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The proliferation of digital information is leading to a wide range of applications which make it desirable to display data easily in many locations, all changeable and updateable. The difficulty in achieving such ubiquitous displays is the cost of signage, the cost of installation, and the software and systems to control the information being sent to each of these signs. In this paper we will talk about a networked system of such signs which are made from gyricon electronic paper. Gyricon electronic paper is a reflective, bistable display which can be made in large web sheets at a reasonable price. Since it does not require a backlight nor does it require power to refresh the display image, such technology is ideal for making signs which can be run on batteries with extremely long battery life, often not needing replacement for years. The display also has a very broad illumination scattering profile which makes it readily viewable from any angle. The basic operating mechanism of the display, its manufacturing technique, and achieved performance will be described, along with the description of a networked solution using many such signs controlled with system software to identify speakers and meetings in conference rooms, hospitality suites, or classrooms in universities. Systems will also be shown which are adapted to retail pricing signage and others which can be used for large format outdoor billboards.

Structured codes are known to be necessary in practical implementations of capacity-approaching "dirty paper schemes". In this paper we study the performance of a recently proposed dirty paper technique, by Erez and ten Brink which, to the authors' knowledge, is firstly applied to data-hiding, and compare it with other existing approaches. Specifically, we compare it with conventional side-informed schemes previously used in data-hiding based on repetition and turbo coding. We show that a significant improvement can be achieved using Erez and ten Brink's proposal. We also study the considerations we have to take into account when these codes are used in data-hiding, mainly related with perceptual questions.

This paper describes pattern processing and recognition for a user interface with real papers rather than display, keyboard and mouse for document editing. We call this UI (User Interface) as 'Paper based User Interface.' In order to realize this UI, we need a technology to detect and recognize correction marks written on printouts of documents. This paper presents a new technology that detects handwritten marks without restricting the way to write correction marks or forcing the users to use a pen of a specific color and a specific scanner. At first, documents are printed out with dotted characters, and they can be easily dropped out to detect correction marks and also they can be easily emphasized to detect where each correction mark is applied. We made a prototype on a PC and verified that the method can extract almost all correction marks correctly.

In this paper, a new data hiding method - writing on wet paper using multi-pixel differencing is presented in order to provide large embedding capacity and improve further steganographic embedding efficiency for the stego-image. It takes into consideration four pixels of a block, and the differences between the lowest gray-value pixel and its surrounding pixels are used to embed the secret data. The receivers can extract secret bits from carrier images only by some matrix multiplications no matter how we embed the secret message. Thus, the invisibility of this steganographic method is achieved in process of secret information transmission. The experimental results show that our scheme has a larger embedding capacity and better robust performance against the methods of active attacks such as noise addition.

Based on the unique characteristic, the paper currency numbers can be put into record and the automatic identification equipment for paper currency numbers is supplied to currency circulation market in order to provide convenience for financial sectors to trace the fiduciary circulation socially and provide effective supervision on paper currency. Simultaneously it is favorable for identifying forged notes, blacklisting the forged notes numbers and solving the major social problems, such as armor cash carrier robbery, money laundering. For the purpose of recognizing the paper currency numbers, a recognition algorithm based on neural network is presented in the paper. Number lines in original paper currency images can be draw out through image processing, such as image de-noising, skew correction, segmentation, and image normalization. According to the different characteristics between digits and letters in serial number, two kinds of classifiers are designed. With the characteristics of associative memory, optimization-compute and rapid convergence, the Discrete Hopfield Neural Network (DHNN) is utilized to recognize the letters; with the characteristics of simple structure, quick learning and global optimum, the Radial-Basis Function Neural Network (RBFNN) is adopted to identify the digits. Then the final recognition results are obtained by combining the two kinds of recognition results in regular sequence. Through the simulation tests, it is confirmed by simulation results that the recognition algorithm of combination of two kinds of recognition methods has such advantages as high recognition rate and faster recognition simultaneously, which is worthy of broad application prospect.

An automatic recognition system for Paper Currency Numbers was developed in this paper. The paper currency number can be recognized and recorded by this system at the same time of binding. At First, an image of 8 bits grey-scale was acquired by CCD camera, and then some output number clusters can be through process of segmentation by Grey Ridge-Valley algorithm, orientation by projection, and character recognition by structure-analyzing algorithm. Results of experiments demonstrated that the proposed algorithm of gray ridges and valleys can extract feature effectively, the algorithm of distance of traversing number body orientation acute, and this system achieves a high recognition rate and a fast recognition speed. It still reached the practical degree. The system has applied national patent.

Hybrid soft computing models, based by neural, fuzzy and evolutionary computation technologies, have been applied to a large number of classification, prediction, and control problems. This paper focuses on one of such applications and presents a systematic process for building a predictive model to estimate time-to-breakage and provide a web break tendency indicator in the wet-end part of paper making machines. Through successive information refinement of information gleaned from sensor readings via data analysis, principal component analysis (PCA), adaptive neural fuzzy inference system (ANFIS), and trending analysis, a break tendency indicator was built. Output of this indicator is the break margin. The break margin is then interpreted using a stoplight metaphor. This interpretation provides a more gradual web break sensitivity indicator, since it uses more classes compared to a binary indicator. By generating an accurate web break tendency indicator with enough lead-time, we help in the overall control of the paper making cycle by minimizing down time and improving productivity.

Exploitation has largely focused on single look, single modality platform collections exploited by a variety of classification algorithms over the last several decades. But multi look, multi modality exploitation is the evolving paradigm for threat classification given the evolution and cost of near term unmanned vehicle system sensor platforms. This paper presents an overview of machine learning algorithms that have previously been applied to automatic target recognition, discusses the evolution of target recognition to the 2010 paradigm of multi look, multi modality valid target identification, and explores how advanced machine learning algorithms can be applied to address the exploitation problems of the next decade.

Since the terrible events of 11 Sep 2001 the response to security vulnerabilities has been to throw “Guns, Gates and Guards” at the problem. Three years later and it is clear that, although this may have had a short-term effect, it is unsustainable and unaffordable in the long term. The war on terrorism is going to be fought for a very long time. Defending against terrorism and enhancing the resilience and robustness of society and its processes now requires constant vigilance. Only technology can provide that vigilance at an efficiency that can provide certainty of detection and fast response. A technology led approach, integrating with people and their processes calls for innovation and a new generation of technology that fuses the physical world with the logical world. This approach is measurable in terms of capability and investment, in the way that the previous Newtonian security approach of cause and effect is not. This paper will address this new security environment and the different approach that R&D has to take to ensure that life and Democracy thrive and terrorism is defeated.

A common approach to the detection of objects in sensor data is to model the target, compare the input data to that model and then if the match is close enough, declare target-present. This is how many automatic target recognition (ATR) systems operate. An alternative approach is to also have confuser models (CMs) and to consider how close the input data is to all of the models in the library. The advantages of CMs can be increased by also modeling the match score likelihoods for targets and confusers. This paper considers several methods for using CMs and likelihood models (LMs) and demonstrates their relative merits with a mean-squared-error based ATR on the MSTAR synthetic-aperture-radar (SAR) public data set. Two benefits of CMs and LMs are demonstrated. They improve the ability of the ATR to discriminate targets and confusers, as one might expect, but they can also help the ATR estimate the confidence it should have in its decisions. In the demonstration, the area-under-the-ROC curve was increased from 0.88 to 0.94 by CM use. For the important case of out-of-library confusers, if the probability of false alarm (Pfa) is set to 0.1 then CMS and LMs increase probability of detection (Pd) from 0.40 to 0.65. On the other hand if the Pd is set to 0.9 then the CMs and LMs decrease Pfa from 0.50 to 0.35. The posterior estimate (i.e., the ATR's confidence) had a reduction in RMS error from 0.27 to 0.09 through the use of CMs and LMs.

This review paper is based on the 2004 UCLA Seminar on Imaging and Informatics (http://www.radnet.ucla.edu/Arrowhead2004/) which is a joint endeavour between the UCLA and the CARS organization, focussing on workflow analysis tools and the digital operating room. Eleven specific presentations of the Arrowhead Seminar have been summarized in this review referring to redesigning perioperative care for a high velocity OR, intraoperative ultrasound process and model, surgical workflow and surgical PACS, an integrated view , interactions in the surgical OR, workflow automation strategies and target applications, visualisation solutions for the operating room, navigating the fifth dimension, and design of digital operating rooms and interventional suites

An IETM is intended to be the functional equivalent of a paper-based Technical Manual (TM), and in most cases a total replacement for paper manual. In this paper, we will describe some of document image understanding technologies applied to the conversion of paper-based TMs to IETMs. Using these advanced technologies allow us to convert paper-based TMs to class 1/2 IETMs. However, these were not sufficient for an automated integrated logistics support system in the ROC Department of Defense. An advanced IETM system is therefore required. Such class 4/5 like IETM system could provide intelligent display of information and other user applications such as diagnostics, intelligent design and manufacturing, or computer-managed training. The author has developed some of the advanced functions, and examples will be shown to demonstrate the new aspect of IETMs.

The Humanitarian Demining Research and Development Program of Night Vision and Electronic Sensors Directorate (NVESD), under the direction of the Office of Assistant Secretary of Defense for Special Operations and Low-Intensity Conflict (OASD/SOLIC) and with participation from the International Test and Evaluation Project (ITEP) for Humanitarian Demining, conducted an in-country field evaluation of HSTAMIDS in the region of Humanitarian Demining Unit #1 (HMAU1) in Thailand. Participants included the US Humanitarian Demining Team of NVESD, ITEP personnel, Thailand Mine Action Center (TMAC), HALO Trust organization from Cambodia, and CyTerra Corporation. The primary objectives were to demonstrate the performance of the U.S. Army's latest handheld multisensor mine detector, the AN/PSS-14, in a demining environment in comparison to the performance of the metal detector being used by the local deminers and also to assess the performance of the trained deminers after limited experience and training with the HSTAMIDS.© (2005) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Dynamic Networked Combat Capability is a transformational concept to enable network centric warfare at the tactical level - the immediate attack of targets of opportunity using any and all assets available: any sensor, any effect generator and any decider against any target. More specifically, the DARPA goal is to provide enabling technologies that will permit the Services to build such a capability.

Current military activity has made great use of small Special Tactics / Special Forces teams operating on the ground in forward areas of battle, directing Battlefield Air Operations (BAO), which include close air support, air traffic control management, and target identification and designation. A recent National Priority has been identified to improve the BAO Kit used by these Special Tactics Groups to reduce errors that may lead to unintended ground casualties. The primary objectives of the upgraded BAO Kit are to 1) improve the range and accuracy of target information; 2) eliminate opportunities for error in weapon delivery; 3) link target coordinate information directly into the weapons computer; and 4) reduce the weight carried by the warfighter by 50%. For these warfighters, L-3 Communications Display Systems and its technology partner, Universal Display Corporation, are utilizing advanced OLED display technology to create a powerful flexible display-based communication device. This will reduce the weight carried by the fighter by combining functions of the present computer, GPS equipment, and radio gear carried into the forward areas of battle. This will give the soldier a larger, higher resolution, increased battery life, and much lighter capability for the viewing of tactical information such as battlefield maps, GIS imaging data, command/control plots, and GPS-assisted navigational maps. Further integration of the device with voice and video messaging options will be explored. Both hand-held roll-up devices and wrist-worn devices are envisioned for the final product.

We consider fundamental limits on information acquisition from localized regions of molecular-scale electronic systems. Our approach is based on a quantitative measure we call the volume accessible information, defined as the (Shannon) mutual information associated with the best possible quantum measurement that can access a system through a specified readout volume. Using results and techniques from quantum measurement theory, we obtain a general expression for an upper bound on the volume accessible information that depends only on the manner in which information is encoded in electron states and specification of the readout volume. An illustrative study of a model tight-binding system indeed reveals that the volume accessible information is sharply reduced at small sampling volumes, where the state distinguishability required for reliable information extraction is diminished.© (2005) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Drinking water is one of the nation's key infrastructure assets that have been deemed vulnerable to deliberate terrorist attacks. While the threat to reservoir systems and water sources is deemed to be minimal, the vulnerability of the drinking water distribution systems to accidental or deliberate contamination due to a backflow event is becoming a well-recognized possibility. The myriad possible points of incursion into a distribution system and the ease of mounting a backflow event, combined with the fact that little or no quality monitoring occurs after the water has left the treatment plant, makes the danger of such an attack acute. This was clearly stated in a General Accounting Office (GAO) report to Congress that listed the vulnerability of the distribution system to attack as the largest security risk to water supplies. Prior to this there has not been a system capable of detecting such an event and alerting the system's managers so that effects of an attack or accident can be contained. The general scientific consensus is that no practical, available, or cost-effective real-time technology exists to detect and mitigate intentional attacks or accidental incursions in drinking water distribution systems. The rapid detection and identification of breaches of security in the water distribution system is crucial in initiating appropriate corrective action. The ability of a technology system to detect incursion on a real time basis and give indications as to the cause could dramatically reduce the impact of any such scenario. As the vulnerability of the distribution system becomes more widely recognized, the development of a system such as the one described will be an invaluable tool in maintaining the integrity of the nations drinking water supply.© (2005) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

There is a growing realization of the need for highly integrated solutions enabled by new bi-directional data 'pipes' between design and manufacturing. Traditional EDA applications should be able to communicate and collaborate with yield analysis software. Simply adding such capabilities to existing EDA applications is not feasible. Thus, there is a need for an infrastructure that would enable such interaction in a standard way. We call this infrastructure the DFM Platform. In this article we present new approach to building such a platform. Brief descriptions of potential applications follow the platform architecture. "Via analysis" application includes test chips capabilities, critical area and critical parameter analysis to predict yield for a real design. The "DFM Cell Grading" module applies the concept of DFM to IP Libraries.

Using Synopsys TCAD tools, several examples of advanced process and device modeling are presented for full-frame CCD image sensors. The topics covered in these examples include channel potential, charge capacity, charge transport, and charge blooming. The simulations provide in depth analysis of the basic principles of operation of CCDs and cover some aspects of antiblooming protection.

The 'LuminusR' that you see in the title of this paper is a registered trade mark of a security paper product which has patent protection to date in six countries, including the United States. Further patent applications are in progress in over forty other countries. The essential characteristic of LuminusR Paper is that it contains a coloured image that is substantially undetectable when viewed in reflected light but becomes apparent when viewed through transmitted light. The stronger the light source, the more pronounced the image becomes. The purpose of this paper is to explain the technical aspects of the product and to explain, in effect, why it is a patentable, completely new technology for protection against counterfeiting. First, it must be explained that the inventors set out to develop a paper that, in conjunction with the surface print on a document, could not be reproduced by the normal means open to a counterfeiter: especially colour photocopying and Offset printing. During the time that Domtar proceeded to bring the concept to the stage of a commercial product, three other advantages became apparent: (1) Durability in use as proven by lab results and a 'live' circulation trial. (2) Design flexibility: giving a third 'side' to the paper that can receive a design. (3) Processing ability in existing equipment including printing presses and cash handling machinery. I am going to concentrate, however, on the anti-counterfeiting aspects of LuminusR Security Paper as security is the theme of this conference. In this respect, I will cover the following subject areas: (1) Make-up of the product, including production methods; (2) Security elements that can be incorporated to deter counterfeiting; (3) Health and safety aspects; (4) Testing against counterfeiting and simulation; (5) Cost-benefit of the product. I am also going to concentrate on one end-use, bank notes, although LuminusR paper has been tested through use in other security documents, in particular travel documents such as passports and visas.© (1998) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

The threat of counterfeiting in Bank Notes and other Security Documents is steadily increasing with the continuous improvement of printing and copying technologies. The perfect reproduction of a document is indeed reachable with the right combination of technique and economics. Our only protection is to make it difficult by introducing artefacts which appear only through the counterfeiting process or reduce significantly the efficiency of that process. Today more than 150 million of banknotes are counterfeited each year and that amount is on the rise. This quantity is small compared to the total amount of currency available in the world which is estimated at 380 billion dollars [1] but it is nevertheless a problem that have to be faced not only for the loss of money but, more importantly for the kind of use made of the counterfeited bills in the troubled world in which we are living today. A number of methods are available to defeat counterfeiting none of which are conpletely satisfactory. The difficulty is not as much the prevention of the duplication than doing it while safegarding the intinsic properties (durability) of the documents as far as permanent readability, resistance to wear etc. satisfactory. The difficulty is not as much the prevention of the duplication than doing it while safegarding the intinsic properties (durability) of the documents as far as permanent readability, resistance to wear etc. Basic approaches are: 1. The light switch: a transparent coating which becomes opaque during any attempt of duplication. 2. An alternative to this first approach is an ink which becomes transparent at the time of duplication. 3. The use of a substrate (Paper stock) whose optical density will rise to that of the printed graphic on the bill at the time of copying. This would be a true interactive paper. 4. Luminescent materials that would blind the photoreceptor during exposure. Such material could be in the substrate or in the printing ink. 5. Introduction of an artefact in the printed copy through a hologram hidden in a transparent coating or embossed in the substrate. All approaches have been investigated to a certain extent with variable success. We will report hereafter the results of investigations made by the author on alternatives (1) and (2) in this Laboratory and at the University of Texas at El Paso under contract from the National Bank of Belgium and with the collaboration of the CNRS in Paris and the University of Marseille France.

How should hospital administrators compare the security risks of paper-based and computerized patient record systems. There is a general tendency to assume that because computer networks potentially provide broad access to hospital archives, computerized patient records are less secure than paper records and increase the risk of breaches of patient confidentiality. This assumption is ill-founded on two grounds. Reasons exist to say that the computerized patient record provides better access to patient information while enhancing overall information system security. A range of options with different trade-offs between access and security exist in both paper-based and computerized records management systems. The relative accessibility and security of any particular patient record management system depends, therefore, on administrative choice, not simply on the intrinsic features of paper or computerized information management systems.

Bioterrorism is no longer a hypothetical construct but a reality. Nevertheless, disease detection and intervention currently remain largely reliant on clinical assessment. Technology providing early detection of disease could impact the care of individual patients and the evolution of epidemic spread. Hyperspectral Imaging (HSI) is a remote sensing technology developed originally by the Department of Defense that combines high-resolution imaging with chemical spectroscopy. In other medical applications HSI is emerging as a new means of early or more sensitive detection of changes in tissue that can be used to define pathology, predict clinical outcomes and adapt therapy. As a small, robust, camera based, non-invasive device, HSI may be well suited to aid in defense against biological warfare or epidemic disease by providing early detection or confirmation of disease and by monitoring the efficacy of vaccination or therapy. Crossover applications exist in the evaluation and treatment of emerging diseases. HSI is well suited to be a screening tool to provide earlier or more accurate detection of disease in an at risk population to better treat and contain disease.

The challenge in providing picture archiving and communication systems (PACS) in the modern managed care environment is to justify its cost, while still providing the required services. The only solution which achieves the economic goal is the elimination of film and its associated costs in favor of PACS. Recognizing that some hardcopy will always be desired, an acceptable inexpensive paper print alternative is required. Our initial networked paper printed solution has replaced all film use in nuclear medicine while still providing economical color and monochrome paper output when required. Extensions of this print architecture for ultrasound, CT and MR are planned for the near future.

Distributing a radiographic image with the verbal report would help to avoid misunderstanding and would aid the referring physician in understanding better the severity of the disease described. With magnetic resonance imaging it is customary in our practice to distribute a copy of the examination to the referring physician. Distributing a film copy of the image is expensive. Based on our initial experience with the Scitex paper printer, we believe that this system provides a paper image of sufficient quality that it would be acceptable as a referrer's copy. Paper copies are cheaper to produce and can be more easily filed with the remainder of the patient's paper based medical record. This presentation discusses the printing method employed by the Scitex printer, demonstrates comparisons between Scitex and laser print images, and discusses the current problems of interfacing the printer to image acquisition devices. As we develop our image management and communication system (IMAC), we anticipate that a need for hard copy images will remain. We discuss the role that we believe this paper printer serves in an IMAC film independent system.© (1994) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

We present two algorithms to construct C1-smooth models of skeletal structures from CT/NMR voxel data. The boundary of the reconstructed models consists of a C1- continuous mesh of triangular algebraic surface patches. One algorithm first constructs C1-continuous piecewise conic contours on each of the CT/NMR data slices and then uses piecewise triangular algebraic surface patches to C1 interpolate the contours on adjacent slices. The other algorithm works directly in voxel space and replaces an initial C0 triangular facet approximation of the model with a highly compressed C1- continuous mesh of triangular algebraic surface patches. Both schemes are adaptive, yielding a higher density of patches in regions of higher curvature.© (1992) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

The SYNOPSYS - expert-systems program XSYS - is a radical departure from traditional methods of lens design. I describe typical problems that were presented to XSYS and show how the program can find useful sometimes unexpected starting points. 1.

Global Optimization (GO) is an area of applied mathemetics that has been active for many years. A large variety of GO algorithms have been constructed in an effort to find a solution to the general GO problem. Some of these have proved to be successful on certain classes of problems. However no general deterministic algorithm exists that can locate the global optimum for every multidimensional problem. Traditionally in the lens design discipline the search for an optimum solution in the design space has been done by optimization methods. The conventional design methods are in principle local search methods and do not pmvide any global information on the design space. As the available computer power increases GO tools can be used also in optical design. A two phase search algorithm based on global optimization techniques is described. In a first phase using a coarse sampling approach the program finds the favorable regions that correspond to potentially promising configurations. In a second phase conventional optimization routines are used to find the best solutions in eh region. Then an optimum solution is determined according to the application at hand. The pro algorithm is analyzed and compared to more conventional design approaches. A further refmement of the algorithm excludes from the systematic search some unfavorable configuration regions through the use of a simple expert system. Search times are further reduced through parallel-processing methods. We believe this approach to lens design represents new initiative towards the determination of the optimum solution in any lens design problem. 1.

Traditionally, multiple format recording emulsions for medical video imaging have utilized a film (transparent) base. The major reason for this is probably because the film and camera manufacturers felt the diagnostician is accustomed to viewing x-ray images on a film base and would prefer to view video images that way also. Because of the need to keep radiation exposure to patients at a minimum and the fact that photographic emulsions are generally very inefficient in utilizing x-ray radiation, a film base was the logical requirement for direct x-ray imaging as it enabled the image to be recorded by two emulsions rather than one. The transparent base thus allows viewing a photograph which is the result of the additive effect of the two emulsions. The use of transparent base imposed specific requirements that necessitated the development of a whole complex of equipment designed for the particular use of film such as the processing machines, their chemical solutions, and the famous viewbox and alternators that characterize the radiology departments of today.

Traditionally, multiple format recording emulsions for medical video imaging have utilized a film (transparent) base. The major reason for this is probably because the film and camera manufacturers felt the diagnostician is accustomed to viewing x-ray images on a film base and would prefer to view video images that way also. Because of the need to keep radiation exposure to patients at a minimum and the fact that photographic emulsions are generally very inefficient in utilizing x-ray radiation, a film base was the logical requirement for direct x-ray imaging as it enabled the image to be recorded by two emulsions rather than one. The transparent base thus allows viewing a photograph which is the result of the additive effect of the two emulsions. The use of transparent base imposed specific requirements that necessitated the development of a whole complex of equipment designed for the particular use of film such as the processing machines, their chemical solutions, and the famous viewbox and alternators that characterize the radiology departments of today.

E-learning device is widely used in our daily life. However mobile e-learning machine is not satisfied as expected. Most mobile e-learning device is a static learning device, unable to fulfill the requirement of collaboration, long standby time, usage under strong sunlight. To meet these requirements, we developed a learning machine based on electronic paper. This paper will discuss the software consideration of the device. The software is a knowledge navigation system, the navigation system is based on Ontology. Research shows that combine frame Ontology and description logic together can afford a uniform interface to user application.

In the knowledge explosion, rapid development of information age, how quickly the user or users interested in useful information for feedback to the user problem to be solved in this article. This paper based on data mining, association rules to the model and classification model a combination of electronic books on the recommendation of the user's neighboring users interested in e-books to target users. Introduced the e-book recommendation and the key technologies, system implementation algorithms, and implementation process, was proved through experiments that this system can help users quickly find the required e-books.

Today, the paper document is fast becoming a thing of the past. With the rapid development of fast, inexpensive computing and storage devices, many government and private organizations are archiving their documents in electronic form (e.g., personnel records, medical records, patents, etc.). Many of these organizations are converting their paper archives to electronic images, which are then stored in a computer database. Because of this, there is a need to efficiently organize this data into comprehensive and accessible information resources and provide for rapid access to the information contained within these imaged documents. To meet this need, Litton PRC and Litton Data Systems Division are developing a system, the Imaged Document Optical Correlation and Conversion System (IDOCCS), to provide a total solution to the problem of managing and retrieving textual and graphic information from imaged document archives. At the heart of IDOCCS, optical correlation technology provide a means for the search and retrieval of information from imaged documents. IDOCCS can be used to rapidly search for key words or phrases within the imaged document archives and has the potential to determine the types of languages contained within a document. In addition, IDOCCS can automatically compare an input document with the archived database to determine if it is a duplicate, thereby reducing the overall resources required to maintain and access the document database. Embedded graphics on imaged pages can also be exploited, e.g., imaged documents containing an agency's seal or logo can be singled out. In this paper, we present a description of IDOCCS as well as preliminary performance results and theoretical projections.

Meyer watershed algorithm, which creates watershed lines to the output image, and its extended split-and-merge version are further analyzed in this paper based on our previous work. Specifically, it is shown that no permanently isolated areas exist in the output of the Meyer watershed algorithm when applied to an eight-connected square grid. The scanning order dependency of watershed algorithms is also studied. First, the different scanning order operations which are implementation dependent are identified; then their effects on the output are analyzed. Finally, an alternative implementation of the split-and-merge watershed algorithm is presented which detects isolated areas using a new technique; the goal is to illustrate and analyze the operations of the split-and-merge watershed algorithm.© (1994) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

The handwritten signature is the most widely employed source of secure identification in the United States, especially for cashing checks, and verifying credit card transactions. Currently, all signature verification is based on visual inspection by a teller or a store clerk. Previous successful techniques for forgery detection have primarily been on-line techniques. This research is an extension of the first author's work on forgery detection and describes an algorithm to detect forgeries perpetrated by using a tracing paper or a glass plate. This algorithm is very successful when used in conjunction with the algorithm developed earlier by the first author.© (1991) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

The first fifty years of magnetic recording centered around one basic format. It was, and is, called longitudinal recording. The medium it-self progressed from wire, first used by Poulsen, to steel tape, to magnetic-coated paper tape, to the magnetic-coated acetate tapes commonly in use in the early 1950's. Quality of reproduction steadily improved over the years as well, and by 1952, magnetic tape recording was becoming the standard for audio mastering, broadcasting, and instrumentation (data) recordings.

With the continuous acceleration of the process of re-electrification and the high proportion of new energy, the power balance pressure of the power grid has increased significantly. The traditional power grid balance regulation method based on deep peak shaving of thermal power has been difficult to meet the needs of the rapid development of new energy in the power grid. There is an urgent need for aggregators to aggregate flexible loads and mobilize them to actively participate in power grid regulation and operation. This paper analyzes the economic benefits of load aggregators by studying the benefits of aggregating load resources in different power transactions, and analyzes the social benefits from promoting power grid construction, promoting the transformation of power sales companies to comprehensive energy service providers, stimulating the development of related industries and alleviating the spear of power supply and demand; from the aspects of promoting energy conservation and emission reduction and saving social resources, this paper analyzes the energy conservation and emission reduction benefits of polymer operators. By determining the comprehensive operation benefits of load aggregators, it is conducive to the distribution of compensation value and value-added income obtained by users when participating in regulation and operation.

Inverse Lithography Technology (ILT) has become one of the key computational lithography solutions, which may produce mask output that results in better process latitude and CD control on wafer than the one using conventional OPC. However, the curvilinear inverse lithography recipe parameter tuning is a non-trivial task, which involves optimization for various common objectives such as common DOF, NILS, and pvband (process variability band) [1]. It is known that test patterns used for recipe development play a critical role in achieving optimized ILT masks in terms of mask-friendliness, OPC convergence or multi-structure common focus range. The traditional way of test pattern selection is usually a clip-level manual search by taking into account of design rules, which inevitably may cause lack of critical design representations. In this paper, we introduce Mentor Graphic’s Calibre SONR[2], a Machine Learning (ML) method to implement design layouts clustering and automatic pattern selections for ILT recipe tuning on a full-chip level. Calibre SONR leverages design layouts and process models to create necessary features as input and then combine with a proprietary clustering algorithm to fully analyze and cluster a full layout in feature vector space. The degree of pattern reduction is controlled by an adjustable similarity metric[2]. The paper has four parts: the first part is on the feature generation and data collection based on the design layout, the lithography and etch process models. A wide range of over 100 features are extracted to represent the layout and process etc. The second is on the SONR model building and pattern selection from the full-chip; the third on the ILT recipe creation process with patterns selected from SONR and the traditional method; the fourth on the pattern coverage analysis and verification by evaluating the recipe quality through OPCV checks, and compare the results between SONR-based and traditional pattern selection methods.

In the past few years, the development of natural language processing has been able to deal with many issues such as emotional analysis, semantic analysis, and so on. This review first introduces the development of natural language processing, and then summarizes their applications in financial technology, which mainly focuses on public opinion analysis, financial prediction and analysis, risk assessment, intelligent question answering, and automatic document generation. The analysis shows that natural language processing can give full play to its advantages in the financial field. Moreover, this paper also discusses the problems and challenges for financial technology that are developed based on natural language processing. Finally, this paper presents two developing trends of natural language processing in financial technology: deep learning and knowledge graph.

As the information technology develops rapidly, the large-scale personal data such as sensors or IoT (Internet of Things) equipment is kept in the cloud or data centers. Sometimes, the data owner in cloud center needs to publish the data. Therefore, in the face of the risk of personal information leakage, how to take full advantage of data has become a hot research topic. When data is published many times, personal privacy is also disclosed. Thus, this paper puts forward a new clustering algorithm based on singular value decomposition to finish the clustering process. The ideas of distance and information entropy are considered to flexibly adjust data availability and privacy protection in this way. Secondly, this paper also puts forward a dynamic update mechanism to ensure that personal data will not be leaked after multiple releases and minimize information loss. Finally, the effectiveness and superiority of this method are verified by experiments.

With the construction of smart grid, a large number of user-side power data has been accumulated. This paper proposes a method for analyzing the user’s power behavior based on clustering algorithm. Firstly, the user load data is classified according to the season, and the user’s seasonal power characteristics are analyzed according to the typical daily load curve of the season. Then the average temperature plus load data is used as the feature, and K-means clustering algorithm is used to explore the influence of temperature and holidays on users’ electricity behavior in summer and winter respectively. This paper proposes a method of classifying and analyzing different power consumption modes of a single user, which provides data support for the subsequent load prediction model training for similar days, as well as the formulation of fine management and demand side management decisions for the power grid.

Object detection has a wide range of applications in daily life and industrial fields. However, the success of object detection depends on a huge amount of manually labeled data. In this paper, based on the YOLO object detection model, two types of pedestrians are identified. After data enhancement and training, the performance of the model is analyzed. This paper also studies the simplification method for the training set, through the down-sampling method, continuously reduces the number of the training set, and finally obtains the simplification strategy when preparing the training set. This paper aims to provide a training set preparation and simplification method for object detection in a specific scene, so as to save computational cost and improve the efficiency of resource use.

In computer vision technology based on deep learning, the backbone network is very important, and its performance can usually affect vision-related tasks such as target detection and target segmentation. This article proposes a Ghostnet improvement strategy combined with Shufflenetv2 named GSnet. This paper uses Shufflenetv2's concatenate and shuffle operations to further improve Ghostnet. And the attention module in Ghostnet has been improved and optimized. This paper uses long-distance non-local features to further improve SA attention and embed it in Ghostnet. Experiments are carried out with the data set of cifar100, and after experimental verification, this method reduces the amount of parameters and calculations on the basis of the original method and improves the accuracy of TOP1 by 1.26%. Code is available at https://github.com/Yipzcc later.

The document images often appear in the digital library, social media, e-mail etc. The duplicate copies of the same content bring burden to the management system and waste network traffic and storage resources. This paper proposes a new algorithm for detecting the duplicate document images in large-scale image data sets. The key idea of the proposed algorithm lies in taking advantage of the characteristics of the document image that is structured because of the page layout. In this paper, the text lines are exacted to be taken as elements features of the document image and the Frechet Distance is introduced to measure the similarity of these features. The experimental results of different types of electronic documents show the advantages of the proposed algorithm in accuracy and stability.

Ocean surface monitoring, especially oil slick detection, has become mandatory due to its importance for oil exploration and risk prevention on ecosystems. For years, the detection task has been performed manually by photo-interpreters using Synthetic Aperture Radar (SAR) images with the help of contextual data such as wind. This tedious manual work cannot handle the increasing amount of data collected by the available sensors and thus requires automation. Literature reports conventional and semi-automated detection methods that generally focus either on oil slicks originating from anthropogenic (spills) or natural (seeps) sources on limited data collections. As an extension, this paper presents the automation of offshore oil slicks on an extensive database with both kinds of slicks. It builds upon the slick annotations of specialized photo-interpreters on Sentinel-1 SAR data for 6 years over 3 exploration and monitoring areas worldwide. All the considered SAR images and related annotation relate to real oil slick monitoring scenarios. Further, wind estimation is systematically computed to enrich the data collection. Paper contributions are the following : (i) a performance comparison of two deep learning approaches: semantic segmentation using FC-DenseNet and instance segmentation using Mask-RCNN. (ii) the introduction of meteorological information (wind speed) is deemed valuable for oil slick detection in the performance evaluation. The main results of this study show the effectiveness of slick detection by deep learning approaches, in particular FC-DenseNet, which captures more than 92% of oil instances in our test set. Furthermore, a strong correlation between model performances and contextual information such as slick size and wind speed is demonstrated in the performance evaluation. This work opens perspectives to design models that can fuse SAR and wind information to reduce the false alarm rate.

Unmanned aerial vehicles (UAV:s) have become an increasing threat in both civilian and military arenas. While military UAV:s often are relatively large and complex, the supply in the civilian hobby market is characterized by small, cheap and simple systems with the capacity to stream high-definition video, carry a variety of other sensors and transport critical goods (eg food or medicine) to hard-to-reach places. The criminal world has quickly realized how UAV:s can be used to smuggle weapons or drugs, for example. Militarily, UAV:s are established for reconnaissance, fire control and electronic warfare operations etc. Laser-guided weapons from a UAV, is an example of a widely used system for precision operations during later conflicts. This paper examines and summarizes various laser functions and their role for detecting, recognizing, tracking and combating a UAV. The laser can be used as a support sensor to others like radar or IR to detect end recognise and track the UAV and it can dazzle and destroy its optical sensors. A laser may also be used to sense the atmospheric attenuation and turbulence in slant paths, which are critical to the performance of a high power laser weapon aimed to destroy the UAV. This paper is part 2 dealing with the laser as a countermeasure towards the UAV optical sensors or used as a weapon.

Objective quality metrics provide cost-efficient methods for quality evaluation, as they are practically algorithms, models, that avoid the necessity of subjective assessment, which is a precise but resource-consuming approach. Their ultimate measure of prediction accuracy fundamentally relies on the correlation between the estimated levels of quality and the actual subjective scores of perceived quality, rated by human individuals. Such metrics have already been developed for every single emerging technology where quality, in general, is relevant. This applies to stereoscopic 3D imaging as well, which is utilized in both industry, healthcare, education and entertainment. In this paper, we introduce an exhaustive analysis regarding the practical applications of objective quality metrics for stereoscopic 3D imaging. Our contribution addresses each and every state-of-the-art objective metric in the scientific literature, separately for image and video quality. The study differentiates the metrics by input requirements and supervision, and examines performance via statistical measures. Machine learning algorithms are particularly emphasized within the paper, such as the Deep Edge and COlor Signal INtegrity Evaluator (DECOSINE) using Segmented Stacked Auto-Encoder (S-SAE), different Convolutional Neural Network (CNN) frameworks, and transfer-learning-based methods like the Xception model, AlexNet, ResNet-18, ImageNet, Caffe, GoogLeNet, and also our very own transfer-learning-based methods. The paper focuses on the actual practical applications of the predictive models, and highlights relevant criteria, along with general feasibility, suitability and usability. The analysis of the investigated use cases also addresses potential future research questions and specifies the appropriate directives for quality-focused, user-centric development.

Paper, as a hygroscopic dielectric material, does not have specific spectral signatures in the Terahertz (THz) range from 0.2-6 THz. However, because of its constituent materials, including dry matter, moisture, and air pockets, it absorbs THz radiation, similar to biological tissues and green leaf. Though the absorption loss is not significant, varying levels of dampness in wet paper are observed over time using continuous wave (CW) based THz Spectroscopic system to quantify the moisture content of wet paper relative to paper at ambient environment. For this purpose, effective medium theory (EMT) approaches including Bruggeman (BM), Landau–Lifshitz–Looyenga (LLL), and Complex Refractive Index (CRI) models are analysed. However, EMT models are dependent on physical and optical properties of paper and water, which are not well-defined and are dependent on assumptions, approximations and rigorous calculations. To remove such dependencies, supervised machine learning regression (SMLR) algorithms in the form of decision tree (DT), random forest (RF), and support vector regression (SVR) are investigated. The conditioning of the training parameters is dependent on spectroscopic data which reduces the processing time and improves efficiency due to elimination of approximations. Prediction efficiency of SMLR models is observed to be better than that of EMT models. RF shows the best results in terms of coefficient of determination, 𝑅2 but the time required for training is more when compared to DT and SVR models. DT models show consistent performance, while predictions using different SVR models show variance with 𝑅2 ranging from 0.42 to 0.98.

With the development of internet technology, artificial intelligence shows its rapid growth in recent year as well. More and more people pay attention to this field, including criminals inevitably. It is noted that one technology called “Deepfake” appeared on the internet at the end of 2017. As the name suggests, it is a portmanteau of “deep learning” and “fake”. In essence, Deepfake is a deep-learning framework in the field of image composite and replacement, which swap faces in images or videos. However detection of face forgery is still in its early stages, due to its novelty and complexity. In this paper we will demonstrate some dataset for deepfake forensics firstly, and then describe various existing detection methods. Those methods are reviewed in two parts: detection based on frame sequences and detection based on single frame. The former is implemented by differences between frames including human features, optical flow, timeline and so on, while the latter is based on features of single frame including extracted features and fusion boundary. Various convolutional Neural Networks (abbreviated as CNN) will be illustrated in this paper. Accordingly, performances of above algorithms are likely to be demonstrated and compared, and a further explanation will be given regarding on their applicable dataset. Finally, further research of face forgery detection of deepfake including methods and applications will be discussed.

This paper describes virtualization in optical networks. The rapid growth of Internet traffic, new services and the rapid development of technologies such as the Internet of Things and next generation 5G mobile networks require greater demands on the bandwidth of optical networks. Variety of network services requires network transport with more flexible bandwidth access capabilities. This means that future transport networks must provide flexible bandwidth for dynamic services through intelligent management. In the same time, optical networks still face certain challenges in introducing and expanding services. The main difficulty is to provide guaranteed performance, including access to routing, convergence and route computation times, path detection time, reliability and resource utilization rate. Current level of operation and maintenance of optical networks does not meet the requirements of customers. For these reasons, optical virtualization is a necessary technology to meet the specified set of network requirements, while providing the necessary isolation between its segments. This document first introduces the concept of optical network virtualization and its application for optical network elements and transport technologies. Then the paper describes the various technologies of virtualization in optical networks and the existing problems.