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This paper presents a low-power, programmable acoustic signal processor for hearing assistive devices with speech intelligibility enhancement. The reprogrammable design provides considerable flexibility for the devices to deal with personal conditions of hearing loss. A spectral-change enhancement (SCE) algorithm is implemented to improve speech intelligibility. The power consumption is minimized by adding dedicated hardware accelerators. The short-time objective intelligibility (STOI) measure is utilized for optimizing the datapath architecture. Optimization on the critical MAC operations results in 34% power and area reductions when compared to the direct-mapped design. A 50% reduction in SRAM storage is also achieved owing to the reduced memory storage for the associated MAC operations. With the aid of the optimized MAC unit and data buffer, the overall execution time is reduced by 99.2%. Designed in a 40-nm CMOS technology, the processor integrates 431k gates in area of 0.3 mm2. The design dissipates 1.5 mW at a clock frequency of 10.5 MHz from a 0.7V supply, with a processing latency of 1.05 ms.

Several prior works have proposed various methods for the task of automatic melody harmonization, in which a model aims to generate a sequence of chords to serve as the harmonic accompaniment of a given multiple-bar melody sequence. In this paper, we present a comparative study evaluating and comparing the performance of a set of canonical approaches to this task, including a template matching based model, a hidden Markov based model, a genetic algorithm based model, and two deep learning based models. The evaluation is conducted on a dataset of 9,226 melody/chord pairs we newly collect for this study, considering up to 48 triad chords, using a standardized training/test split. We report the result of an objective evaluation using six different metrics and a subjective study with 202 participants., 20 pages, 6 figures, published in Journal of New Music Research (JNMR), Volume 50 Issue 1

Information and communication technologies have been widely used to achieve the objective of smart city development. A smart air quality sensing and forecasting system is an important part of a smart city. One of the major challenges in designing such a forecast system is ensuring high accuracy and an acceptable computation time. In this paper, we show that it is possible to accurately forecast fine particulate matter (PM2.5) concentrations with low computation time by using different clustering techniques. An Internet of Things framework comprising of Airbox devices for PM2.5 monitoring has been used to acquire the data. Our main focus is to achieve high forecasting accuracy with reduced computation time. We use a hybrid model to do the forecast and a grid based system to cluster the monitoring stations based on the geographical distance. The experiments and evaluation is done using Airbox devices data from 557 stations deployed all over Taiwan. We are able to demonstrate that a proper clustering based on geographical distance can reduce the forecasting error rate and also the computation time. Also, in order to further evaluate our system, we have applied wavelet-based clustering to group the monitoring stations. A final comparative analysis is done for different clustering schemes with respect to accuracy and computational time.

Fluorescent Ag nanoclusters (AgNCs) stabilized by carboxymethyl dextran (CMD) were prepared by the photochemical reduction of a mixture of CMD and [Ag(NH3)2]+ under ultraviolet C (UVC) light irradiation. Further study showed that the as-prepared AgNCs had the maximum fluorescence emission peak near the located surface plasmon resonance (LSPR) wavelength of AgNCs. Thus, these AgNCs are defined as LSPR-AgNCs. The maximum excitation wavelength of AgNCs is 250 nm and the maximum emission wavelength is 425 nm. In solution, the fluorescence of AgNCs could be quenched in the presence of cysteine. Based on this phenomenon, AgNCs were developed as a fluorescent probe for the detection of cysteine. The fluorescence intensity quenching value (ΔI) has a linear relationship with the concentration of cysteine, which follows a linear equation of ΔI = 4.25 × 109c + 36.84 with the correlation coefficient of r = 0.9967 in the linear range of 5.0 × 10−10 to 1.0 × 10−7 mol L−1. The detection limit (3σ/k) was 3.2 × 10−10 mol L−1. It has been applied to the cysteine concentration detection with the accepted results in the synthesized samples and compound amino acids injection sample.

Research on automatic music generation has seen great progress due to the development of deep neural networks. However, the generation of multi-instrument music of arbitrary genres still remains a challenge. Existing research either works on lead sheets or multi-track piano-rolls found in MIDIs, but both musical notations have their limits. In this work, we propose a new task called lead sheet arrangement to avoid such limits. A new recurrent convolutional generative model for the task is proposed, along with three new symbolic-domain harmonic features to facilitate learning from unpaired lead sheets and MIDIs. Our model can generate lead sheets and their arrangements of eight-bar long. Audio samples of the generated result can be found at https://drive.google.com/open?id=1c0FfODTpudmLvuKBbc23VBCgQizY6-Rk, Comment: 7 pages, 7 figures and 4 tables

Information and communication technologies have been widely used to achieve the objective of smart city development. A smart air quality sensing and forecasting system is an important part of a smart city. In this paper, we present an approach to accurately forecast hourly fine particulate matter (PM2.5). An Internet of Things (IoT) framework comprising of Airbox Devices for PM2.5 monitoring has been used to acquire the data. Our main focus is to achieve high forecasting accuracy with reduced computation time. We use a hybrid model to do the forecast and a grid based system to cluster the monitoring stations based on the geographical distance. The experimentation and evaluation is done using Airbox Devices data from 119 stations in Taichung area of Taiwan. We are able to demonstrate that a proper clustering based on geographical distance can reduce the forecasting error rate and also the computation time.

In this paper, we present an energy, data and cost efficient model for mobile opportunistic PM2.5 sensing via bicycles. To facilitate the implementation of such systems, we first investigate the accuracy issue of different Inertial Monitoring Unit (IMU) built into the Arduino 101 for stop detection. Then, by curve fitting and optimization calculation on system parameter tuning and modeling, each sensor could start up at the optimum time in order to achieve minimum energy consumption and maximum data usability. Also we propose a formula that can count the minimum number of required PM2.5 sensors under the condition of total experiment time spent and total expected number of sampling point. Finally, we conduct a field experiment to evaluate the proposed model in a real world setting. The results show that total time spent of PM2.5 data collection is similar to the expected time derived from the system modeling.

In this paper, we present a system calibration model (SCM) for mobile PM2.5 sensing systems using COTS low-cost particle sensors. To implement such systems, we first assess the accuracy of low-cost dust sensors and identify the most reliable sensor through a comprehensive set of evaluations. We also investigate the inner working principle of the selected sensor. By conducting a set of lab-scale controlled experiments, we obtained a logarithmic regression model that models the impacts of mobility and ambient wind velocity on PM2.5 sensing results. Moreover, using a low-cost water flow sensor, we design a customized micro anemometer and apply a linear regression model to convert the flow rate readings from the sensor to wind velocity values. Finally, we conduct a field experiment to evaluate the proposed calibration model in a real-world setting. The results show that the accuracy of the PM2.5 measurement results improves significantly when the model is utilized. The calibration model is simple and effective, and it can be utilized by other mobile sensing applications that facilitate micro-scale environmental sensing on the move.

Steel material is one of the indispensable raw materials for human, and its mechanical property is one of the most popular measurements used for weighing the mass. In this paper, we study the microstructure of steel how to affect the mechanical property. Some conclusions are drawn about the structure of pearlite, bainite and martensite have different mechanical properties while the constitution of steel is regular, and we can vary the mechanical properties of steel through the way of metal heat treatment.

This paper presents a low-power acoustic signal processor for fully-implantable cochlear implants. The developed processor supports adaptive beamforming, frequency-domain analysis, envelope detection, channel combination, and magnitude compression. Power and area are minimized by leveraging dedicated real-valued FFT, register count minimization, data allocation optimization, hardware complexity reduction, and minimum-energy-point operation. Compared to complex-valued FFT, real-valued FFT achieves 44.36% power reduction. Register count minimization and data allocation for FFT output reordering yields 28.07% and 27.09% area and power reduction, respectively. Envelope detection and log-compression are realized by hardware-efficient CORDIC engines. The processor is scalable to support various numbers of channels. This chip is implemented in 90-nm CMOS and the core area is 0.47 mm2. It dissipates 98.6 μW at 50 kHz, 0.33 V for a latency of 3 ms.