Background: The aim of this study is to evaluate the efficiency of a recently developed smart digital toothbrush monitoring and training system (DTS) in terms of correct brushing motion and grip axis orientation in an at‐home environment.Methods: Twenty‐one participants (11 test individuals [DTSG] and 10 control individuals [COG]) received instructions on the modified Bass technique (MBT) after their toothbrushing performance was monitored and they received professional tooth cleaning (T0). After 36 hours (T1), without mechanical oral hygiene measures, plaque and gingival indices were recorded, and the brushing technique was reviewed. After randomization, participants individually performed oral hygiene for 6 weeks (T2) with the provided oral hygiene kits. The DTSG group additionally used DTS. During the following 8 weeks (T3), participants used their original brushing devices without any additional interference. Investigators at each visit were masked regarding group identity. Data were statistically evaluated using Mann‐Whitney U, Friedman, Wilcoxon, and paired tests and Pearson correlation.Results: At T0, 27.27% of DTSG participants used the MBT correctly (COG, 50%), increasing to 54.55% (COG, 60%) after professional instruction (T1) and further to 90.91% at T2 (COG, 60%) (P <0.001). Plaque scores were reduced in DTSG (P <0.05). At T3, 80% of the DTSG (COG, 40%) totally adopted the MBT (P <0.05). The plaque scores on buccal surfaces of the DTSG showed an additional slight improvement between T2 and T3, in contrast to a decline on oral surfaces (P <0.001). At T2 and T3, the DTSG brushed >120 seconds (COG, 90% and 50%) (P <0.05).Conclusion: Apparently, the tested DTS effectively improves the brushing technique and leads to a prolonged learning effect, including improved oral hygiene.
Today research interests in underwater (UW) communication and navigation technologies are steadily growing. However, the design of robust UW communication and navigation systems demands a deep knowledge of the transmission medium. Acoustic UW (AUW) communication is widely used due to the good propagation characteristics of sound waves in water compared to electromagnetic waves that are highly attenuated. Besides its advantage -the low attenuation compared to electromagnetic waves -AUW communication suffers from multipath propagation, severe Doppler spread due to the low propagation speed of sound, and shadow zones, to name some of the most challenging effects. Evaluation of new communication devices under realistic conditions in sea trials is expensive and time-consuming. Therefore, a simulator modeling the AUW communication channel accurately is a valuable tool for development and evaluation of AUW communication devices. In this paper an Acoustic Underwater Channel and Network Simulator is proposed that uses ray tracing to model the AUW channel. It uses channel impulse responses (CIRs) generated by theBELLHOP ray tracing model to simulate multipath propagation. These CIRs for static constellations of receiver and transmitter are post-processed to be in agreement with the mobility of transmitters and receivers. Thereby, Doppler spread is introduced into the channel model. An empirical noise model is used to superimpose received signals with noise. Different modulation schemes can be evaluated using this AUW channel model in laboratory before expensive sea trials are conducted. In this paper a frequency hopping and an OFDM implementation are realized besides the channel model. Multiple mobile transmitters and receivers can be considered to simulate UW networks.
A convolutionally coded M -ary frequency shift keying (MFSK) modulation scheme for underwater acoustic communication is introduced. It uses a rate 1/ log 2 M inner convolutional code, whose coded symbols are used as transmission symbols. An interleaver in the frequency domain is applied to improve the average channel clearing time. It thereby achieves almost the same data rate as a comparable uncoded frequency hopped frequency shift keying (FH-FSK) modulation scheme, but obtains the error correcting properties of the convolutional code. Bit and packet error rates are evaluated on the additive white Gaussian noise (AWGN) channel and the so-called Watermark channel model, which is a benchmark for underwater acoustic modulation schemes based on sea trial measurements. The JANUS standard which uses convolutionally coded FH-FSK is used for comparison. On the AWGN channel, the proposed scheme achieves a gain of 4 dB with respect to the required E b /N 0 for a given packet error rate (PER) compared with JANUS. In the Watermark scenarios, the best proposed-scheme achieves a PER < 10 −3 at more than 10 dB lower E b /N 0 than the JANUS implementation.INDEX TERMS Underwater acoustics, underwater communication, frequency shift keying, convolutional codes.
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