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Acrylate photoresists have gained considerable attention in recent years owing to their high resolution, high sensitivity, and versality. In this work, a series of thermally stable copolymers are synthesized by introducing an isobornyl group, and well characterized using Fourier transform infrared spectroscopy (FT-IR) and nuclear magnetic resonance spectra (1H-NMR). The effects of polymerization conditions on the molecular weight and their further influence on lithography are explored. By analyzing the thermal properties, film-forming capabilities, and the patterning behavior of these copolymers, a direct correlation between lithography performance and polymerization conditions is established via the molecular weight. In addition, the baking temperature of lithography is also optimized by atomic force microscopy (AFM), after which a line resolution of 0.1 μm is observed under the exposure of a 248 nm UV light and electron beam. Notably, our synthesized photoresist displays dual-tone resist characteristics when different developers are applied, and the reaction mechanism of acid-catalyzed hydrolysis is finally proposed by comparing the structural changes before and after exposure.

With the progress of medical science and technology and the healthy changes in eating habits, the proportion of aged population is gradually increasing. Smart-home elderly care has thus attracted a lot of research attention in the recent past, and remains an active issue. Internet of Things (IoT) has been recognized as a key enabler for smart-home elderly care realization. In the literature, a large number of IoT services/applications and platforms have been proposed for health/elderly care. However, research reports showed that their adoption ratio is very low. This is mainly because these packet-switched IoT data communication/networking approaches are too complicated from the perspective of older people. Statistical data reflect that circuit-switched voice telephony is still their most favorable communication mechanism. In this paper, we aim to implement a circuit-switched approach to realizing smart-home elderly care IoT remote control based on our IoT platform, IoTtalk. To the best of our knowledge, our IoTtalk incorporating such capability is the first generic IoT platform in the literature that provides a telecommunication solution for smart-home elderly care. We design and implement an Android App, DTMFTalk, following the application development/execution framework of IoTtalk, to support IoT remote control via circuit-switched Dual Tone MultiFrequency (DTMF) signaling during a phone call conversation. Our real testbed deployment demonstrates that DTMFTalk can constantly and accurately recognize DTMF keys as long as the user holds the desired DTMF keys with enough periods, justifying that DTMFTalk can serve as an effective approach to IoT remote control for smart-home elderly care.

PurposeDue to the continuous and rapid evolution of telecommunication equipment, the demand for more efficient and noise-robust detection of dual-tone multi-frequency (DTMF) signals is most significant.Design/methodology/approachA novel machine learning-based approach to detect DTMF tones affected by noise, frequency and time variations by employing the k-nearest neighbour (KNN) algorithm is proposed. The features required for training the proposed KNN classifier are extracted using Goertzel's algorithm that estimates the absolute discrete Fourier transform (DFT) coefficient values for the fundamental DTMF frequencies with or without considering their second harmonic frequencies. The proposed KNN classifier model is configured in four different manners which differ in being trained with or without augmented data, as well as, with or without the inclusion of second harmonic frequency DFT coefficient values as features.FindingsIt is found that the model which is trained using the augmented data set and additionally includes the absolute DFT values of the second harmonic frequency values for the eight fundamental DTMF frequencies as the features, achieved the best performance with a macro classification F1 score of 0.980835, a five-fold stratified cross-validation accuracy of 98.47% and test data set detection accuracy of 98.1053%.Originality/valueThe generated DTMF signal has been classified and detected using the proposed KNN classifier which utilizes the DFT coefficient along with second harmonic frequencies for better classification. Additionally, the proposed KNN classifier has been compared with existing models to ascertain its superiority and proclaim its state-of-the-art performance.

In response to the slow processing speed, weak anti-interference, and low accuracy of autonomous call models in current emergency command scenarios, the research focuses on the fire scenario, aiming to improve the emergency response efficiency through technological innovation. The research innovatively integrates digital signal processing algorithm and two-tone multi-frequency signal detection algorithm to develop a hybrid algorithm. Then, a novel autonomous call model based on the hybrid algorithm is constructed. The comparative experimental results indicated that the accuracy of the hybrid algorithm was 0.9 and the error rate was 0.05, which was better than other comparison models. The average accuracy and comprehensive performance score of the model were 0.95 and 97 points, respectively, both of which were better than comparison models. The results confirm that the autonomous call model proposed in this study can accurately and quickly judge emergency scenarios and handle calls, and provide new ideas and theoretical basis for emergency command and rescue of fire and other disasters, with broad application prospects.

A novel tone detection approach, designated as the multi-frequency detecting (MFD) algorithm, is presented in this work as an alternative to conventional single point detection approaches but it is an efficient way to achieve the aim of further computational load reduction for a dual-tone multi-frequency (DTMF) signal detection. The idea is that an optimal phase search is performed over the frequency band of interest in each tone detection, and then the optimal frequency response of a detector is built accordingly. In this manner, a DTMF detection task is done following one-time detection computation. This proposal demonstrates an overall computational load reduction of 80.49% and 74.06% in comparison with a discrete Fourier transform (DFT) approach and the Goertzel algorithm, respectively. This detection complexity reduction is an advantage and an important issue for applying DTMF detection technique to embedded devices.

This paper proposes replacement of a tele-controller system to base on DTMF in spite of tele-system web. It is found that telesystem with Dual Tone Multiple Frequency has a large capability in send-receiving of data in hardware and software. It dos not need to install of a PC for receiving and processing of data as tone modulated form from slave side. In other hand, its adaptive ability with different phone lines and no any need to spare devices of network such as net card or modem and no contribution from local networks of internet and finally different master interfacing capabilities with a single slave are examples of the advantages of the system. Also, synchronous using of internet lines like ADSL in other applications such as video image sending (due to system development) is one of the other advantages of it. This paper describes several interface system and control system with experimentally tests according to the ISO9283 standard and Delay-Time in this the Tele-Robot.

This paper designs a kind of intelligent remote control device of household telephone channel decoding chip MT8870 based on the use of multi frequency signal decoding telephone sent into SCM system after processing, give the operator prompts by controlling ISD4004 chip voice recording playback speech, realize remote control of household appliances. After the actual test: the device is economic, safe and reliable, consistent with the appliance control development direction, has a certain practical value.

This chapter introduce the CMSIS-DSP library and gives examples (DTMF, Least Square motion tracking) of using CMSIS-DSP in application programming.

This chapter contains sections titled: DTMF Signal Detection Using Correlation, FFT, and Goertzel Algorithm Beat Detection Using Onboard LEDs FIR with RTDX Using Visual C++ for Transfer of Filter Coefficients Radix-4 FFT with RTDX Using Visual C++ and MATLAB for Plotting Spectrum Display Through EMIF Using a Bank of 32 LEDs Spectrum Display Through EMIF Using LCDs Time-Frequency Analysis of Signals with Spectrogram Audio Effects (Echo and Reverb, Harmonics, and Distortion) Voice Detection and Reverse Playback Phase Shift Keying��-��BPSK Encoding and Decoding with PLL Binary Phase Shift Keying Modulation Schemes��-��PAM and PSK Selectable IIR Filter and Scrambling Scheme Using Onboard Switches Convolutional Encoding and Viterbi Decoding Speech Synthesis Using Linear Prediction of Speech Signals Automatic Speaker Recognition ��-Law for Speech Companding SB-ADPCM Encoder/Decoder: Implementation of G.722 Audio Coding Encryption Using the Data Encryption Standard Algorithm Phase-Locked Loop Miscellaneous Projects Acknowledgments References

This isn't your mother's mothballed minkit's fur for foxes: wickedly rich, enviably lean, and liable to set off flashbulbs.