Abstract: Disclosed are physical unclonable functions (“PUFs”) that provide both a hardware timestamp and an encryption key. The timestamp is more accurate than traditional timestamps generated by software calls to the computing device's operating system, while the encryption key can be used for, among other things, securing time-synchronization packets sent across a network. By combining timestamp generation with encryption key generation, the PUFs provide enhanced security while being cheaper to build and to operate than the specialized cryptographic hardware that they replace.

Abstract: Disclosed are physical unclonable functions (“PUFs”) that provide both a hardware timestamp and an encryption key. The timestamp is more accurate than traditional timestamps generated by software calls to the computing device's operating system, while the encryption key can be used for, among other things, securing time-synchronization packets sent across a network. By combining timestamp generation with encryption key generation, the PUFs provide enhanced security while being cheaper to build and to operate than the specialized cryptographic hardware that they replace.

Abstract: A WBAN system for real-time telemonitoring health of a subject, involving a wearable biosignal sensors, each sensor of the sensors configured to measure a plurality of biosignals, ultra-low-power radios correspondingly coupled with the sensors; and a processor operable with at least one of the sensors and the radios, each radio of the radios configured to receive the biosignals from each corresponding sensor and to transmit the biosignals to a processor via a WBAN, and the processor configured to: receive the biosignals from the radios, process the biosignals via a set of executable instructions storable in relation to a nontransitory memory device, the set of executable instructions comprising an instruction for synchronizing time of the biosignals by using the broadcasted R peak reference, whereby real-time health data is providable, and transmit the real-time health data to a healthcare provider.

Abstract: A method for estimating the position of a mobile device. Initializing an Extended Kalman Filter. Moving mobile device from a first unknown position (xt1, yt1) at a first time t1 to a second unknown position (xt2, yt2) at a second time t2. Measuring distance traveled d and angle traveled ? between the first unknown position and the second unknown position. Calculating a first possible position (x1, y1) at the first time and a second possible position (x2, y2) the second time. Using the Extended Kalman Filter, a predicted first unknown position ({circumflex over (x)}, ?) is calculated at the first time. A first final value is calculated according to ?{square root over (({circumflex over (x)}?x1)2+(??y1)2)}. A second final value is calculated according to ?{square root over (({circumflex over (x)}?x2)2+(??y2)2)}. If the first final value is less than or equal to the second final value, the first possible position (x1, y1) is output. Otherwise, the second possible position (x2, y2) is output.

Abstract: A method for predicting a GPS pseudorange error includes receiving a first plurality of pseudorange errors at a plurality of different times for a plurality of global positioning system (GPS) satellites. The method also includes creating, using a processor, a first matrix. Each element of the first matrix is determined using a portion of the first plurality of pseudorange errors. The method yet further includes creating, using the processor, a second matrix that includes at least a portion of the first plurality of pseudorange errors. A size of the second matrix is determined by comparing each element of the first matrix to a predetermined threshold. The method still further includes predicting, using the processor, a second plurality of pseudorange errors for the plurality of GPS satellites using the first plurality of pseudorange errors and the second matrix.

Abstract: A system and method involve receiving a plurality of samples of at least one orthogonal frequency division multiplex (OFDM) signal, the samples containing at least one complete OFDM symbol including data samples and a cyclic prefix, using an initial symbol timing offset (STO) estimate to initialize an N×1 vector of occupation probabilities ?, where Nis the number of sub-carriers of the OFDM signal, and, for each subsequent set m of OFDM samples received containing at least one complete OFDM symbol, determining a STO estimate from a set of candidate STO estimates, updating the vector of occupation probabilities ?, and determining a final STO estimate. The final STO estimate is used to determine a carrier frequency offset and may be determined using the STO estimate with the highest occupation probability.

Abstract: A system and method involve receiving a plurality of samples of at least one orthogonal frequency division multiplex (OFDM) signal, the samples containing at least one complete OFDM symbol including data samples and a cyclic prefix comprising inter-symbol interference (ISI) samples and one or more ISI-free samples, and determining a symbol time offset estimate ? that minimizes the squared difference between the ISI-free samples and their corresponding data samples and satisfies a correlation based boundary condition.

Abstract: A system and method involve subtracting a positive bias from a symbol timing offset estimate determined by an estimator in an orthogonal frequency division multiplexing (OFDM) receiver. The bias may be determined based upon a channel order and/or the length of a cyclic prefix of a received OFDM symbol. If based upon the length of the cyclic prefix, the bias may be less than or equal to half the length of the cyclic prefix. The estimator may be a blind estimator, a coarse estimator, or a blind coarse estimator. The OFDM receiver may have N cp 2 + 1 parallel channels, where Ncp is the number of samples of a cyclic prefix of the received OFDM symbol, where the positive bias is different for each channel.

Abstract: A method involves predicting a GPS pseudorange error according to the equation (??i)k=?j=1n?i,j(??j)k-1, where (??i)k is a predicted pseudorange error from GPS satellite i at time k, (??j)k-1 is a pseudorange error of a signal measured from GPS satellite j at time k?1, and ?i,j is a parameter relating the contribution of (??j)k-1 to (??i)k. Parameter ?i,j is determined by collecting N pseudorange errors for n GPS satellites, forming an n×n matrix C, where element (i,j) is given by Cij=(ATA)?1AT{right arrow over (b)}, where A is an (N?1)×1 vector of GPS satellite j's N?1 pseudorange errors and b is an (N?1)×1 vector of GPS satellite i's N pseudorange errors, and forming an (N?1)×f matrix D, where f is the number of pseudorange errors that demonstrate a correlation with GPS satellite i's pseudorange error, where ?i,j=(DTD)?1DT{right arrow over (b)}.

Abstract: A system and method involve receiving a plurality of samples of an orthogonal frequency division multiplex (OFDM) signal containing a complete OFDM symbol. The OFDM symbol includes data samples and a cyclic prefix comprising inter-symbol interference (ISI) samples and ISI-free samples. The ISI samples are used to limit the search region of possible symbol time offset (STO) estimates made using the ISI-free samples. This may involve determining a first cost function using the ISI-free samples and using data samples that correspond to the ISI-free samples, determining a second cost function using the ISI samples and using data samples that correspond to the ISI samples, using regression coefficients to determine a set of STOs yielding the smallest second cost function, and determining a joint estimate of STO and carrier frequency offset (CFO) by finding STO and CFO values within the set that result in minimization of the first cost function.