Miracle-dz/UniFAR-FTUs huggingface dataset:
Intrinsic Light Field Decomposition and Disparity Estimation with Deep Encoder-Decoder Network

We present an encoder-decoder deep neural network that solves non-Lambertian intrinsic light field decomposition, where we recover all three intrinsic components: albedo, shading, and specularity. We learn a sparse set of features from 3D epipolar volumes and use them in separate decoder pathways to reconstruct intrinsic light fields. While being trained on synthetic data generated with Blender, our model still generalizes to real world examples captured with a Lytro Illum plenoptic camera. The proposed method outperforms state-of-the-art approaches for single images and achieves competitive accuracy with recent modeling methods for light fields.
CascadeNet: Modified ResNet with Cascade Blocks

Different enhanced convolutional neural network (CNN) architectures have been proposed to surpass very deep layer bottleneck by using shortcut connections. In this paper, we present an effective deep CNN architecture modified on the typical Residual Network (ResNet), named as Cascade Network (CascadeNet), by repeating cascade building blocks. Each cascade block contains independent convolution paths to pass information in the previous layer and the middle one. This strategy exposes a concept of “cross-passing” which differs from the ResNet that stacks simple building blocks with residual connections. Traditional residual building block do not fully utilizes the middle layer information, but the designed cascade block catches cross-passing information for more complete features. There are several characteristics with CascadeNet: enhance feature propagation and reuse feature after each layer instead of each block. In order to verify the performance in CascadeNet, the proposed architecture is evaluated in different ways on two data sets (i.e., CIFAR-10 and HistoPhenotypes dataset), showing better results than its ResNet counterpart.
An HMM-Based Localization Scheme Using Adaptive Forward Algorithm For LTE Networks

User location is important for network operators to perform the network management and services. This paper considers the problem of fingerprinting localization in the LTE networks. Fingerprint information is extracted from measurement reports (MRs) gathered at the network side. We present an Adaptive Forward (AFW) localization algorithm based on a hidden Markov model (HMM), where the hidden states are the locations of the User Equipments (UEs), and the observations are the Reference Signal Received Power (RSRP) vectors extracted from MRs. The transition probability of the HMM is trained based on the neighboring relationship between the RSRP vectors to eliminate the need for any sensor or additional information. Furthermore, in AFW algorithm, the adaptive switching strategy between forward algorithm and single point localization is proposed to improve the localization accuracy by suppressing the potential accumulated errors. Extensive experiments operated with data sets from real LTE networks show that, the median localization error of the proposed algorithm is around 23m for outdoor UEs, which is comparable to the related works which need additional information.
Formal Verification of Datarace in Safety Critical ARINC653 compliant RTOS

Safety properties of a real time embedded system must be verified throughout the execution trace of the system. Formal verification through model checking complements the conventional testing. We propose a technique for verification of datarace in the kernel library of an ARINC 653 based safety critical real time operating system. The property to be verified is specified in-terms of linear temporal logic formula. We also define a methodology to model the kernel of a real time operating system with two level scheduling viz. partition level and process level.
Fully Differential Single Resonator FM Gyroscope Using CW/CCW Mode Separator

This paper reports a fully differential frequency modulated (FM) gyroscope where the superposed clockwise (CW) and counter clockwise (CCW) modes are independently controlled on a single resonator. The CW and CCW mode detectors were developed to control both modes simultaneously on the resonator. A frequency and quality factor mismatch compensation method was developed to perfectly orthogonalize these two modes. A FM gyroscope operation with excellent linearity between applied angular rate and frequency difference was demonstrated. The temperature coefficient of scale factor was as small as −52 ± 136 ppm/°C. The scale factor and bias were stable over 700 hours with the drift less than 0.062 ± 0.100 ppm/hr and 31 ± 121 ppm/hr, respectively. [2018-0103]
Modulation Techniques for Common-Mode Voltage Reduction in the Z-Source Ultra Sparse Matrix Converters

Common-mode voltage (CMV) is known as a major cause of premature motor failures. The form and amplitude of the CMV generated by any converter depends on both the circuit topology and modulation technique. Among single stage ac–ac converters employed in drive applications, the Z-source ultra sparse matrix converter (ZSUSMC) is a desirable choice due to reduced number of switches in its structure and voltage boosting capability. Based on these considerations, this paper aims to: 1) theoretically determine and compare the CMV of the ZSUSMC with four well-known impedance networks, namely, cascaded, switched-inductor, quasi, and series, in its structure and 2) propose modulation techniques for CMV reduction in the ZSUSMCs. To this end, first, it is shown that the ZSUSMC, depending on the impedance network used in its structure, provides more freedom to choose from redundant switching states compared to the ultra sparse matrix converter. Then, by taking advantage of this feature, novel modulation strategies that attenuate the CMV without affecting the boosting capability are developed. The proposed modulation schemes are based on avoiding the switching states most contributing to the CMV during a switching period. Through hardware-in-the-loop studies, the effectiveness of the proposed modulation strategies in reducing the CMV of the ZSUSMCs is verified.
Hardware Design of Real Time Epileptic Seizure Detection Based on STFT and SVM

Closed-loop stimulation of many neurological disorders, such as epilepsy, is an emerging technology and regarded as a promising alternative for surgical and drug treatment. In this paper, a real-time seizure detection algorithm based on STFT and support vector machine (SVM) and its field-programmable gate array (FPGA) implementation are proposed. With a two-stage patient-specific channel selection and feature selection mechanism, those redundant and uncorrelated spectral features are removed from the entire feature set. The evaluation results on CHB-MIT epilepsy database show that the mean detection latency of the proposed algorithm is 6 s, the sensitivity is 98.4%, and the false detection rate is 0.356/h. The performance of our proposed algorithm is comparable to other existing seizure detection algorithms. Moreover, we implement the proposed seizure detection algorithm on Xilinx Zynq-7000 XC7Z020 with high level synthesis. Each classification of the input electroencephalography signal can be finished within 313 <inline-formula> <tex-math notation="LaTeX">$\mu \text{s}$ </tex-math></inline-formula>, and the power consumption of the programmable logic is only 380 mW at 100 MHz. In hardware implementation, an optimization strategy for the nested-loop structure within nonlinear SVM is proposed to improve pipeline efficiency. Compared with existing method, the experimental result shows that our method can speed up the nonlinear SVM by <inline-formula> <tex-math notation="LaTeX">$1.70\times $ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$1.53\times $ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$1.37\times $ </tex-math></inline-formula>, and <inline-formula> <tex-math notation="LaTeX">$1.26\times $ </tex-math></inline-formula> with the unroll factor equal to 1–4 at the same DSP utilization rate. The evaluation results affirm the possibility of integrating the proposed algorithm and FPGA implementation into a wearable seizure control device.
Interactive Segmentation of Glioblastoma for Post-surgical Treatment Follow-up

In this paper, we present a novel framework for interactive segmentation of glioblastoma in contrast-enhanced Tl-weighted magnetic resonance images. U-net based-fully convolutional network is combined with an interactive refinement technique. Initial segmentation of brain tumor is performed using U-net, and the result is further improved by including complex foreground regions or removing background regions in an iterative manner. The method is evaluated on a research database containing post-operative glioblastoma of 15 patients. Radiologists can refine initial segmentation results in about 90 seconds, which is well below the time of interactive segmentation from scratch using state-of-the-art interactive segmentation tools. The experiments revealed that the segmentation results (Dice score) before and after the interaction step (performed by expert users) are similar. This is most likely due to the limited information in the contrast-enhanced Tl-weighted magnetic resonance images used for evaluation. The proposed method is computationally fast and efficient, and could be useful for post-surgical treatment follow-up.
Experimental Recording and Assessing Gait Phases Using Mobile Phone Sensors and EEG

Human manner of walking characterized by kinematic parameters measure posture-gait control characterizing the dynamic changes in body parts with the involvement of multi-sensory patterns processed by different parts of the brain. In this study, low-cost sensors have been used to collect gait signals and identify the features responsible for differentiating the gait phases (swing/stance). Dataset was obtained for a total of 160 trails with 5 gait cycles per trail from healthy volunteers $(\mathbf{n}=20)$. Torque involved during progressive gait was also estimated to model regulation of the body for maintaining balance in gait and posture. Additionally, we also investigated EEG and gait correlates by identifying the brain regions that are active during movement initiation and during stance and swing (a progressive gait) using electroencephalography. While identifying key biomarkers relevant for posture control and gait, this could enhance low-cost detection of movement related diseases in technically challenged regions.
ACORB – An ACO and ORB based Hybrid Image Feature Detector

Feature detection is most crucial stage in image identification in computer vision, which helps computer recognizing the image. This work is part of a research for the Indian currency recognition for blind people. Each country has its own currencies with unique features, colors, denominations and international value. As we move towards first quarter of 21st Century, world is facing various issues like terror-funding, smuggling and that has lead to the printing of fake currencies. Due to this, many a times a person would never be able to know that the currency which one is holding is genuine or fake. This can only be decided if one knows all the features of the currency. However, for a common man, it is not possible to remember the features of the currency; especially for blind person it is not at all possible. Though the denomination can easily be recognized for a currency but it becomes difficult to identify a counterfeit currency from the real one. This paper proposes an ACO based novel concept for feature detection using ORB feature detector, named ACORB. It has been tested thoroughly to check its effectiveness and the concept seems promising based on its results. The main motive of this work is to design and develop an algorithm for Indian currency recognition in the regional languages to help the visually challenged people to recognize the currency denomination and to check if the currency is fake or genuine.
Fuzzy-Model-Based Nonfragile Control for Nonlinear Singularly Perturbed Systems With Semi-Markov Jump Parameters

This paper is concerned with the fuzzy-model-based nonfragile control problem for discrete-time nonlinear singularly perturbed systems with stochastic jumping parameters. The stochastic parameters are generated from the semi-Markov process. The memory property of the transition probabilities among subsystems is fully considered in the investigated systems. Consequently, the restriction that the transition probabilities are memoryless in widely used discrete-time Markov jump model can be removed. Based on the T-S fuzzy model approach and semi-Markov kernel concept, several criteria ensuring $\delta$-error mean square stability of the underlying closed-loop system are established. With the help of those criteria, the designed procedures which could well deal with the fragility problem in the implementation of the proposed fuzzy-model-based controller are presented. A technique is developed to estimate the permissible maximum value of singularly perturbed parameter for discrete-time nonlinear semi-Markov jump singularly perturbed systems. Finally, the validity of the established theoretical results is illustrated by a numerical example and a modified tunnel diode circuit model.
Influence of Subwavelength Grating Asymmetry on Long-Wavelength Infrared Transmittance Filters

Subwavelength dielectric gratings provide a means of narrowband spectral filtering by coupling to quasi-guided leaky modes, providing a high reflectivity background and narrowband transmittance via Fano resonance behavior. Geometrically asymmetric two-step gratings enable normal incidence filtering by accessing symmetry-protected leaky modes. The impact of asymmetry on leaky mode coupling and optical filter response are studied via simulation and described by the Fano resonance theory. The influence of asymmetry introduced by grating step height and angular cone of incidence are verified experimentally in silicon/air gratings in the long-wavelength infrared (8–14 μm).
New Indicator For Edema Evaluation

A new calculated index is proposed for estimating the volume of extracellular fluid in the soft tissues of the extremities: impedance fluid index (IFI). This indicator, calculated when performing bioempedance measurements of tissues, can be used to assess the severity and nature of the edema of tissues of any etiology. The use of the index for performing low-frequency phase-selective impedanceometry in patients with secondary (postmastectomic) lymphedema of the upper limb is described. The high informative value of the indicator was demonstrated, both in the initial diagnosis and in evaluating the effectiveness of the treatment.
Resource Constrained Deep Reinforcement Learning

In urban environments, resources have to be constantly matched to the “right” locations where customer demand is present. For instance, ambulances have to be matched to base stations regularly so as to reduce response time for emergency incidents in ERS (Emergency Response Systems); vehicles (cars, bikes among others) have to be matched to docking stations to reduce lost demand in shared mobility systems. Such problems are challenging owing to the demand uncertainty, combinatorial action spaces and constraints on allocation of resources (e.g., total resources, minimum and maximum number of resources at locations and regions). Existing systems typically employ myopic and greedy optimization approaches to optimize resource allocation. Such approaches typically are unable to handle surges or variances in demand patterns well. Recent work has demonstrated the ability of Deep RL methods in adapting well to highly uncertain environments. However, existing Deep RL methods are unable to handle combinatorial action spaces and constraints on allocation of resources. To that end, we have developed three approaches on top of the well known actor-critic approach, DDPG (Deep Deterministic Policy Gradient) that are able to handle constraints on resource allocation. We also demonstrate that they are able to outperform leading approaches on simulators validated on semi-real and real data sets.
Automatic Assessment Item Bank Calibration for Learning Gap Identification

The benefit of assessments (formative or summative) is fully realized when they result in accurate identification of learning gaps. AMrita Personalized Learning and Evaluation (AMPLE) platform adopts a unified approach to pinpoint learning gaps by integrating student performance data from paper-and-pen assessments and computerized adaptive tests. In order to aid the estimation of learning gaps, we have also developed a calibration technique for automatic assignment of difficulty levels to assessment items. This calibration technique uses statistical features derived from student performance data and a Gaussian Mixture Model (GMM) to effectively identify difficulty levels of assessment items. We also show our verification of this model using a diverse data set of student assessments spread over six subjects and 6000 students. Our model achieved about 91% accuracy by comparing the model-generated output with teacher-supplied difficulty levels. By auto-calibrating the assessment items, we pave the way for accurate learning gap analysis, obviating significant efforts from the teacher.
Effect of Edge passivation on the Electronic Properties of Zigzag Phosphorene Nanoribbons (ZPNRs) Antidots

In this paper, we report the electronic properties of Zigzag Phosphorene Nanoribbons(ZPNRs) when passivated using different atoms. All electronic properties were calculated using the Density Functional Based Tight Binding(DFTB) Method which provide result similar to DFT methodbut saves computational time. Single layer PNRs structure was optimized and simulated with Hydrogen, Oxygen and without any passivation. The bandstructure, Density of States(DOS) both confirm the superiority of H passivation over others. The same calculation is repeated for the ZPNRs based antidot lattice. We observe that although ZPNRs reduce bandgap in antidot structure irrespective of the passivation, H passivated antidot lattice provide a bandgap in semiconducting range whereas other passivations return metallic behaviour.
Materials, Processes, and Markets for Monolithic III-V Devices in Silicon Integrated Circuits

Materials and processes to create monolithic 111-V+CMOS circuits have been developed iteratively with inputs from potential market application needs and semiconductor industry infrastructure. The GaN LED and III-V HEMT platforms are earliest to develop and driven by currently anticipated market needs. The developed process flow involves conventional 200mm CMOS front-end processing in a CMOS foundry, merging the CMOS wafer with a III-V/Si wafer, processing III-V devices in a silicon fabrication environment, and final interconnection via returning the wafer to a CMOS foundry for back-end interconnection. III-V+CMOS silicon ICs are designed in a Cadence environment using foundry PDKs modified with insertion of the integrated III-V device models. The overall method described above is invariant for the different III-V's that are integrated into silicon ICs. Circuits have been designed using GaN LED+CMOS, GaN HEMT+CMOS, InGaAs HEMT+CMOS, and InGaP LED+CMOS platforms.
From attributes to faces: a conditional generative network for face generation

Recent advances in computer vision have aimed at extracting and classifying auxiliary biometric information such as age, gender, as well as health attributes, referred to as soft biometrics or attributes. We here seek to explore the inverse problem, namely face generation based on attribute labels, which is of interest due to related applications in law enforcement and entertainment. Particularly, we propose a method based on deep conditional generative adversarial network (DCGAN), which introduces additional data $( e.g.$, labels) towards determining specific representations of generated images. We present experimental results of the method, trained on the dataset CelebA, and validate these based on two GAN-quality-metrics, as well as based on three face detectors and one commercial off the shelf (COTS) attribute classifier. While these are early results, our findings indicate the method’s ability to generate realistic faces from attribute labels.
Novel Ku-Band Series-Fed Patch Antenna Array With Enhanced Impedance and Radiation Bandwidth

In this paper, a novel design of a patch antenna with two symmetrical slits and two closely spaced resonances is proposed. A novel patch antenna demonstrates a great flexibility in impedances adjustment at both resonances that are of a great importance in the design of series-fed antenna arrays with a large number of radiating elements. Printed antenna with two linear arrays consisting of 16 serially fed patches is designed and fabricated. Measured results agree very well with simulations. The antenna operation frequency range is 15.6–17.3 GHz that is 10.3% of a relative bandwidth. The measured antenna gain is 19.5 dBi with a small variation of 1.2 dB within the whole operating bandwidth, sidelobe levels are below −22 dB in the H-plane and −11.5 dB in the E-plane. The half-power beamwidth is less than 8° and 40°, in H- and E-planes, respectively. The antenna array exhibits the enhanced impedance and radiation bandwidth in comparison with similar antenna structures and is very suitable for various radar applications, notably for compact, low-cost frequency-modulated continuous wave systems.
Unambiguous Velocity Estimation for Multichannel SAR Systems

Multi-channel synthetic aperture radar (SAR) systems is widely used in ground motion target indication and motion parameter estimation. This paper presents a new unambiguous speed estimation algorithm for multi-channel SAR systems. This paper presents a novel unambiguous velocity estimation algorithm for multichannel SAR systems. The proposed velocity estimation algorithm is a multistage procedure: the first stage is to generate a group of multichannel data with the reduced PRF; in the second stage, adaptive clutter suppression and velocity estimation using spatial spectral processing technique are applied for the two groups of multichannel data to calculate two ambiguity velocities for each moving target. Finally, the unambiguous radial velocity can be estimated using the two ambiguity velocities. The effectiveness of the proposed unambiguous velocity estimation approach is verified by simulated multichannel SAR data.
Online Estimation of Power System Inertia Using Dynamic Regressor Extension and Mixing

The increasing penetration of power-electronic-interfaced devices is expected to have a significant effect on the overall system inertia and a crucial impact on the system dynamics. In future, the reduction of inertia will have drastic consequences on protection and real-time control and will play a crucial role in the system operation. Therefore, in a highly deregulated and uncertain environment, it is necessary for transmission system operators to be able to monitor the system inertia in real time. We address this problem by developing and validating an online inertia estimation algorithm. The estimator is derived using the recently proposed dynamic regressor and mixing procedure. The performance of the estimator is demonstrated via several test cases using the 1013-machine ENTSO-E dynamic model.