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Optical character recognition - Wikipedia
Optical character recognition or optical character reader (OCR) is the electronic or mechanical .... OCR software often "pre-processes" images to improve the chances of successful recognition. Techniques include: De-skew – If the document ...

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Six-sigma methodology can take your thinking about availability from the binary uptime-versus-downtime model to one in which you look at the user experience. It requires that you measure variables that are related to things you can control, reducing variability by removing the cause of defects in the process. If you ve defined a long-running transaction as a defect, then capacity planning and resource allocation become part of your remediation. If that variability in response time is caused by system behavior during a failover, then you may have to design for a more complex recovery model. Deciding where to start the process what gives you the most benefit for the effort is at the heart of our Availability Index that debuts in 3. Think of it as a Pareto of availability techniques for the six-sigma-minded.



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nature, the process of generating network traf c is suggested by the model under consideration There are two issues of special interest to self-similar traf c and workload generation that can bene t from further investigation Closed-loop Workload Generation Many traf c generation models are time series models that output a sequence of values interpreted as packets or bytes per time unit which are then fed to a network system (simulation or physical) These ``openloop'' synthetic traf c generation models can be used to evaluate queueing behavior of nite=in nite buffer systems with self-similar input, they can be used to generate background or cross traf c impinging on bottleneck routers, and they can serve as traf c ows that are controlled in an open-loop fashion including traf c shaping.

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Resource management isn t thought of as a canonical part of availability; however, it s essential for providing predictability in quality of service. There are several knobs to turn in the resource management domain: Process scheduling. Normally the Unix scheduler tries to allocate CPU cycles evenly, giving priority to processes that were waiting for I/O to complete. The default behavior isn t ideal if you want to skew scheduling in favor of critical, user-visible processes or push various housekeeping tasks into the background. In addition to the default round-robin scheduler, many vendors offer a fair share scheduler that allows you to create classes of processes and CPU (and memory) allocations for each class. There are also batch schedulers that take specified processes and only run them when the system is otherwise idle. Bandwidth management and allocation. We looked at the end-to-end networking problem in 9, Networking ; if your packets aren t making it from application to database server or from client to web server, you have a perceived availability problem. When you are dealing with congested networks, or looking to enforce a minimum quality of service, bandwidth management is a tool that carves out slots for traffic.

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In network systems governed by feedback traf c controls where source output behavior is a function of network state, open-loop traf c generation is, in general, ill-suited due to its a priori xed nature, which does not incorporate dependence on network state Traf c emitted from a source is in uenced by speci c control actions in the protocol stack for example, TCP Tahoe, Reno, Vegas, and ow-controlled UDP and capturing network state and protocol dependence falls outside the scope of openloop traf c generation models A closed-loop traf c generation model for selfsimilar traf c that captures both network and protocol dependence based on physical modeling works by generating le transmission events with heavytailed le size distribution at the application layer and lets each le transmission event pass through the protocol stack (eg.

, TCP in the transport layer), which then results in packet transmission events at the network=link layer The consequent traf c ow re ects both the control actions such as reliability, congestion control, fragmentation, and buffering undertaken in the protocol stack, as well as feedback from the network The closed-loop workload generation framework allows the effect of different control actions on network traf c and performance to be discerned and evaluated Several issues remain: Which connection arrival model should be used at the application layer (eg, exponential versus heavy-tailed interconnection arrival times) and for what purpose Should the arrival time of the next connection be counted from the time of completion of the previous connection or independently= concurrently How sensitive are the induced traf c properties and network performance to details in the application layer workload model (cf.

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