Miracle-dz/UniFAR-FTUs huggingface dataset:
Simulation of voltage/current waveforms and contact area of pulsed surface discharge on water

Propagation of a surface discharge on water is evaluated using simulation results of the voltage/current waveforms and discharge contact area. Voltage/current waveforms are calculated using an exponential function which assumes the resistance of water decreases with increasing discharge contact area. A conductive disk having a given potential is used to model the discharge contact area and the temporal variation of the radius of the disk is discussed as the discharge propagation. The calculation in the current field is replaced by a calculation of the electrostatic field using the similarity between current and electrostatic fields. The calculation of the electrostatic field is conducted by a charge simulation method. The discharge phenomena are classified into two stages which are the breakdown of needle-to-water gap and the surface discharge propagation on water. The electrostatic field calculation at needle-to-water gap is performed to determine the initial discharge contact area. Expansion of the contact area in the creepage direction, is evaluated by the electric field calculation at the edge of the conductive disk. The expansion terminates when the field becomes lower than 26 kV/cm. The velocity of the expansion increases with the applied voltage. The maximum contact radius increases with decreasing conductivity of water under the same applied voltage. The tendency of simulation results of the expansion of the contact area are consistent with the previous observation results of the discharge propagation.
24.1 A 1Mb Multibit ReRAM Computing-In-Memory Macro with 14.6ns Parallel MAC Computing Time for CNN Based AI Edge Processors

Embedded nonvolatile memory (NVM) and computing-in-memory (CIM) are significantly reducing the latency (tMAC) and energy consumption (EMAC) of multiply- and-accumulate (MAC) operations in artificial intelligence (AI) edge devices [1, 2]. Previous ReRAM CIM macros demonstrated MAC operations for lb-input, ternary- weighted, 3b-output CNNs [1] or lb-input, 8b-weighted, 1b-output fully-connected networks with limited accuracy [2]. To support higher-accuracy convolution neural network heavy applications NVM-CIM should support multibit inputs/weights and multi-bit output (MAC-OUT) for CNN operations. One way to achieve multibit weights is to use a multi-level ReRAM cell to store the weight. However, as shown in Fig. 24.1. 1, multibit ReRAM CIM faces several challenges. (1) a tradeoff between area and speed for multibit input/weight/MAC-OUT MAC operations; (2) sense amplifier’s high input offset, large area, and high parasitic load on the read-path due to large BL currents (IBL) from multibit MAC; (3) limited accuracy due to a small read/sensing margin (ISM) across MAC-OUT or variation in cell resistance (particularly MLC cells). To overcome these challenges, this work proposes, (1) a serial-input non-weighted product (SINWP) structure to optimize the tradeoff between area, tMAC and EMAC, (2) a down-scaling weighted current translator (DSWCT) and positive-negative current- subtractor (PN-ISUB) for short delay, a small offset and a compact read-path area; and (3) a triple-margin small-offset current-mode sense amplifier (TMCSA) to tolerate a small ISM. A fabricated 55nm 1Mb ReRAM-CIM macro is the first ReRAM CIM macro to support CNN operations using multibit input/weight MAC-OUT. This device achieves the shortest CIM-MAC-access time (tAC) among existing ReRAM-CIMs (tMAC=14.6ns with 2b-input, 3b-weight with 4b-MAC-OUT) and the best peak EMAC of 53.17 TOPS/W (in binary mode).
17.5 A 0.8mm3 Ultrasonic Implantable Wireless Neural Recording System With Linear AM Backscattering

Miniaturization of implantable neural recording systems to micron-scale volumes will enable minimally invasive implantation and alleviate cortical scarring, gliosis, and resulting signal degradation. Ultrasound (US) power transmission has been demonstrated to have high efficiency and low tissue attenuation for mm-scale implants at depth in tissue [1, 2, 3], but has not been demonstrated with precision recording circuitry. We present an US implantable wireless neural recording system scaled to 0.8mm3, verified to safely operate at 5cm depth with state of the art neural recording performance an average circuit power dissipation of 13μW, and 28. 8μW including power conversion efficiency. Sub-mm scale is achieved through single-link power and communication on a single piezocrystal (Lead Zirconate Titanate, PZT) utilizing linear analog backscattering, small die area, and eliminating all other off-chip components.
27.7 A Synthesizable Digital AOT 4-Phase Buck Voltage Regulator for Digital Systems with 0.0054mm2 Controller and 80ns Recovery Time

Achieving a fast-transient response is a major challenge when designing a switching regulator for a processor. Furthermore, high-frequency operation with small passive devices and robust control with a small area footprint in a CMOS-logic process are essential for integrated digital-system voltage regulators. A 4-phase hysteretic converter [1] has a superior transient response using a 3.3V input voltage. However, the analog control circuit under a low input voltage suffers from limited dynamic range. Although a time-based design [2] minimizes the use of analog circuits, it still requires an accurate analog-to-time converter and exhibits only a moderate transient response time. A buck voltage regulator (VR) with a digital controller has been proposed for modern digital systems [3], [4], since it can take advantage of the advanced digital process. However, with a conventional digital proportional-integral-derivative (PID) controller it is difficult to offer high bandwidth due to the large power and chip area of the PID and the required multi-bit ADC. Thus, an additional circuit, the resistive transient assist (RTA), was proposed [4]. This paper presents an all-digital synthesizable VR using a digitally-adaptive on-time (DAOT) controller implemented in a CMOS logic process to achieve a fast recovery time.
Bipolar high voltage pulse generator without H-bridge based on cascade of positive and negative Marx generators

A parameterized bipolar high-voltage pulse generator without H-bridge based on cascade of positive and negative Marx generators is proposed. In the proposed circuit topology, the positive and negative Marx generators are connected directly without any auxiliary circuits. Each stage of the Marx generators consists of a diode, a storage capacitor, two MOSFETs switches. A 1 kV DC power supply is employed to charge the storage capacitors of the two kinds of Marx generators. The control signals of switches generated by FPGA circuit according to the setting parameters of high-voltage pulses and loads are sent to high-voltage pulse circuit via optical fiber. There are two remarkable features in the developed prototype: (1) the ability to adjust all of the parameters of the high-voltage pulse, especially the variable rising and falling time, (2) the better match properties to different loads. The key parameters of the developed bipolar high-voltage generator show as following: adjustable rising/falling time 50–500 ns, pulse width of top flat 0–1 ms, repetition rate 10 kHz (at ±15 kV and 200 pF), overshoot/undershoot less than 5%, and amplitude 0 to ±15 kV. The shape of waveform can be the rectangle and/or triangle. All the pulse parameters can be programmed arbitrarily.
Fixed-Point Convolutional Neural Network for Real-Time Video Processing in FPGA

Modern mobile neural networks with a reduced number of weights and parameters do a good job with image classification tasks, but even they may be too complex to be implemented in an FPGA for video processing tasks. The article proposes neural network architecture for the practical task of recognizing images from a camera, which has several advantages in terms of speed. This is achieved by reducing the number of weights, moving from a floating-point to a fixed-point arithmetic, and due to a number of hardware-level optimizations associated with storing weights in blocks, a shift register, and an adjustable number of convolutional blocks that work in parallel. The article also proposed methods for adapting the existing data set for solving a different task. As the experiments showed, the proposed neural network copes well with real-time video processing even on the cheap FPGAs.
Single Ring Slot-Based Antennas for Metal-Rimmed 4G/5G Smartphones

In this paper, multiband antennas based on a single ring slot are proposed for 4G/5G smartphone applications. The basic structure of the antenna is consisted of a large metal ground and an unbroken metal rim, in which a single 2 mm-wide ring slots is realized between the metal ground and rim. Here, a reconfigurable 4G antenna (820–960 and 1710–2690 MHz) is initially devised by loading multiple grounded stubs and a simple dc controlling circuit with varactor diode into the upper section of the ring slot. To further cover the sub-6 GHz spectrum (3400–3600 MHz) for future 5G communications, a four-element multi-input multi-output (MIMO) slot antennas configuration is designed by utilizing the lower section of the ring slot. A prototype antenna was fabricated, and good agreement is shown between the measured and simulated results. Due to the advantages such as multiband operation, MIMO configuration for 5G communications, high isolation, and compact structure, the proposed antenna design is attractive for 4G/5G smartphones.
ABNE: An Attention-Based Network Embedding for User Alignment Across Social Networks

User alignment across social networks can facilitate more information/knowledge transferring across networks and thereby benefit several applications, including social link prediction, cross-domain recommendation, and information diffusion. Several works try to learn a common subspace for networks by preserving the structural proximities, such that different contribution weights of neighbors are ignored as users were always connected by unweighted edges. In this paper, we propose an attention-based network embedding model that exploits the social structures for user alignment. In particular, two main components are contained in our model framework: a masked graph attention mechanism which tries to learn the alignment task driven attention weights by the supervision of pre-aligned user pairs, and an embedding algorithm tries to learn a common vector space by explicitly modeling the weighted contribution probabilities between follower-ships and followee-ships. With the learned weights and embeddings transferring between these two components, we construct a unified model for user embedding and alignment. Stochastic gradient descent and negative sampling are adopted for efficient learning and scalability. The extensive experiments on real-world social network data sets demonstrate the effectiveness and robustness of the proposed model compared with several state-of-the-art methods.
Traffic Event Related Blog Post Classification by Using Traffic Related Named Entities

Real-time monitoring of traffic flow requires physical sensors to be deployed on road networks. Development of such systems might be impractical due to deployment costs of sensors on large scale networks. This study presents a method to extract traffic event related tweets from social streams in order to employ users of social media as human sensors of traffic conditions or events. The proposed method offers a cost effective way of monitoring events or conditions affecting traffic flow. The method consists of three steps. The first step involves natural language processing tasks for preprocessing the blog posts. The second step extracts a set of traffic event related named entities from blog post texts using the model that is constructed with Conditional Random Fields. The third step includes classification in order to detect blog posts reporting events or conditions affecting traffic flow. The proposed method is experimentally evaluated on a set of tweets collected in one month under varying feature sets. The results show the potential of the approach for traffic monitoring and reveals that the use of traffic related named entities increases the classification accuracy.
Enhanced Extinction Ratio in an Asymmetric Mode-Locked Laser by Modulating Two Saturable Absorbers

The complexity of photonic integrated circuits is increasing, and the need for on-chip optical isolators becomes important in order to maintain stable on-chip laser light generation. A way to circumvent the need for such isolators is to make the on-chip lasers insensitive to feedback. A first step towards this goal could be to operate a ring laser unidirectionally. In this paper the extinction ratio between the two propagation directions in an asymmetric ring semiconductor mode-locked laser with two saturable absorbers (SAs), operating at 19.342 GHz, is experimentally investigated when modulated. The results show that the extinction ratio is increased by around 1.4 dB when the SAs are modulated with a phase difference of 90 degrees in the favored direction. According to an extrapolation of the data, unidirectional operation should be possible if the SAs are modulated with a larger depth. The modulation depth in this work is expected to be limited by the limited signal transmission through the external RF interface circuitry.
Determining sites of interaction between prenisin and its modification enzymes NisB and NisC

Although nisin is a model lantibiotic, our knowledge of the specific interactions of prenisin with its modification enzymes remains fragmentary. Here, we demonstrate that the nisin modification enzymes NisB and NisC can be pulled down in vitro from Lactococcus lactis by an engineered His‐tagged prenisin. This approach enables us to determine important intermolecular interactions of prenisin with its modification machinery within L. lactis. We demonstrate that (i) NisB has stronger interactions with precursor nisin than NisC has, (ii) deletion of the propeptide part keeping the nisin leader intact leads to a lack of binding, (iii) NisB point mutants of highly conserved residues W616, F342A, Y346F and P639A are still able to dehydrate prenisin, (iv) NisB Δ(77–79)Y80F mutant decreased the levels of NisB–prenisin interactions and resulted in unmodified prenisin, (v) substitution of an active site residue H331A in NisC leads to higher amounts of the co‐purified complex, (vi) NisB is present in the form of a dimer, and (vii) the region FNLD (‐18 to ‐15) of the leader is an important site for binding not only to NisB, but also to NisC.
Compact Model of Nonlinear Dynamics While the Cycling of a Memristor

The experimentally observed deviation of memristor’s switching voltages at the cycling is considered as a phenomenon of deterministic chaos, and not as a truly random phenomenon. It is assumed that there is a nonlinear dynamics of two phase quantities: one of them is the size of the filament (along with current direction), and the other is intrinsic. We associate it with the small charge accumulated in the filament region. The model does not claim to accurately reproduce sophisticated processes and is presented by Verilog-A description for embedding in CAD.
A functional variant at the miR‐885‐5p binding site of CASP3 confers risk of both index and second primary malignancies in patients with head and neck cancer

Caspases are important regulators and executioners in the apoptosis pathways and play crucial roles in carcinogenesis. We tested the hypothesis that functional variants of CASP genes are associated with risk of squamous cell carcinoma of the head and neck (SCCHN) and second primary malignancy (SPM). We genotyped 7 selected, potentially functional single nucleotide polymorphisms (SNPs) located in the microRNA binding sites of the 3′ untranslational region (UTR; 2 in CASP3, 1 in CASP6, and 4 in CASP7) and evaluated their associations first with risk of SCCHN in 1066 patients with SCCHN and 1074 cancer‐free control subjects and then with SPM in 846 patients in the same non‐Hispanic white study population. We found that compared with the CASP3 TT genotype of rs1049253, the variant TC/CC genotypes were associated with significantly increased risk of SCCHN (adjusted odds ratio = 1.29 and 95% confidence interval 1.07‐1.56) and SPM (adjusted hazard ratio = 1.79 and 95% CI=1.02–3.16) and worse SPM‐free survival (log‐rank P = 0.020), but no associations were found for the other 6 SNPs. We then performed additional experiments to seek functional relevance of the rs1049253 SNP. First, the luciferase activity and miR‐885‐5p mimic transfection tests suggested that CASP3 was the target of miR‐885‐5p and that rs1049253T>C resulted in altered regulation of the CASP3 expression. Second, the rs1049253 CC genotype was associated with reduced levels of CASP3 mRNA in peripheral blood mononuclear cells from 118 SCCHN patients and 103 cancer‐free control subjects and lower levels of CASP3 protein expression in 11 head and neck cancer cell lines, compared with the TT genotype. Taken together, our data suggest that the miR‐885‐5p binding site rs1049253T>C SNP in the 3′‐UTR of CASP3 modulates CASP3 expression at both mRNA and protein levels and thus contributes to SCCHN susceptibility. —Guan, X. , Liu, Z. , Liu, H. , Yu, H. , Wang, L. ‐E. , Sturgis, E. M. , Li, G. , and Wei, Q. A functional variant at the miR‐885‐5p binding site of CASP3 confers risk of both index and second primary malignancies in patients with head and neck cancer. FASEB J. 27, 1404–1412 (2013). www.fasebj. org
On the Impact of the Physical Topology on the Optical Network Performance

Network performance optimization is a poly-parametric task where usually the physical network topology is already provided and given. The latter is mainly defined by the geographical needs and the business models developed in a region that reflect the particular data traffic requirements. However, during the design of a new network, the physical topology can be considered as an additional parameter, and this work aims to study the potential impact of a network topology in the performance of the network. Two relevant metrics of optical WDM networks are evaluated, throughput and blocking ratio. Through the simulation of 350k implementations of more than 500 network topologies, we demonstrate a performance variation on networks with the same number of nodes and edges up to 73.9% on the total network load under same levels of requests blocking ratio, and up to 17.3% on the network throughput.
Autonomous Navigation of Nanomachines through Biological Motion Planning

Navigation is the process of conscious movement based on motion planning. Brain representations, investigation of those representation to predict environmental reactions, choices optimization and execution of the selected actions are several main components of Biological Motion Planning (BMP). Evidence shows that interaction and learning have an influence on BMP. In this paper considering the cellular delivery process, we use the autonomous navigation among a swarm of nanomachines. Here, cancer Targeted Drug Delivery (TDD) is considered as the case study for the proposed method. The cancer place is defined where the concentration of Vascular Endothelium Growth Factor (VEGF) is high. To do this, we first form the swarm, and then using the two types of potential functions, the swarm is guided toward cancer. Two types of potential functions as two objectives include healthy cells which should be avoided and cancer cells which should be approached. By defining a multi-objective function, we consider both types of objectives in the proposed navigation. As soon as the swarm reaches the cancer site, they start their collective therapies. The result shows the remedy of the approach in less drug usage by the healthy cells and proper drug uptake by cancer cells.
Estimation of Global Luminance for Holovizio 3D Display

Current 3D (light field, LF) displays are capable to visualize the synthetic scenes simultaneously for different viewing positions. The user-perceived image changes smoothly within the field of view of such display. The global luminance level the of user-perceived image may change drastically with the viewing direction. This level is often used in image processing algorithms, such as tone mapping, in order to improve the image quality.In this paper, we suggest a method of evaluating the global luminance level for a particular type of horizontal-parallax light field display. This method requires only a set of projector images needed for the actual rendering, and can work in real-time. We test the visual performance of this method on a synthetic scene, and as a part of the conventional spatially-uniform tone mapping algorithm.
Adaptive GA-based Potential Field Algorithm for Collision-free Path Planning of Mobile Robots in Dynamic Environments

While detecting the obstacles is the first step in the proper operation of an autonomous robot, the most vital part is the path planning. Many path planning methods like potential field rely only on the position of the obstacles and the target to determine a valid path. Since the obstacles may move in the environment, the generated path based on these algorithms won’t be optimum. Recent algorithms tend to solve this problem by adding the dynamic information of the objects to the path planning method. While this approach seems to solve the problem completely, the lack of adaptiveness in many cases will cause problems if the mobile robot moves in various environment setups. This paper proposes an adaptive GA-based potential field algorithm for collision-free path planning. The modification is done by adding a term to the negative field based on the obstacle dynamics. To enhance the quality of the potential field in different situations, the required coefficients are calculated online with an adaptive genetic algorithm. The adaptiveness of the algorithm is achieved by changing the impact of population generation methods of genetic algorithm in each iteration. To validate the method, a series of simulations and experiments are conducted and the results confirm the effectiveness of the algorithm.
Generative approach using the noise generation models for DNN-based speech synthesis trained from noisy speech

This paper proposes a generative approach to construct high-quality speech synthesis from noisy speech. Studio-quality recorded speech is required to construct high-quality speech synthesis, but most of existing speech has been recorded in a noisy environment. A common method to use noisy speech for training speech synthesis models is reducing the noise before the vocoder-based parameterization. However, such multi-step processes cause an accumulation of spectral distortion. Meanwhile, statistical parametric speech synthesis (SPSS) without vocoders, which directly generates spectral parameters or waveforms, has been proposed recently. The vocoder-free SPSS will enable us to train speech synthesis models considering the noise addition process generally used in signal processing research. In the proposed approach, newly introduced noise generation models trained by a generative adversarial training algorithm randomly generates spectra of the noise. The speech synthesis models are trained to make the sum of their output and the randomly generated noise close to spectra of noisy speech. Because the noise generation model parameters fit the spectrum of the observed noise, the proposed method can alleviate the spectral distortion found in the conventional method. Experimental results demonstrate that the proposed method outperforms the conventional method in terms of synthetic speech quality.