Miracle-dz/UniFAR-FTUs huggingface dataset:
Integrating Denoising Autoencoder and Vector Taylor Series with Auditory Masking for Speech Recognition in Noisy Conditions

We propose a new front-end feature compensation technique to improve the performance of Automatic Speech Recognition (ASR) systems in noisy environments. First, a Time Delay Neural Network (TDNN) based Denoising Autoencoder (DAE) is considered to compensate the noisy features. The DAE provides good gain in performance when it has been trained using the noise present in the test utterances (“seen” conditions). However, if the noise present in the test utterance is different to what was used in the training of the DAE (“un-seen” conditions), then the performance degrades to a great extent. To improve the ASR performance in such unseen conditions, a model compensation technique, namely the Vector Taylor Series with Auditory Masking (VTS-AM) is used. We propose a new Signal-to-Noise Ratio (SNR) based measure, which can reliably choose the type of compensation to be used for best performance gain. We show that the proposed technique improves the ASR performance significantly on noise corrupted TIMIT and Librispeech databases.
Robust Scene Text Detection with Deep Feature Pyramid Network and CNN based NMS Model

Scene text detection has attracted great interest from the computer vision and pattern recognition communities since text information plays an important role in image indexing and scene understanding. Deep neural networks have become popular for the task of scene text detection, especially for their ability to learn strong text features. However, existing deep learning based state-of-the-art scene text detection methods detect texts only from a single feature map which is unable to capture semantic information at all scales. In this paper, we propose a novel deep learning based model that leverages the pyramid structure of feature maps for accurate scene text detection. We also design a deep convolutional neural network model for non-maximum suppression. In addition, we develop a novel loss function and training method for end-to-end training. The experimental results validate that our end-to-end system is simple, fast, and achieves high accuracy on standard datasets, namely, ICDAR 2015 and MSRA-TD500. We also create a dataset for scene text detection.
Maximum Conditional Probability Stochastic Controller for Scalar Linear Systems with Additive Cauchy Noises

In this work a stochastic controller, motivated by the sliding mode control methodology, is proposed for linear, single-state system with additive Cauchy distributed noises. The control law utilizes the time propagated probability density function (pdf) of the system state given measurements that has been derived in recent studies addressing the Cauchy estimation problem. The motivation for the proposed approach is mainly the high numerical complexity of the currently available methods for such systems. The controller performance is evaluated numerically and compared to an alternative approach presented recently and to a Gaussian approximation to the problem. A fundamental difference between the Cauchy and the Gaussian controllers is their response to noise outliers. While all controllers respond to process noises, even to the outliers, the Cauchy controllers drive the state faster towards zero after those events. On the other hand, the Cauchy controllers do not respond to measurement noise outliers, while the Gaussian does. The newly proposed Cauchy controller exhibits similar performance to the previously proposed one, while requiring lower computational effort.
Methodology to Generate Approximate Circuits to Reduce Process Induced Degradation in CNFET Based Circuits

A systematic methodology is presented to generate approximate circuits with fewer nodes and shorter paths to reduce process induced degradation due to imperfect process in emerging technologies such as CNFET. In a 16-bit CNFET adder example, at PCNTopen =5%, two resulted approximate adders achieve 80.5% and 90.2% circuit-level pass rate with a penalty of 3.3% and 24.0% in relative logic error, respectively, in comparison with 12.5% pass rate for the precision counterpart. The study paves the path to practically utilize such technology for error-resilient applications.
SIW H-Spaced Head slot antenna for WLAN/GIFI Applications

In this paper, Proposes SIW slot antenna for 57-64 GHz frequency with resonant frequency of 60GHz and shape of slot is used for this design is H-Spaced Head. This frequency is used for WLAN$/\mathrm{G}\mathrm{I}\Gamma \mathrm{I}$ applications. This antenna is designed by using rogers substrate material with $\epsilon_{\gamma}=2.2$ and substrate height is 0.381 mm. The microstrip feed is used for this design with an input impedance of 50ohms. The structure provides 3. 96GHz impedance bandwidth in between 57-64 GHz band and this band is perfectly matched with VSWR 2:1. The values of the Reflection coefficient, VSWR, Gain, transmission and radiation efficiency of proposed antenna at 60GHz are −30.862dB, 1.05, 7. 2dBi, 92.35% and 82.5%.
Sliding Mode Controller-A Nonlinear Approach to Non-Isolated Cuk Converter for Constant Voltage Application

In this work a non-isolated CUK converter is modelled and output voltage is controlled by using sliding mode controller. First CUK converter is designed and analysed by a set of non linear equations, then it is converted into transfer function model using averaging technique. A sliding mode controller is designed to maintain load voltage constant. The efficiency of the converter system is evaluated against step load and input voltage variations. All the results are analysed in Matlab/Simulink platform and presented in this paper.
Computer Vision and Machine Learning for Viticulture Technology

This paper gives two contributions to the state-of-the-art for viticulture technology research. First, we present a comprehensive review of computer vision, image processing, and machine learning techniques in viticulture. We summarize the latest developments in vision systems and techniques with examples from various representative studies, including, harvest yield estimation, vineyard management and monitoring, grape disease detection, quality evaluation, and grape phenology. We focus on how computer vision and machine learning techniques can be integrated into current vineyard management and vinification processes to achieve industry relevant outcomes. The second component of the paper presents the new GrapeCS-ML database which consists of images of grape varieties at different stages of development together with the corresponding ground truth data (e.g., pH and Brix) obtained from chemical analysis. One of the objectives of this database is to motivate computer vision and machine learning researchers to develop practical solutions for deployment in smart vineyards. We illustrate the usefulness of the database for a color-based berry detection application for white and red cultivars and give baseline comparisons using various machine learning approaches and color spaces. This paper concludes by highlighting future challenges that need to be addressed prior to successful implementation of this technology in the viticulture industry.
A Consolidated Approach to Convolutional Neural Networks and the Kolmogorov Complexity

The ability to precisely quantify similarity between various entities has been a fundamental complication in various problem spaces specifically in the classification of cellular images. Contemporary similarity measures applied in the domain of image processing proposed by the scientific community are mainly pursued in supervised settings. In this work, we will explore the innovative algorithmic normalized compression distance metric based on the information theoretic concept of Kolmogorov Complexity. Additionally we will observe its possible implementation in Convolutional Neural Networks to facilitate and automate the classification of Retinal Pigment Epithelial cell cultures for use in Age Related Macular Degeneration Stem Cell therapy in an unsupervised setting.
Multilayered and Multiband SRR Loaded CB-CPW Fed Antenna for On-Body Monitoring Applications

The SRR loaded circular ring encircled by an proposed structure with conductor backed coplanar wave guide fed with small size antenna is outstandingly necessary part for wearable transceiver device module for healthcare interfacing application. This antenna designed to fine-tune at resonating frequency 3.5GHz, 3.66GHz, 4.1GHz, 6.7GHz, and 9.3GHz. The simulated results for the magnitude of reflection coefficients are −14dB at 3.5GHz, −13dB at 2.48GHz, −35dB at 4.1GHz, −31dB at 6.7GHz, −40dB at 9.3GHz, for the proposed wearable antenna. These values are more suitable for standard UWB band and other wireless interfacing communication bands.
Rethinking ReLU to Train Better CNNs

Most of convolutional neural networks share the same characteristic: each convolutional layer is followed by a nonlinear activation layer where Rectified Linear Unit (ReLU) is the most widely used. In this paper, we argue that the designed structure with the equal ratio between these two layers may not be the best choice since it could result in the poor generalization ability. Thus, we try to investigate a more suitable method on using ReL U to explore the better network architectures. Specifically, we propose a proportional module to keep the ratio between convolution and ReLU amount to be N:m (n>m). The proportional module can be applied in almost all networks with no extra computational cost to improve the performance. Comprehensive experimental results indicate that the proposed method achieves better performance on different benchmarks with different network architectures, thus verify the superiority of our work.
A Deep CNN Model for Student Learning Pedagogy Detection Data Collection Using OCR

Student learning pedagogy detection requires a huge amount of data from students. Efficient process to collect the data is a major fact here. This paper proposes an approach based on Convolutional Neural Network (CNN) for Optical Character Recognition (OCR) and mainly shows a method to use this OCR system to extract information of a student filled in a specialized form. This form contains 170 cells. Some of these cells are to be filled with capital English alphabets and others are to be filled with English numerals. This paper discusses a method for feature extraction and use of CNN to identify each cell. Using this method we could predict 96.87% of numeric data and 94.36% of alphabetic data accurately.
Human Crawl vs Animal Movement and Person with Object Classifications Using CNN for Side-view Images from Camera

An optical camera-based intrusion classification system (Light Intrusion DeTection systEm named as acronym LITE) for an outdoor setting was recently developed by a superset of the authors. The system classified between human and animal images captured in a side-view manner based on the height. Based on the system and algorithm design, most probably human-crawl would be classified as animal by the LITE. In this paper, classification between human-crawl and animal is addressed. In addition to this work, classification of person with weapon versus person with vehicle is also addressed (referred as person with object) to provide more information about the type of intrusions. A Convolutional Neural Network (CNN) based approach is used to solve the above stated two problems. In the case of “person with object” classification, a study of different CNN architectures was carried out and analysis corresponding to that is presented. In case of human crawl vs animal movement, performance results corresponding to only the best architecture model is provided among the many tried models. Further on, additional insights are provided about the classification using the attention heat maps and t-SNE plots. The test classification accuracies for human-crawl vs animal and person with object classification on the recorded data are close to 95.65% and 90%, respectively. The LITE, having the Odroid C2 (OC2) Single-Board Computer (SBC) with CNN-based classification algorithm for human-crawl versus animal task ported on it, was deployed in an outdoor setting for a realtime deployment. It provided a classification accuracy close to 92%.
Low-Cost, High-Resolution, Fault-Robust Position and Speed Estimation for PMSM Drives Operating in Safety-Critical Systems

In this paper, it is shown how to obtain a low-cost, high-resolution, and fault-robust position sensing system for permanent-magnet synchronous motor drives operating in safety-critical systems, by combining binary Hall-effect sensors with high-frequency signal injection. It is shown that the position error signal obtained via signal injection can be merged easily into the quantization-harmonic-decoupling vector-tracking observer that is used to process the Hall-effect sensor signals. The resulting algorithm provides accurate, high-resolution estimates of speed and position throughout the entire speed range; compared to the state-of-the-art drives using Hall-effect sensors alone, the low-speed performance is greatly improved in healthy conditions and also following position sensor faults. It is envisaged that such a sensing system can be successfully used in applications requiring IEC 61508 SIL 3 or ISO 26262 ASIL D compliance, due to its extremely high mean time to failure and to the very fast recovery of the drive following Hall-effect sensor faults at low speeds. Extensive simulation and experimental results are provided on a 3.7-kW interior permanent-magnet drive.
A Unified Deep Learning Based Polar-LDPC Decoder for 5G Communication Systems

In the 5G communication systems, a hybrid approach to support polar codes for control plane and LDPC codes for data plane has been identified as the channel coding solution for enhanced mobile broadband (eMBB) scenario. One of the major challenges to implement this approach is to design powerful decoders at the terminal side. Inspired from a useful machine learning based polar decoder, we proposed a deep learning based unified polar-LDPC by concatenating an indicator section. Through numerical experiments, we show that the proposed deep neural network (DNN) based decoding architecture can achieve the similar decoding performance compared with the traditional BP-based decoding algorithm. Meanwhile, the proposed unified approach shares the same network architecture and parameters with isolated approaches, which saves significant implementation resources consequently.
Compact LTCC Packaging and Printing Technologies for Sub–THz Modules

Low Temperature Co-fired Ceramics (LTCC) is a ceramic multilayer material with properties that suit well as both as a substrate and packaging material. LTCCs multilayer capabilities, excellent electrical properties, extremely high reliability and full hermetic nature suit it to a wide range of RF applications. Several frequency bands of interest from 100 GHz up to 300 GHz could potentially benefit from the use of LTCC system in package devices. To test the ability of LTCC materials at these frequencies, several grounded coplanar waveguide (GCPW) on 35-60 μm thick LTCC substrates were manufactured with the help of high-resolution Aerosol Jet printing technique and measured from 1-320 GHz (Ferro A6M-E) and 1-220 GHz, respectively (DuPont 9k7). For the first time, LTCC material are characterized as mm-wave circuit substrates at the frequency band of 220-320 GHz.
Opportunities for Increased Efficiency in Monochromatic Photovoltaic Light Conversion

We study efficiency loss mechanisms and the Potential for improvement of a record-setting monochromatic photovoltaic device, which has demonstrated efficiency of 70% at $830 \mathrm {n}\mathrm {m}$. We combine experimental and modeling-based estimates of losses due to reflection, transmission, thermalization, and series resistance into an extended detailed-balance model that further includes losses due to nonradioactive recombination, quantified by an external radioactive efficiency (ERE). From the device's efficiency, we estimate ERE $\geq10$%. The device efficiency could be improved by increased material quality, operation at a longer wavelength closer to the Gas band gap, and increased light intensity. Each order of magnitude increase in incident light intensity or material ERE increases the efficiency by 4% absolute. Redesigning the device to operate at 850 nm reduces thermalization losses by 2% absolute. Overall device efficiency over 80% could be attained by combining these opportunities.
Estimation of Arecanut Yield in Various Climatic Zones of Karnataka using Data Mining Technique: A Survey

Agriculture is the widely practiced job in India which has the major share in the country's Gross Domestic Profit (GDP). Agriculture in India is being practiced as traditional job because of which agriculture is not being practiced as the technology-driven or technology-oriented job. As a result, the farming practice in India is not producing good economic outcome. Data mining in agriculture is one such emerging trend which aims at improving the farming practice by considering the crop yield data. This crop yield data can be subjected to weather data and soil data in order to analyze the individual or combined effect of weather parameters and soil type on crop yield. This paper highlights some of the data analytical techniques used to analyze the crop yield based on which the current experiment is inspired.
Study of the Electrical Characteristics of Light-Emitting Diode Luminaires at Amendment of the Supply Voltage

Results of the study of the change of electrical characteristics of light-emitting diode (LED) luminaires with different types of drivers and applications at amendment of the supply voltage are presented in the paper. Trends of the change in the electric current, real power, harmonic currents, total harmonic distortion and power factor for eleven LED luminaires with two type of drivers - pulse width modulation and bridge rectifier with capacitive divider, when varying the supply voltage are measured, analysed and presented in tabular, graphical and analytical form.
Opportunity Selection in Self-Managing Organizations: The Limits of Crowds

This paper takes a critical perspective on self-managing organizations, i.e., crowds and online communities. I theorize that they are limited in their ability to select and integrate new opportunities (e.g., ideas, project proposals) and cumulatively develop technical systems (e.g., software systems). I compare self-managing organizations to traditional organizations under different complexity conditions. Therefore, I distinguish two opposing selection mechanisms: centralized opportunity selection, typifying traditional organizations, and decentralized opportunity selection, typifying self-managing organizations. Using a computational model, I let these mechanisms cumulatively develop a technical system architecture, which I formalize as a design structure matrix. The model shows that the selection mechanisms’ advantages relative to each other depend on the complexity conditions. Centralized opportunity selection is more effective in achieving rich functionality under high interdependence and many available opportunities, while decentralized opportunity selection is more effective in this regard when few opportunities are available. Surprisingly, when considering indicators of the architecture maintenance costs, decentralized opportunity selection can be outperformed by centralized opportunity selection. The paper contributes to the discussion on digitally enabled organizational forms by outlining the boundary conditions of self-managing organizations imposed by complexity. It also contributes to research on systems architectures by relating organizational characteristics to organizational outcomes.
Incremental Learning in Deep Convolutional Neural Network VIA Adaptive Regularization

Class-incremental learning is an unsolved problem in the domain of deep learning. The challenging point of class-incremental learning is to maintain the performance of original classes while learning new classes. Without class-incremental learning, deep learning based classification models cannot adaptively evolve to newly introduced environments and this is a significant barrier to deep learning based consumer electronics. In this paper, we address the problem of the softmax layer in the class-incremental learning process and tackle the problem by adaptively regularizing incremental weights. Our method impressively improves the performance of class- incremental learning tasks compared to naive approaches.
Millimeter-Wave Substrate-Based Dielectric Reflectarray

A linearly polarized millimeter-wave substrate-based dielectric reflectarray fabricated out of a single low-cost printed circuit board is presented in the frequency range from 30 to 40 GHz. Its unit cell has a cylindrical dielectric resonator (DR) to obtain the required reflection phase. To avoid the alignment problem, the DR has four dielectric arms for connecting itself to its adjacent DR elements. By varying the radius of the DR element, a linear reflection-phase response that fully covers 360° can be obtained at 35 GHz. For demonstration, a circular DR reflectarray is designed and fabricated at 35 GHz and a measured peak antenna gain of 23.9 dBi is obtained.
Size-Controllable Synthesis of Bimodal Cu Particles by Polyol Method and Their Application in Die Bonding for Power Devices

A simple and large-scale polyol method was utilized to successfully synthesize bimodal Cu particles in the air. The size, size distribution, and morphology of Cu particles were determined by the reaction parameters, such as Cu source and additives. The insoluble Cu source is suitable for the formation of bimodal Cu particles as they provide a slow and steady supply of fresh Cu nuclei, which modify the nucleation and growth of Cu particles. In addition, sodium sulfide (Na2S) additive has the ability to control the size of Cu particles due to its reduction capability. As-synthesized bimodal Cu particles were used to fabricate sintered Cu joints for high-power electronic packaging. The results show that the shear strength of sintered Cu joints was determined by the size and size distribution of Cu particles. Sintered Cu joints with high shear strength above 20 MPa can be readily achieved at 300 °C with a slight applied pressure of 0.4 MPa. This strength is superior to joints utilizing nanoparticle Cu paste. The size-matching effect and the high oxidation resistance of the bimodal Cu particles were attributed to the excellent mechanical performance of the sintered Cu joints. Our results indicate that this affordable paste utilizing bimodal Cu particles of a suitably designed size and with a uniform distribution is highly promising as the die-bonding material for next-generation power devices.
Asymptotic and finite-time almost global attitude tracking: representations free approach

In this paper, the attitude tracking problem is considered using the rotation matrices. Due to the inherent topological restriction, it is impossible to achieve global attractivity with any continuous attitude control system on SO(3). Hence in this work, we propose some control protocols achieving almost global tracking asymptotically and in finite time, respectively. In these protocols, no world frame is needed and only relative state information are requested. For finitetime tracking case, Filippov solutions and non-smooth analysis techniques are adopted to handle the discontinuities. Simulation examples are provided to verify the performances of the control protocols designed in this paper.