Miracle-dz/UniFAR-FTUs huggingface dataset:
An Ultrasonically Powered and Controlled Ultra-High-Frequency Biphasic Electrical Neurostimulator

This paper presents the design of a neurostimulator performing biphasic ultra-high-frequency electrical stimulation while being driven from ultrasound energy. Unlike conventional constant current or constant voltage stimulators or state-of-the-art ultra-high-frequency stimulators, the system does not convert the input AC signal into regulated DC for storing power and supplying the elements of the circuits. Instead, it uses the received ultrasonic signal frequency (≥1 MHz) for electrically stimulating the tissue directly, and it achieves biphasic stimulation with external control and without storing extra power. This results in a highly efficient and miniature circuit, which has the potential to be used in bioelectronic medicine for stimulating small peripheral nerves deep inside the body. The operation of the circuit was first simulated in LTSpice using a lumped elements model for the impedance of the piezoelectric receivers and the load. Finally, a prototype was tested in vitro with commercial transducers and platinum-iridium electrodes as load.
(In)scribing body/landscape relations

Chapter 1 Prologue or, Where does this book come from? Part 2 (In)scribing body/landscape relations: Australia Chapter 3 Landscapes and Bodies Chapter 4 Writing stories of (be)longing Chapter 5 Australian men talk about becoming environmentalists [with Hilary Whitehouse] Part 6 (In)scribing body/landscape relations: Japan Chapter 7 Remembering Japanese childhoods Chapter 8 Traveling in Japan Chapter 9 Japanese environmentalists talk about Japanese body/landscape relations Part 10 Subjection and the eclipsing of the constitutive power of discourse through fictional texts Chapter 11 An exploration of body/landscape relations in Kawabata's Yama no Oto [with Takeshi Osanai] Chapter 12 Reading and writing The Kadaithcha Sung: A novel by Sam Watson [with Sam Watson] Chapter 13 The Second Bridegroom: A Narrative of captivity in Australian landscapes Chapter 14 (Be)longing in the writing of Janette Turner Hospital:Eclipsing the constitutive force of discourse Chapter 15 Conclusion: The ways and the song of the book
Quasi-Steady-State Free Carrier Absorption Measurements of Effective Carrier Lifetime in Silicon

The paper presents a semiconductor carrier lifetime measurement technique, quasi-steady-state free carrier absorption (QSS-FCA), that is an all-optical analog of the quasi-steady-state photoconductivity technique. The effective electron–hole recombination lifetime of silicon is measured under quasi-steady-state conditions using an above-bandgap pump and a below-bandgap probe of the free carrier absorption (FCA). A mathematical model is developed to relate the experimentally measured probe signal to the lifetime. QSS-FCA is calibrated self-consistently by measuring the quasi-steady-state signal simultaneously with the true recombination lifetime in the same apparatus. This directly establishes the relationship between the quasi-steady-state signal and lifetime, ensuring the accuracy of QSS-FCA measurements. The calibration constant is the FCA cross section of silicon, which is a material constant. QSS-FCA is a simplification of traditional FCA lifetime measurement approaches where the lifetime is extracted from a time- or frequency-resolved carrier decay. Both of these approaches require a bandwidth commensurate with the inverse carrier lifetime of the wafer and require fitting multiple data points in order to extract the lifetime. In contrast, QSS-FCA is a quasi-steady-state measurement, so it is inherently low frequency, and as a single point measurement technique, fitting is not required. Finally, since QSS-FCA is a purely optical technique, it opens up the possibility of in situ lifetime characterization that would be inaccessible to the traditional RF/microwave-based lifetime measurement techniques.
Recent Developments of Photon Counting and Energy Discriminating Detectors for Radiographic Imaging

The direct detection of X-ray photons into electrical signals is enabled by usage of highly absorbing photo conducting materials (e.g. CdTe) as detection layer of an underlying CMOS semiconductor X-ray image detector. If the read-out speed is high enough (ca. 50 - 100 ns dead time) single X-ray photons can be counted and their energy measured. Read-out noise and dark image correction can be diminished. By setting energy thresholds, selected energy ranges of the X-ray spectrum can be detected or suppressed. This allows material discrimination or reduction of scattered radiation, which results in an enhanced contrast sensitivity.
Adaptive Traffic Light Control with Statistical Multiplexing Technique and Particle Swarm Optimization in Smart Cities

Vehicular traffic in Urban areas of the globe is continuously increasing and the resulting congestion has become a major concern for transportation management. The traffic signal controls are the major way to manage vehicular flow at the intersections in these urban areas. However, traditional systems fail to adjust the timing pattern based on traffic which demands for need of developing adaptive systems. The focus is this study is to develop an intelligent system that is adaptive to the traffic flow at an intersection point of the real scenarios. A hybrid system comprising of Statistical Multiplexing and Particle Swarm Optimization(PSO) has been developed to control the flow of traffic. The performance of the developed algorithm was tested with both simulated and real traffic count of some major traffic congestion intersection of Kathmandu valley. It was observed that the average waiting time of vehicles on a junction has been reduced.
ASM GaN: Industry Standard Model for GaN RF and Power Devices—Part-II: Modeling of Charge Trapping

Because of charge trapping in GaN HEMTs, dc characteristics of these devices are not representative of high-frequency operation. The advanced spice model GaN model presented in Part I of this paper is combined with a Shockley–Reed–Hall-based trap model, yielding a comprehensive FET model for GaN HEMTs which can accurately model GaN devices exhibiting trapping-related dispersion effects. Measurement results of the dc and pulsed output and transfer characteristics of a commercially available GaN HEMT are presented, trapping in the device is modeled, and excellent fit to the measured data is shown. This paper presents an accurate model of trapping which is validated for eight different quiescent bias points of pulse measurements, with quiescent drain voltage ranging from 5 to 20 V and quiescent gate voltage ranging from −2.8 to −3.8 V, and a large range of gate and drain voltages to which the device was pulsed in the pulse measurements and at which the device was measured in the dc measurements, with gate voltage ranging from −4 to 0.4 V and drain voltage ranging from 0 to 40 V. This paper also presents high-frequency (10 GHz) large-signal RF validation of the model for optimal complex load condition.
Measurement-Based Burst-Error Performance Modeling for Cooperative Intelligent Transport Systems

Safety applications for cooperative intelligent transport systems are limited in their performance by the latency of the communication more than by the achieved throughput. However, there exist few models at packet level that are able to capture the burstiness of the communication. We therefore introduce a packet error model that considers burst lengths through second-order statistics and mean packet errors. The foundation of our approach is the Gilbert–Elliot model, which is able to model not only the packet error rate, but also the burst durations of the packet errors, which we interpret in a nonstationary fashion. Based on this, we formulate maximum likelihood expressions for the time variant model fits, and then proceed to fit the parameters to extensive recorded measurements. We consider the fading statistics of the measured channel and the signal-to-noise ratio and present how they influence the channel burstiness. Our analysis demonstrates that the communication shows strong bursts at packet level, proving the demand for such models. The approach we demonstrate here remains of low computational complexity, allowing future employment in large-scale simulations.
Circumferential Current Field Testing Method for Innerwall Cracks Detection in Aluminum Tube

This paper presents a novel circumferential current field testing method for the detection of innerwall cracks in aluminum tube. The finite element method (FEM) model of the circumferential current field testing method is proposed to analyze the electromagnetic field around the crack. The circumferential current field testing probe with tunnel magneto resistance (TMR) sensor array and testing system are developed. The inner-wall crack detection experiments are carried out. The results show that the inner-wall cracks in aluminum tube can be detected, positioned and evaluated by the circumferential current field testing method.
Control of an Iguana Character Using Soft-Body Simulation

The locomotion of lizards is characterized by lateral bending of the body, which is distinct from quadrupedal mammals such as dogs. We propose a method to control a physically simulated iguana character as a representative species of lizards, which can move while physically interacting with the environment, and reproduces the flexible characteristics of a lizard in real time. The main challenges lie in expressing the deformable characteristics of an iguana and adapting low-quality captured motions to various terrain conditions and iguana poses. Our iguana character is designed as a soft-body character, which models the elasticity of an iguana body so that it can express flexible and realistic motions. Applying the motion capture data obtained from an iguana is problematic because it is captured using only a sparse set of markers in an environment different to the simulation environment. To resolve these problems, we transform the low-quality captured motion into full-body motion and adapt it to the terrain in real time using our motion adaptation algorithm. To control the various movements of an iguana, a motion graph is constructed to choose an appropriate motion depending on the situation. The chosen reference motion is adapted to the local terrain, which has irregular height, in real time. A soft-body iguana model is then simulated by physically tracking the time-varying reference motion. We demonstrate that our approach can generate natural and flexible movements of an iguana on hilly terrain.
Alleviating Conductance Nonlinearity via Pulse Shape Designs in TaOx Memristive Synapses

Analog resistive switching behavior in emerging nonvolatile memories has facilitated a wide range of potential applications in neuromorphic computing and cognitive tasks. However, the intrinsic nonlinearity (NL) of conductance update has been proven to be highly unfavorable for the implementation of analog synapse-based hardware neural network (HNN). In this brief, we show that the conductance update characteristics of our Pt/TaOx/Ta memristor will be significantly improved by carefully designing the potentiation and depression pulse shapes. Furthermore, measured conductance update characteristics with different degrees ofNL are applied in the simulation of a face classification task. The results show that both the recognition accuracy and the learning speed of this supervised learning scenario are substantially improved by the proposed optimization approach.
System Design and Experiment of Bionics Robotic Arm with Humanoid Characteristics

Aiming at the problem of lack of humanoid characteristics of the current bionic robotic arm, a bionic robotic arm with anthropomorphic characteristics is designed. Based on ergonomic principles and kinematics analysis, the system design of RYRPRR-RP-RRRP structure was determined. The motion control of the 23 DOF (degree of freedom) of the entire arm is realized with 10 steering gears. Through the three-dimensional modeling, the structure of the arm’s mechanism components is made and the virtual simulation of the system is carried out. It was found that the motion space of the arm covered the commonly used work space of the human arm. The components are printed by 3D printing technology and are assembled, which the hardware and software of the control system are carried out. Operator’s arm color, depth image and bones are got by Kinect and the space vector method is used to calculate the joint angle data when the human arm moves. By setting communication with micro-controller, Arduino, by serial port, remote synchronization control of humanoid manipulator with gestural body language is achieved. The humanoid experiment of bionic robotic arm and human arm was carried out to finish the humanoid motion control of arm joint and the grasping, holding and handling of the object, which verified the rationality and feasibility of the system design. The bionic robotic arm has the characteristics of smart structure, convenient control, flexible movement, good human nature and low cost, and has important reference value for carrying out research on human bionics, medical prostheses and special environment teleoperation arms.
Wavelet Transform-Spectral Kurtosis Based Hybrid Technique for Disturbance Detection in a Microgrid

This paper proposes a combined Wavelet Transform-Spectral Kurtosis based approach for detecting islanding and power quality (PQ) issues in microgrids. Islanding and PQ disturbances are generated in a microgrid comprising renewable energy sources such as wind and solar photovoltaic apart from diesel generators, fuel cells and flywheel/battery energy storage systems (FESS/BESS). Different microgrid configurations are considered to test the detection capabilities of the proposed approach. The negative sequence component of the voltage signal is measured at the point of common coupling (PCC) and processed through Short Time Fourier Transform (STFT), Wavelet Transform (WT) and Wavelet Transform-Spectral Kurtosis (SK) under no-noise and 20-dB noise conditions. Furthermore, performance indices such as energy and kurtosis are calculated in all case studies to detect disturbances based on a suitably selected threshold. The results of the case studies demonstrate the superior performance and robustness of SK when compared with STFT and WT for detecting islanding and PQ disturbances in MGs.
User Influence on Mobile Terminal Antennas: A Review of Challenges and Potential Solution for 5G Antennas

This paper presents a comprehensive review of mobile terminal antenna researches performed in the past seven years and the current challenges related to the user’s influence on the performance of fifth generation (5G) terminal antennas. The main challenges for the designing of mobile terminal antennas are to meet the compact size requirements of built-in structures and their multiband capabilities. The antenna design techniques that are used to achieve broader operating bandwidths with smaller antenna dimensions will be first discussed. This is followed by the effects of user interactions with the head/hand for mobile antennas in terms of radiation efficiency and, consequently, the correlation of multiple input multiple output (MIMO) antenna systems. The ultimate aims of this paper are as follows: 1) to highlight the different frequencies of mobile terminal antennas for different applications; 2) to highlight mobile terminal antennas that have been developed for 5G application; 3) to study and discuss the effects of user’s hand on 5G mobile terminal antennas; and 4) to discuss the research gap, issues, and challenges in the field of user’s effects on mobile terminal antennas for 5G applications. In addition to that, an investigation of the users’ hand effects on two MIMO mobile terminal antennas operational in the sub-6-GHz 5G band is presented. This investigation performed using two MIMO antennas is an attempt to formulate guidelines on efficient mobile terminal antenna design in the presence of user’s hand in C Band (from 3.4 to 3.6 GHz) and LTE-U Band 46 (from 5.15 to 5.925 GHz).
Improving Nepali News Recommendation Using Classification Based on LSTM Recurrent Neural Networks

News classification is the process of grouping news documents into some predefined categories. Due to the increasing volume of the Nepali news content being generated every day by thousands of online news portals, appropriate classification of these news items has become a necessity for the news readers. This research was targeted to improve the Nepali news classification based on Recurrent Neural Networks, that uses deep layers of neural networks to classify the news to an appropriate category. In this research paper, five popular news portals website across eight different categories was used for the purpose of data gathering and their classification accuracies were compared among these websites as well as overall accuracy was measured. The model was compared with the Support Vector Machine based on the parameters Accuracy, Precision, Recall and F1 Score. The use of Long Short Term Memory Recurrent Neural Network has improved the precision with the use of word2vec model. The presented model in the research has achieved a good accuracy of 84.63% and precision of 89% in compared to the SVM where the accuracy was 81.41% and precision 85%. Based on the categories of the news, sports news was classified more accurately by the model and economy was least accurately classified.
A CMOS Envelope Detector for Low Power Wireless Receiver Applications

Recent studies have shown that the power consumption of a wake-up receiver can be reduced by using an envelope detector as its first stage. Designing a low power envelope detector is challenging because of stringent requirements such as data rate, operating frequency, conversion gain and sensitivity. This paper describes the design of a 915 MHz envelope detector which uses a combination of active inductors and an input matching circuit to improve its conversion gain. The proposed design consumes 2 $\mu \mathrm{A}$ of bias current, achieves a 37.8 dB of conversion gain, a sensitivity of -60 dBm and a data rate of 200 kb/s in a 0.13 $\mu \mathrm{m}$ CMOS process.
Partial Contention-Free D2D Discovery for Proximity-Based Services in Cellular Networks

In this paper, we design a beacon-based Device-to-Device (D2D) neighbor discovery scheme as a key enabler for proximity-based services in the Long-Term Evolution-Advanced (LTE-A) system. The proposed scheme is based on the random access procedure of LTE-A system and it opts for contention-free access to enable the connection of D2D receivers while contention-based strategy is used to connect D2D transmitters. To allow neighbor discovery, D2D users first listen to peer uplink signals in order to detect beacons sent by D2D transmitters within their neighborhood. Then, the received beacons are forwarded by D2D receivers to the evolved Node B (eNB) to verify their unicity and to ensure the feasibility of establishing a D2D link, based on the location of involved users. At the successful D2D receiver side, new beacons will be assigned by the eNB while the initial beacons are kept to be used for D2D transmitters. This strategy allows to greatly reduce the collision probability for the whole network by avoiding collisions at the receiver side. It is worth noting that as the proposed scheme is based on almost the same principle of the random access procedure in the LTE-A system, it can readily be applied to the current architecture without significant modification. By means of simulations, we show that nearby user equipments can be found with low collision probability while achieving a high discovery rate proportional to the total number of users willing to initiate direct links with their neighbors.
A Multi-Mode Accelerator for Pruned Deep Neural Networks

Convolutional Neural Networks (CNNs) are constituted of complex, slow convolutional layers and memory-demanding fully- connected layers. Current pruning techniques can reduce memory accesses and power consumption, but cannot speed up the convolutional layers. In this paper, we introduce a pruning technique able to reduce the number of kernels in convolutional layers of up to 90% with negligible accuracy degradation. We propose an architecture to accelerate fully- connected and convolutional computations within a single computational core, with power$/$energy consumption below mobile devices budget. The proposed pruning technique speeds up convolutional computations by up to $ 6.9\times $, reducing memory accesses by the same factor.
An Ultra-low-power 28nm CMOS Dual-die ASIC Platform for Smart Hearables

This paper presents an ultra-low-power dual-die platform for (medical) smart hearables. It pairs two custom ASICs: i) Blackghost - a 28nm CMOS near-threshold-VDDpowered and highly integrated SoC with embedded PMU, MCU, 16-issue DSP engine and hardened audio sub-system island; ii) DIRAC - a 28nm CMOS always-on voiceband RF & mixed-signal audio codec frontend. With ~90dB of dynamic range, DIRAC codec consumes <200µW of total power. For fast wakeup, sleep and standby of Blackghost, DIRAC also features low latency microphone activity detection (MAD) and TX-RX cross fading scheme. This dual-die platform enables miniaturized hearable devices capable of running emerging audio algorithms like deep learning at an extremely low enerzy budget.
Influence of RC snubbers on the Steepness of Switching Transients caused by Vacuum Circuit Breakers

Vacuum circuit breakers are known to cause steep-fronted traveling waves in electrical systems as a result of pre- and restrike processes. Especially when very steep wave fronts encountering transformers or electrical machines, they will lead to inhomogeneous voltage distributions inside the windings. Therefore, this paper presents high resolution measurements of prestrikes stressing a transformer and analyses the influence of a RC snubber on the steepness of the occurring traveling waves.The results show that the oscillations caused by prestrikes are damped expectedly by the RC snubber. But it also shows, that RC snubbers do not affect the steepness of the initial wave front at all, as well as its initial height. Thus, it can be concluded that a snubber is not reducing all effects of the prestrike phenomenon. This leads to the conclusion that prestrikes may excite overvoltages inside windings independent of the snubber, possibly causing interturn faults.
Analysis of Color Space Quantization in Split-Brain Autoencoder for Remote Sensing Image Classification

This paper investigates the importance of different parameters of split-brain autoencoder to performance of learned image representations for remote sensing scene classification. We investigate the usage of LAB color space as well as color space created using PCA applied to RGB pixel values. We show that these two spaces give almost equal results, with slight favor towards the LAB color space. We also investigate choices of different quantization methods of color targets and number of quantization bins. We have found that using k-means clustering for quantization works slightly better than using uniform quantization. We also show that even when using really small number of bins it is possible to get only slightly worse results.