This paper introduces a Trimmed-Tapped-Delay-Line-based Time-to-Digital Converter (TTDL-TDC) architecture. Unlike state-of-the-art works, where the propagation time of the TDL is roughly equivalent to the system clock period, the TDL of the proposed architecture can be shortened to use only 63% of the necessary length to propagate a clock cycle without loss of resolution. To achieve this reduction, the clock signal is propagated throughout this Trimmed TDL (TTDL), capturing its position with the rising edge of a latching signal. An encoder based on transition detectors and ones-counters modules is employed to determine the clock’s phase from the captured code in TTDL. The architecture was implemented in a Cyclone V FPGA, nevertheless, the proposed design methodology allows for migrating the design to any other FPGA device. The experimental results showed a resolution (LSB) of 5.98 ps and a overall precision of 7.6 ps. Additionally, the proposed design strategy saves hardware resources, allowing the exploration of multichannel measurement systems in low-end devices.

Traumatic brain injury (TBI), resulting from external forces, is a leading cause of disability and death, often leading to cognitive deficits that affect attention, concentration, speech and language, learning and memory, reasoning, planning, and problem-solving. Given the diverse mechanisms underlying TBI symptoms, it is essential to characterize its neurophysiological and neuropsychological effects. To address this, we employed weighted coherence (WC) analysis in patients performing the Halstead–Reitan categorization task, alongside a control group of eight healthy individuals. Our findings indicate a significant decrease in WC within the theta and delta bands in the temporal regions during cognitive tasks in the TBI group compared to controls. Additionally, we observed a significant increase in WC in the beta and gamma bands in the parietal region during both rest and cognitive tasks in the TBI group, relative to the control group. Furthermore, there was a strong correlation between WC and task performance scores in the temporal regions.

In this article, motor imagery Electro-Encephalographic (EEG) signals for Brain-Computer interfaces (BCI) are processed under a weak signal detection (WSD) paradigm, with the goal to improve the low EEG Signal to Noise Ratio (SNR). Based on our previous results, Stochastic resonance (SR) is proposed, as a WSD method on EEG signals, for the first time. This novel methodology takes advantage of noise transitions on a non-linear, bi-stable, double well system. These transitions are synchronized with brain oscillations embedded in the EEG signal, achieving the desired brain waves enhancement. More over, our methodology modifies the bistable well separation and depth modulation on the Duffing system, having as target point the maximum SNR hence, recovering most of the input waveform shape. Dataset from the international BCI competition IV was used in this research, with an estimated SNR of −24dB. After stochastic resonance processing, the resulting signal enhancement on the desired μ − β band was observed, with an output SNR of 1.31dB during motor imagery and 1.41dB on resting states. In order to demonstrate the signal processing methodology applicability, a single EEG trial is reported from 17 channels processed. Also, for comparative purposes, dyadic wavelet transform (DWT) was applied to the same EEG signal. The results exhibit comparable, or even a better, signal enhancement with SR methodology than DWT processing.

The Silicon Photomultipliers (SiPMs) are commonly used in the construction of radiation detectors such as those used in high energy experiments and its applications where an excellent time resolution is required for triggering. In most of this cases, the trigger systems electric charge information is discarded due to limitations in data acquisition. In this work we propose a method, using a simple radiation detector based on an organic plastic scintillator 2×2×0.3 cm³ size, to estimate the electric charge obtained from the acquisition of the fast output signal of a SensL SiPM model C-60035-4P-EVB. Our results suggest a linear relation between the reconstructed electric charge from the fast output of the SiPM used with respect to the one reconstructed with its standard signal output. Using our electric charge reconstruction method, we compared the sensitivity of two plastic scintillators, BC404 and BC422Q, under the presence of ⁹⁰Sr, ¹³⁷Cs, ⁶⁰Co and ²²Na radiation sources.

Cryptographic algorithms have played an important role in information security for protecting privacy. The literature provides evidence that many types of chaotic cryptosystems have been proposed. These chaotic systems encode information to obviate its orbital instability and ergodicity. In this work, a pseudo-random cryptographic generator algorithm with a symmetric key, based on chaotic functions, is proposed. Moreover, the algorithm exploits dynamic simplicity and synchronization to generate encryption sub-keys using unpredictable seeds, extracted from a chaotic zone, in order to increase their level of randomness. Also, it is applied to a simulated electrical energy consumption signal and implemented on a prototype, using low hardware resources, to measure physical variables; hence, the unpredictability degree was statistically analyzed using the resulting cryptogram. It is shown that the pseudo-random sequences produced by the cryptographic key generator have acceptable properties with respect to randomness, which are validated in this paper using National Institute of Standards and Technology (NIST) statistical tests. To complement the evaluation of the encrypted data, the Lena image is coded and its metrics are compared with those reported in the literature, yielding some useful results.