samples.

Performing Fast Addition and Multiplication by Transferring Single Electrons

This paper proposes novel fast addition and multiplication circuits that are based on non-binary redundant number systems and single electron (SE) devices. The circuits consist of MOSFET-based single-electron (SE) turnstiles. We use the number of electrons to represent discrete multiple-valued logic states and we finish arithmetic operations by controlling the number of electrons transferred. We construct a compact PD2,3 adder and a 12x12bit multiplier using the PD2,3 adder. The speed of the adder can be as high as 600MHz with 400nW power dissipation. The speed of the adder is regardless of its operand length. The proposed circuits have much smaller transistors than conventional circuits.