Miracle-dz/UniFAR-FTUs huggingface dataset:
A PAM-4 10S/12S line coding scheme with equi-probable levels

We propose an equi-probable line coding scheme for pulse amplitude modulation (PAM)-4. This encoding scheme can be used for improving the spectral efficiency of high speed serial links. Equi-probable PAM symbols also ensure atleast one zero-crossing transition in every encoded word, which is sufficient enough to recover the symbol clock from the received data. The equi-probable encoded words make design of the comparators at the receiver easy by aiding an automatic threshold tracking mechanism. This proposed encoding technique has a maximum contiguous symbol run length of 8, and ensures a DC balancing of encoded signals. The proposed 10S/12S encoding scheme has an overhead of 20%, as compared to the 25% overhead in the commonly used 8B/10B schemes.
Block matching frame based material reconstruction for spectral CT

Spectral computed tomography (CT) has a great potential in material identification and decomposition. To achieve high-quality material composition images and further suppress the x-ray beam hardening artifacts, we first propose a one-step material reconstruction model based on Taylor’s first-order expansion. Then, we develop a basic material reconstruction method named material simultaneous algebraic reconstruction technique (MSART). Considering the local similarity of each material image, we incorporate a powerful block matching frame (BMF) into the material reconstruction (MR) model and generate a BMF based MR (BMFMR) method. Because the BMFMR model contains the L0-norm problem, we adopt a split-Bregman method for optimization. The numerical simulation and physical phantom experiment results validate the correctness of the material reconstruction algorithms and demonstrate that the BMF regularization outperforms the total variation and no-local mean regularizations.
F-LauReLxp: A gameful learning experience in forecasting

It is beyond question that technology determines various aspects of students’ learning process. Gamification, the application of gaming elements in non-gaming environment such as education, taking advantage of information technology, has recently gained perpetual attention as a method to increase motivation and ameliorate learning outcomes. F-LauReL is a web-based platform that hosts three gamified applications related to statistical, judgmental forecasting and forecasting accuracy respectively. Additionally, F-LauReL aims to enhance educational process around milestones research conclusions of forecasting and promote learning performance through students’ engagement. This study presents a quantitative analysis of true experimental design, using treatment and control groups. Our main result is that using gamified applications as a complementary teaching tool in a forecasting course could have a positive impact on students’ learning performance.
Maximizing Wireless Power Transfer with Single-Turn Coreless Coupled Coils

New GaN, SiC and diamond based devices are able to work up to 275°C with transients reaching 100V/nS. These specifications require a new design to supply the gate driver. At these temperatures, under these isolation constraints, the power transmission has to be realized without any magnetic material, via a magnetic field using inductive coupling between coils of wire. However, this kind of isolated power supply is currently limited by the area needed for the windings. In order to minimize the space used, this paper highlights the impact of each converter element. To this end, a model of the whole system is presented and validated experimentally. Based on the analytical expression of each element of the system, we demonstrate that single turn coils allow the power transmitted to be maximized for a given space used. Based on the proposed design, we transmitted 200mW through a 9cm2 prototype.
Active Control of μLED Arrays for Optogenetic Stimulation

Optical neural stimulation using optogenetic technique plays an important role in neuroscience applications and neuroprosthetic solutions. Most optogenetic applications require power efficient photostimulation tools that can provide sufficient optic power for stimulation of photosensitized neural cells with desired spatial and temporal resolutions. In this paper, we propose an active control of custom-assembled μLED arrays for optogenetic photostimulation tools. The designed circuit provides required forward voltage (3.3V) and current (20mA) to the corresponding μLEDs without sacrificing optic power or temporal resolution. The active control system has been realized using AMS 0.35 μm CMOS process and the performance of the design has been verified through post-layout simulation. The proposed design can operate under different forward voltages and hence, can be adopted for driving several types of μLEDs.
A 12-bit, 2.5-bit/cycle, 1 MS/s two-stage cyclic ADC, for high-speed CMOS Image sensors

A 12-bit, 1 MS/s, two-stage cyclic ADC, with novel 2.5-bit/cycle architecture is proposed in this paper. A 1.5-bit algorithm is used in a 2.5-bit framework, which reduces the required number of accurate comparators and power consumption by 42%. Further, the ADC shows 46% improvement in the conversion rate as compared to the state-of-the-art two-stage cyclic ADC. The proposed ADC is designed and fabricated in a standard 180 nm CMOS technology. The obtained values of DNL and INL are +0.5/-0.5 LSB and +0.8/-0.9 LSB respectively. The ADC consumes 0.8 mW of power and occupies an area of 0.045 mm2 with a FoM of 0.19 pJ/conversion-step. The proposed ADC when designed in a column pitch of 5.6 μm, will result in a frame-rate of 1000 frames/sec for a 1 Mpixel array.
A Scalable Fully Synthesized Phase-to-Digital Converter for Phase and Duty-Cycle Measurement of High-Speed Clocks

This work presents a Phase-to-Digital Converter (PDC) synthesisable from a standard cell library that enables the measurement of phase and duty-cycle of high-speed clocks. The resolution and sample rate of the PDC can be adjusted by the choice of the frequency of an additional input clock. This allows the use of the PDC in closed-loop systems for phase or duty-cycle adjustments and provides a way to minimize the power consumption of the circuit. Also a calculation method is proposed to model the PDC's behavior with respect to jitter. The design is tested on an FPGA with up to 650 MHz and implementation results at 2.5 GHz in a 65 nm CMOS process show the potential use of the PDC for phase alignment and duty-cycle adjustment in multi-gigabit transceivers with low hardware cost and low power.
Dermatologist Level Dermoscopy Skin Cancer Classification Using Different Deep Learning Convolutional Neural Networks Algorithms

In this paper, the effectiveness and capability of convolutional neural networks have been studied in the classification of 8 skin diseases. Different pre-trained state-of-the-art architectures (DenseNet 201, ResNet 152, Inception v3, InceptionResNet v2) were used and applied on 10135 dermoscopy skin images in total (HAM10000: 10015, PH2: 120). The utilized dataset includes 8 diagnostic categories - melanoma, melanocytic nevi, basal cell carcinoma, benign keratosis, actinic keratosis and intraepithelial carcinoma, dermatofibroma, vascular lesions, and atypical nevi. The aim is to compare the ability of deep learning with the performance of highly trained dermatologists. Overall, the mean results show that all deep learning models outperformed dermatologists (at least 11%). The best ROC AUC values for melanoma and basal cell carcinoma are 94.40% (ResNet 152) and 99.30% (DenseNet 201) versus 82.26% and 88.82% of dermatologists, respectively. Also, DenseNet 201 had the highest macro and micro averaged AUC values for overall classification (98.16%, 98.79%, respectively).
An Automatic LO Leakage Calibration Method for Class-AB Power Mixer Based RF Transmitters

Mixers in a transmitter typically use pseudo differential common-source baseband amplifiers driving local oscillator (LO) switches. The common-source amplifiers are biased near cut-off for class-AB operation which yields higher efficiency. To mitigate their non-linearity, they are usually preceded by feedback controlled pre-distortion circuitry. DC bias differences between the two pseudo-differential halves of the common-source amplifiers turns out to be a major source of LO leakage. This paper presents an automatic calibration method to reduce the LO leakage. Major contributors of LO leakage in class-AB based power mixers with enhanced linearity and baseband noise filtering is analyzed. Monte Carlo analysis shows that LO leakage is less than −35dBc for 17% of the cases without calibration and 98% of the cases with calibration.
Optimal SDRAM Buffer Allocator for Efficient Reuse of Layer IO in CNNs Inference Framework

Deep Learning based applications are becoming increasingly ubiquitous. The new generation smart phones are adapting lot of applications built on deep learning technology. However, adapting complex Deep Neural Network (DNN) applications on embedded processors is a huge challenge not only due to huge computational requirement, but also due to the massive SDRAM memory requirements of network layer IO buffers. Hence, an efficient reuse of layer IO buffers is required. However, it is challenging to reuse layer IO buffers because of complex network topology and large number of layers in the network. In this paper, we present an optimal SDRAM buffer allocator to minimize the overall SDRAM memory requirement of layer IO buffers, which works for any complex networks. The proposed SDRAM buffer allocator is integrated with ICNN (Inference only Convolutional Neural Networks) framework which is an extension of Caffe framework. Our framework with optimal SDRAM buffer allocator is experimented with popular AlexNet, GoogLeNet, ResNet-50 and Inception-ResNet-v2 CNNs. The results show 2× to 30× reduction in SDRAM footprint when compared with Caffe/TensorFlow frameworks and 26% to 47% reduction when compared with MXNet framework.
QuanFuzz: Fuzz Testing of Quantum Program

Nowadays, quantum program is widely used and quickly developed. However, the absence of testing methodology restricts their quality. Different input format and operator from traditional program make this issue hard to resolve. In this paper, we present QuanFuzz, a search-based test input generator for quantum program. We define the quantum sensitive information to evaluate test input for quantum program and use matrix generator to generate test cases with higher coverage. First, we extract quantum sensitive information -- measurement operations on those quantum registers and the sensitive branches associated with those measurement results, from the quantum source code. Then, we use the sensitive information guided algorithm to mutate the initial input matrix and select those matrices which improve the probability weight for a value of the quantum register to trigger the sensitive branch. The process keeps iterating until the sensitive branch triggered. We tested QuanFuzz on benchmarks and acquired 20% - 60% more coverage compared to traditional testing input generation.
A 65 nm Compact High Performance Fully Synthesizable Clock Multiplier Based on an Injection Locked Ring Oscillator

This paper presents a compact high performance clock multiplier based on an injection locked ring oscillator (ILRO) with replica oscillator tuning. All cells were synthesized utilizing vendor supplied digital cells and automatically placed and routed to improve scalability and speed up the design process. The ILRO, fabricated in TSMC 65 nm CMOS process technology, occupies 102 μm × 170 μm chip area. At 1.1 V and 1.7 GHz, the measured phase noise for third harmonic injection locking was found to be −128 dBc/Hz at 1 MHz offset. Furthermore, it consumes 11.77 mW and achieves 200 fs RMS jitter (1 kHz to 40 MHz) and 86 MHz locking bandwidth.
Periodontal Disease and Pregnancy Outcomes: Overview of Systematic Reviews

Periodontal disease is very common during pregnancy. Although it has been linked to adverse pregnancy outcomes, systematic reviews have reached discrepant conclusions on these links. Therefore, we conducted a systematic overview of systematic reviews studying the association between periodontal disease and adverse pregnancy outcomes. We searched 6 online databases up to November 2016 and hand-searched references and citations of eligible papers. Systematic reviews of studies comparing pregnancy outcomes among women with and without periodontal disease were eligible for inclusion. Primary outcomes were maternal mortality, preterm birth, and perinatal mortality. Two reviewers extracted data and assessed risk of bias of individual systematic reviews. Findings are described in tabular and narrative form. Twenty-three systematic reviews (including between 3 and 45 studies) were included. None reported the association between periodontal disease and maternal or perinatal mortality. Systematic reviews with the lowest risk of bias consistently demonstrated positive associations between periodontal disease and preterm birth (relative risk, 1.6; 95% confidence interval, 1.3 to 2.0; 17 studies, 6,741 participants), low birth weight (LBW; relative risk, 1.7; 95% CI, 1.3 to 2.1; 10 studies, 5,693 participants), preeclampsia (odds ratio, 2.2; 95% CI, 1.4 to 3.4; 15 studies, 5,111 participants), and preterm LBW (relative risk 3.4; 95% CI, 1.3 to 8.8; 4 studies, 2,263 participants). Based on these figures, estimated population-attributable fractions for periodontal disease were 5% to 38% for preterm birth, 6% to 41% for LBW, and 10% to 55% for preeclampsia. In terms of limitations, as several primary studies did not adjust for confounding, meta-analyses may have overestimated the strength of the associations under study. Due to substantial overlap in included primary studies, we could not aggregate results across reviews. Consistent evidence from systematic reviews with low risk of bias indicates that pregnant women with periodontal disease are at increased risk of developing preeclampsia and delivering a preterm and/or LBW baby (PROSPERO: CRD42015030132). Knowledge Transfer Statement: This study highlights that periodontal disease is an important risk factor for several common adverse pregnancy outcomes. Clinicians should be aware of this link to guide risk selection. Research is needed to develop novel preventive and treatment strategies.
Linearity- and Gain-Enhanced Wideband Transconductor Using Digitally Auto-Tuned Negative Conductance Load

A wideband, gain enhanced, high frequency, fully differential operational transconductance amplifier (OTA) with enhanced linearity is proposed. The OTA uses a negative conductance to cancel its output parasitic conductance. An automatic, digitally controlled, feedback tuning loop ensures that the parasitic conductance is tracked across corners. High linearity is achieved by voltage biasing the transistors and allowing higher headroom. The OTA uses a common mode feedback (CMFB) loop for common mode stabilization instead of diode connected transistors which enhances the achievable bandwidth. Simulations in a standard 130 nm CMOS process show a dc gain enhancement from 14 dB to 42 dB when negative conductance in incorporated across process voltage and temperature (PVT). The OTA has an unity gain bandwidth of 20 GHz. It has an input referred noise of 1.75 nV/√Hz and has −40dB total harmonic distortion for an input of 330 mVppd.
Ultra-Low Latency and High Throughput Key-Value Store Systems Over Ethernet

Key-value store (KVS) systems are playing important roles as the caches of database to improve the data access efficiency. In this paper, we propose general architectures of KVS systems based on field programmable gate arrays (FPGAs) which can adapt to different application scenarios. Data hazards introduced by the pipeline are fully handled to improve the throughput. The batch operation is proposed to support the variable-length key-value pairs. The memory address management system reduces CPU load further by managing memory addresses on hardware. We implement and evaluate our KVS systems on VC709 evaluation board targeting throughput, latency and capacity respectively. The ultra-low latency KVS system can achieve 160 ns latency and about 155 million request per second (MRPS) to retrieve 96-bit values with 96-bit keys. The ultra-high throughput KVS system can achieve about 200 ns latency and 200 MRPS throughput to retrieve the same size key-value pairs. The ultra-capacity KVS system retrieves 64-byte values with 12-byte keys, serving a maximum request rate of more than 35 MRPS with latency less than 500 ns.
Development of Hand-Held Point-of-Care Diagnostic Device for Detection of Multiple Cancer and Cardiac Disease Biomarkers

This paper presents a hand-held point-of-care diagnostic device that incorporates a lab-on-a-chip module with interdigitated capacitive biosensors for label-free detection of multiple cancer and cardiovascular disease biomarkers. The developed prototype is comprised of a capacitive bio-detection chip, a sensitive capacitive readout electronics enclosed in a hand-held unit and a data analysis software calculating the quantity of biomarkers using previously stored reference database for different concentrations of each biomarker. The capacitive bio-detection chip is based on interdigitated circular electrodes, which are pre-activated with single (for detecting one biomarker) or multiple specific antibodies (for detecting multiple disease biomarkers). Detection principle of capacitive biosensor is based on measuring the level of capacitance change between interdigitated electrode pairs of the capacitive biosensor chip induced by the change in dielectric constant due to affinity-based electron exchange in between antibodies and antigens. The more antibody-antigens binding occurs, the more capacitance change is measured due to the change in dielectric constant of the capacitance media. The device uses pre-activated ready-to-use cartridges, composed of capacitive biosensors, which can be stored 3 months under optimal conditions, and is capable of on-site diagnosis and can report the result in less than 30 minutes. The device is verified with 16 real patient blood samples for 6 different disease biomarkers.
Assessing Perturbations to Neural Spiking Response Dynamics Caused By Electrical Microstimulation

This study assesses the feasibility of latent factor analysis via dynamical systems (LFADS) for evaluating differences in the observed spiking response dynamics imposed by two electrical microstimulation regimes in awake rats. LFADS is a recently-developed deep learning method that uses stimulus-aligned neural spiking data to determine the initial neural state of each trial, as well as infer a set of time-dependent perturbations to the learned neural dynamics within trials. We show that time-dependent perturbations inferred by an LFADS model trained on spikes from trials on a single session can distinguish between different stimulation conditions. Furthermore, we use these data to exemplify how LFADS inferences track the evolution of stimulus-related spiking responses during chronic microstimulation experiments.
Changes in the Brain-Derived Neurotrophic Factor Are Associated with Improvements in Diabetes Risk Factors after Exercise Training in Adolescents with Obesity: The HEARTY Randomized Controlled Trial

Obesity in youth increases the risk of type 2 diabetes (T2D), and both are risk factors for neurocognitive deficits. Exercise attenuates the risk of obesity and T2D while improving cognitive function. In adults, these benefits are associated with the actions of the brain-derived neurotrophic factor (BDNF), a protein critical in modulating neuroplasticity, glucose regulation, fat oxidation, and appetite regulation in adults. However, little research exists in youth. This study examined the associations between changes in diabetes risk factors and changes in BDNF levels after 6 months of exercise training in adolescents with obesity. The sample consisted of 202 postpubertal adolescents with obesity (70% females) aged 14–18 years who were randomized to 6 months of aerobic and/or resistance training or nonexercise control. All participants received a healthy eating plan designed to induce a 250/kcal deficit per day. Resting serum BDNF levels and diabetes risk factors, such as fasting glucose, insulin, homeostasis model assessment (HOMA-B—beta cell insulin secretory capacity) and (HOMA-IS—insulin sensitivity), and hemoglobin A1c (HbA1c), were measured after an overnight fast at baseline and 6 months. There were no significant intergroup differences on changes in BDNF or diabetes risk factors. In the exercise group, increases in BDNF were associated with reductions in fasting glucose (β = −6.57, SE = 3.37, p = 0.05) and increases in HOMA-B (β = 0.093, SE = 0.03, p = 0.004) after controlling for confounders. No associations were found between changes in diabetes risk factors and BDNF in controls. In conclusion, exercise-induced reductions in some diabetes risk factors were associated with increases in BDNF in adolescents with obesity, suggesting that exercise training may be an effective strategy to promote metabolic health and increases in BDNF, a protein favoring neuroplasticity. This trial is registered with ClinicalTrials.gov NCT00195858, September 12, 2005 (funded by the Canadian Institutes of Health Research).
A Rectifier-less Energy Harvesting Interface Circuit for Low-Voltage Piezoelectric Transducers

This paper proposes a rectifier-less, topology for harvesting energy from low-voltage piezoelectric transducers. The proposed system is based on Synchronous Electric Charge Extraction and utilizes a bi-directional switching converter which inherently produces both positive and negative voltages that enables rectifier-free operation. A PCB prototype implemented using discrete components is tested with a commercially available transducer. The measurement results confirm the effectiveness of the topology in harvesting energy from low voltages. The bi-directional converter achieves a peak power conversion efficiency of 73 % while the control circuits consume only 7.5 μW at 3 V.
Brain-inspired recurrent neural network with plastic RRAM synapses

The development of neuromorphic systems capable of mimicking the behavior of the human brain has recently received an increasing deal of interest. However, the building of such artificial systems has been hindered by the lack of commercial technologies with nanoscale integration of synaptic devices as well as the complexity of the biological neural architecture in terms of connectivity, parallelism, and plasticity behavior. In particular, there is a wide consensus on the relevance of recurrent connections in the human brain, and their key role for associative learning and pattern classification. Fundamental primitives of cognitive computing can therefore be demonstrated by means of Recurrent Neural Networks (RNNs). In this work, we design and simulate a Hopfield-type RNN with HfO2 RRAM devices capable of learning via spike-timing dependent plasticity (STDP). We first demonstrate learning and recall of a single attractor state in a 4-neuron RNN. Based on this result, we then simulate signal restoration of two orthogonal patterns in a 64-neuron RNN, thus supporting RRAM-based RNN with cognitive computing functionalities.