Crafting large prototypes

Contour crafting utilizes computer-aided ancient sculpting techniques for fabrication of large components. The article presents the essentials of the contour crafting process, the status of research and development of the process, experiments with thermoplastics and ceramics materials, engineering analysis of certain aspects of the technology, and its potential application areas     

Reading cursive handwriting by alignment of letter prototypes

We describe a new approach to the visual recognition of cursive handwriting. An effort is made to attain human-like performance by using a method based on pictorial alignment and on a model of the process of handwriting. The alignment approach permits recognition of character instances that appear embedded in connected strings. A system embodying this approach has been implemented and tested on five different word sets. The performance was stable both across words and across writers. The system exhibited a substantial ability to interpret cursive connected strings without recourse to lexical knowledge.SU is partially supported by NSF grant IRI-8900267.     

Semantic database prototypes

Abstract. This paper describes a technique for improving the semantic consensus of conceptual database designs. Semantic consensus is a condition where there is pragmatic agreement among database designers and all of the users about which aspect of reality is being represented by a particular database element, and how that representation is being coded. The technique, called semantic database prototyping (SDP), involves a prototype that has been designed and constructed purely as a consequence of the semantic data model. The purpose of the semantic database prototype is to promote direct user validation during the conceptual database design phase of information systems analysis and design. Its distinguishing characteristic is its capture of data element occurrences within the context of the database design. The research method was action research, and the project is also briefly described.     

Data compression by volume prototypes for streaming data

In these years, we often deal with an enormous amount of data in a large variety of pattern recognition tasks. Such data require a huge amount of memory space and computation time for processing. One of the approaches to cope with these problems is using prototypes. We propose volume prototypes as an extension of traditional point prototypes. A volume prototype is defined as a geometric configuration that represents some data points inside. A volume prototype is akin to a data point in the usage rather than a component of a mixture model. We show a one-pass algorithm to have such prototypes for data stream, along with an application for classification. An oblivion mechanism is also incorporated to adapt concept drift.     

Evaluating expert system prototypes

There is a disparity between the multitude of apparently successful expert system prototypes and the scarcity of expert systems in real everyday use. Modern tools make it deceptively easy to make reasonable prototypes, but these prototypes are seldom made subject to serious evaluation. Instead the development team confronts their product with a set of cases, and the primary evaluation criterion is the percentage of correct answers: we are faced with a 95% syndrome. Other aspects related to the use of the system are almost ignored. There is still a long way to go from a promising prototype to a final system.It is maintained in the article that a useful test must be performed by future users in a situation that is as realistic as possible. If this is not done claims of usefulness cannot be justified. It is also stated that prototyping does not make traditional analysis and design obsolete, although the contents of these activities will change.In order to discuss the effects of using the systems a distinction between expert systems as media, tools and experts is proposed.     

Self-generating prototypes for pattern classification

Prototype classifiers are a type of pattern classifiers, whereby a number of prototypes are designed for each class so as they act as representatives of the patterns of the class. Prototype classifiers are considered among the simplest and best performers in classification problems. However, they need careful positioning of prototypes to capture the distribution of each class region and/or to define the class boundaries. Standard methods, such as learning vector quantization (LVQ), are sensitive to the initial choice of the number and the locations of the prototypes and the learning rate. In this article, a new prototype classification method is proposed, namely self-generating prototypes (SGP). The main advantage of this method is that both the number of prototypes and their locations are learned from the training set without much human intervention. The proposed method is compared with other prototype classifiers such as LVQ, self-generating neural tree (SGNT) and K-nearest neighbor (K-NN) as well as Gaussian mixture model (GMM) classifiers. In our experiments, SGP achieved the best performance in many measures of performance, such as training speed, and test or classification speed. Concerning number of prototypes, and test classification accuracy, it was considerably better than the other methods, but about equal on average to the GMM classifiers. We also implemented the SGP method on the well-known STATLOG benchmark, and it beat all other 21 methods (prototype methods and non-prototype methods) in classification accuracy.     

Automated merging of software prototypes

As software becomes more complex, more sophisticated development and maintenance methods are needed to ensure software quality. Computer-aided prototyping achieves this via quickly built and iteratively updated prototypes of the intended system. This process requires automated support for keeping track of many independent changes and for exploring different combinations of alternative changes and refinements. This article formalizes the update and change merging process, extends the idea to multiple changes to the same base prototype, and introduces a new method of slicing prototypes. Applications of this technology include automatic updating of different versions of existing software with changes made to the baseline version of the system, integrating changes made by different design teams during development, and checking consistency after integration of seemingly disjoint changes to the same software system.     

Document clustering using synthetic cluster prototypes

The use of centroids as prototypes for clustering text documents with the k-means family of methods is not always the best choice for representing text clusters due to the high dimensionality, sparsity, and low quality of text data. Especially for the cases where we seek clusters with small number of objects, the use of centroids may lead to poor solutions near the bad initial conditions. To overcome this problem, we propose the idea of synthetic cluster prototype that is computed by first selecting a subset of cluster objects (instances), then computing the representative of these objects and finally selecting important features. In this spirit, we introduce the MedoidKNN synthetic prototype that favors the representation of the dominant class in a cluster. These synthetic cluster prototypes are incorporated into the generic spherical k-means procedure leading to a robust clustering method called k-synthetic prototypes (k-sp). Comparative experimental evaluation demonstrates the robustness of the approach especially for small datasets and clusters overlapping in many dimensions and its superior performance against traditional and subspace clustering methods.     

Combining classifiers using nearest decision prototypes

We present a new classifier fusion method to combine soft-level classifiers with a new approach, which can be considered as a generalized decision templates method. Previous combining methods based on decision templates employ a single prototype for each class, but this global point of view mostly fails to properly represent the decision space. This drawback extremely affects the classification rate in such cases: insufficient number of training samples, island-shaped decision space distribution, and classes with highly overlapped decision spaces. To better represent the decision space, we utilize a prototype selection method to obtain a set of local decision prototypes for each class. Afterward, to determine the class of a test pattern, its decision profile is computed and then compared to all decision prototypes. In other words, for each class, the larger the numbers of decision prototypes near to the decision profile of a given pattern, the higher the chance for that class. The efficiency of our proposed method is evaluated over some well-known classification datasets suggesting superiority of our method in comparison with other proposed techniques.     

Learning graph prototypes for shape recognition

This paper presents some new approaches for computing graph prototypes in the context of the design of a structural nearest prototype classifier. Four kinds of prototypes are investigated and compared: set median graphs, generalized median graphs, set discriminative graphs and generalized discriminative graphs. They differ according to (i) the graph space where they are searched for and (ii) the objective function which is used for their computation. The first criterion allows to distinguish set prototypes which are selected in the initial graph training set from generalized prototypes which are generated in an infinite set of graphs. The second criterion allows to distinguish median graphs which minimize the sum of distances to all input graphs of a given class from discriminative graphs, which are computed using classification performance as criterion, taking into account the inter-class distribution. For each kind of prototype, the proposed approach allows to identify one or many prototypes per class, in order to manage the trade-off between the classification accuracy and the classification time.Each graph prototype generation/selection is performed through a genetic algorithm which can be specialized to each case by setting the appropriate encoding scheme, fitness and genetic operators.An experimental study performed on several graph databases shows the superiority of the generation approach over the selection one. On the other hand, discriminative prototypes outperform the generative ones. Moreover, we show that the classification rates are improved while the number of prototypes increases. Finally, we show that discriminative prototypes give better results than the median graph based classifier.     

Manufacturing prototypes for paper-based diagnostic devices

Diagnostic assays created using simple and inexpensive materials have the potential to transform healthcare management systems in resource-limited settings. This change can be caused by making assays more accessible (e.g., by improving portability) or by inspiring the development of tests that do not exist currently but would solve a considerable need. Microfluidic devices produced from patterned paper have emerged as a promising candidate for a general platform capable of supporting the development of these diagnostic assays. The ability to manufacture paper-based diagnostic assays reproducibly and in large volumes—potentially hundreds of millions of devices per year per assay—is a critical component of the successful translation of these tools out of the laboratory and into the hands of users. This subject, however, has been largely overlooked in lieu of efforts to develop new tools and techniques. In this review, we summarize the recent advances in two processes that are required to fabricate paper-based diagnostic assays: patterning microfluidic channels into paper and assembling devices. In addition, we discuss the advantages and disadvantages of each process in the context of scalability and potential use as a method to manufacture point-of-care devices.     

Using paper prototypes in home-page design

I previously discussed (see ibid., May 1995) how we redesigned the Sun home page after observing users interacting with the Web sites of several companies, including our own. The change we made shows the benefits of this type of testing. We discovered early on that users didn't recognize the “What's New at Sun” button as a link, and (because it was easy to do) we redesigned the button immediately. Our log data for the two months following the change shows that use of the “What's New at Sun” button increased by 416 percent-ample proof that even small interface changes can lead to dramatic changes in user behavior. The theme of home-page redesign continues with a look at the value of low-fidelity prototyping     

Wheelchair ergonomics and physiological testing of prototypes

Abstract

Two hand rim propelled wheelchairs, a daily-use (active) wheelchair (R) and a marathon sports wheelchair (S), were compared to a three-wheeled crank (C) and a (synchronic) lever (L) propelled wheelchair. All wheelchairs were analysed with respect to cardio-respiratory parameters ([Vdot] _E, [Vdot] _O2 , HR, RER), power output and gross mechanical efficiency during a continuous exercise test (speed of the treadmill V = 0·96 ms^-1; every third minute a one degree increase of the slope). Non-wheelchair users (NW: N = 10) were compared to wheelchair sportsmen (WS: N = 3).

The cardio-respiratory strain of hand rim propulsion increases more swiftly for both R and S wheelchairs than for the C and L systems. Mechanical efficiency is significantly lower for the hand rim wheelchairs with a remarkably low efficiency for the S wheelchair. The S wheelchair however showed the lowest energy losses and a lower [Vdot]_{O 2} compared to the R wheelchair, these being important modalities for high speed and long distance wheeling. The trend in the data was similar for the C and L wheelchairs. These systems showed a 2–3% higher mechanical efficiency in the NW group than for R and S wheelchairs. The trend in the data between wheelchairs for both groups is more or less identical, although the WS group showed lower cardiorespiratory responses and a higher mechanical efficiency. It is concluded that physiological testing of prototypes is useful and should be extended to biomechanical and anatomical aspects to learn more about the optimizing factors for efficiency and energy expenditure in the wheelchair/user interface.     

Convex sets as prototypes for classifying patterns

We propose a general framework for nonparametric classification of multi-dimensional numerical patterns. Given training points for each class, it builds a set cover with convex sets each of which contains some training points of the class but no points of the other classes. Each convex set has thus an associated class label, and classification of a query point is made to the class of the convex set such that the projection of the query point onto its boundary is minimal. In this sense, the convex sets of a class are regarded as “prototypes” for that class. We then apply this framework to two special types of convex sets, minimum enclosing balls and convex hulls, giving algorithms for constructing a set cover with them and for computing the projection length onto their boundaries. For convex hulls, we also give a method for implicitly evaluating whether a point is contained in a convex hull, which can avoid computational difficulty for explicit construction of convex hulls in high-dimensional space.     

Micromechanical machining of high aspect ratio prototypes

 Micromechanical machining uses physical cutting tools in high precision machines to fabricate parts with micrometers features and sub-micrometer tolerances. An advantage of this process is the ability to use any machinable material, quick process planning and material removal, and three-dimensional geometry only limited by the machine and tools used. Disadvantages are that forces are placed on micro cutting tools causing deflection and possible breaking. Deflection reduces process precision and tool breakage results in repeated set up, slower production, and poorer tolerances. Nevertheless, these processes have created many diverse prototypes ranging from biomedical to space applications. Received: 10 August 2001/Accepted: 24 September 2001     

Unsupervised learning of prototypes and attribute weights

In this paper, we introduce new algorithms that perform clustering and feature weighting simultaneously and in an unsupervised manner. The proposed algorithms are computationally and implementationally simple, and learn a different set of feature weights for each identified cluster. The cluster dependent feature weights offer two advantages. First, they guide the clustering process to partition the data set into more meaningful clusters. Second, they can be used in the subsequent steps of a learning system to improve its learning behavior. An extension of the algorithm to deal with an unknown number of clusters is also proposed. The extension is based on competitive agglomeration, whereby the number of clusters is over-specified, and adjacent clusters are allowed to compete for data points in a manner that causes clusters which lose in the competition to gradually become depleted and vanish. We illustrate the performance of the proposed approach by using it to segment color images, and to build a nearest prototype classifier.     

Gender Recognition by MULBP

Gender recognition has gained more attention. However, most of the studies have focused on face images acquired under controlled conditions. In this paper, we investigate gender recognition on real-life faces. We proposed a gender recognition scheme, which is composed of four parts: face detection, median filter, feature extraction, and gender classifier. MULBP features are adopted and combined with a SVM classifier for gender recognition. The MULBP feature is robust to noise and illumination variations. In the experiment, we obtain 98.32% using LFW database and 97.30% on Samsung Gender dataset, which shows the superior performance in gender recognition compared with the conventional operators.     

Beyond prototypes: challenges in deploying ubiquitous systems

The authors discuss the problems in creating the next generation of widely deployed ubiquitous computing systems and articulate current technical and sociological challenges to inspire researchers in the field. They discuss significant research challenges that have yet to be addressed. Central to documenting these challenges is recognizing the context within which we are operating, so they first describe the technical and social changes of the 1990s that directly affected ubiquitous computing then.     

Some novel classifiers designed using prototypes extracted by a newscheme based on self-organizing feature map

We propose two new comprehensive schemes for designing prototype-based classifiers. The scheme addresses all major issues (number of prototypes, generation of prototypes, and utilization of the prototypes) involved in the design of a prototype-based classifier. First we use Kohonen's self-organizing feature map (SOFM) algorithm to produce a minimum number (equal to the number of classes) of initial prototypes. Then we use a dynamic prototype generation and tuning algorithm (DYNAGEN) involving merging, splitting, deleting, and retraining of the prototypes to generate an adequate number of useful prototypes. These prototypes are used to design a "1 nearest multiple prototype (1-NMP)" classifier. Though the classifier performs quite well, it cannot reasonably deal with large variation of variance among the data from different classes. To overcome this deficiency we design a "1 most similar prototype (1-MSP)" classifier. We use the prototypes generated by the SOFM-based DYNAGEN algorithm and associate with each of them a zone of influence. A norm (Euclidean)-induced similarity measure is used for this. The prototypes and their zones of influence are fine-tuned by minimizing an error function. Both classifiers are trained and tested using several data sets, and a consistent improvement in performance of the latter over the former has been observed. We also compared our classifiers with some benchmark results available in the literature.