Miracle-dz/UniFAR-FTUs huggingface dataset:
Reflectance-based skin detection in the short wave infrared band and its application to video

Abstract. Robust reflectance-based skin detection is a potentially powerful tool for security and search and rescue applications, especially when applied to video. However, to be useful it must be able to account for the variations of human skin, as well as other items in the environment that could cause false detections. This effort focused on identifying a robust skin detection scheme that is appropriate for video application. Skin reflectance was modeled to identify unique skin features and compare them to potential false positive materials. Based on these comparisons, specific wavelength bands were selected and different combinations of two and three optical filters were used for actively identifying skin, as well as identifying and removing potential false positive materials. One wavelength combination (1072/1250 nm) was applied to video using both single- and dual-camera configurations based on its still image performance, as well as its appropriateness for video application. There are several important factors regarding the extension of still image skin detection to video, including light available for detection (solar irradiance and reflectance intensity), overall intensity differences between different optical filters, optical component light loss, frame rate, time lag when switching between filters, image coregistration, and camera auto gain behavior.
Quasiflats in CAT.0/ 2-complexes

We show that if X is a piecewise Euclidean 2‐complex with a cocompact isometry group, then every 2‐quasiflat in X is at finite Hausdorff distance from a subset Q which is locally flat outside a compact set, and asymptotically conical. 20F65
Prenatal Diagnosis of Dilated Cavum Septum Pellucidum et Vergae

The cavum septum pellucidum is a ﬂuid-ﬁlled cavity located between the membranes of the septi pellucidi, which should always be visualized sonographi-cally in normal fetuses between 18 and 37 weeks gestation and in (cid:1) 50% of term infants. The cavum vergae, a cavity within the septum pellucidum, is located posterior to an arbitrary vertical plane formed by the columns of the fornix. We present a case in which markedly dilated cavum septum pellucidum et vergae noted at 29 weeks gestation was associated with midgut malrotation and volvulus, and review the literature pertaining to this unusual prenatal ultrasonographic diagnosis.
Variability Analysis Via Parametrized Model Order Reduction

In this thesis, two efficient algorithms are presented for statistical analysis of high-speed circuits with multiple stochastic parameters in both frequency and time domains. In the frequency domain, using the proposed algorithm, a set of local reduced-order parameterized circuits are derived via an implicit multi-moment matching projection technique. The local models preserve the stochastic parameters as symbolic quantities. As a result, stochastic response of the circuit can be obtained by simulating the local reduced models instead of the original large system leading to significant reduction in the computational cost compared to traditional Monte-Carlo techniques. In the time domain, the proposed method combines the merits of the parametrized model order based techniques and Numerical Inversion of Laplace Transform (NILT). Evaluation of the time-domain response of the reduced-order models using NILT is more efficient and highly parallelizable compared to time-stepping numerical integration techniques.
Simultaneous Inference Procedures in the Presence of Heteroscedasticity

Simultaneous inference has become an increasingly important statistical tool in handling many real world problems. In a frequentist analysis, performing simultaneous inference typically requires exploring the null distribution of the multivariate test statistic in order to control the resulting inference at the pre-determined nominal level. As the probability calculation of the true null distribution involves high-dimensional integration that was computationally too difficult before the prevalence of computing resources, simultaneous inferences were only available for some special designs with specific sample size patterns in the one-way analysis of variance setting. With the dramatic rise in computing power, numerical calculation of the multivariate t probability becomes accessible, enabling one to perform simultaneous analyses in the general linear model framework assuming homogeneous variance. In this case, the null distribution of the test statistic for any set of linear hypotheses is a multivariate t distribution. In this dissertation, we devise a computationally efficient algorithm to calculate the tail areas and find the critical values of any arbitrary multivariate t distribution. The performance of the algorithm is compared to the existing method with regard to the accuracy of inference and computing time. In particular, the restriction on integer degrees of freedom of the current practice is relaxed in the new algorithm. An additional benefit of the new algorithm is the allowance for the error probability for each individual comparison to vary.
Reduction algorithms for the cryptanalysis of lattice based asymmetrical cryptosystems

REDUCTION ALGORITHMS FOR THE CRYPTANALYSIS OF LATTICE BASED ASYMMETRICAL CRYPTOSYSTEMS The theory of lattices has attracted a great deal of attention in cryptology in recent years. Several cryptosystems are constructed based on the hardness of the lattice problems such as the shortest vector problem and the closest vector problem. The aim of this thesis is to study the most commonly used lattice basis reduction algorithms, namely Lenstra Lenstra Lovasz (LLL) and Block Kolmogorov Zolotarev (BKZ) algorithms, which are utilized to approximately solve the mentioned lattice based problems. Furthermore, the most popular variants of these algorithms in practice are evaluated experimentally by varying the common reduction parameter delta in order to propose some practical assessments about the effect of this parameter on the process of basis reduction. These kind of practical assessments are believed to have non-negligible impact on the theory of lattice reduction, and so the cryptanalysis of lattice cryptosystems, due to the fact that the contemporary nature of the reduction process is mainly controlled by the heuristics.
SARS Time Series Modeling and Spatial Data Analysis

By using ArcGIS9.3 SARS information on data collection and management, it was established that the spatial database containing information on SARS epidemic factors; the activities of SARS cases range control was simulated, and the best path for treatment of SARS cases was got by spatial analysis. S-type non-linear regression function is utilized in the cumulative number of confirmed deaths and cumulative curve fitting during SARS lasted; for cumulative number of confirmed, Logistic of s-shape function fitted better, while for cumulative number of deaths, Gompertz of s-shape function fitted better. Experimental results show that the fitted curve can effectively predict the SARS epidemic, and all preparedness and proactive actions would decrease SARS fatalities.
Improvements in Nonconvective Aviation Turbulence Prediction for the World Area Forecast System

For the next generation of the World Area Forecast System (WAFS), the global Graphical Turbulence Guidance (G-GTG) has been developed using global numerical weather prediction (NWP) model outputs as an input to compute a set of turbulence diagnostics, identifying strong spatial gradients of meteorological variables associated with clear-air turbulence (CAT) and mountain-wave turbulence (MWT). The G-GTG provides an atmospheric turbulence intensity metric of energy dissipation rate (EDR) to the 1/3 power (m2/3 s–1), which is the International Civil Aviation Organization (ICAO) standard for aircraft reporting. Deterministic CAT and MWT EDR forecasts are derived from ensembles of calibrated multiple CAT and MWT diagnostics, respectively, with the final forecast provided by the gridpoint-by-gridpoint maximum of the CAT and MWT ensemble means. In addition, a probabilistic EDR forecast is produced by the percentage agreement of the individual CAT and MWT diagnostics that exceed a certain EDR threshold for turbulence (i.e., multidiagnostic ensemble). Objective evaluations of the G-GTG against global in situ EDR measurement data show that both deterministic and probabilistic G-GTG significantly improve the current WAFS CAT product, mainly because the G-GTG takes into account turbulence from various sources related to CAT and MWT. The probabilistic G-GTG forecast is more reliable at predicting light-or-greater (EDR > 0.15)- than moderate-or-greater (EDR > 0.22)-level turbulence, although it suffers from overforecasting. This will be improved in the future when we use this methodology with NWP ensembles and more observation data will be available for calibration. We expect that the new G-GTG forecasts will be beneficial to aviation users globally.
YARB: a Methodology to Characterize Regular Expression Matching on Heterogeneous Systems

The continuous growth of data pushes novel and efficient approaches for information retrieval. In this context, Regular Expression (RE) matching is widely employed and represents a relevant computational kernel that carries control-and memory-related issues. Among the several solutions to relieve these burdens, accelerators seem a promising alternative to general-purpose systems. However, state-of-the-art benchmarking presents a highly fragmented scenario without consensus on the approach and lacks an open-source strategy. Therefore, to fairly characterize existing execution engines, this work presents YARB, an open benchmarking methodology. It builds upon literature solutions, a comprehensive approach, and an in-depth characterization of heterogeneous systems. Moreover, YARB's openness will enable future integrations and engines comparison.
A High-Speed Capacitor Less LDO with Multi-Loop Fast Feedback and Bandwidth Enhancement Control

This paper presents a high-speed low dropout (LDO) regulator with wide dynamic range. The use of piecewise speed enhancement technique dividing the loop dynamic into three phases in which the current regulation circuits (CRC), large-signal derivative path control circuits addressing the design challenge of slew rate limitation, and the hybrid passive-active frequency compensation (PAFC) for small signal settling time improvements are introduced lends the proposed LDO to providing constant output voltage under the condition of large load variations. The LDO is designed in TSMC 180-nm 1.8 V standard CMOS technology with 0.17 mm2 active area. The quiescent current is 380 $\mu \mathrm{A}$ at no load. With regulated 1.2 V output, the input voltage ranges from 1.3 V to 1.8 V. The measured overshoot and undershoot with load steps of 0 to 100 mA at 50 ns edge time are 135 mV and 105 mV, respectively. The settling time at 25 mA, 50 mA and 100 mA are 2.6 $\mu \mathrm{s}, 4.5\mu \mathrm{s}$, and 9.8 $\mu \mathrm{s}$, respectively. The LDO is competent in handling a wide range of output capacitance from 0 to 5 nF while the overshoot and undershoot exhibits small variation in the load step response.
“It’s up to the Consumer to be Smart”: Understanding the Security and Privacy Attitudes of Smart Home Users on Reddit

Smart home technologies offer many benefits to users. Yet, they also carry complex security and privacy implications that users often struggle to assess and account for during adoption. To better understand users’ considerations and attitudes regarding smart home security and privacy, in particular how users develop them progressively, we conducted a qualitative content analysis of 4,957 Reddit comments in 180 security- and privacy-related discussion threads from /r/homeautomation, a major Reddit smart home forum. Our analysis reveals that users’ security and privacy attitudes, manifested in the levels of concern and degree to which they incorporate protective strategies, are shaped by multi-dimensional considerations. Users’ attitudes evolve according to changing contextual factors, such as adoption phases, and how they become aware of these factors. Further, we describe how online discourse about security and privacy risks and protections contributes to individual and collective attitude development. Based on our findings, we provide recommendations to improve smart home designs, support users’ attitude development, facilitate information exchange, and guide future research regarding smart home security and privacy.
System on Chip Testbed for Deep Neuromorphic Neural Networks

This paper describes a first prototype of a testbed System on chip (SoC) to design and evaluate different Neuromorphic Deep Neural Networks (NN) cores. The $1.25mm\times 1.25mm$ SoC was fabricated in a 65nm CMOS technology and implements a system composed of an ARM based microprocessor, two memory banks of 32KB, a QSPI serial interface and two NN accelerators. The first one is a novel neuromorphic accelerator consisting of a $5\times 5$ kernel Symmetrical Simplicial (SymSimp) core with a depthwise separable structure, which allows to efficiently implement multi-channel convolutional layers by breaking 3D kernels into 2D kernels. The second is a 3×3 conventional MAC engine to implement the fully connected layers. Experimental results show an energy efficiency of 0.49pJ/OP, which is competitive when compared to similar technology ICs, and extrapolated to the MobileNetworkV2 ImageNet represents a factor of 2 improvement with respect to NVIDIA Jetson Nano.
Powerline Energy Harvesting with Maximum Power Point Tracking for a Wide Current Range

This paper presents a powerline energy harvesting circuit to power wireless sensor nodes for powerline safety monitoring. The magnetic harvester is a ring-shaped nano-crystalline magnetic core, which transforms the ac powerline current to ac voltage. The major building blocks of the circuit are a buck-boost converter operating in discontinuous conduction mode (DCM) and a microcontroller unit (MCU) for maximum power point tracking (MPPT). The MPPT algorithm based on the perturb and observe senses the current flowing into the load and adjusts the duty cycle of the buck-boost converter to match the source impedance. The magnetic core delivers 6.98 W to an optimal $200\ \Omega$ resistor directly attached to the core under the powerline current of 30 A. The output power of the proposed circuit is 4.86 W with the optimal load resistance of $R_{L}=250\ \Omega$, resulting in the conversion efficiency of 70%.
Self-Tuned Series Resonant Power Oscillator with Load-independent Operation

This study proposes a self-tuned series resonant power oscillator capable of load-independent operation. The proposed power oscillator has a self-tuning feedback loop that provides a gate-driving voltage with a constant phase shift and amplitude. Therefore, it is robust against component tolerances in a series resonant filter. It is also robust against load variations owing to its load-independent design. Consequently, it maintains a high-efficiency and constant AC output against load variations and component tolerances. Experimental results demonstrated the effectiveness of the proposed power oscillator.
Methods to Detect Breast Cancer Using Traditional Machine Learning Technique

In this research paper, a computer-aided diagnostic system based on image processing techniques and deep learning is proposed for the classification of breast cancer in mammograms. In the preprocessing sets for image processing pectoral muscle removal, An improved mammography is created by applying contrast-limited adaptive histogram equalization algorithms for image advancement. Finally, the region of interest was generated using gradient field coverage features based thresholding technique. Next, the system uses a reformed ResNet-50 model for feature extraction. The gleaned features are then classified using three classifiers and their performance is compared. The classifiers belonging to different classification families namely AdaBoost, support vector machine (SVM), and random forest. The overall framework achieved high accuracy, sensitivity, and specificity, demonstrating its potential as a powerful tool for breast cancer diagnosis. Furthermore, the proposed image processing and feature extraction technique shows superior performance compared to individual feature sets, illustrating the significance of the suggested approach.
Reverse Engineering techniques for Android systems: A Systematic approach

The Google Play Store has over 3 million Android apps available for download, making it the largest app store in the world. With such a vast number of apps available, there is a growing need for security and analysis, which has led to an increase in the use of reverse engineering tools and techniques. In this paper we provide an overview of the main tools used in Android reverse engineering, which are used for various activities including decompiling apk files, converting Java into Intermediate Languages (IL), and inspecting an application's Java code. The study's finding shows App Cloner and Mt manager tools have the highest precision and capabilities in optimizing and analyzing the security of the applications. This paper concludes with a discussion of the most effective reverse engineering tools and the future prospects of improvements possible for these tools.
Redox-Enabled Microscale Opto-Electronically Transduced Electrodes (ReMOTEs)

We present the elements of a pH-measuring autonomous microsystem, a Redox-Enabled Microscale Opto-Electronically Transduced Electrode (ReMOTE). The ReMOTE contains two electrodes of different materials, where the nearby $\mathbf{pH}$ dictates the voltage between the two electrodes. The ReMOTE is a tetherless, all-optical microsystem which is powered and is transmitting its data optically. The ReMOTE is designed in a 180 nm CMOS SOI process, occupies merely $\mathbf{417}\ \mu \mathbf{m}\times \mathbf{48}\ \mu \mathbf{m}$, and it only consumes $\mathbf{2}\ \mu\mathbf{W}$. For the functional verification, we have conducted pH measurements using a thin film titanium nitride working electrode and a Ag/AgCl reference electrode, of which the voltage per pH sensitivity was 42.53 mV/pH.
Hardware Design of Memristor-based Oscillators for Emulation of Neurological Diseases

One of the most prominent examples of a complex system in nature is the nervous system, which exhibits oscil-lation phenomena across its structures, from single neurons to sophisticated neural networks. Memristors have been utilized in the past decade as a promising technology for building neuro-morphic systems due to their intrinsic neuromorphic properties. In this paper, we introduce a novel Memristor-based Oscillator (MBO) circuit design that implements both artificial neurons and artificial synapses in the same MBO-based medium, addressing scalability issues. The circuit design is transistor-free and simple, allowing high integration density. We utilize passive unipolar memristor devices based on the JART memristor model to design MBO neurons, which have been shown to reproduce bio-plausible spiking and bursting activities. Moreover, the MBO circuit is also realized as an artificial synapse incorporating synapse and axon mechanisms. Finally, the MBO neurons are modeled as Parkinson-related neuron types and their direct bidirectional coupling demonstrates that MBO neurons are effective in driving spiking activity in non-externally stimulated MBO neurons. This qualifies them for implementation in brain-inspired networks, providing low-cost Neurological-disease-related emulators.
A 7 nW, 1 kHz, −40–170°C Relaxation Oscillator with Switch-Leakage Compensation for Low-Power High-Temperature IoT Systems

This paper proposes a low-power relaxation oscillator for low-power high-temperature IoT systems. It generates a 959 Hz clock signal from −40 to 170°C, consuming 6.75 nW at 0.65 V. A proposed switch-leakage compensation scheme nullifies the effects of body diode and subthreshold leakages on oscillator output frequency at high temperatures, thereby obtaining a wide operating temperature range. The oscillator implemented in a 180 nm CMOS process achieves a temperature coefficient of 40 ppm/°C from −40 to 170 °C at 0.65 V and a line sensitivity of 0.5 %/V from 0.65 to 2.4 V at room temperature, in simulation. Compared with state-of-the-art sub-$\mu\mathrm{W}$ oscillators, this circuit obtains the highest operating temperature and the maximum temperature range.
Urban Macrocell Propagation Model Proposed For Future 5G Rollout

This paper presented a new Urban Macrocell (UMa) path loss model based on the classic single-frequency Close-In (CI) model improvement. We add a new parameters on the classic CI model such as the height of the transmitting and receiving stations, and a coefficient of correction. The results of a first estimation of the propagation channel model based on the standard CI model showed a large deviation (an average of 19.8 dB and 46.5 dB respectively for the LOS and NLOS case) between the two models estimated respectively when the antennas are in co- and cross-polarizations. The coefficient of correction that we added takes into account both the path loss obtained in co- and cross-polarizations in modeling the path loss of each polarization case, thereby reducing the deviation (less than 10 dB for the both LOS and NLOS conditions). The variation of the TX and RX heights combined with the coefficient of correction showed better results compared to the models estimated with the classic CI model and the 3GPP UMa model.
Photoplethysmography Based Blood Glucose Estimation Using Convolutional Neural Networks

This paper introduces the design and measurements for non-invasive blood glucose level (BGL) estimation using a convolutional neural network (CNN) based on photoplethysmography (PPG). The prototype consists of a PPG sensor connected to a microcontroller (MCU) Arduino Nano 33 BLE Sense. The PPG-only based CNN model deployed on the MCU showed 89.28% of the predicted samples in zone A of a Clarke error grid (CEG). When the mean power spectrum feature from PPG signals was included, the results demonstrated an improvement in the accuracy to be 92.85%. The proposed system is real-time and non-invasive that can be used to replace the existing invasive glucometers.
Fabrication and Assembly Techniques for Distributed Battery-Free Brain Implants

In the past three decades, we have witnessed unprecedented progress in wireless implantable medical devices (IMDs) that can interface with the nervous system. To provide an even more stable, safe, and distributed interface, a new class of implantable devices is being developed; single-channel sub-mm scale wireless devices. In this research, we describe a new and simple technique for the fabrication and assembly of a sub-mm wirelessly powered stimulating implant. The fabricated implant is composed of an ASIC that measures $\mathbf{900} \times \mathbf{450}\times \mathbf{80}\ \boldsymbol{\mu} \mathbf{m}^{\mathbf{3}}$, two PEDOT-coated microelectrodes, an SMD inductor, and a SU-8 coating. The electrodes and the SMD are directly mounted onto the ASIC. The ultra-small device is powered via electromagnetic (EM) in near-field using a 2-coil inductive link and shows a power transfer efficiency (PTE) of 0.17% in the air with coil separation of 0.5 cm.