Abstract: The proposed technology includes ophthalmic apparatuses and systems that allow transmission of a digital slit-lamp output (both of the visible light as well as of infra-red wavelength) over the web in “real-time” alongside face-to-face audio-visual communication between the various parties. The digital output of the camera will be in a format that is compatible with direct transmission over the web. This allows a person(s) at the remote site to see the slit-lamp imagery as if the remote examiner(s) were on site in the presence of the patient and simultaneously video-chat with the operator of the microscope to provide tele-consultation. The features also facilitate collaborative care between several providers, as well as patient representative(s), by enhancing the audio-visual communication between parties with the actual slit lamp imagery of the patient in real time.

Abstract: This disclosure provides systems, methods and apparatuses for classifying traffic flow using a plurality of learning machines arranged in multiple hierarchical levels. A first learning machine may classify a first portion of the input stream as malicious based on a match with first classification rules, and a second learning machine may classify at least part of the first portion of the input stream as malicious based on a match with second classification rules. The at least part of the first portion of the input stream may be classified as malicious based on the matches in the first and second learning machines.

Abstract: This disclosure provides systems, methods and apparatuses for classifying traffic flow using a plurality of learning machines arranged in multiple hierarchical levels. A first learning machine may classify a first portion of the input stream as malicious based on a match with first classification rules, and a second learning machine may classify at least part of the first portion of the input stream as malicious based on a match with second classification rules. The at least part of the first portion of the input stream may be classified as malicious based on the matches in the first and second learning machines.

Abstract: This disclosure provides systems, methods and apparatuses for classifying traffic flow using a plurality of learning machines arranged in multiple hierarchical levels. A first learning machine may classify a first portion of the input stream as malicious based on a match with first classification rules, and a second learning machine may classify at least part of the first portion of the input stream as malicious based on a match with second classification rules. The at least part of the first portion of the input stream may be classified as malicious based on the matches in the first and second learning machines.

Abstract: This disclosure provides systems, methods and apparatuses for classifying traffic flow using a plurality of learning machines arranged in multiple hierarchical levels. A first learning machine may classify a first portion of the input stream as malicious based on a match with first classification rules, and a second learning machine may classify at least part of the first portion of the input stream as malicious based on a match with second classification rules. The at least part of the first portion of the input stream may be classified as malicious based on the matches in the first and second learning machines.

Abstract: This disclosure provides systems, methods and apparatuses for classifying traffic flow using a plurality of learning machines arranged in multiple hierarchical levels. A first learning machine may classify a first portion of the input stream as malicious based on a match with first classification rules, and a second learning machine may classify at least part of the first portion of the input stream as malicious based on a match with second classification rules. The at least part of the first portion of the input stream may be classified as malicious based on the matches in the first and second learning machines.

Abstract: Upon receiving malware detection rules that are to be identified with respect to an input traffic stream, a rule database that requires less storage capacity than the malware detection rules is generated by substituting tokens for selected symbol strings within the malware detection rules. A compressed traffic stream is generated by substituting the tokens for instances of the selected symbol strings within the input traffic stream, and then compared with the rule database to determine whether the input traffic stream contains one or more symbol sequences that correspond to any of the malware detection rules.

Abstract: In a malware detection device, first characters in a network traffic flow are compared with a plurality of entries within a ternary content addressable memory (TCAM), the plurality of entries including a first entry that constitutes a first segment of a malware signature. In response to an output from the first TCAM indicating that the first characters match the first entry, a variable-character expression engine determines whether second characters in the network traffic flow match a first variable-length regular expression, the variable-length regular expression corresponding to a second segment of the malware signature. A comparand value is generated that includes third characters in the network traffic flow and an expression-match value that indicates whether the second characters match the first variable-length regular expression. The TCAM compares the first comparand value with the plurality of entries therein as part of a determination whether the network traffic flow contains the malware signature.

Abstract: Upon receiving malware detection rules that are to be identified with respect to an input traffic stream, a sequence of state definitions are generated for each of the rules. The state definitions for each rule correspond to respective segments of the rule and specify conditions under which a state machine is to transition between search states corresponding to those segments, at least one of the segments corresponding to multiple characters within the input traffic stream. A state machine transitions between search states corresponding to one or more of the rules in accordance with contents of the input traffic stream and the conditions specified by the sequence of state definitions.

Abstract: Upon receiving malware detection rules that are to be identified with respect to an input traffic stream, a rule database that requires less storage capacity than the malware detection rules is generated by substituting tokens for selected symbol strings within the malware detection rules. A compressed traffic stream is generated by substituting the tokens for instances of the selected symbol strings within the input traffic stream, and then compared with the rule database to determine whether the input traffic stream contains one or more symbol sequences that correspond to any of the malware detection rules.

Abstract: Upon receiving malware detection rules that are to be identified with respect to an input traffic stream, a rule database that requires less storage capacity than the malware detection rules is generated by substituting tokens for selected symbol strings within the malware detection rules. A compressed traffic stream is generated by substituting the tokens for instances of the selected symbol strings within the input traffic stream, and then compared with the rule database to determine whether the input traffic stream contains one or more symbol sequences that correspond to any of the malware detection rules.

Abstract: Upon receiving malware detection rules that are to be identified with respect to an input traffic stream, a sequence of state definitions are generated for each of the rules. The state definitions for each rule correspond to respective segments of the rule and specify conditions under which a state machine is to transition between search states corresponding to those segments, at least one of the segments corresponding to multiple characters within the input traffic stream. A state machine transitions between search states corresponding to one or more of the rules in accordance with contents of the input traffic stream and the conditions specified by the sequence of state definitions.

Abstract: Upon receiving malware detection rules that are to be identified with respect to an input traffic stream, a sequence of state definitions are generated for each of the rules. The state definitions for each rule correspond to respective segments of the rule and specify conditions under which a state machine is to transition between search states corresponding to those segments, at least one of the segments corresponding to multiple characters within the input traffic stream. A state machine transitions between search states corresponding to one or more of the rules in accordance with contents of the input traffic stream and the conditions specified by the sequence of state definitions.

Abstract: In a malware detection device, first characters in a network traffic flow are compared with a plurality of entries within a ternary content addressable memory (TCAM), the plurality of entries including a first entry that constitutes a first segment of a malware signature. In response to an output from the first TCAM indicating that the first characters match the first entry, a variable-character expression engine determines whether second characters in the network traffic flow match a first variable-length regular expression, the variable-length regular expression corresponding to a second segment of the malware signature. A comparand value is generated that includes third characters in the network traffic flow and an expression-match value that indicates whether the second characters match the first variable-length regular expression. The TCAM compares the first comparand value with the plurality of entries therein as part of a determination whether the network traffic flow contains the malware signature.

Abstract: Upon receiving malware detection rules that are to be identified with respect to an input traffic stream, a sequence of state definitions are generated for each of the rules. The state definitions for each rule correspond to respective segments of the rule and specify conditions under which a state machine is to transition between search states corresponding to those segments, at least one of the segments corresponding to multiple characters within the input traffic stream. A state machine transitions between search states corresponding to one or more of the rules in accordance with contents of the input traffic stream and the conditions specified by the sequence of state definitions.

Abstract: A job searching and matching system and method is disclosed that gathers job seeker information in the form of job seeker parameters from one or more job seekers, gathers job information in the form of job parameters from prospective employers and/or recruiters, correlates the information with past job seeker behavior, parameters and behavior from other job seekers, and job parameters and, in response to a job seeker's query, provides matching job results based on common parameters between the job seeker and jobs along with suggested alternative jobs based on the co-relationships.

Abstract: Upon receiving malware detection rules that are to be identified with respect to an input traffic stream, a rule database that requires less storage capacity than the malware detection rules is generated by substituting tokens for selected symbol strings within the malware detection rules. A compressed traffic stream is generated by substituting the tokens for instances of the selected symbol strings within the input traffic stream, and then compared with the rule database to determine whether the input traffic stream contains one or more symbol sequences that correspond to any of the malware detection rules.

Abstract: A job searching and matching system and method is disclosed that gathers job seeker information in the form of job seeker parameters from one or more job seekers, gathers job information in the form of job parameters from prospective employers and/or recruiters, correlates the information with past job seeker behavior, parameters and behavior from other job seekers, and job parameters and, in response to a job seeker's query, provides matching job results based on common parameters between the job seeker and jobs along with suggested alternative jobs based on the co-relationships.

Abstract: A CAM device including a CAM array, multiple match resolution (MMR) circuitry, and a priority encoder allows the addresses of multiple matching locations resulting from a first search operation to be generated without losing the match results generated in second search operation initiated prior to detection of the multiple match condition for the first search operation. When the multiple match condition is detected, the MMR circuitry asserts a stall signal that stalls search operations in the CAM array. The asserted stall signal also causes the match results of the first and second search operations to be stored in separate memory elements so that the addresses of all matching locations for the first search operation can be generated without disturbing the match results of the second search operation.

Abstract: A CAM device includes a CAM array that can implement column redundancy in which a defective column segment in a selected block can be functionally replaced by a selected column segment of the same block, and/or by a spare column segment of the same block.