1. A method of leading edge detection, comprising:
receiving a signal;
determining at least one characteristic of the received signal;
determining a threshold based on the at least one characteristic; and
identifying a leading edge associated with the received signal based on the threshold.
2. The method of claim 1, wherein the at least one characteristic is indicative of a peak amplitude associated with the received signal.
3. The method of claim 1, wherein:
the determination of the at least one characteristic comprises identifying a signal mode associated with the received signal; and
the determination of the threshold is based on the identified signal mode.
4. The method of claim 3, wherein the signal mode is indicative of a signal-to-noise ratio associated with the received signal.
5. The method of claim 3, wherein:
the reception of the signal comprises receiving sets of pulses using different gain levels; and
the identification of the signal mode comprises:
determining gain level metrics that indicate how the use of the different gain levels affects the reception of the sets of pulses,
using the gain level metrics to determine a minimum gain level that provides adequate received signal detection, and
selecting the signal mode based on the minimum gain level.
6. The method of claim 3, wherein the identification of the leading edge comprises:
determining a timing estimate of the leading edge; and
performing bias correction based on the signal mode to adjust the timing estimate.
7. The method of claim 1, further comprising:
generating a reconstruction of the received signal; and
identifying a signal lobe associated with a first local maximum of the signal reconstruction,
wherein the identification of the leading edge comprises identifying timing of a leading edge of the signal lobe based on the threshold.
8. The method of claim 7, wherein:
the identification of the leading edge further comprises performing zero insertion on the signal reconstruction to generate a higher resolution signal lobe; and
the identification of the timing comprises determining where a leading edge of the higher resolution signal lobe crosses the threshold.
9. The method of claim 1, wherein the reception of the signal comprises using one-bit quantization to sample received pulses.
10. The method of claim 1, wherein:
the determination of the at least one characteristic is performed by a hardware module;
the hardware module sends an indication of the at least one characteristic to a software module; and
the identification of the leading edge is performed by the software module.
11. The method of claim 1, wherein:
the identification of the leading edge comprises estimating a time of arrival of the leading edge; and
the method further comprises determining a distance between an apparatus that receives the signal and another apparatus based on the estimated time of arrival.
12. An apparatus for leading edge detection, comprising:
a receiver configured to receive a signal;
a characteristic determiner configured to determine at least one characteristic of the received signal;
a threshold determiner configured to determine a threshold based on the at least one characteristic; and
a leading edge identifier configured to identify a leading edge associated with the received signal based on the threshold.
13. The apparatus of claim 12, wherein the at least one characteristic is indicative of a peak amplitude associated with the received signal.
14. The apparatus of claim 12, wherein:
the determination of the at least one characteristic comprises identifying a signal mode associated with the received signal; and
the determination of the threshold is based on the identified signal mode.
15. The apparatus of claim 14, wherein the signal mode is indicative of a signal-to-noise ratio associated with the received signal.
16. The apparatus of claim 14, wherein:
the reception of the signal comprises receiving sets of pulses using different gain levels; and
the identification of the signal mode comprises:
determining gain level metrics that indicate how the use of the different gain levels affects the reception of the sets of pulses,
using the gain level metrics to determine a minimum gain level that provides adequate received signal detection, and
selecting the signal mode based on the minimum gain level.
17. The apparatus of claim 14, wherein the identification of the leading edge comprises:
determining a timing estimate of the leading edge; and
performing bias correction based on the signal mode to adjust the timing estimate.
18. The apparatus of claim 12, further comprising a signal reconstructor configured to generate a reconstruction of the received signal, wherein:
the leading edge identifier is further configured to identify a signal lobe associated with a first local maximum of the signal reconstruction; and
the identification of the leading edge comprises identifying timing of a leading edge of the signal lobe based on the threshold.
19. The apparatus of claim 18, wherein:
the identification of the leading edge further comprises performing zero insertion on the signal reconstruction to generate a higher resolution signal lobe; and
the identification of the timing comprises determining where a leading edge of the higher resolution signal lobe crosses the threshold.
20. The apparatus of claim 12, wherein the reception of the signal comprises using one-bit quantization to sample received pulses.
21. The apparatus of claim 12, wherein:
the determination of the at least one characteristic is performed by a hardware module;
the hardware module sends an indication of the at least one characteristic to a software module; and
the identification of the leading edge is performed by the software module.
22. The apparatus of claim 12, wherein:
the identification of the leading edge comprises estimating a time of arrival of the leading edge; and
the apparatus further comprises a distance determiner configured to determine a distance between the apparatus and another apparatus based on the estimated time of arrival.
23. An apparatus for leading edge detection, comprising:
means for receiving a signal;
means for determining at least one characteristic of the received signal;
means for determining a threshold based on the at least one characteristic; and
means for identifying a leading edge associated with the received signal based on the threshold.
24. The apparatus of claim 23, wherein the at least one characteristic is indicative of a peak amplitude associated with the received signal.
25. The apparatus of claim 23, wherein:
the determination of the at least one characteristic comprises identifying a signal mode associated with the received signal; and
the determination of the threshold is based on the identified signal mode.
26. The apparatus of claim 25, wherein the signal mode is indicative of a signal-to-noise ratio associated with the received signal.
27. The apparatus of claim 25, wherein:
the reception of the signal comprises receiving sets of pulses using different gain levels; and
the identification of the signal mode comprises:
determining gain level metrics that indicate how the use of the different gain levels affects the reception of the sets of pulses,
using the gain level metrics to determine a minimum gain level that provides adequate received signal detection, and
selecting the signal mode based on the minimum gain level.
28. The apparatus of claim 25, wherein the identification of the leading edge comprises:
determining a timing estimate of the leading edge; and
performing bias correction based on the signal mode to adjust the timing estimate.
29. The apparatus of claim 23, further comprising:
means for generating a reconstruction of the received signal; and
means for identifying a signal lobe associated with a first local maximum of the signal reconstruction,
wherein the identification of the leading edge comprises identifying timing of a leading edge of the signal lobe based on the threshold.
30. The apparatus of claim 29, wherein:
the identification of the leading edge further comprises performing zero insertion on the signal reconstruction to generate higher resolution signal lobe; and
the identification of the timing comprises determining where a leading edge of the higher resolution signal lobe crosses the threshold.
31. The apparatus of claim 23, wherein the reception of the signal comprises using one-bit quantization to sample received pulses.
32. The apparatus of claim 23, wherein:
the determination of the at least one characteristic is performed by a hardware module;
the hardware module sends an indication of the at least one characteristic to a software module; and
the identification of the leading edge is performed by the software module.
33. The apparatus of claim 23, wherein:
the identification of the leading edge comprises estimating a time of arrival of the leading edge; and
the apparatus further comprises means for determining a distance between the apparatus and another apparatus based on the estimated time of arrival.
34. A computer-program product for leading edge detection, comprising:
computer-readable medium comprising codes executable to:
receive a signal;
determine at least one characteristic of the received signal;
determine a threshold based on the at least one characteristic; and
identify a leading edge associated with the received signal based on the threshold.
35. A headset, comprising:
a receiver configured to receive a signal;
a characteristic determiner configured to determine at least one characteristic of the received signal;
a threshold determiner configured to determine a threshold based on the at least one characteristic;
a leading edge identifier configured to identify a leading edge associated with the received signal based on the threshold; and
a transducer configured to provide an audio output as a result of the identification of the leading edge.
36. A watch, comprising:
a receiver configured to receive a signal;
a characteristic determiner configured to determine at least one characteristic of the received signal;
a threshold determiner configured to determine a threshold based on the at least one characteristic;
a leading edge identifier configured to identify a leading edge associated with the received signal based on the threshold; and
a user interface configured to provide an indication as a result of the identification of the leading edge.
37. A sensing device, comprising:
a receiver configured to receive a signal;
a characteristic determiner configured to determine at least one characteristic of the received signal;
a threshold determiner configured to determine a threshold based on the at least one characteristic;
a leading edge identifier configured to identify a leading edge associated with the received signal based on the threshold; and
a sensor configured to provide data for transmission as a result of the identification of the leading edge.
The claims below are in addition to those above.
All refrences to claim(s) which appear below refer to the numbering after this setence.
1. A system (100) for detecting, evaluating and preventing cyber-threats, the system (100) comprising:
at least one online-analytical-processing (OLAP) resource (102);
at least one data mining engine (104) coupled to the OLAP resource (102);
at least one reporting resource (106) coupled to the OLAP resource (102); and
at least one processor (108) coupled to the OLAP resource (102) and coupled to at least one data source (110), the processor (108) having at least one extract-transform-load (ETL) module (112) associated therewith, the ETL module (112) configured, upon execution by the at least one processor (108), to:
extract at least one data tuple from the data source (110);
transform the data tuple, wherein transforming the data tuple comprises normalizing the data tuple according to a predetermined policy and removing sensitive information from the at least one data tuple; and
load the transformed data tuple (TDT) to the OLAP resource (102),
wherein the OLAP resource (102) is configured to:
store the loaded TDT,
analyze the loaded TDT with reference to data acquired by the data mining engine (104) and with reference to at least one previously stored TDT according to at least one predetermined algorithm wherein at least one measure of similarity between the loaded TDT and the previously stored TDT is a factor, the predetermined algorithm including a reliability score associated with the data source (110) from which the TDT was extracted,
assign at least one threat score to the data source (110) from which the TDT was extracted, according to the predetermined algorithm, and
upload the threat score to the at least one reporting resource (106); and
wherein the reporting resource (106) is configured to collate and store the threat score for subsequent retrieval.
2. The system (100) of claim 1, wherein the data mining engine (102) is coupled to a cloud.
3. The system (100) of claim 1, wherein the data mining engine (102) is coupled to an external network.
4. The system (100) of claim 1, wherein the ETL module (112) is configured for interoperability with at least one firewall.
5. The system (100) of claim 1, wherein the sensitive information is stored locally to the data source (110).
6. The system (100) of claim 1, where the OLAP resource (102) resides within a distributed network.
7. The system (100) of claim 1, wherein the OLAP resource (102) is hosted by at least one web server.
8. The system (100) of claim 1, wherein the data source 110 is assigned a unique identifier (ID), thereby making the data source 110 anonymous with respect to at least one device which is external to the data source 110.
9. The system (100) of claim 8, wherein the unique ID is randomly assigned by an encryption algorithm.
10. The system (100) of claim 1, wherein the system (100) utilizes a Domain Name System-modeled protocol to manage a hierarchical system of servers hosting the at least one data source (110), thereby enabling the system (100) to operate in real-time or in near real-time.
11. A method (300) of detecting, evaluating and preventing cyber-threats, the method comprising:
extracting (310), by at least one processor (108) running at least one extract-transform-load (ETL) module (112), at least one data tuple from at least one data source (110);
transforming (312) the data tuple, wherein transforming the data tuple comprises normalizing the data tuple according to a predetermined policy and removing sensitive information from the data tuple;
loading (314) the transformed data tuple (TDT) to at least one OLAP resource (102);
storing (316) the loaded TDT;
analyzing (318) the loaded TDT with reference to data acquired by at least one data mining engine (104) and with reference to at least one previously stored TDT, according to at least one predetermined algorithm in which at least one measure of similarity between the loaded TDT and the previously stored TDT is a factor, the predetermined algorithm including a reliability score associated with the data source (110) from which the TDT was extracted;
assigning (320) at least one threat score to the data source (110) from which the TDT was extracted, according to the predetermined algorithm; and
uploading (322) the threat score to at least one reporting resource (106),
wherein the reporting resource (106) is configured to collate and store (324) the threat score for subsequent retrieval.
12. The method (300) of claim 11, wherein the data mining engine (102) is coupled to a cloud.
13. The method (300) of claim 11, wherein the data mining engine (102) is coupled to an external network.
14. The method (300) of claim 11, wherein the ETL module (112) is configured for interoperability with at least one firewall.
15. The method (300) of claim 11, wherein the sensitive information is stored locally to the data source (110).
16. The method (300) of claim 11, where the OLAP resource (102) resides within a distributed network.
17. The method (300) of claim 11, wherein the OLAP resource (102) is hosted by at least one web server.
18. The method (300) of claim 11, wherein the data source (110) is assigned a unique identifier (ID), thereby making the data source (110) anonymous with respect to at least one device which is external to the data source 110.
19. The method (300) of claim 18, wherein the unique ID is randomly assigned by an encryption algorithm.
20. The method (300) of claim 11, wherein the method (300) utilizes a Domain Name System-modeled protocol to manage a hierarchical system of servers hosting the at least one data source (110), thereby enabling the method (300) to operate in real-time or in near real-time.