Miracle-dz/UniFAR-FTUs huggingface dataset:
Bioacoustics Data Analysis – A Taxonomy, Survey and Open Challenges

Biodiversity monitoring has become a critical task for governments and ecological research agencies for reducing significant loss of animal species. Existing monitoring methods are time-intensive and techniques such as tagging are also invasive and may adversely affect animals. Bioacoustics based monitoring is becoming an increasingly prominent non-invasive method, involving the passive recording of animal sounds. Bioacoustics analysis can provide deep insights into key environmental integrity issues such as biodiversity, density of individuals and present or absence of species. However, analysing environmental recordings is not a trivial task. In last decade several researchers have tried to apply machine learning methods to automatically extract insights from these recordings. To help current researchers and identify research gaps, this paper aims to summarise and classify these works in the form of a taxonomy of the various bioacoustics applications and analysis approaches. We also present a comprehensive survey of bioacoustics data analysis approaches with an emphasis on bird species identification. The survey first identifies common processing steps to analyse bioacoustics data. As bioacoustics monitoring has grown, so does the volume of raw acoustic data that must be processed. Accordingly, this survey examines how bioacoustics analysis techniques can be scaled to work with big data. We conclude with a review of open challenges in the bioacoustics domain, such as multiple species recognition, call interference and automatic selection of detectors.
Deep Learning for Consumer Devices and Services 4—A Review of Learnable Data Augmentation Strategies for Improved Training of Deep Neural Networks

Learnable data augmentation is a technique where a neural netowrk learns to create modified data samples that improve the training outcome from a second, parallel neural network. This is a relatively new approach to dataset augmentation that has inspired many variations in the last few years. In this article the most signficiant of these advanced data augmentation strategies are summarised and discussed.
Zigbee Protocol-Based Communication Network for Multi-Unmanned Aerial Vehicle Networks

This paper proposes a communication protocol for Unmanned Aerial Vehicles (UAVs) using ZigBee technology. A review of the state-of-the-art of Flying Ad-hoc Networks (FANETs) and its main respective technologies is presented in detail. A comparison among Long Term Evaluation (LTE), WiFi, and ZigBee is performed, thus showing that ZigBee stands out as a good alternative to scenarios without proper infrastructure. A Raspberry Pi 3 Model B board associated with module XBEE PRO S3B 915 MHz is embedded in a UAV model DJI Phantom 3 Standard to carry out the tests. The obtained results show that the adopted protocol is capable of sending and receiving images between the UAV and the ground station. Tests are also performed using a flying aircraft, where it is demonstrated that the transmission is successfully executed for all cases and the communication protocol operates accurately. In addition, a brief analysis of the time interval required by the process is presented, as there are no significant differences among the existing scenarios.
Centrifugation of Microparticles Inside a Sessile Droplet on a Micromachined Silicon Chip Using Acoustic Tweezers

This paper reports the first demonstration of acoustic tweezers to manipulate microparticles in a sessile droplet over a functional surface-micromachined silicon chip fabricated with polysilicon micromechanical sensing structures. The proposed plug-and-play setup comprises two interchangeable parts: a detachable silicon micromachined sensor chip (acting as a superstrate) and a surface acoustic wave (SAW) device substrate. The SAW device provides the off-chip acoustic source from which Rayleigh waves couple into the functional silicon chip. We demonstrate concentration of initially randomly dispersed microparticles in a water droplet loaded on the functional silicon chip by means of acoustic streaming forces using a drive power of 1 W.
A Smart Binaural Hearing Aid Architecture Leveraging a Smartphone APP With Deep-Learning Speech Enhancement

This paper presents a smartphone-based binaural hearing aid architecture for improving the speech intelligibility of hearing aid users. The proposed system consists of an earpiece, a smartphone and an application that performs real-time speech enhancement. The speaker’s voice, which is picked up by the microphone of the earpiece that is worn on the ear, is transmitted to the smartphone via wireless technology. After the speech intelligibility is improved in real time by the deep learning speech enhancement application, it is returned to the earpiece and generates sound. Deep learning speech enhancement algorithms can be used without performing burdensome calculations on the processors in the hearing aid. The results showed that the average usage of the central processing unit in the smartphone was approximately 26%, and the signal-to-noise ratios improve by at least 20%. The presented objective and subjective results show that the proposed method achieves comparatively more noise suppression without distorting the audio.
Visualization of Course Discussion Forums: A Short Review from Online Learning Perspective

This paper presents a short review of existing visualization systems that focus on online course discussion forums. Discussion forums in online courses are used as an important means for students-students and students-instructors communications. However, a large amount of asynchronous and heterogeneous posts in the discussion forums and their complex relationships make it difficult to track and extract meaningful information about the students’ activities and progress in the online courses. Information visualization has shown promise on this to facilitate online learning, especially for predicting students’ performance on tasks and assessments, understanding students’ affects and sentiments, and predicting students’ social behavior pattern and awareness. In this review, we detailed the applications of the existing visualization systems, their design considerations, and their strengths and weaknesses in the context of discussion forum analyses. This information will provide insight into how to build an effective visualization system for the online courses’ discussion forums.
Sound Pressure and Low Frequency Enhancement Using Novel PZT MEMS Microspeaker Design

This study presents the piezoelectric MEMS microspeaker design in Fig. 1a with four triangular plates, a connection mass, and dual driving electrodes (inner and outer electrodes) to increase the sound pressure level (SPL). By integrating the microspeaker on chip, for example the array with 5 microspeakers in Fig. 1b, the SPL could be further increased. This study exhibits two merits: (1) The inner and outer electrodes on triangular plates (Fig. 1c) are driven with 180° out-of-phase to actuate the piston mode of structure to increase the SPL; (2) The connection mass synchronizes the vibration of the four plates to reduce the gap size surrounding the plates for low frequency sound pressure enhancement. Measurements show the SPL enhancement of the proposed dual electrode driving is 9.5dB as compared with the single (inner or outer) electrode driving. Measurements in the standard ear simulator indicate the presented microspeaker array has the SPL of 81.4dB at 100Hz and the SPL of 84.7dB at 1kHz. The results show that the presented design exhibits good SPL at low frequency. Moreover, the proposed microspeaker array (5 microspeakers) presents ±3dB bandwidth between 100Hz∼2.1kHz, and the highest SPL of 118.1dB at 11.9kHz with only 2Vpp driving voltage.
A 0.003-mm2 440fsRMS-Jitter and -64dBc-Reference-Spur Ring-VCO-Based Type-I PLL Using a Current-Reuse Sampling Phase Detector in 28-nm CMOS

This paper reports a current-reuse sampling phase detector for a type-I phase-locked loop (PLL) to simultaneously achieve both wide loop bandwidth and low control voltage ripple, resulting in low RMS jitter and reference spur, while minimizing the chip area by avoiding an explicit loop filter. Fabricated in 28-nm CMOS, the PLL prototype measures a jitter of 440 fsRMS, and a spur level of -64 dBc at 3.296 GHz. The die area is 0.003 mm2.
Optimization of Weight Backpropagation with Particle Swarm Optimization for Student Dropout Prediction

Students who drop out of school are the cases that should be of concern in college. An uncontrollable decline affects the quality of the university. Dropout could happen for a variety of reasons, one of which has carried out a maximum study period. The Undergraduate program has a maximum finished within less than eight years. In this study, student dropouts prediction was conducted for students who had the possibility of exceeding the maximum study period. The predictions are made by digging the data patterns in the student’s academic database by utilizing the Academic Achievement index data of each semester and class attendance. This research aims to accelerate training and improve the accuracy of predictions with backpropagation (BP) optimized with particle swam optimization (PSO). Evaluation of the classification model is done with 10 fold cross-validation, where the data are divided into 10 fold, and the test is done 10 x until the highest accuracy is obtained. Further accuracy results was compared with a simple backpropagation algorithm without optimization. The algorithm of backpropagation, which is optimized with particle swarm optimization, produces the best accuracy of 100% with 6 epoch. Meanwhile, the backpropagation algorithm generates an accuracy rate of 77.78% with 17 epoch. The highest accuracy gained using the best architecture is 8-8-1 and the number of particles 10. Optimization of a network weight backpropagation with particle swarm optimization can improve the accuracy and number of decreased iterations
What do Customers Really Need in Ride-Hailing Applications? : Signaling Electronic Service Quality via E-CRM Features

Fierce competition on ride-hailing applications in Indonesia encourages the development of the application that is not only reliable but also has to be customer-oriented. In the context of gig economy, customers consist of clients and gig workers. Lack of customer engagement can reduce competitiveness, images, and profit. In 2017, Indonesian Consumers Foundation exposed that 13 percent ride-hailing customer’s disappointment was caused by the application. This research evaluated Electronic Customer Relationship Management (E-CRM) features in ride-hailing applications, and then found out if those affect electronic service quality (ESERVQUAL); in other words, it is about customers’ perception. Data analysis was conducted using Partial Least Square Structural Equation Modelling (PLS-SEM) method with the total number of valid responses processed was 204 respondents. The results implied that navigation, privacy and security, online community, and customer service are the E-CRM features that are really needed by the customers. By knowing the results, the researchers want to show the asymmetry signal between the providers and customers’ perception. Thus, it can lead providers to develop better E-CRM features in ride-hailing application and conquering the customers’ dissatisfactions in the context of gig economy.
Effective Capacity Based Power Allocation for the Coexistence of an Integrated Radar and Communication System and a Commercial Communication System

Considerable interest has been shown in the coexistence between airborne radar and commercial communication systems in recent years. In particular, the integrated radar and communication system (IRCS) is promising for the airborne platforms like Unmanned Air Vehicles (UAVs). However, due to fast varying channels caused by high mobility, it is a great challenge for the fusion center to collect detection information within a given delay threshold through the air-to-ground (A2G) communication. Based on slowly varying components of the channel, i.e, target spectrum, power spectral densities of the signal dependent clutters, path loss and shadow fading, this paper considers the problem of power minimization for an IRCS and a base station (BS) coexisting in the same frequency band. The latency bound, latency violation probability (LVP), and channel capacity for the A2G communication to the fusion center are considered based on the effective capacity (EC) theory. The detection performance for radar and the rate requirement of BS user are also taken into account. The power allocation problem is non-convex and formulated to a monotonic optimization problem. Afterward, an efficient heuristic scheduling algorithm with acceptable computational complexity is proposed to solve the formulated problem. Then, the robust power allocation with channel estimation error is considered. Simulation results demonstrate the effectiveness of the proposed heuristic algorithm from the perspectives of the total transmit power, EC, and LVP of the IRCS.
AC Loss Behavior of Wireless Power Transfer Coils

Wireless power transfer (WPT) systems, based on near field magnetic induction, typically operate in the frequency range of a few hundreds of $\mathbf{kHz}$. Although, most commercially available transmission coils are made of high frequency litz wire to suppress skin and proximity effects, the power losses under AC conditions are significantly bigger than under DC conditions. In the design process of a WPT system it is essential to know the power losses and the resulting self heating of the transmission coils under operating conditions. In this paper we demonstrate the analytic calculation of power losses and we present a new setup to measure the self heating of transmission coils under AC conditions with large current amplitudes. We compare the results to measurements in the small signal regime and we evaluate the frequency dependent derating of the rated current over a broad spectrum of coils. Finally, we investigate how the choice of coil geometry and litz wire influence the AC loss.
An IoT Sensor Node SoC with Dynamic Power Scheduling for Sustainable Operation in Energy Harvesting Environment

This paper describes a low-power IoT sensor node SoC that can be used for factory automation and wearable healthcare applications. It features dynamic power scheduling method suitable for the environment that works with an energy harvester. Our proposed SoC architecture is composed of a low power (60 nA) RTC, a normally-off RF which has fast start-up time (0.5 µs), and other low-power components. A mixed-signal SoC has been fabricated using the TSMC 65 nm LP process. The sensor node system equipped with dynamic power scheduling consumes about 3.4 µW when the activity ratio is 0.1%. Evaluation results show that the proposed method can reduce about 51% of the power consumption compared with the case without dynamic power scheduling in sensor node SoC. Also, the parameters of the system in energy harvesting environment can be adjusted under the trade-off with power consumption and measurement accuracy according to the application requirements.
A unified modeling framework for explaining the electrical resistivity trend of composites with nanostructures

Theoretical models for 1-D nanowire networks with particulate fillers are provided to explain how the electrical conductivity increases or decrease with respect to the shape and dimension of the particulate filler. The present research aims at a comprehensive understanding of the size effect of secondary particulate fillers on the electrical conductivity of CNT polymer composites. The change of the resistivity of CNT polymer composites due to the size of particulate fillers could be interpreted via the combination of Voronoi geometry induced from Swiss cheese models and underlying fiber percolation theory. This indicates that there is the transition of phases in excluded-volume effects, i.e., the enhancement in conductivity was measured in cooperation with micro-sized second fillers whereas the decrease in conductivity was observed for nano-sized fillers.
Simulation of 5G Secondary Synchronization Signal Detection

In the mobile communication, the cell search is used to implement time synchronization and frequency synchronization between the user and the cell, and acquire the physical cell identification number. A new synchronization signal structure is defined in the 5G communication system to complete the cell search, wherein the secondary synchronization signal is greatly changed, and the cell group ID in the physical cell identifier can be obtained by the effective secondary synchronization signal sequence detection. This paper focuses on the secondary synchronization signal detection process, and proposes to use the gold sequence characteristics of the secondary synchronization signal to simplify the operation process of obtaining the cell group ID in the physical layer cell identifier. The simulation results show that the proposed improved secondary synchronization signal detection algorithm can reduce the complexity of the cell group ID in the physical cell identifier detection while ensuring the correct rate.
1D-MV Position Detection Method for Wireless Power Transfer System of Electric Vehicle

In order to achieve good coils' alignment, position detection (PD) needs to be used in the electric vehicle which has wireless power transfer function (WPTEV), so that the WPT system can provide a better transfer performance. Through the analysis of PD functional requirements and research of existing positioning technology, this paper proposes a magnetic vectoring PD method with one source coil (1D- MV) which greatly simplifies the complexity of hardware and software in PD function. Two calculation principles based on different distance ranges are described in detail. Finally, an experimental system is built to verify the 1D-MV PD method. The results show that the 1D-MV PD method can achieve relative error accuracy within 10% in the range of one-meter distance, and maintain a positioning accuracy of 2cm error during short-distance positioning,
Design and Simulation of single-phase 3 kW PV inverter

This research provides a complete analysis of photovoltaic (PV) inverter of 3kW output power by merging the design of PV array and DC-AC inverter; The design includes a PV module to configure a large array that agrees with the inverter power output, the DC-DC step-up (boost) converter, next the full-bridge using MOSFET switches controlled by PWM (Pulse Width modulation) is used to generate three levels signal and the output LCL filter design, then to the load. Also studying the inverter parameters that affect the losses and inverter efficiency. The design is verified using Matlab-Simulink simulation using parameters of a real PV module, switches and passive elements to be close to practical work. The simulation results prove the design output consistency.
Full-Rate Full-Diversity Distributed Orthogonal Space-Time Block Codes

In this paper, we proposed two Distributed Extended Orthogonal Space-Time Blok Coding (D-EO-STBC) schemes with two terminal nodes, four relay nodes all equipped with single antenna, represented in the first scheme, and two terminal nodes, two relay nodes equipped with dual antennas as a second scheme. Full-data rate and Full-transmission diversity are achievable. Moreover, the second scheme has an advantage of minimizing the decoding complexity at the relay nodes . The simulation results show that both proposed schemes have good performance.
High-Speed and High-Resolution On-Chip Pumping Utilizing Asymmetric Flow Resistors

We present a revolutionary on-chip pumping with high-speed response, high-resolution and bidirectional continuous pumping. Generally, there is a tough challenge of on-chip pumping to break through the trade-off between response time and volumetric resolution with the function of bidirectional continuous pumping. In order to confront this challenge, we propose a concept of “asymmetric flow resistors (AFRs)” whose flow resistance is temporally changed by utilizing the spatiotemporal vortices generated by physical interaction between ultrafast flow and uniquely designed asymmetric microstructures of microchannel. Thus, we achieved an unexplored specification of on-chip pumping with resolution of 1.4 nl with the single forward-backward actuation and response time of <50 ms with continuous flow. Furthermore, we demonstrated bidirectional pumping of picking-up and dispensing of single motile microalga by using a microfluidic pipette utilizing two proposed on-chip pumps with opposite direction.
Referee: A Pattern-Guided Approach for Auto Design in Compiler-Based Analyzers

Coding rules become more critical for security-oriented softwares, which prefer compilers as their base platforms due to simultaneous demands not only in a mature grammatical analysis, but also in compilation and optimization techniques. However, engineering such a compiler-based analyzer, exploring proper launch points before integrating hundreds of rules one by one in the frontend of compilers, is a completely manual decision-making process with heavy redundant efforts exhausted. To improve this, we introduce a novel pattern-guided approach, named Referee, to facilitate this process. Referee improves the manual approach significantly by making three advances: (1) our pattern-guided approach can significantly reduce the amount of redundant manual efforts required, (2) a twin-graph aided broadcasting process is developed to enable rule patterns to be characterized with partially developed rules, and (3) a reliable recommendation mechanism is used to pinpoint the launch point for a new rule based on the accumulated experience from handling earlier rules. We have implemented Referee in GCC 8.2 with 163 rules from SPACE-C and MISRA-C standards. Referee achieves an accuracy of 89.9% on recommendation of launch points for new rules to our GCC-based analyzer automatically when trained with 70% of all the rules. Decreasing the training data size to 60% and 50% still yields an accuracy of 87.7% and 81.5%, respectively. Therefore, Referee can significantly reduce the amount of manual efforts that would otherwise be required, with a careful selection of seeding rule patterns, providing an interesting and fruitful avenue for further research.
A 7.5 - 42V Input High-VCR Monolithic DC-DC Converter Using Stacked Isolated SC Cores

A fully integrated switched-capacitor DC-DC power converter that converts input voltages up to 42 V into 3 V is presented. The converter achieves a wide input operating range with multiple VCRs based on stacking of several isolated switched-capacitor converters. Stacking simplifies the design to a low voltage unit converter with only a few VCRs, while the wide input range high voltage converter achieves the efficiency of the unit converter. The DC-DC converter is designed in a 0.35 µm CMOS technology and achieves an output power of 2.1 mW.
A Comparison among Different Levels of Abstraction in Genetic Programming

In this paper we compare the performance of variants of Genetic Programming (GP) typically used for high dimensional machine learning problems. First we propose a taxonomy based on GP primitives that allow us to clearly differentiate GP variants found in literature; then we implement and test three GP variants in a set of image denoising tasks. Results show a clear advantage of the variant most commonly used for those kind of problems. We then compare our results with those reported in other GP works as well as those obtained by a Deep Neural Network (DNN). Comparisons suggest that GP cannot compete with deep learning unless it is embedded with expert’s knowledge of the problem domain.