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If a reference to the server object is returned (marshalled by reference), then the methods invoked on the callee will be executed on the server object s machine (the machine where the Remote COS is deployed) We discussed marshalling and unmarshalling in Section 14311, and we discussed reference types within the Common Type System (CTS) in Section 18114 The two scenarios we just discussed are essentially the combination of those two concepts with additional functionality being provided by the NET Remoting subplatform facilities The second scenario is the situation we are most interested in for DDBE development, at least most of the time There are many options to choose from and restrictions to follow when creating a Remote COS using NET Remoting.

TCP Fields Control bits: URG Urgent pointer: Used to send a message to the destination that urgent data is waiting to be sent to it. This could be sent to a destination station, when the destination station has closed the Receive window to the sender. However, the receiver will still accept packets with this bit set.

While the detailed discussion is beyond the scope of this chapter, many of the relevant details are described very clearly in the online documentation for the Microsoft NET Framework-based platform We will brie y summarize the basic concepts here We say that a server (remote) object is consumed by a client and exposed by a server The server object can be client activated (referred to as activated) or server.

indicate which higher level protocol should receive the data of the datagram (i.e., TCP, UDP, OSPF, or possibly other protocol). This field allows for multiplexing. There are many protocols that may reside on top of IP. Currently, the most common transport implementations are TCP and UDP. If the protocol field is set to a number that identifies TCP, the data will be handed to the TCP process for further processing. The same is true if the frame is set to UDP or any other upper layer protocol. This field becomes very apparent to anyone who troubleshoots networks. Simply stated, it allows for IP to deliver the data (after it strips off and processes its fields) to the next intended protocol. The second field is a Cyclic Redundancy Check (CRC) of 16 bits. How this number is arrived at is beyond the scope of this book, but the idea behind it is to ensure the integrity of the header. A CRC number is generated from the data in the IP data field and placed into this field by the transmitting station. When the receiving station reads the data, it will compute a CRC number. If the two CRC numbers do not match, there is an error in the header and the packet will be discarded. Stretching it, you may think of this as a fancy parity check. As the datagram is received by each router, each router will recompute the checksum. Why change it Because the TTL field is changed by each router the datagram traverses.

this frame (the target name)

applied in PbD Kang et al [4] describe a system that observes, recognizes, and maps human grasp to a robot manipulator using a stereo vision system and a data glove Zoellner et al [5] use a data glove with integrated tactile sensors where the recognition is based on support vector machines (SVMs) Ikeuchi et al [6] apply hidden Markov models (HMMs) to segment and recognize grasp sequences Ekvall and Kragic [7] also use HMM methods and address the PbD problem using arm trajectory as an additional feature for grasp classi cation Li et al [8] use singular value decomposition (SVD) for the generation of feature vectors of human grasp and SVMs, which are applied to the classi cation problem Aleotti and Caselli [9] describe a virtual-reality-based PbD system for grasp recognition where only nal grasp postures are modeled based on nger joint angles.

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Palm and Iliev presented two methods based on fuzzy models [10] and [11] These two methods can be seen as evolving fuzzy systems as they are able to learn robotic behaviors or task primitives from experience However, looking at the rich variety of cited methods, it is evident that they do not provide equally successful results Therefore, three methods are compared with one another, two of them being already described in detail in [10] and [11] and the third one being a hybrid method of fuzzy clustering and HMM methods All three methods start with fuzzy time clustering The rst method, which is the simplest one, classi es a given test grasp using the distances of the time clusters between the test grasp and a set of model grasps [10].