Miracle-dz/UniFAR-FTUs huggingface dataset:
A Picosecond-Resolution Digitally-Controlled Timing Generator With One-Clock-Latency at Arbitrary Instantaneous Input

This brief presents an open-loop all-digital burst-mode digital-to-time converter (DTC). The output timing is converted in one-clock latency in response to any instant change of input which is a time range with a digital code. The given time range is coarsely estimated using a ring oscillator based edge-counting. It is then processed with a self-referenced phase interpolation which greatly reduces the actual input range for interpolation and achieves a good linearity in the whole conversion range. The implemented DTC in 28nm LP CMOS shows the maximum peak INL error of 5.34ps in a seamless conversion range of from 16ps to 2850ps.
Court Similar Case Recommendation Model Based on Word Embedding and Word Frequency

Mining valuable services from the massive big data of court judgment texts is the focus of current judicial informatization research. The paper proposed a novel TF-W2V comprehensive similarity model based on deep neural network to calculate the similarity of judgment texts.The proposed approach can implement semantic retrieval of judgment texts similar with cases that the users want to search, and recommend the critical information of similar cases needed by courts in the process of handling cases, which is helpful for the new case decision-making. Experimental results show that the accuracy of the proposed model is up to 37% higher than other models.
Wavelet based Fine-to-Coarse Retinal Blood Vessel Extraction using U-net Model

Segmentation of retinal blood vessels is a crucial preliminary step in the diagnosis of any retinal disease. While extracting vessel-map, the biggest challenge is to segment fine vessels which are in poor contrast with the non-vessel background. Key contribution of this work is a fine-to-coarse retinal vessel extraction model with high sensitivity. Proposed algorithm uses a directional wavelet to generate a novel multiscale, three-channel image. To generate this image, only the real coefficients of wavelet transform are used, which facilitate the extraction of fine vessel-ends. Multiple scales cover different thicknesses of vessels. The vessel-enhanced image catalyzes the learning of deep U-net model for pixel classification. This work uses STARE and DRIVE databases for experimentation. Algorithm has performed robustly well in cross-database testing, even in pathological environment. Proposed method has produced state-of-the-art results. The vessel segmentation is outstanding in terms of sensitivity measure which validates better extraction of fine vessels. In this paper, an elaborate comparison with the other existing methods is also presented.
On Effective Stochastic Mechanisms for On-The-Fly Codebook Regeneration

This paper proposes an effective mechanism for stochastic codebook generation for lossy coding, using source examples. Earlier work has shown that the rate-distortion bound can be asymptotically achieved by a "natural type selection" (NTS) mechanism which iteratively considers asymptotically long source strings (from given distribution P) and regenerates the codebook according to the type of the first codeword to "dmatch" the source string (i.e., satisfy the distortion constraint), where the sequence of codebook generating types converges to the optimal reproduction distribution. While ensuring optimality, earlier results had a significant practical flaw, due to the order of limits at which the convergence is achieved. More specifically, NTS iterations indexed by n presume asymptotically large, but the codebook size grows exponentially with ℓ. The reversed order of limits is practically preferred, wherein most codebook regeneration iterations involve manageable string lengths. This work describes a dramatically more efficient mechanism to achieve the optimum within a practical framework. It is specifically shown that it is sufficient to individually encode many source strings of short fixed length ℓ, then find the maximum likelihood estimate for the distribution Qn+1that would have generated the observed sequence of d-matching codeword strings, then use Qn+1to generate a new codebook for the next iteration. The sequence of distributions Q1,Q2,… converges to the optimal reproduction distribution $Q_\ell ^{\ast}(P,d)$, achievable at finite length ℓ. It is further shown that $Q_\ell ^{\ast}(P,d)$ converges to the optimal reproduction distribution Q∗(P,d) that achieves the rate-distortion bound R(P,d), asymptotically in string length ℓ.
Application of Duality-Based Equivalent Circuits for Modeling Multilimb Transformers Using Alternative Input Parameters

The principle of duality is applied for electromagnetic transient (EMT) modeling of industry scale (i.e. 50, 390 MVA) multilimb transformers. While saturation, hysteresis, deep-saturation, and remanent flux are accounted for, the need for transformer internal design information such as core dimension or material is eliminated. This is achieved by formulating the equivalent circuits with an alternative set of parameters that are either provided by the manufacturer or can be determined using conventional techniques. Open-circuit tests confirm that the models produce accurate excitation currents at different saturation levels when compared with measurement results. Furthermore, the models facilitate correct short-circuit condition with support for arbitrary number of windings. Upon validating the models, inrush current is simulated and the worst-case scenario is determined due to potential remanent flux values. The findings agree with an established EMT simulation model as well as manufacturer analytical approximations. Simulated hysteresis loops are also investigated.
Localization of Anatomical Features in Vascular-enhanced Enface OCT Images

Optical coherence tomography (OCT) allows in vivo volumetric imaging of the eye. Identification and localization of anatomical features in enface OCT are important steps in OCT-based image analysis. However the visibility of anatomical features in both structural OCT or vascular OCT angiography is limited. In this paper, we propose to use vascular-enhanced enface OCT image for the concurrent detection of anatomical features, using a FasterRCNN object detection framework based on convolutional networks. Transfer learning was applied to adapt pre-trained models as the backbone networks. Models were evaluated on a dataset of 419 images. The results showed that VGG-FasterRCNN achieved a mean average precision 0.77, with localization errors of 0.18 ± 0.10 mm and 0.24 ± 0.13 mm for the macula and optic disc respectively. The results are promising and suggest that this network could potentially be used to automatically and concurrently detect anatomical features.Clinical Relevance— Localization of anatomical features in enface OCT is needed for the automation of OCT image analysis protocols. The use of fast detection networks could potentially suggest image-based real-time tracking during image acquisition.
Muscles Cooperation Analysis Using Akaike Information Criteria for Anterior Cruciate Ligament Injury Prevention

In this paper, we propose the analysis method for finding out the similarity of the muscle force patterns to mine the risk factor of the anterior cruciate ligament (ACL) injury. Akaike information criteria (AIC) under the assumption of the auto-regression model is adapted to analyze the similarities of muscle force patterns in time-series. The difference of AIC values between 2 muscles is considered to be the distance between 2 muscle force patterns and the dexterity of the maneuver is expected to be discussed. We measured drop vertical jump (DVJ) and use the data around the contact timing of whom hadn’t had ACL injury experiments. The results showed that we could successfully calculate AIC distance according to the similarity of the time-series data pattern and it can be useful to discuss one’s dexterity of controlling body maneuvers soon after contact timing of DVJ motion.
Automated Detection of Juvenile Myoclonic Epilepsy using CNN based Transfer Learning in Diffusion MRI*

Epilepsy is one of the largest neurological diseases in the world, and juvenile myoclonic epilepsy (JME) usually occurs in adolescents, giving patients tremendous burdens during growth, which really needs the early diagnosis. Advanced diffusion magnetic resonance imaging (MRI) could detect the subtle changes of the white matter, which could be a non-invasive early diagnosis biomarker for JME. Transfer learning can solve the problem of insufficient clinical samples, which could avoid overfitting and achieve a better detection effect. However, there is almost no research to detect JME combined with diffusion MRI and transfer learning. In this study, two advanced diffusion MRI methods, high angle resolved diffusion imaging (HARDI) and neurite orientation dispersion and density imaging (NODDI), were used to generate the connectivity matrix which can describe tiny changes in white matter. And three advanced convolutional neural networks (CNN) based transfer learning were applied to detect JME. A total of 30 participants (15 JME patients and 15 normal controls) were analyzed. Among the three CNN models, Inception_resnet_v2 based transfer learning is better at detecting JME than Inception_v3 and Inception_v4, indicating that the "short cut" connection can improve the ability to detect JME. Inception_resnet_v2 achieved to detect JME with the accuracy of 75.2% and the AUC of 0.839. The results support that diffusion MRI and CNN based transfer learning have the potential to improve the automated detection of JME.
Drosophila Brain Functional Data Analysis: A Unified Framework

A unified framework for the analysis of fluorescence data taken by a two-photon imaging system is presented. As in the processing of blood-oxygen-level-dependent signals of functional magnetic resonance imaging, the acquired functional images have to be co-registered with a structural brain atlas before delineating the regions activated by a given stimulus. The voxels whose calcium traces are highly correlated with the predicted responses are demarcated without the need for subjective reasoning. Experimental data acquired while presenting olfactory stimuli are used to demonstrate the efficacy of the proposed schemes. The results indicate that the functional images of a Drosophila individual can be normalized into a standard stereotactic space, and the expected brain regions can be delineated adequately. This framework provides an opportunity to enable the development of a Drosophila functional connectome database.
200-Gbit/s PAM4 Generation by a Dual-Polarization Mach-Zehnder Modulator Without DAC

In this experiment, we generate PAM4 signals based on a dual-polarization Mach-Zehnder modulator (DP-MZM) without DAC. Two high-speed binary OOK signals with different driving voltages are used to modulate a DP-MZM. After modulation, the intensity of the optical OOK signal in a polarization state is 0.7 times of the other. In this way, up to 200-Gbit/s PAM4 signals can be successfully generated. We also measure the BER performance of the generated PAM4 signals after 1-km dispersion shifting fiber (DSF) transmission. Based on the experimental results, the generated 160-Gbit/s PAM4 signal can be successfully transmitted over 1-km DSF, satisfying the hard-decision forward error correction (HD-FEC) threshold of <inline-formula> <tex-math notation="LaTeX">$3.8\times 10 ^{\mathbf {-3}}$ </tex-math></inline-formula>. Moreover, the system can support up to 200-Gbit/s PAM4 signal transmission over 1-km DSF when satisfying the soft-decision forward error correction (SD-FEC) threshold of <inline-formula> <tex-math notation="LaTeX">$2\times 10 ^{\mathbf {-2}}$ </tex-math></inline-formula>.
A Nano-Watt Dual-Output Subthreshold CMOS Voltage Reference

A dual-output CMOS voltage reference is presented for ultra-low power applications that require two or more different voltage references. The <inline-formula> <tex-math notation="LaTeX">$\text{V}_{\mathrm{ REF1}}$ </tex-math></inline-formula> is designed by employing the <inline-formula> <tex-math notation="LaTeX">$\text{V}_{\mathrm{ TH}}$ </tex-math></inline-formula> difference between two devices to compensate the temperature coefficient (TC) of the thermal voltage. The <inline-formula> <tex-math notation="LaTeX">$\text{V}_{\mathrm{ REF2}}$ </tex-math></inline-formula> is generated by feeding a current mirrored from the first reference voltage’s supply current into a diode-connected-transistor load. In such a way, two different voltage references can be generated in one compact and simple design to reduce the devices and chip area significantly, compared to two separate voltage references in a conventional design. Fabricated in a 0.18-<inline-formula> <tex-math notation="LaTeX">${\mu }\text{m}$ </tex-math></inline-formula> CMOS process, the proposed CMOS voltage reference can provide two references of 331.8 and 660.3 mV with variation coefficients of 0.53% and 0.42%, respectively. The average TCs of <inline-formula> <tex-math notation="LaTeX">$\text{V}_{\mathrm{ REF1}}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$\text{V}_{\mathrm{ REF2}}$ </tex-math></inline-formula> for a temperature range of −40 to 125°C are measured as 41.7 and 24.5 ppm/°C, respectively. The line sensitivity (LS) of <inline-formula> <tex-math notation="LaTeX">$\text{V}_{\mathrm{ REF1}}$ </tex-math></inline-formula> is 0.0505 %/V with 0.6-1.8 V supply, and the LS of <inline-formula> <tex-math notation="LaTeX">$\text{V}_{\mathrm{ REF2}}$ </tex-math></inline-formula> is 0.114 %/V with 0.8-1.8 V supply. The measured results show a competitive power supply ripple rejection, and the power consumption is only 4.12 nW with 0.8-V minimum supply at 25°C, while the active area is 0.0108 mm<sup>2</sup>.
Spatio-temporal deep learning for EEG-fNIRS brain computer interface

In this paper the classification of motor imagery brain signals is addressed. The innovative idea is to use both temporal and spatial knowledge of the input data to increase the performance. Definitely, the electrode locations on the scalp is as important as the acquired temporal signals from every individual electrode. In order to incorporate this knowledge, a deep neural network is employed in this work. Both motor-imagery EEG and bi-modal EEG-fNIRS datasets were used for this purpose. The results are compared for different scenarios and using different methods. The achieved results are promising and imply that combining both temporal and spatial information of the brain signals could be really effective and increases the performance.
Low-Power Binary Neuron Circuit With Adjustable Threshold for Binary Neural Networks Using NAND Flash Memory

Recent studies have demonstrated that binary neural networks (BNN) could achieve a satisfying inference accuracy on representative image datasets. BNN conducts XNOR and bit-counting operations instead of high-precision vector-matrix multiplication (VMM), significantly reducing the memory storage. In this work, an analog bit-counting scheme is proposed to decrease the burden of neuron circuits with a synaptic architecture utilizing NAND flash memory. A novel binary neuron circuit with a double-gate positive feedback (PF) device is demonstrated to replace the sense amplifier, adder, and comparator, thereby reducing the burden of the complementary metal-oxide semiconductor (CMOS) circuits and power consumption. By using the double-gate PF device, the threshold voltage of the neuron circuits can be adaptively matched to the threshold value in the algorithms eliminating the accuracy degradation introduced by the process variation. Thanks to the super-steep SS characteristics of the PF device, the proposed neuron circuit with the PF device has an off-state current of 1 pA, representing 105 times improvement compared to the neuron circuit with a conventional metal-oxide-semiconductor field effect transistor (MOSFET) device. A system simulation of a hardware-based BNN shows that the low-variance conductance distribution (8.4 %) of the synaptic device and the adjustable threshold of the neuron circuit implement a highly efficient BNN with a high inference accuracy.
Physical Layer Secrecy Performance Analysis of Imperfect Feedback Based 4×1 MISO System

In this paper, we analyze the physical layer secrecy performance of two-bit feedback based 4 × 1 multiple-inputsingle-output communication system in the presence of a single antenna equipped passive eavesdropper over Rayleigh faded channel. To achieve full-rate and spatial diversity real orthogonal space-time block code is employed in the considered wiretap communication system. In a practical wireless communication system, it is not always possible to acquire full channel knowledge. Thereby, in this paper, we exploit the partial channel state information at the transmitter with the help of two-bit quantized feedback based diagonal precoder. Further, exact closed-form expressions of the secrecy outage probabilities are derived over perfect and imperfect feedback bits by using order statistics. It is shown in the numerical results that an optimized power allocation scheme successfully improves secrecy performance gain under erroneous feedback in comparison to other schemes.
Multilocus coalescent species delimitation reveals widespread cryptic differentiation among Drakensberg mountain-living freshwater crabs (Decapoda : Potamonautes)

Abstract. Cryptic lineages present major challenges for evolutionary and conservation studies, particularly where these lineages remain undiscovered. Freshwater crabs are known to harbour cryptic diversity, in most cases with limited morphological differences. During the present study, we used a multilocus (12S rRNA, 16S rRNA, COI, 28S rRNA, DecapANT and PEPCK) Bayesian species delimitation to examine cryptic diversity within a freshwater crab species complex (Potamonautes clarus/P. depressus). We sampled 25 highland rivers in the Tugela and uMkomazi River drainage systems of the Drakensberg Mountain range, in the KwaZulu–Natal province of South Africa. Our results showed there to be at least eight lineages: six novel potamonautid freshwater crabs, and two described taxa P. clarus and P. depressus. Divergence from the most recent common ancestor occurred between the mid- and late Miocene (12.1 Mya), while divergence within the species complex occurred ∼10.3 Mya up until the Holocene (0.11 Mya). The discovery of six novel lineages of freshwater crabs from a seemingly restricted distribution range has conservation implications, but to date most conservation planning strategies have focussed on freshwater vertebrates. By conducting a fine-scale phylogenetic survey using invertebrates, this study provides a platform for the inclusion of freshwater invertebrates in future conservation assessments.
Photoplethysmographic Subject Identification by Considering Feature Values Derived from Heartbeat and Respiration

This research proposes a subject identification method using PPG (Photoplethysmogram) signals towards continuous authentication. The proposed method uses feature values derived from heartbeat and respiration extracted from PPG signals by means of frequency filtering and MFCC (Mel-Frequency Cepstrum Coefficients) to identify subjects. An experiment was conducted using an open dataset containing PPG signals to investigate the identification performance of the method. The feature values were extracted from the PPG signals and classifiers were generated to evaluate the performance of the method. As a result, the proposed method was found to be capable of identifying 46 people with the accuracy of 92.9 % by using feature values derived from heartbeat and respiration.
First-Order Typed Fuzzy Logics and their Categorical Semantics: Linear Completeness and Baaz Translation via Lawvere Hyperdoctrine Theory

It is known that some fuzzy predicate logics, such as Łukasiewicz predicate logic, are not complete with respect to the standard real-valued semantics. In the present paper we focus upon a typed version of first-order MTL (Monoidal T-norm Logic), which gives a unified framework for different fuzzy logics including, inter alia, Hajek’s basic logic, Łukasiewicz logic, and Gödel logic. And we show that any extension of first-order typed MTL, including Łukasiewicz predicate logic, is sound and complete with respect to the corresponding categorical semantics in the style of Lawvere’s hyperdoctrine, and that the so-called Baaz delta translation can be given in the first-order setting in terms of Lawvere’s hyperdoctrine. A hyperdoctrine may be seen as a fibred algebra, and the first-order completeness, then, is a fibred extension of the algebraic completeness of propositional logic. While the standard real-valued semantics for Łukasiewicz predicate logic is not complete, the hyperdoctrine, or fibred algebraic, semantics is complete because it encompasses a broader class of models that is sufficient to prove completeness; in this context, incompleteness may be understood as telling that completeness does not hold when the class of models is restricted to the standard class of real-valued hyperdoctrine models. We expect that this finally leads to a unified categorical understanding of Takeuti-Titani’s fuzzy models of set theory.
A Game Theoretic Approach to Decision Making in Neuronal Networks

We formulate as a game, the dynamic interaction scenario among three populations of neurons with different functions and capabilities. Specifically, we consider two populations of excitatory neurons where each population is responsible for a direction of movement, say, right and left. Both the excitatory neuron populations are connected to an inhibitory neurons population. Each excitatory population wants to take control of the movement. We formulate a game theoretic view of this competition. Specifically, we assume that the activity level of each neuronal population is quantized in to two levels. We transform the dynamical system model that exists in the literature, see for example [1], for this problem to create the game utilities. We characterize, through the replicator dynamics of the evolutionary game, the evolutionary stable strategies and find conditions under which they hold. Finally, we use the phase portrait to show the evolution of the evolutionary stable strategies from different initial conditions. We find that mixed strategies cannot be a part of the game solution. In addition, we find that the case of no or low activity is the worst case and there are no initial conditions or neurons coordination that can overcome it.
The Structural Model of Diffuse Equations in Solar Photovoltaic Cells and the Economic Development for a Social Extreme Climate: México

The study is constructed on the upper-middle class families with an economic and social development based on monetary savings derived from the implementation of solar photovoltaic cells and the related consequent social benefit in cities with extreme climates in Mexico. The heuristic proposal that includes diffuse logic (Modus Ponens, Mamdani-Zadhe) in the latent variables for the Structural Equation Model (SEM) and, therefore, be able to find unobserved values of qualitative and quantitative indicators easily. As well as to obtain the value applied in multiple linear regression to generate equations that represent the phenomenon.
Development and Characterization of a PCB-Based Microfluidic YChannel*

Since the introduction of microfluidics in the 1990s, the community has investigated numerous methods for their fabrication. However, there is interest for their inexpensive rapid prototyping. PCB technology as a low-cost, massfabrication approach for the realization of sensors has attracted attention, while its use for microfluidics is also gaining ground. In this paper the development and characterization of a microfluidic Y-channel is presented. The proposed design and assembly process for realizing the device is described in detail and the flow rate and mixing within the microchannel are characterized, demonstrating the feasibility of the proposed novel technology for lab-on-PCB devices.