Miracle-dz/UniFAR-FTUs huggingface dataset:
ROBOCRANE AND EMMA APPLIED TO WASTE STORAGE TANK REMEDIATION

AbstractThe RoboCrane is a revolutionary concept in crane automation. The RoboCrane, based on the Stewart platform parallel link manipulator design, consists of cables as the parallel links and winches as the actuators. The result is a work platform that can maneuver tools and/or manipulators with full six degree-of-freedom control. EMMA, a serpentine manipulator, is currently being developed for sev-eral applications, including the delivery and orien-tation of end-effectors throughout nuclear waste storage tanks. The combination of Robo- Crane and EMMA can provide an adaptable and highly effective means for waste storage tank remedia-tion.Keywords: RoboCrane, EMMA, parallel link manipulator, Stewart platform devices, cable-based robots, cranes. 1.0 RoboCrane Since the late 1980's, the National Institute of Standards and Technology (NIST) has been work-ing on the RoboCrane ® . The RoboCrane is a cable driven, multi-purpose manipulator based on the Stewart platform parallel link manipulator [1]. It provides full six degree-of-freedom (6 DoF) load control via tele-operative, semi-autonomous and autonomous control modes. Originally, the RoboCrane was developed under a Defense Advanced Research Project Agency (DARPA) contract to stabilize crane loads [2]. Currently, the conﬁguration has advanced to include air-lifted, land, sea, and space applications. It can be designed for high lift-to-weight ratio, stable gantry conﬁgurations, ﬂexibility, precise maneuverability, and mobility over a variety of surfaces including very rough terrain. As part of the NIST mission, the Intelligent Sys-tems Division (ISD) provides ". research in, develops methods for, and applies intelligent sys-tems technology to improve U.S. industry compet-itiveness. ” The RoboCrane provides an intelligent machine enhancement to current large scale manu-facturing methods. It also provides a means for manipulating tools and equipment, such as long-range manipulators and end-effectors, into waste storage tanks for inspection and remediation.Multiple RoboCrane prototypes have been devel-oped to study a variety of applications. A 6 m octa-hedral system (see FIGURE 1) has performed heavy load maneuverability, sawing, grinding, gripping, welding, and assembly tasks under tele-operative, preprogrammed, off-line, and/or hybrid control modes. The 6 m RoboCrane prototype has been subjected to a variety of performance measurements and computer simulations [3]. Experimental tests were conducted to verify its functional work volume, static loading capability, and load positioning accuracy. These experimental results compared favorably to associated computer analysis. Posi-tioning uncertainty of this 6 m prototype has been measured at up to 1 mm in translation and angular precision of approximately 0.5° throughout a min-imum 100 m
Understanding Ricin from a Defensive Viewpoint

The toxin ricin has long been understood to have potential for criminal activity and there has been concern that it might be used as a mass-scale weapon on a military basis for at least two decades. Currently, the focus has extended to encompass terrorist activities using ricin to disrupt every day activities on a smaller scale. Whichever scenario is considered, there are features in common which need to be understood; these include the knowledge of the toxicity from ricin poisoning by the likely routes, methods for the detection of ricin in relevant materials and approaches to making an early diagnosis of ricin poisoning, in order to take therapeutic steps to mitigate the toxicity. This article will review the current situation regarding each of these stages in our collective understanding of ricin and how to defend against its use by an aggressor.
Efficient Security Mechanisms for Routing Protocolsa

As our economy and critical infrastructure increasingly rely on the Internet, securing routing protocols becomes of critical importance. In this paper, we present four new mechanisms as tools for securing distance vector and path vector routing protocols. For securing distance vector protocols, our hash tree chain mechanism forces a router to increase the distance (metric) when forwarding a routing table entry. To provide authentication of a received routing update in bounded time, we present a new mechanism, similar to hash chains, that we call tree-authenticated oneway chains. For cases in which the maximum metric is large, we present skiplists, which provides more efficient initial computation cost and more efficient element verification; this mechanism is based on a new cryptographic mechanism, called MW-chains, which we also present. For securing path vector protocols, our cumulative authentication mechanism authenticates the list of routers on the path in a routing update, preventing removal or reordering of the router addresses in the list; the mechanism uses only a single authenticator in the routing update rather than one per router address. We also present a simple mechanism to securely switch one-way chains, by authenticating the next one-way chain using the previous one. These mechanisms are all based on efficient symmetric cryptographic techniques and can be used as building blocks for securing routing protocols.
The Classification of decoherence functionals: An Analog of Gleason's theorem

Gell‐Mann and Hartle have proposed a significant generalization of quantum theory with a scheme whose basic ingredients are ‘‘histories’’ and decoherence functionals. Within this scheme it is natural to identify the space UP of propositions about histories with an orthoalgebra or lattice. This raises the important problem of classifying the decoherence functionals in the case where UP is the lattice of projectors P(V) in some Hilbert space V; in effect, one seeks the history analog of Gleason’s famous theorem in standard quantum theory. In the present article the solution to this problem for the case where V is finite dimensional is presented. In particular, it is shown that every decoherence functional d(α,β), α,β∈P(V) can be written in the form d(α,β)=trV⊗V(α⊗βX) for some operator X on the tensor‐product space V⊗V.
Soft Robotic Grippers Using Gecko-Inspired Fibrillar Adhesives for Three-Dimensional Surface Grasping

Researches on biological adhesive systems in nature have changed a perspective view on adhesion that it is not only the area of surface chemistry, but also mechanics of interfacial geometry which can significantly effect on fracture strength and load distribution on the contact interface. Various synthetic fibrillar adhesives in previous works have shown enhanced interfacial bond strength with the capacity of adhesion control by exploiting mechanical deformation of the elastomeric fibrillar structures inspired by geckos. However, control of the interfacial load distribution has been focused on the size of micro-contact with single or a few of micro-/nano-fibers on planar surface, and not for a large contact area on complex three-dimensional (3D) surfaces. This thesis work aims at investigating principles of the interfacial load distribution control in multi-scale, ranging from micro-contact with single micro-fiber to a centimeter-scale contact with a membrane-backed micro-fiber array on non-planar 3D surfaces. The findings are also applied for developing a soft robotic gripper capable of grasping a wide range of complex objects in size, shape, and number, expanding the area of practical applications for bio-inspired adhesives in transfer printing, robotic manipulators, and mobile robots. This paper comprises three main works. First, we investigate the effect of tip-shapes on the interfacial load sharing of mushroom-shaped micro-fibrillar adhesives with precisely defined tipgeometries using high resolution 3D nano-fabrication technique. For a large area of non-planar contact interface, we fabricate fibrillar adhesives on a membrane (FAM) by integrating micro-fibers with a soft backing, which enables robust and controllable adhesion on 3D surfaces. Picking and releasing mechanism for the maximal controllability in adhesion are discussed. Finally, we propose a soft robotic architecture which can control the interfacial load distribution for the FAM on 3D surfaces, solving an inherit dilemma between conformability and high fracture strength with the equal load sharing on complex non-planar 3D surfaces.
A robust H∞ control design for swarm formation control of multi-agent systems: A decentralized adaptive fuzzy approach

In this paper, a decentralized adaptive control scheme for multi-agent formation control is proposed. This control method is based on artificial potential functions integrated with adaptive fuzzy H∞ technique. We consider fully actuated mobile agents with partially unknown models, where an adaptive fuzzy logic system is used to approximate the unknown system dynamics. The H∞ control theory is used to attenuate the adaptive fuzzy approximation error to a prescribed level. Therefore the agents motion is forced to obey the dynamics defined by the simple inter-agent artificial potential functions. Stability proof is given using Lyapunov functions, which shows the robust behavior of controller with respect to disturbances and system uncertainties. Finally, simulation results are demonstrated for a multi-agent formation problem of a group of six agents, illustrating the effective attenuation of fuzzy logic approximation error.
A Multi-View Nonlinear Active Shape Model Using Kernel PCA

Recovering the shape of any 3D object using multiple 2D views requires establishing correspondence between feature points at different views. However changes in viewpoint introduce self-occlusions, resulting nonlinear variations in the shape and inconsistent 2D features between views. Here we introduce a multi-view nonlinear shape model utilising 2D view-dependent constraint without explicit reference to 3D structures. For nonlinear model transformation, we adopt Kernel PCA based on Support Vector Machines.
Improvement of spatial and spectral resolution in Raman microscopy

The classical limit of the spatial resolution, known as the diffraction limit, has been overcome by the recent development of super-resolution techniques. However, the main application of the technique is limited to fluorescence imaging. In our research, we have developed two techniques to improve the spatial resolution in Raman scattering imaging. We introduced the structured illumination to improve the spatial resolution in line-illumination Raman microscopy. We also utilized the saturation effect of coherent anti-Stokes Raman scattering to improve both spatial and spectral resolution simultaneously.
Identification of Unmodeled Objects from Symbolic Descriptions

Successful human-robot cooperation hinges on each agent's ability to process and exchange information about the shared environment and the task at hand. Human communication is primarily based on symbolic abstractions of object properties, rather than precise quantitative measures. A comprehensive robotic framework thus requires an integrated communication module which is able to establish a link and convert between perceptual and abstract information. The ability to interpret composite symbolic descriptions enables an autonomous agent to a) operate in unstructured and cluttered environments, in tasks which involve unmodeled or never seen before objects; and b) exploit the aggregation of multiple symbolic properties as an instance of ensemble learning, to improve identification performance even when the individual predicates encode generic information or are imprecisely grounded. We propose a discriminative probabilistic model which interprets symbolic descriptions to identify the referent object contextually w.r. t. \ the structure of the environment and other objects. The model is trained using a collected dataset of identifications, and its performance is evaluated by quantitative measures and a live demo developed on the PR2 robot platform, which integrates elements of perception, object extraction, object identification and grasping.
A Fast Data Collection and Augmentation Procedure for Object Recognition

When building an application that requires object class recognition, having enough data to learn from is critical for good performance, and can easily determine the success or failure of the system. However, it is typically extremely labor-intensive to collect data, as the process usually involves acquiring the image, then manual cropping and hand-labeling. Preparing large training sets for object recognition has already become one of the main bottlenecks for such emerging applications as mobile robotics and object recognition on the web. This paper focuses on a novel and practical solution to the dataset collection problem. Our method is based on using a green screen to rapidly collect example images; we then use a probabilistic model to rapidly synthesize a much larger training set that attempts to capture desired invariants in the object's foreground and background. We demonstrate this procedure on our own mobile robotics platform, where we achieve 135x savings in the time/effort needed to obtain a training set. Our data collection method is agnostic to the learning algorithm being used, and applies to any of a large class of standard object recognition methods. Given these results, we suggest that this method become a standard protocol for developing scalable object recognition systems. Further, we used our data to build reliable classifiers that enabled our robot to visually recognize an object in an office environment, and thereby fetch an object from an office in response to a verbal request.
Theory of Alignment Generators and Applications to Statistical Machine Translation

Viterbi Alignment and Decoding are two fundamental search problems in Statistical Machine Translation. Both the problems are known to be NP-hard and therefore, it is unlikely that there exists an optimal polynomial time algorithm for either of these search problems. In this paper we characterize exponentially large subspaces in the solution space of Viterbi Alignment and Decoding. Each of these subspaces admits polynomial time optimal search algorithms. We propose a local search heuristic using a neighbourhood relation on these subspaces. Experimental results show that our algorithms produce better solutions taking substantially less time than the previously known algorithms for these problems.
Robust 2D Principal Component Analysis: A Structured Sparsity Regularized Approach

Principal component analysis (PCA) is widely used to extract features and reduce dimensionality in various computer vision and image/video processing tasks. Conventional approaches either lack robustness to outliers and corrupted data or are designed for one-dimensional signals. To address this problem, we propose a robust PCA model for two-dimensional images incorporating structured sparse priors, referred to as structured sparse 2D-PCA. This robust model considers the prior of structured and grouped pixel values in two dimensions. As the proposed formulation is jointly nonconvex and nonsmooth, which is difficult to tackle by joint optimization, we develop a two-stage alternating minimization approach to solve the problem. This approach iteratively learns the projection matrices by bidirectional decomposition and utilizes the proximal method to obtain the structured sparse outliers. By considering the structured sparsity prior, the proposed model becomes less sensitive to noisy data and outliers in two dimensions. Moreover, the computational cost indicates that the robust two-dimensional model is capable of processing quarter common intermediate format video in real time, as well as handling large-size images and videos, which is often intractable with other robust PCA approaches that involve image-to-vector conversion. Experimental results on robust face reconstruction, video background subtraction data set, and real-world videos show the effectiveness of the proposed model compared with conventional 2D-PCA and other robust PCA algorithms.
Influence of molecular structure on the foamability of polypropylene: Linear and extensional rheological fingerprint

The foaming structure and rheological properties of four different isotactic homo-polypropylenes with various molecular weights and an isotactic long chain branched polypropylene were investigated to find a suitable rheological fingerprint for PP foams. The molecular weight distribution and thermal properties were measured using GPC-MALLS and differential scanning calorimetry, respectively. Small amplitude oscillatory shear data and uniaxial extensional experiments were analyzed using the frameworks of van Gurp-Palmen plot (δ vs. |G*|) and the molecular stress function model, respectively. These analyses were used to find a correlation between the molecular structure, rheological properties and foaming structures of linear and long chain branching polypropylenes. Two linear viscoelastic characteristics, |G*| at δ = 60° and |η*| at ω = 5 rad/s were used as criteria for foamability of these polymers, where decreasing of both parameters by increasing the long chain branching content results in smaller cell size and higher cell density. The molecular stress function model was able to quantify the strain hardening properties of long chain branching blends using small amplitude oscillatory shear data and two nonlinear material parameters, 1 ≤ β ≤ 2.2 and 1 ≤ f max 2 ≤ 600, where the minimum and maximum values of these parameters belong to the linear and long chain branched polypropylene, respectively. Increasing the long chain branched polypropylene content of the PP blends increased strain hardening, and therefore improved the foaming characteristics significantly by suppressing the coalescence of cells. Dilution of linear PP with only 10 wt% of long chain branched polypropylene enhanced the cell density from 5.7 × 106 to 2.7 × 107 cell/cm3 and reduced the average cell diameter from 58 to 26 µm, respectively, while their volume expansion ratio remained in the same range of 2–3. Increasing of long chain branching to 50 and 100 wt% enhanced the V.E. R. to 6.2 and 7.8, respectively.
Macro Interleaver Design for Bit Interleaved Coded Modulation with Low-Density Parity-Check Codes

Bit interleaved coded modulation (BICM) is a pragmatic approach to achieve power and bandwidth efficient transmission. At this low-density parity-check codes (LDPCC) can be utilized which are then concatenated with a modulator. The concatenation of LDPCC and modulator is done via a bit interleaver which has the task to spread information. While the matched design of an optimal LDPC degree distribution with respect to a given mapping scheme is well observed it is often assumed that the probabilistic values passed from demodulator to the LDPCC decoder are independent due to the mapping interleaver. However for higher order modulations, e.g. 16-QAM or 256-QAM, more and more bits are getting correlated during the demodulation process. The connectivity of these correlated bits have to be considered during the design of the mapping interleaver with respect to the graph of the concatenated LDPC codes. In this paper, we present the design of mapping interleavers of BICM schemes utilizing LDPC codes. Simulation results show the strong influence of correlated inputs to the performance of LDPC decodes. This correlations can cause a high error floor if the mapping interleaver is not carefully designed.
System-Level Performance Modeling with BACPAC - Berkeley Advanced Chip Performance Calculator

In this paper, a new system-level performance model is introduced. The model, called BACPAC (Berkeley Advanced Chip Performance Calculator), is comprised of a large set of analytical models that have been newly developed and/or compiled to collectively describe the performance characteristics of future high-performance designs. New deep submicron effects are considered in BACPAC that were ignored in previous system-level performance models, such as noise and enhanced power modeling. BACPAC users can explore the capabilities of future VLSI designs given certain expected input conditions. The model can be used to predict clock frequency, chip area, power consumption, yield and other important characteristics of future systems.
Emotional Memory in Patients with Alzheimer's Disease: A Report of Two Cases

Highly emotional events in daily life can be preserved in memory and such memory is generally referred to as emotional memory. Some reports have demonstrated that emotional memory is also found in patients with Alzheimer's disease (AD). However, to our knowledge, there have been no reports about how long memory retention for emotional events can continue in patients with AD. In this paper, we present two patients with AD who lost an immediate family member during followup and retained the memory over a long period despite progression of the AD.
When doctors cry wolf: a systematic review of the literature on clinical lycanthropy

This paper provides an overview and critical reassessment of the cases of clinical lycanthropy reported in the medical literature from 1850 onwards. Out of 56 original case descriptions of metamorphosis into an animal, only 13 fulfilled the criteria of clinical lycanthropy proper. The remaining cases constituted variants of the overarching class of clinical zoanthropy. Forty-seven cases involved primary delusions, and nine secondary delusions on the basis of somatic and/or visual hallucinations which may well have affected the patients’ sense of physical existence, also known as coenaesthesis. Cases of secondary delusions in particular warrant proper somatic and auxiliary investigations to rule out any underlying organic pathology, notably in somatosensory areas and those representing the body scheme.
Displacive Excitation of Coherent Phonons

Abstract : Recently, we have shown that coherent lattice vibrations can be induced and subsequently detected in certain semimetals and semiconductors (e.g. Bi, Sb, Te, Ti2O3) via time-resolved optical pump-probe measurements using a CPM oscillator. The experimental data show in each case that only totally symmetric lattice modes (i.e., A1 symmetry) are coherently excited, even though other symmetry modes of comparable Raman cross-section exist. Furthermore, careful measurement of the coherent phonon phase reveals that the excitation mechanism for coherent phonons in these materials appears to be qualitatively different from that responsible for impulsive stimulated Raman scattering previously seen in organic dyes and crystals.
Application of MPC to an active structure using sampling rates up to 25kHz

In this paper we demonstrate the implementation of model predictive control (MPC) for vibration suppression of the first five bending modes of an active structure. For adequate performance, this requires a 5kHz sampling rate, which is achieved using a standard active-set optimisation technique running on a 200MHz digital signal processor. Experimental results show that MPC offers improved performance for this application when compared with other standard approaches.
Economy-based Content Replication for Peering Content Delivery Networks

Existing content delivery networks (CDNs) by nature are closed delivery networks which do not cooperate with other CDNs and in practice, islands of CDNs are formed. The current logical separation between contents and services in this context results in two content networking domains. In addition to that, meeting the quality of service requirements of clients according to negotiated service level agreements is crucial for a CDN. Present trends in content networks and content networking capabilities give rise to the interest in interconnecting these networks. Hence, in this paper, we present an open, scalable, and service-oriented architecture (SOA)-based system that assist the creation of open content and service delivery networks (CSDNs), which scale and support sharing of resources through peering with other CSDNs. To encourage resource sharing and peering arrangements between different CDN providers at global level, we propose using market-based models by introducing an economy-based strategy for content replication.