SYSTEM AND METHOD FOR EVALUATING ACCURACY OF OPERATION OF PLURALITY OF SENSORS OF CLIMATE CONTROL UNIT IN PREMISES

A system and method for evaluating accuracy of operation of sensors of a climate control unit in a premises are disclosed. The method includes receiving, from a plurality of sensors, output signals indicative of a climate within the premises. The method further includes determining one or more climate parameters within the premises based on the received output signals. The method further includes receiving one or more historical climate parameters. The method further includes generating a virtual model configured to generate one or more virtual climate parameters. The method further includes determining, based on comparison of the climate parameters and the virtual climate parameters, a range of variation for the climate parameters. The method further includes determining an accuracy of operation of the plurality of sensors based on comparison of variation between the climate parameters and the virtual climate parameters with the determined range of variation for the climate parameters.

CROSS REFERENCE TO A RELATED APPLICATION

The application claims the benefit of EP patent application Ser. No. 23/382,851.6 filed Aug. 16, 2023, the contents of which are hereby incorporated in their entirety.

TECHNICAL FIELD

This invention relates to evaluating operation of climate control unit in a premises, and more particularly, to evaluating accuracy of operation of one or more sensors monitoring a climate within the premises.

BACKGROUND

Climate within a premises may refer to any or a combination of comfort parameters for persons within the premises, such as, without limitations, temperature, relative humidity, levels of carbon dioxide, levels of particulate matter, etc. A goal of a competent climate control unit may be to operate various climate control apparatuses within the premises to provide optimal and/or preferred climate conditions to persons within the premises. Generally, the climate control unit relies on information relayed through sensors to determine a current condition of climate at various locations within the premises. Based on deviation of current climate conditions from the preferred climate conditions, the climate control unit may operate the different climate control apparatuses to rectify or minimise the deviation. As a result, for the optimal functioning of the climate control unit, it is important that the sensors provide accurate information. Due to the vast number of sensors generally deployed in premises, particularly, large premises, it may be cumbersome and inefficient to individually check functioning of each of the sensors. However, a malfunction in even a single sensor may lead to inefficient or errant operation of the climate control unit, leading to increased costs of operation of the climate control unit.

SUMMARY

Disclosed herein is a method for evaluating accuracy of operation of sensors of a climate control unit in a premises. The method includes receiving, by a computing device, from a plurality of sensors communicably coupled to it, output signals indicative of a climate within the premises. The method further includes determining, by the computing device, one or more climate parameters within the premises based on the received output signals. The method further includes receiving, by the computing device, from a database communicably coupled to it, one or more historical climate parameters within the premises. The method further includes generating, by the computing device, through a learning engine communicably coupled to it, based on the historical climate parameters, a virtual model of the plurality of sensors. The virtual model is configured to generate one or more virtual climate parameters pertaining to the climate within the premises. The method further includes determining, by the computing device, through the learning engine, based on a comparison of the climate parameters and the virtual climate parameters, a range of variation for the climate parameters. The method further includes determining, by the computing device, an accuracy of operation of a sensor of the plurality of sensors based on comparison of a variation between the climate parameters determined from the output signals from the sensor and the virtual climate parameters, with the determined range of variation for the climate parameters.

In one or more embodiments, the method further includes determining, by the computing device, that the sensor of the plurality of sensors is accurate when the variation between the climate parameters determined from the output signals from the sensor and the virtual climate parameters lies within the determined range of variation for the climate parameters.

In one or more embodiments, the method further includes determining, by the computing device, that the sensor of the plurality of sensors is faulty when the variation between the climate parameters determined from the output signals from the sensor and the virtual climate parameters deviates from the determined range of variation for the climate parameters.

In one or more embodiments, the method further includes indicating, by the computing device, through an indication unit communicably coupled to it, the accuracy of operation of the plurality of sensors.

In one or more embodiments, to determine the range of variation for the climate parameters, the method further includes determining, by the computing device, through the learning engine, based on comparison of the climate parameters determined from the output signals from the plurality of sensors with the virtual climate parameters, a corresponding plurality of deviations for the climate parameters. The method further includes determining, by the computing device, through the learning engine, a mean value and standard deviation for the plurality of deviations. The method further includes determining, by the computing device, through the learning engine, the range of variation for the climate parameters by as being between an upper limit and a lower limit. The upper and lower limits are determined as a difference of a function of the standard deviation from the mean value.

In one or more embodiments, the one or more historical climate parameters includes first and second parts. The first part is different from the second part. The virtual model is generated based on the first part. The range of variation for the climate parameters is determined based on the second part.

In one or more embodiments, the one or more historical climate parameters includes any or a combination of historical data from the plurality of sensors, and simulated data.

In one or more embodiments, the learning engine includes a wavelet neural network (WNN).

In one or more embodiments, the learning engine is configured to determine the range of variation for the climate parameters by using an extended Kalman filter (EKF).

In one or more embodiments, the plurality of sensors includes air quality sensors. The climate parameters include any one or a combination of relative humidity, temperature, level of carbon dioxide, and level of particulate matter.

Further disclosed herein is a system for evaluating accuracy of operation of sensors of a climate control unit in a premises. The system includes a plurality of sensors disposed at different locations in the premises, and configured to detect climate parameters relating to climate within the premises. The system further includes a computing device communicably coupled to the plurality of sensors. The computing device includes a processor and a memory. The memory stores instructions executable by the processor. The computing device is configured to receive, from the plurality of sensors, output signals indicative of a climate within the premises. The computing device is further configured to determine one or more climate parameters within the premises based on the received output signals. The computing device is further configured to receive, from a database communicably coupled to the computing device, one or more historical climate parameters within the premises. The computing device is further configured to generate, through a learning engine communicably coupled to the computing device, based on the historical climate parameters, a virtual model of the plurality of sensors. The virtual model is configured to generate one or more virtual climate parameters pertaining to the climate within the premises. The computing device is further configured to determine, through the learning engine, based on a comparison of the climate parameters and the virtual climate parameters, a range of variation for the climate parameters. The computing device is further configured to determine an accuracy of operation of a sensor of the plurality of sensors based on comparison of a variation between the climate parameters determined from the output signals from the sensor and the virtual climate parameters, with the determined range of variation for the climate parameters.

In one or more embodiments, the computing device is configured to determine that the sensor of the plurality of sensors is accurate when the variation between the climate parameters determined from the output signals from the sensor and the virtual climate parameters lies within the determined range of variation for the climate parameters.

In one or more embodiments, the computing device is configured to determine that the sensor of the plurality of sensors is faulty when the variation between the climate parameters determined from the output signals from the sensor and the virtual climate parameters deviates from the determined range of variation for the climate parameters.

In one or more embodiments, the computing device is further configured to indicate, through an indication unit communicably coupled to the computing device, the accuracy of operation of the plurality of sensors.

In one or more embodiments, to determine the range of variation for the climate parameters, the learning engine is configured to determine, based on comparison of the climate parameters determined from the output signals from the plurality of sensors with the virtual climate parameters, a corresponding plurality of deviations for the climate parameters. The learning engine is further configured to determine a mean value and standard deviation for the plurality of deviations. The learning engine is further configured to determine the range of variation for the climate parameters by as being between an upper limit and a lower limit. The upper and lower limits are determined as a difference of a function of the standard deviation from the mean value.

In one or more embodiments, the one or more historical climate parameters includes first and second parts. The first part is different from the second part. The virtual model is generated based on the first part. The range of variation for the climate parameters is determined based on the second part.

In one or more embodiments, the one or more historical climate parameters includes any or a combination of historical data from the plurality of sensors, and simulated data.

In one or more embodiments, the learning engine includes a wavelet neural network (WNN).

In one or more embodiments, the learning engine is configured to determine the range of variation for the climate parameters by using an extended Kalman filter (EKF).

In one or more embodiments, the plurality of sensors includes air quality sensors. The climate parameters include any one or a combination of relative humidity, temperature, level of carbon dioxide, and level of particulate matter.

The foregoing summary is illustrative only and is not intended to be in any way limiting. In addition to the illustrative aspects, embodiments, and features described above, further aspects, embodiments, features, and techniques of the invention will become more apparent from the following description taken in conjunction with the drawings.

DETAILED DESCRIPTION

Referring toFIG.1, a schematic representation of a system100for evaluating accuracy of operation of sensors of a climate control unit120in a premises150is shown. In some embodiments, the premises150may refer to any structure, such as a building, an outdoor venue, etc. In the illustrated embodiment, the premises150is a building. The building may be any, such as an office building, a residential building, public spaces, such as restaurants, malls, theaters, etc. A climate within the premises150may refer to a state of air within the premises. Specifically, the climate may refer to regulation and/or control of different parameters of the air, such as relative humidity, temperature, carbon dioxide content, particulate matter content, etc. within the premises. The climate control unit120is generally adapted to regulate and/or control the different parameters of the climate within the premises. The climate control unit120may be configured to operate various devices and/or apparatuses configured within the premises150that are configured to control a climate within the premises150. The devices and/or apparatuses may include, without limitations, HVAC systems, thermal regulation systems, thermal control systems, illumination systems, fans and/or blowers, etc.

The system100may further include a plurality of sensors102disposed at various locations within the premises150. The illustrated embodiment ofFIG.1shows sensors102-1,102-2. . .102-N. The sensors102-1,102-2. . .102-N may be collectively interchangeably referred to as “the sensors102”. The sensors102may be configured to detect a climate parameters relating to climate within the premises150. As the climate within the premises150may be dependent on optimal operation of the climate control unit120, the sensors102may thus, indicate a state of operation of the climate control unit120within the premises150. In one or more embodiments, the sensors102includes air quality sensors. The sensors102may be configured to measure or detect climate parameters pertaining to the climate within the premises150. In one or more embodiments, the climate parameters may include any one or a combination of relative humidity, temperature, level of carbon dioxide, and level of particulate matter. In some embodiments, the sensors102may be communicably coupled to one another via a communication network104.

In some embodiments, the communication network104may be a wireless communication network. The wireless communication network may be any wireless communication network capable of transferring data between entities of that network such as, without limitations, a carrier network including circuit switched network, a public switched network, a Content Delivery Network (CDN) network, a Long-Term Evolution (LTE) network, a Global System for Mobile Communications (GSM) network and a Universal Mobile Telecommunications System (UMTS) network, an Internet, intranets, local area networks, wide area networks, mobile communication networks, Bluetooth low energy (BLE) networks, and combinations thereof. Through the communication network104, the sensors102may be configured to transmit signals to each other or to an external device.

In some embodiments, the communication network104may be a hardwired communication network. The hardwired communication network may be an optic cable, or a metallic cable provided in the structure of the premises150in which the sensors102are disposed.

The system100further includes a server110. The sensors102may be communicably coupled to the server110. In some embodiments, the server110may be a remote server. In some embodiments, the server110may be a cloud-based server. The server110may further be communicably coupled to a database112through the communication network104. The database112may be configured within the server110or may be a cloud-based storage device.

The server110may be configured with a computing device200. The computing device200may be configured for evaluating the state of operation of the climate control unit120in a premises150. The computing device200may be implemented by way of a single device or a combination of multiple devices that may be communicably coupled or networked together. The computing device200may be implemented in hardware or a suitable combination of hardware and software. The computing device200may be a hardware device including a processor executing machine-readable program instructions. The “hardware” may include a combination of discrete components, an integrated circuit, an application-specific integrated circuit, a field programmable gate array, a digital signal processor, or other suitable hardware. The “software” may include one or more objects, agents, threads, lines of code, subroutines, separate software applications, two or more lines of code or other suitable software structures operating in one or more software applications or on one or more processors. The processor may include, for example, without limitations, microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuits, any devices that manipulate data or signals based on operational instructions, and the like. Among other capabilities, the processor may fetch and execute computer-readable instructions in the memory operationally coupled with the computing device200for performing tasks such as data processing, input/output processing, feature extraction, and/or any other functions. Any reference to a task in the present disclosure may refer to an operation being or that may be performed on data.

The system100further includes an indication unit130configured to indicate information to related to operation of the sensors102and the climate control unit120within the premises150. The indication unit130may include units, such as, without limitations, a display unit, an audio unit, a notification unit, an input unit, an output unit, electronic devices, and the like; however, the same are not shown in theFIG.1, for the purpose of clarity. Also, inFIG.1, only few units are shown; however, the computing device200may include multiple such units or the computing device200may include any such numbers of the units, obvious to a person skilled in the art or as required to implement the features of the present invention.

Conventionally, climate control units (e.g., the climate control unit120) rely on information relayed through sensors (e.g., the sensors102) to determine a current condition of climate at various locations within a premises. Based on deviation of current climate conditions from the preferred climate conditions, the climate control unit may operate the different climate control apparatuses to rectify or minimise the deviation. As a result, for the optimal functioning of the climate control unit, it is important that the sensors provide accurate information. Due to the vast number of sensors generally deployed in premises, particularly, large premises, it may be cumbersome and inefficient to individually check functioning of each of the sensors. However, a malfunction in even a single sensor may lead to inefficient or errant operation of the climate control unit, leading to increased costs of operation of the climate control unit.

Thus, there is a requirement for a means to determine operation of sensors accurately, and identify and notify at the earliest, any instance of a sensor malfunctioning or being faulty.

Referring toFIG.2, a detailed schematic block diagram of the computing device200, is shown. The computing device200includes a processor202, and a memory204communicably coupled to the processor202. The memory204may store instructions executable by the processor202to implement the computing device200. The computing device200further includes an interface206. The interface206may include a variety of interfaces, for example, interfaces for data input and output devices, referred to as I/O devices, storage devices, and the like. The interface206may also provide a communication pathway for one or more components of the computing device200.

Referring now toFIGS.1and2, the computing device200is communicably coupled to the database112(shown inFIG.1). The database112may be configured to store data generated during execution of instructions by the processor202in order to implement the computing device200. The database112may further be configured to store additional data required for implementing the computing device200.

The computing device200includes a processing engine210. The processing engine210may be implemented as a combination of hardware and programming (for example, programmable instructions) to implement one or more functionalities of the processing engine210. In some examples, the processing engine210may be implemented by electronic circuitry.

The processing engine210may include a sensor data engine212, a learning engine214, an evaluation engine220, an indication engine222, and other engine(s)226. The learning engine214may further include a training engine216, and a variation determination engine218. The other engine(s)224may include engines configured to perform one or more functions ancillary functions associated with the processing engine210.

The sensor data engine212is configured to receive, from the sensors102, output signals indicative of the climate within the premises150. The output signals may include climate parameters pertaining to the climate within the premises150. In one or more embodiments, the climate parameters may include any one or a combination of relative humidity, temperature, level of carbon dioxide, and level of particulate matter. In one or more embodiments, the sensor data engine212may be further configured to store the received output signals in the database112. In one or more embodiments, the database112may have a record of a plurality of such received output signals over a plurality of time periods. Such a record may be collated to form a first dataset containing the plurality of output signals.

The learning engine214is configured to determine a reference range for the climate parameters. The climate parameters determined from the received output signals from the sensors102may be compared with the reference range to ascertain if the sensors102are functioning optimally. Functioning of the sensors102may be indicative of an overall state of operation of the climate control unit120.

The training engine216is configured to receive, from the database112, one or more historical climate parameters relating to climate within the premises. In one or more embodiments, the one or more historical climate parameters may include the stored climate parameters stored in the first dataset. The stored climate parameters may be determined from a plurality of output signals over a plurality of time periods. However, in some other embodiments, at least a portion of the historical climate parameters may be simulated data.

In one or more embodiments, the historical climate parameters includes first and second parts. The first and second parts may each include climate parameters determined from a plurality of output signals over a plurality of time periods; however, the first and second parts are different from one another. In one or more embodiments, the first part may be used to train the learning engine214, and the second part may be used to determine the reference range.

The training engine216is configured to generate, based on the historical climate parameters, a virtual model of the sensors102. The virtual model may be adapted to operate in a manner that is analogous or similar to how the sensors102operate. The virtual model may be configured to generate one or more virtual climate parameters indicative of the climate within the premises150.

In one or more embodiments, the learning engine214may include a neural network. Specifically, the learning engine214may include a wavelet neural network (WNN). The WNN may be more specifically suited to model the sensors102as the WNN has a greater capacity to be modelled based on non-linearities inherent in the sensors102. Furthermore, the training engine216may be trained using an extended Kalman filter (EKF) technique to enhance the accuracy of the virtual model.

The WNN includes an input layer, one or more hidden layers, and an output layer. In some embodiments, the WNN may include two hidden layers. A combination of Mexican hat wavelet function and logistic sigmoid may be utilized.

The variation determination engine218is configured to determine, based on a comparison of the climate parameters, and the virtual climate parameters, a range of variation for the climate parameters. In one or more embodiments, the variation determination engine218is configured to determine, based on comparison of the climate parameters determined from the output signals from the plurality of sensors102with the virtual climate parameters, a corresponding plurality of deviations for the climate parameters. The variation determination engine218is further configured to determine a mean value and standard deviation for the plurality of deviations. The variation determination engine218is further configured to determine the range of variation for the climate parameters by as being between an upper limit and a lower limit. In one or more embodiments, the upper and lower limits are determined as a difference of a function of the standard deviation from the mean value. In an example, the upper and lower limits may be one standard deviation above and below the mean value, respectively. The accuracy of operations of any sensor102may be determined by determining a variation between the climate parameters determined from the output signal received from the respective sensor102and the corresponding virtual climate parameters. The upper and lower limits may signify the range of values for the variation between the climate parameters and the virtual climate parameters indicative of optimal functioning of the sensors102. In case, for any one or more sensors102, the variation deviates from the range of variation, the respective one or more sensors102may be faulty.

The evaluation engine220is configured to determine an accuracy of operation of a sensor (e.g., the sensor102-1) of the sensors102based on comparison of the variation between the climate parameters determined from the output signals from the sensor (102-1) and the virtual climate parameters, with the determined range of variation for the climate parameters.

In one or more embodiments, the evaluation engine220is configured to determine that the sensor is accurate when the variation between the climate parameters determined from the output signals from the sensor and the virtual climate parameters lies within the determined range of variation for the climate parameters.

In one or more embodiments, the evaluation engine220is configured to determine that the sensor is faulty when the variation between the climate parameters determined from the output signals from the sensor and the virtual climate parameters deviates from the determined range of variation for the climate parameters.

Further, in one or more embodiments, the evaluation engine220is configured to generate an alert in the event that any one or more of the sensors102is determined to be faulty. The alert may include an identity of the any one or more sensors102, a value of the climate parameters indicated by the respective one or more sensors102, and a deviation of the variation from the range of variation for the climate parameters.

The indication engine222is configured to indicate, through the indication unit130, the accuracy of operation of the sensors102. The indication engine222is further configured to indicate, through the indication unit130, any generated alert pertaining to faulty states of any one or more sensors102.

Referring toFIG.3, a schematic flow diagram for a method300for evaluating accuracy of operation of sensors of the climate control unit120in the premises150is shown. Referring now toFIGS.1to3, at step302, the method300includes receiving, by the computing device200, from the plurality of sensors102communicably coupled to it, output signals indicative of the climate within the premises150. At step304, the method300includes determining one or more climate parameters within the premises150based on the received output signals. At step306, the method300further includes receiving, by the computing device200, from the database112communicably coupled to it, one or more historical climate parameters within the premises150. At step308, the method300further includes generating, by the computing device200, through the learning engine214communicably coupled to it, based on the historical climate parameters, the virtual model of the plurality of sensors102. The virtual model is configured to generate one or more virtual climate parameters pertaining to the climate within the premises150. At step310, the method300further includes determining, by the computing device200, through the learning engine214, based on a comparison of the climate parameters and the virtual climate parameters, a range of variation for the climate parameters. At step312, the method300further includes determining, by the computing device200, an accuracy of operation of any the sensor of the plurality of sensors102based on comparison of the variation between the climate parameters determined from the output signals from the sensor and the virtual climate parameters, with the determined range of variation for the climate parameters.

In one or more embodiments, the method300further includes determining, by the computing device200, that the sensor of the plurality of sensors102is accurate when the variation between the climate parameters determined from the output signals from the sensor and the virtual climate parameters lies within the determined range of variation for the climate parameters.

In one or more embodiments, the method300further includes determining, by the computing device200, that the sensor of the plurality of sensors102is faulty when the variation between the climate parameters determined from the output signals from the sensor and the virtual climate parameters deviates from the determined range of variation for the climate parameters.

In one or more embodiments, the method300further includes indicating, by the computing device200, through an indication unit130communicably coupled to it, the accuracy of operation of the plurality of sensors102.

In one or more embodiments, to determine the range of variation for the climate parameters, the method300further includes determining, by the computing device200, through the learning engine214, based on comparison of the climate parameters determined from the output signals from the plurality of sensors with the virtual climate parameters, a corresponding plurality of deviations for the climate parameters. The method300further includes determining, by the computing device200, through the learning engine214, the mean value and standard deviation for the plurality of deviations. The method300further includes determining, by the computing device200, through the learning engine214, the range of variation for the climate parameters by as being between an upper limit and a lower limit. The upper and lower limits are determined as a difference of the function of the standard deviation from the mean value.

FIG.4is an exemplary an exemplary schematic flow diagram for a process300for evaluating accuracy of operation of sensors102of a climate control unit120in a premises150. Historical sensor data420(which includes the one or more historical climate parameters) may be utilized to develop a virtual model404. The virtual model may be configured to estimate the operation of the sensors102, and generate values for the climate parameters. Further, the virtual model, based on the historical sensor data may further generate control limits406. The control limits may include upper and lower limits. The control limits may be generated based on comparison of the historical sensor data and the sensor data generated by the virtual model. The variation between the two sets of data may be plotted, and a mean and standard deviation may be determined, based on which the upper and lower control limits may be ascertained.

Further, a control block408may be configured to receive sensor data from the virtual model, as well as from physical sensors. The control block may be configured to determine a variation between the sensor data received from the virtual model and the physical sensor. At step410, it may be checked if the variation determined by the control block is within the control limits. If yes, at step412, the physical sensor is deemed to be accurate. If the variation determined by the control block is outside the control limits, the sensor may not be accurate. At step414, corrective action may be applied to the faulty sensor, until the faulty sensor operation is rectified.

FIG.5is an exemplary schematic block diagram of a hardware system used for implementing the computing device200. As shown inFIG.5, a computer system500can include an external storage device510, a bus520, a main memory530, a read only memory540, a mass storage device550, communication port560, and a processor570. A person skilled in the art will appreciate that the computer system may include more than one processor and communication ports. Examples of processor570include, but are not limited to, an Intel® Itanium® or Itanium 2 processor(s), or AMD® Opteron® or Athlon MP® processor(s), Motorola® lines of processors, FortiSOC™ system on chip processors or other future processors. Processor570may include various modules. Communication port560can be any of an RS-232 port for use with a modem-based dialup connection, a 10/100 Ethernet port, a Gigabit or 10 Gigabit port using copper or fibre, a serial port, a parallel port, or other existing or future ports. Communication port560may be chosen depending on a network, such a Local Area Network (LAN), Wide Area Network (WAN), or any network to which computer system connects. Memory530can be Random Access Memory (RAM), or any other dynamic storage device commonly known in the art. Read-only memory540can be any static storage device(s) e.g., but not limited to, a Programmable Read Only Memory (PROM) chips for storing static information e.g., start-up or BIOS instructions for processor570. Mass storage550may be any current or future mass storage solution, which can be used to store information and/or instructions. Exemplary mass storage solutions include, but are not limited to, Parallel Advanced Technology Attachment (PATA) or Serial Advanced Technology Attachment (SATA) hard disk drives or solid-state drives (internal or external, e.g., having Universal Serial Bus (USB) and/or Firewire interfaces), e.g. those available from Seagate (e.g., the Seagate Barracuda7102family) or Hitachi (e.g., the Hitachi Deskstar 7K1000), one or more optical discs, Redundant Array of Independent Disks (RAID) storage, e.g. an array of disks (e.g., SATA arrays), available from various vendors including Dot Hill Systems Corp., LaCie, Nexsan Technologies, Inc. and Enhance Technology, Inc.

Bus520communicatively couples processor(s)570with the other memory, storage, and communication blocks. Bus520can be, e.g., a Peripheral Component Interconnect (PCI)/PCI Extended (PCI-X) bus, Small Computer System Interface (SCSI), USB or the like, for connecting expansion cards, drives and other subsystems as well as other buses, such a front side bus (FSB), which connects processor570to software system.

Optionally, operator and administrative interfaces, e.g., a display, keyboard, and a cursor control device, may also be coupled to bus520to support direct operator interaction with a computer system. Other operator and administrative interfaces can be provided through network connections connected through communication port560. The external storage device510can be any kind of external hard-drives, floppy drives, IOMEGA® Zip Drives, Compact Disc-Read Only Memory (CD-ROM), Compact Disc-Re-Writable (CD-RW), Digital Video Disk-Read Only Memory (DVD-ROM). Components described above are meant only to exemplify various possibilities. In no way should the aforementioned exemplary computer system limit the scope of the present disclosure.

While the invention has been described with reference to exemplary embodiments, it will be understood by those skilled in the art that various changes may be made and equivalents may be substituted for elements thereof without departing from the scope of the invention as defined by the appended claims. Modifications may be made to adopt a particular situation or material to the teachings of the invention without departing from the scope thereof. Therefore, it is intended that the invention not be limited to the particular embodiment disclosed, but that the invention includes all embodiments falling within the scope of the invention as defined by the appended claims.

In interpreting the specification, all terms should be interpreted in the broadest possible manner consistent with the context. In particular, the terms “comprises” and “comprising” should be interpreted as referring to elements, components, or steps in a non-exclusive manner, indicating that the referenced elements, components, or steps may be present, or utilized, or combined with other elements, components, or steps that are not expressly referenced. Where the specification claims refer to at least one of something selected from the group consisting of A, B, C . . . and N, the text should be interpreted as requiring only one element from the group, not A plus N. or B plus N, etc.