939 results about "Activity detection" patented technology

A sensor is used to detect movement of a mobile device. For example, data received from an accelerometer may be averaged together and compared to a threshold value. If the average of the data is greater than a threshold value, the mobile device may be assumed to be moving. This information may be used to control the frequency of data collection by, for example, a GPS receiver in order to maximize utility and minimize power use.

In one embodiment, eyewear having an activity monitoring capability is disclosed. Activity, such as motion, steps or distance, can be measured by an activity detector. The measured activity can then be used in providing activity-related information to a user of the eyewear. Advantageously, the user of the eyewear is able to easily monitor their degree of activity.

A method of transferring a real-time audio signal transmission, including: registering voice patterns (or other characteristics) of on more users to be used to identify the voices of the users, accepting an audio signal as it is created as a sequence of segments, analyzing each segment of the accepted audio signal to determine if it contains voice activity (314), determining a probability level that the voice activity of the segment is of a registered user (320&322); and selectively transferring the contents, of a segment responsive to the determined probability level (324).

An epileptiform activity patient-specific template creation system permits a user to efficiently develop an optimized set of patient-specific parameters for epileptiform activity detection algorithms. The epileptiform activity patient template creation system is primarily directed for use with an implantable neurostimulator system having EEG storage capability, in conjunction with a computer software program operating within a computer workstation having a processor, disk storage and input/output facilities for storing, processing and displaying patient EEG signals. The implantable neurostimulator is operative to store records of EEG data when neurological events are detected, when it receives external commands to record, or at preset or random times. The computer workstation operates on stored and uploaded records of EEG data to derive the patient-specific templates.

A voice activity detection apparatus (1) comprising: a signal condition analyzing unit (3) which analyses at least one signal parameter of an input signal to detect a signal condition SC of said input signal; at least two voice activity detection units (4-i) comprising different voice detection characteristics, wherein each voice activity detection unit (4-i) performs separately a voice activity detection of said input signal to provide a voice activity detection decision VADD; and a decision combination unit (5) which combines the voice activity detection decisions VADDs provided by said voice activity detection units (4-i) depending on the detected signal condition SC to provide a combined voice activity detection decision cVADD.

The disclosure addresses the detection of anomalous activity. Some embodiments are directed towards consolidating information from multiple data feeds. Exemplary information may relate to: extrusion violations, failed logins, sensitive fields access, sensitive objects access, escalation of privileges, or activity during a specific time frame within a time period, and combinations of one or more of these. In one embodiment, duplicate data may be removed or consolidated.

A device and method for estimating a tonality of a sound signal comprise: calculating a current residual spectrum of the sound signal; detecting peaks in the current residual spectrum; calculating a correlation map between the current residual spectrum and a previous residual spectrum for each detected peak; and calculating a long-term correlation map based on the calculated correlation map, the long-term correlation map being indicative of a tonality in the sound signal.

An integrated circuit may have separate clock control for a number of different functional units. Ancillary to some of the functional units may be an activity detector and clock control unit which monitors input to its functional unit to determine when the functional unit will be inactive. When an activity detector and clock control unit determines that a particular functional unit is or will be inactive, it may disable clocking to its functional unit while the functional unit is inactive. When activity detector and clock control unit determines that activity will resume for its functional unit, it enables clocking to its functional unit. Thus, the activity detector and clock control unit for each such functional unit functions to control clocking to its respective functional unit so that during periods of inactivity, inactive functional units are not clocked to reduce the overall static and/or dynamic power consumption for the integrated device.

An electroencephalogram (EEG) utilizing epileptiform activity detection, warning and recording system adapted for use by non-healthcare professionals or healthcare professionals. The system is simple enough for use by untrained personnel and will be self-contained, not requiring technical setup or point of use maintenance. The system includes a small number of passive or active scalp electrodes for capturing the electrical signals in the subject's brain allowing for detection of epileptiform activity. The system will recognize epileptiform activity and will either transmit signals upon detection of epileptiform activity to either a localized warning device or to a cellular or radio receiving device. The system will note epileptiform activity in a recording component and allow for review of the epileptiform data to allow non-clinical and clinical personnel to verify such activity.

A computerized method is established to detect suspicious and fraudulent activities in a group of subjects by defining and dynamically integrating multidimensional risks, which are based on the characteristics of the subjects, into a mathematical model to produce a set of the most up-to-date representative risk values for each subject based on its activities and background. These multidimensional risk definitions and representative risk values are used to select a subset of multidimensional risk-weighted detection algorithms so that suspicious or fraudulent activities in the group of subjects can be effectively detected with higher resolution and accuracy. A priority sequence, which is based on the set of detection algorithms that detect the subject and the representative risk values of the detected subject, is produced to determine the priority of each detected case during the investigation process. To assist the user to make a more objective decision, any set of multidimensional risks can be used to identify a group of subjects that contain this set of multidimensional risks so that group statistics can be obtained for comparison and other analytical purposes. Furthermore, to fine-tune the system for future detections and analyses, the detection results are used as the feedback to adjust the definitions of the multidimensional risks and their values, the mathematical model, and the multidimensional risk-weighted detection algorithms.

Disclosed are systems and methods which detect unauthorized, impermissible, or otherwise undesired call activity through a silence detection technique employing multiple samples. One embodiment monitors a call in progress to detect an event such as might be indicative of unauthorized call activity. Thereafter, a sampling process may be invoked which analyzes multiple samples of the call to make a determination as to whether or not a particular unauthorized call activity is being attempted. A party to the call may be muted to thereby prevent that party from thwarting the unauthorized call activity determination. Embodiments implement techniques for defeating parties to calls learning the unauthorized call activity detection system in order to avoid detection, such as by implementing a plurality of detection techniques.

A voice activity detector to analyze a short-term averaged energy (STAE), a long-term averaged energy (LTAE), and a peak-to-mean likelihood ratio (PMLR) in order to determine whether a current audio frame being transmitted represents voice or silence. This is accomplished by determining whether a sum of the STAE and a factor is greater than the LTAE. If not, the current audio frame represents silence. If so, a second set of determinations is performed. Herein, a determination is made as to whether the difference between the LTAE and the STAE is less than a predetermined threshold. If so, the current audio frame represents voice. Otherwise, the PMLR is determined and compared to a selected threshold. If the PMLR is greater than the selected threshold, the current audio frame represents a voice signal. Otherwise, it represents silence.

A method and a system for monitoring network activities associated with a computer connected to a network are provided. The method may include detecting a bot activity associated with the computer; attributing a bot status to the computer, based on a bot activity type associated with the bot activity, prior detections of bot activities, and considering time stamps. The method may also include updating the bot status attributed to the computer, based upon detection of subsequent bot activities associated with the computer, the bot activity types associated with the subsequent bot activities, and one or more other criteria. In one example embodiment, the network activities may include network transmissions and behavioral patterns. According to example embodiments, the system may include a network monitor, a bot activity detection module, a bot status module, and a bot status update module.

Wireless and other external connectivity technology is used in various ways to enhance or improve upon existing radar detector and police activity detection systems. External memory interfaces, such as SD cards or USB, provide external storage. Wireless interfaces such as Bluetooth, Zigbee, 802.11, and wireless personal area network communication protocols, allow a detector processor to interact wirelessly with external devices, such as a Bluetooth headset, a cellular network device providing a server connection, or toggle buttons used to indicate the presence of police activity at a current position. Further, radar detectors are upgraded to provide GPS capabilities, using the existing power/data connector of the radar detector.

A voice activity detector (100) filters (204) out noise energy and then computes a high-frequency (2400 Hz to 4000 Hz) versus low-frequency (100 Hz to 2400 Hz) signal energy ratio (224), total voiceband (100 Hz to 4000 Hz) signal energy (214), and signal periodicity (208) on successive frames of signal samples. Signal periodicity is determined by estimating the pitch period (206) of the signal, determining a gain value of the signal over the pitch period as a function of the estimated pitch period, and estimating a periodicity of the signal over the pitch period as a function of the estimated pitch period and the gain value. Voice is detected (230–232) in a segment if either (a) the difference between the average high-frequency versus low-frequency signal energy ratio and the present segment's high-frequency versus low-frequency energy ratio either exceeds (310) a high threshold value or is exceeded (312) by a low threshold value, or (b) the average periodicity of the signal is lower (306) than a low threshold value, or (c) the difference between the average total signal energy and the present segment's total energy exceeds (304) a threshold value and the average periodicity of the signal is lower (304) than a high threshold value, or (d) the average total signal energy exceeds (412) a minimum average total signal energy by a threshold value and voice has been detected (410) in the preceding segment.

The invention discloses a method for targeted knockout of a specific gene of a Suqin yellow chicken embryonic stem cell. The method comprises the following steps: firstly, checking out an exon sequence of a gene, cloning the gene, sequencing to obtain a complete exon sequence, designing a CRISPR/Cas9 knockout target site on the sequence as gRNA, and constructing a CRISPR/Cas9 dual-promoter knockout vector; performing SSA activity detection on the constructed Cas9 vector, transfecting a control group with an empty vector, detecting a luciferase signal, and obtaining a result that the more the luciferase activity is increased relative to the control group, the higher the gRNA shearing activity is shown; transfecting the CRISPR/Cas9 vector which is high in SSA activity with ESCs, using flow cytometry to screen a GFP-positive cell, extracting genome DNA, and designing a primer.

An integrated circuit may have separate power control for a number of different functional units. Ancillary to some of the functional units may be an activity detector and power control unit which monitors input to its functional unit to determine when the functional unit will be inactive. When an activity detector and power control unit determines that a particular functional unit is or will be inactive, it may disable power to its functional unit while the functional unit is inactive. When activity detector and power control unit determines that activity will resume for its functional unit, it enables power to its functional unit. Thus, the activity detector and power control unit for each such functional unit functions to control power to its respective functional unit so that during periods of inactivity, inactive functional units are powered down to reduce the overall static and/or dynamic power consumption for the integrated device.