Abstract: A device that includes a node engine configured to define a number of child correlithm objects for a string correlithm object. The node engine is further configured to set a starting correlithm object as a first parent correlithm object and set an ending correlithm object as a second parent correlithm object. The node engine is further configured to randomly select a correlithm object less than the standard distance away from the first parent correlithm object, define the selected correlithm object as a child correlithm object, and link the child correlithm objects with the first parent correlithm object. The node engine is further configured to randomly select a correlithm object less than the standard distance away from the second parent correlithm object, define the selected correlithm object as a child correlithm object, and link the child correlithm objects with the second parent correlithm object.

Abstract: A device configured to emulate a unary correlithm object logic function gate comprises a memory and a logic engine. The memory stores a logical operator truth table that includes a plurality of input logical values and a plurality of output logical values. These logical values are represented by correlithm objects. The logic engine receives an input and determines the Hamming distance between the correlithm object of the input and the correlithm objects of the truth table to determine the appropriate output.

Abstract: A device configured to emulate a binary correlithm object logic function gate comprises a memory and a logic engine. The memory stores a logical operator truth table that includes first and second groups of input logical values and a group of output logical values. These logical values are represented by correlithm objects. The logic engine receives first and second inputs and determines the Hamming distance between the correlithm objects of the inputs and the correlithm objects of the truth table to determine the appropriate output.

Abstract: A device configured to emulate a correlithm object system includes a memory that stores a sensor table. The sensor table identifies a plurality of real-world value entries and a plurality of corresponding input correlithm objects. A sensor receives a first input signal associated with a first timestamp, the first input signal representing a first real-world value entry in the sensor table. The sensor identifies a first input correlithm object in the sensor table linked with the first real-world value entry and outputs the first input correlithm object. The memory further stores a sensor output table that identifies the first real-world value entry associated with the first input correlithm object and the first timestamp.

Abstract: A device configured to emulate a string correlithm object velocity detector includes a memory that stores a first string correlithm object comprising a plurality of sub-string correlithm objects. The device further includes a sensor coupled to the memory and configured to determine a time between performing data processing associated with the plurality of sub-string correlithm objects, and represent those times as correlithm objects.

Abstract: A correlithm object processing system uses one or more mobile correlithm object devices to emulate the functionality of one or more of sensors, nodes, and actors. The mobile correlithm object devices may be deployed to different parts of a system or network to perform particular tasks. The mobile correlithm object devices may periodically communicate with one another or with other elements of the correlithm object processing system.

Abstract: A correlithm object processing system includes a reference table that stores a plurality of correlithm objects, a demultiplexer configured to split a particular one of the plurality of correlithm objects into a first portion of the binary string and a second portion of the binary string, and a multiplexer communicatively coupled to the demultiplexer by at least first and second communication channels. The multiplexer receives the first and second portions of the particular correlithm object over the first and second communication channels, respectively, and combines the first and second portions into a received correlithm object. A node communicatively coupled to the multiplexer node determines distances between the received correlithm object and each of the plurality of correlithm objects stored in the reference table, identifies one of the plurality of correlithm objects from the reference table with the shortest distance, and outputs the identified correlithm object.

Abstract: A device configured to emulate a correlithm object system includes a memory that stores a node table. The node table identifies a plurality of source correlithm objects and a corresponding plurality of target correlithm objects. A node receives a first input correlithm object associated with a first timestamp, computes distances between the first input correlithm object and each of the source correlithm objects in the node table, and identifies a first source correlithm object from the node table with the shortest distance. The node identifies a first target correlithm object from the node table linked with the identified first source correlithm object, and outputs the first target correlithm object. The memory stores a node output table that identifies the first target correlithm object associated with the first source correlithm object, the first timestamp, and the computed distance between the first input correlithm object and the first source correlithm object.

Abstract: A device configured to emulate a correlithm object processing system includes a sensors coupled to a node. A first sensor receives a first sample text string comprising a plurality of characters and assigns correlithm objects to corresponding subsets of the plurality of characters of the first sample text string. A second sensor receive a second sample text string comprising a plurality of characters and assigns a correlithm objects to corresponding subsets of the plurality of characters of the second sample text string. A third sensor receives a test text string comprising a plurality of characters and assigns correlithm objects to corresponding subsets of the plurality of characters of the test text string. The node determines which of the first and second sample text string is the closest match to the test text string by determining which is closer to the test text string in n-dimensional space using the correlithm objects.

Abstract: A device configured to emulate a correlithm object processing system includes a memory that stores a node table that identifies a plurality of source correlithm objects and a plurality of corresponding target correlithm objects. The system further includes a node coupled to the memory and configured to receive an input correlithm object, identify a source correlithm object from the node table with the shortest n-dimensional distance to the input correlithm object, and identify a first target correlithm object from the node table linked with the identified source correlithm object. The node further generates a second target correlithm object that is offset in n-dimensional space from the first target correlithm object by the distance between the input correlithm object and the identified source correlithm object. The node outputs the second target correlithm object.

Abstract: A correlithm object processing system includes a reference table that stores a plurality of correlithm objects, and a first node communicatively coupled to a second node by a communication channel. The first node is configured to receive a particular one of the plurality of correlithm objects from the second node over the communication channel. The first node determines distances between the received correlithm object and each of the plurality of correlithm objects stored in the reference table. The first node further identifies one of the plurality of correlithm objects from the reference table with the shortest distance, and outputs the identified correlithm object.

Abstract: A device configured to emulate a correlithm object system includes a memory configured to store a plurality of correlithm objects associated with different levels of string correlithm objects. The device further includes a node and an actor coupled to the memory and configured to receive an input correlithm object representing a task to be performed and output real-world data based on a comparison in n-dimensional space between the input correlithm object and one or more of the different levels of string correlithm objects.

Abstract: A system that includes a first device and a second device. The first device is configured to send correlithm objects having a first bit having a first bit string length and to send a test correlithm object having the first bit string length to the second device. The second device is configured to receive the test correlithm object, to determine a distance between the test correlithm object and a reference correlithm object, and to send the switch command to the first device in response to determining the distance between the test correlithm object and the reference correlithm object exceeds a distance threshold value. The first device is further configured to receive a switch command, and to send correlithm objects having a second bit string length that is greater than the first bit string length to the second device in response to receiving the switch command.

Abstract: A device configured to emulate image mapping in a correlithm object processing system comprises a memory and one or more processors. The memory stores a correlithm object mapping table configured with multiple source image elements and multiple corresponding target correlithm objects. The processors receive an input image element comprising an n-pixel array of binary values and determine n-dimensional distances between the input image element and each of the source image elements. The processors then identify a source image element with the closest determined n-dimensional distance. The processors determine whether a deviation between the input image element and the identified source image element is within a predetermined tolerance. The processors identify a target correlithm object corresponding to the identified source image and determine a perturbation to be applied to the identified target correlithm object based on the determined deviation.

Abstract: A device that includes a sensor engine and a node engine. The sensor engine is configured to receive an input signal representing a data sample and identify a real world value entry in a sensor table based on the input signal. The sensor engine is further configured to fetch an input correlithm object in the sensor table linked with the real world value entry and send the input correlithm object to a node engine. The node engine is configured to determine distances between the input correlithm object and each of the child correlithm objects in a node table in response to receiving the input correlithm object and identify a child correlithm object from the node table with the shortest distance. The node engine is further configured to fetch a parent correlithm object from the node table linked with the identified child correlithm object and output the identified parent correlithm object.

Abstract: A device that includes a model training engine implemented by a processor. The model training engine is configured to obtain a set of data values associated with a feature vector. The model training engine is further configured to generate a set of gradients by dividing separation distances by an average separation distance and to compare each gradient to a gradient threshold value. The model training engine is further configured to identify a boundary in response to determining a gradient exceeds the gradient threshold value, to determine a number of identified boundaries, and to determine a number of clusters based on the number of identified boundaries. The model training engine is further configured to train the machine learning model to associate the determined number of clusters with the feature vector.

Abstract: A device that includes a model training engine implemented by a processor. The model training engine is configured to obtain a set of data values associated with a feature vector. The model training engine is further configured to transform a first data value and a second data value from the set of data value into sub-string correlithm objects. The model training engine is further configured to compute a Hamming distance between the first sub-string correlithm object and the second sub-string correlithm object and to identify a boundary in response to determining that the Hamming distance exceeds a bit difference threshold value. The model training engine is further configured to determine a number of identified boundaries, to determine a number of clusters based on the number of identified boundaries, and to train the machine learning model to associate the determined number of clusters with the feature vector.

Abstract: A device comprising a cluster engine implemented by a processor. The cluster engine is configured to obtain a reference correlithm object and compute a set of Anti-Hamming distances between the reference correlithm object and the set of correlithm objects. The cluster engine is further configured to identify a subset of correlithm objects from the set of correlithm objects that are associated with an Anti-Hamming distance that is greater than a first bit threshold value. The cluster engine is further configured to compute a set of Hamming distances between the reference correlithm object and the subset of correlithm objects and to identify correlithm objects associated with a Hamming distance that exceeds a second bit threshold value. The cluster engine is further configured to remove the identified correlithm objects that are associated with a Hamming distance that exceeds the second bit threshold value and generate the cluster.

Abstract: A device that includes a model training engine implemented by a processor. The model training engine is configured to select a first sub-string correlithm object and a second sub-string correlithm object from a set of sub-string correlithm objects. The model training engine is further configured to compute a Hamming distance between the first sub-string correlithm object and the second sub-string correlithm object and to compare the Hamming distance to a bit difference threshold value. The model training engine is further configured to determine that the Hamming distance is less than the bit difference threshold value and to compute an average of the first sub-string correlithm object and the second sub-string correlithm object in the n-dimensional space in response to the determination. The model training engine is further configured to train the machine learning model to define the average as a centroid for the first cluster.

Abstract: A correlithm object processing system includes a memory that stores a first string correlithm object comprising a first plurality of sub-string correlithm objects, and a second string correlithm object comprising a second plurality of sub-string correlithm objects. A string correlithm object engine communicatively coupled to the memory determines the anti-Hamming distances between each of the sub-string correlithm objects of the first string correlithm object pairwise with each corresponding sub-string correlithm object of the second string correlithm object, and stores the determined anti-Hamming distances in a distance table. The engine identifies a group of neighboring anti-Hamming distances stored in the distance table that are greater than a predetermined number of standard deviations beyond a standard distance and, in response, determines the corresponding sub-string correlithm objects of the first and second string correlithm objects to be a match.