Miracle-dz/UniFAR-FTUs huggingface dataset:
Design of a Millimeter Wave Microstrip Patch Antenna and Its Array for 5G Applications

A millimeter wave microstrip patch antenna and its array for 5G applications is proposed. The 5G Microstrip patch is designed on rogers RT Duroid 5880 substrate with standard thickness 0.787 mm having relative dielectric constant (∊r) = 2.2 and tan δ = 0.0013. The antenna resonates at 24.85 GHz with a return loss -19.5 dB and a bandwidth 1.318 GHz. An array of 1x4 element of the proposed antenna is designed using tapered line feeding. The antenna array resonants at four different frequencies i.e. 23.2 GHz, 27.09 GHz, 31 GHz and 42.5 GHz. Appreciable improvement in gain is observed with the array of antennas. The antenna and its array can be used for 5G mobile communication because of its compactness.
Audio Segmentation and Classification Approach Based on Adaptive CNN in Broadcast Domain

Audio segmentation and classification is the basis of broadcast audio processing in the broadcast domain. A number of methods have been developed over the years in order for the broadcast station to segment and classify the audio into 7 classes, which are: male speech, female speech, speech with noise, speech with music, noise, music, and silence. Some of the major methods include SVM, GMM and HMM among others, however the overall accuracy of these existing methods is not satisfactory. This paper, therefore proposes an adaptive CNN method which classifies clips directly based on audio sample points. To our knowledge, this is the first work to do segmentation and classification in broadcast domain by Adaptive CNN. Each convolutional layer is equivalent to the clip feature extraction. The study uses the method of Adaptive CNN by increasing the amount of computation to ensure that the most suitable features can be extracted. In other words, Adaptive CNN increases the process of feature selection compared to CNN. A large number of experiments were carried out and compared with other approaches and the results show that the Adaptive CNN technique achieved a significant error reduction.
An Affective Computing in Virtual Reality Environments for Managing Surgical Pain and Anxiety

Pain and anxiety are common accompaniments of surgery. About 90% of people indicate elevated levels of anxiety during pre-operative care, and 66% of the people report moderate to high levels of pain immediately after surgery. Currently, opioids are the primary method for pain management during postoperative care, and approximately one in 16 surgical patients prescribed opioids becomes a long-term user. This, along with the current opioid epidemic crisis calls for alternative pain management mechanisms. This research focuses on utilizing affective computing techniques to develop and deliver an adaptive virtual reality experience based on the user's physiological response to reduce pain and anxiety. Biofeedback is integrated with a virtual environment utilizing the user's heart rate variability, respiration, and electrodermal activity. Early results from Total Knee Arthroplasty patients undergoing surgery at Patewood Memorial Hospital in Greenville, SC demonstrate promising results in the management of pain and anxiety during pre and post-operative care.
Nuclei Detection Using Residual Attention Feature Pyramid Networks

Detection of cell nuclei in microscopy images is a challenging research topic due to limitations in acquired image quality as well as due to the diversity of nuclear morphology. This has been a topic of enduring interest with promising success shown by deep learning methods. Recently, attention gating methods have been proposed and employed successfully in a diverse array of pattern recognition tasks. In this work, we introduce a novel attention module and integrate it with feature pyramid networks and the state-of-the-art Mask R-CNN network. We show with numerical experiments that the proposed model outperforms the state-of-the-art baseline.
A Hexa Band Antenna Having Frequency Reconfigurable Feature for Portable Devices Communication

Frequency reconfigurable antenna has a variety of applications in portable wireless applications. In this paper, we are presenting the frequency reconfigurable antenna for Six frequency bands including portable wireless applications and X-bands applications. A T-shape truncated slot antenna for hexa band is described here. We use the FR4 lossy material as a substrate in design the antenna with a dimension of 56 × 44 × 0.8mm3. This antenna can tune into six different frequencies band with the help of pin diode switches. The operate single band (WLAN at 5.5GHz) and Penta band (GPS at 1.56 GHz, WIFI at 2.402 GHz, WIMAX at 3.48 GHz, 7.3 GHz and X bands 10.7 GHz). The simulated result show that the antenna achieved better performance of return loss, gain, directivity and radiation pattern for wireless applications. This antenna is design using the CST MWS solely.
Co-Scheduling of Advance and Immediate Bandwidth Reservations for Inter-Data Center Transfer

As scientific applications and business services increasingly migrate to clouds, big data of various types with different priorities need to be transferred between geographically distributed cloud-based data centers. It has become a critical task for Cloud Service Providers (CSP) to fully utilize the expensive bandwidth resources of the links connecting such data centers while guaranteeing users’ Quality of Experience (QoE). Most high-performance networks based on software-defined networking (SDN) provide the capability of advance bandwidth reservation. This paper focuses on the scheduling of multiple user requests of two different types with different priorities, namely, advance bandwidth reservation with a lower priority and immediate bandwidth reservation with a higher priority, to maximize the total satisfaction of user requests.We formulate this co-scheduling problem as a generic optimization problem, which is shown to be NP-complete. We design a heuristic algorithm to maximize the number of successfully scheduled requests and minimize the number of preempted advance reservation requests, while minimizing the completion time of each request. Extensive simulation results show that our scheduling scheme significantly outperforms greedy approaches in terms of user satisfaction degree.
Identification of individuals using palm vein classification

In this paper, a new algorithm is introduced for palm identification using Gabor filter. First, our proposed method processes images using Gaussian filter and histogram equalization methods. The features are then extracted using bank of Gabor filters. We apply L2-max norm of superposition into output of Gabor filter to reduce the dimension of the features vector. Finally support vector machine (SVM) and Nearest Neighbours classifiers are used for palm vein verification. Our proposed method is evaluated using a public dataset, named VP. The efficiency of the identification process by the proposed methods is highly compared to the traditional one where simplified extraction features methods is used. The experimental results confirm that the introduced approach is efficient compared to the traditional methods.
On High Frequency Analysis of Gm-boosted High Performance Flipped Voltage Followers with Very Low Output Impedance

This paper presents high frequency analysis of various gm-boosted flipped voltage followers (FVF). It has been shown that gm-boosting technique has contributed in higher bandwidth and lower output impedance. A high-gain amplifier is employed at the negative feedback of FVF and its other versions the linearity and unity-gain have been improved. The small signal high frequency analyses of gm-boosted FVF have been done to calculate the mid-band gain and its bandwidth. Moreover, the effect of gm-boosting technique on other variants of FVF cell has also been observed and studied. The simulations are performed in TSMC 0.13um CMOS, BSIM3v3 and Level 53 technology by using Eldo-Spice simulator of Mentor Graphics tool.
Design of Automotive Digital Instrument Cluster Adjustable to Driver’s Cognitive Characteristics

In this paper, we propose an adjustable digital instrument cluster designed to provide an effective user interface and user experience (UI/UX) depending on the driver’s age, gender, personal driving experience, and driver’s emotions and conditions changes in driving. To make this possible, firstly we build a theme structure that can link with scripts to reconfigure flexibly the predetermined digital instrument cluster according to various situations that occur during driving, such as elderly drivers, female drivers, novice drivers, and general drivers. Secondly, we defined objects and elements to provide a digital instrument cluster that can change dynamically according to the script that makes up the cluster, and this library designed provides a work environment for users to personalize some themes. In addition, the proposed system can basic research to derive effective interaction methods by providing an environment suitable for evaluating the usefulness of each driver groups.
A Greedy Algorithm for Jobs Allocation in a Multiprocessor System

We present a greedy 0.5-approximation algorithm for allocation indivisible jobs in a multiprocessor system. The algorithm uses an ordering of processors according to the non-decreasing of size, and two orderings of items: in nonincreasing utility order and in nonincreasing order of the utility/ size ratio. These orderings create two lexicographic orderings on the set $ I\times J$ (here I is the set of jobs and J is the set of processors). Based on these lexicographic orderings, the algorithm creates an admissible allocation by looking through the pairs $(i,\ j) \in I\times J$ in the corresponding order and allocating the job i to processor j if this job is not allocated yet and can be allocated to processor j. The algorithm chooses the best of the two obtained solutions. This algorithm is 0.5-approximate and has running time $O(mn)$ (without sorting), where m and n are the sizes of the sets I and J correspondingly.
Single-mode Fiber Coated with Zinc Oxide (ZnO) Nanorods for H2 Gas Sensor Applications

A Hydrogen (H2) gas sensor was successfully developed using optical fiber coated with Zinc Oxide (ZnO) nanorods. The single-mode fiber (SMF) used as a sensing device has been prepared by etching the SMF fiber and coated with ZnO nanorods. The etching of the fiber was performed using hydrofluoric acid (HF) to enhance the evanescent field around the fiber core. The ZnO nanorods were prepared by hydrothermal method through seeding and growth solution technique. The diameter of cladding and core are 125 µm and 8 µm, respectively, before etching and goes down to 11µm after etching. Around 2 cm of ZnO nanorods were coated in the middle of the etched fiber. The sensing layer was characterized through Scanning Electron Microscopy (SEM), Energy Dispersive X-Ray (EDX) and X-Ray Diffraction (XRD) to verify the properties of ZnO. The developed sensor's response and recovery time were observed to be 7 min and 3 min, respectively, for a low concentration of 0.25% H2 gas. The aim of this study is to understand the gas sensing properties towards the spectral intensity variations in etched optical fiber coated with ZnO nanorods.
Compact Flexible and Wearable AMC Based Antenna for Wireless Body Area Network

This paper presents a flexible, wearable, and reconfigurable antenna, including a slot and a stub. It tends to achieve the reconfigurability by switching different states of switches and results in altering the stub's radiation parameters. Both the slot and stub have different characteristic polarization and operating frequency. Thus, the antenna is joined with a dual band and polarization-dependent artificial magnetic conductor (AMC) surface to decrease the specific absorption rate (SAR) and progress the radiation characteristics. A flexible substrate is used in designing of this antenna. Simulations concluded that the SAR level is decreased when the AMC Surface is integrated with antenna, and it works as an isolator between the antenna and the human body. Two switches are embedded in the stub. While keeping both the switches off, i.e., switches behaving as non-conducting elements, the antenna tends to resonate on multiple frequencies covering three bands with gain enhanced at a certain level. This proposed antenna is wearable and proves an appropriate choice for Worldwide Interoperability for Microwave Access and Wireless Body Area Network for wireless body-worn devices.
Three-Dimensional Laplacian Spatial Filter of a Field of Vectors for Geometrical Edges Magnitude and Direction Detection in Point Cloud Surfaces

Detecting geometrical features and edges magnitudes and directions in 3-D surfaces remains an important problem that has a wide range of application such as 3-D object recognition and detection. The objective of the current research is to detect geometrical edges magnitude and direction of the surface of the three-dimensional images using the high-pass spatial filters of the second-order (the Laplacian) derivative that handle the vectors as one solid quantity, instead of separate layers of axes, in a way similar to using the second order derivative to detect intensity edges in the gray-scale images. The problem of the current research is that the state of the art high-pass spatial filter of the Laplacian operators are not applicable to the vector quantities because they use the component-wise Cartesian vector subtraction. In the current research, based on Solid Vector Subtraction operation, we: (1) propose a novel definition for the second-order (the Laplacian) derivative of a field of vectors geometrical edges magnitude and direction detector, where the field is the group of the pixels of the 3-D image and the vectors are the normal vectors to the surface of that field; (2) do behavioral analyses on the geometrical Step, Plane, and Ramp areas; and (3) performance analyses on TUM data set, and comparison study on NYUD data set that shows our Laplacian edge detector is efficient.
Emotion Classification Using Single-Channel EEG

The discovery of EEG signal in 1929 by the German psychiatrist Hans Berger changed the way we understand the structure and functioning of brain.The interaction between a machine and computer is increasing day by day and the need of the hour is to develop a Brain Computer Interface(BCI) which can help the humanity.EEG Emotion recognition system can be used to predict the emotions felt by disabled people.Studies have found that corticolimbic Theta electroencephalographic (EEG) oscillation is responsible for the emotions that one feels. Alpha, Theta, Beta and Delta sub-bands of EEG play a major role in brains emotion processing. The goal of this study is to identify emotions from an EEG data collected from a Single Channel EEG headset.13 subjects of varying age participated in the EEG experiment which were shown videos that helped in evoking three emotional states: neutral, calm and fear. After each video the participants were asked to rate the video on the basis of SAM Model and Valence-Arousal scale.The method used is based on Digital Signal Processing Techniques in order to remove arte-facts,clubbed with machine learning in order to design a system for predicting emotions using EEG signal.Stationary Wavelet Transform (SWT) with haar wavelet at level 6 decomposition with Garrote Thresholding is used to clean the signal and remove the noise. Higuchi Fractal Dimension is also calculated and added as one of the features and is found to have increase the classification accuracy due to its ability to identify the patterns from the data.Experimental results show that EEG based emotion classification can predict emotions with an average of 76% in case of pure EEG signal and 85% in case of EEG signals with Valence-Arousal scale using Recurrent Neural Networks.
A Uniform Modeling Methodology for Benchmarking DNN Accelerators

Deep Neural Networks (DNNs) have achieved tremendous success in many application domains. Inspired by its success, specialized accelerators have been and continue to be developed to process DNN workloads in an energy-efficient manner. The design space for DNN accelerators can be extremely large since they can employ different datapaths, data mapping strategies, circuits, and device technologies. To explore the design space for developing DNN accelerators, it is important to quickly estimate the energy cost associated with an accelerator. This paper introduces a uniform modeling framework, Eva-DNN, to estimate the dynamic energy (a major component of total energy) consumed by a DNN accelerator. Specifically, we model the number of accesses and associated energy cost at different levels of memory and functional units. We derive a uniform expression that estimates the number of accesses as a function of the number of basic operations normalized by data reuse and activity factor of corresponding units. To model the energy cost of an individual functional unit operation, we employ a device-level benchmarking approach. Eva-DNN can accurately model energy contributions from device technology, circuits, architecture, data mapping strategy, and network. We applied our model on three accelerator architectures from the literature, namely: Eyeriss, ShiDianNao, and TrueNorth. Results suggest that Eva-DNN can accurately estimate energy contributions from different architectural units, achieving 4.5% to 8.0% of deviation from energy costs obtained from hardware measurements for different DNN workloads.
Security Enhancement for IoT Device using Physical Unclonable Functions

In this paper, we propose a device authentication method using PUF (Physical Unclonable Function) for IoT (Internet of Things) device security enhancement. We propose security functions such as device identifier and secret key generation using PUF, efficient management of device authentication information on the server side, update of secret keys, and continuous authentication. It improves the security of IoT devices against various existing attacks.
The BioRace system: safe and efficiency

Ampliar a crítica zona apical a diâmetros biológicos capazes de otimizar a efetividade da substância química auxiliar e da medicação intracanal tem sido um grande desafio. Os instrumentos rotatórios de NiTi presentes no mercado apresentam kits que limitam em muito a ampliação desta região. O sistema BioRace da FKG Dentaire permite um trabalho eficiente e seguro, ampliando a região apical a diâmetros maiores, aumentando a efetividade do processo de sanificação.
Signal Analysis of Brain Computer Interface (BCI)

Electroencephalography (EEG) equipment are becoming more available in the public market, which enables more diverse research in a currently narrow field. The Brain-Computer Interface (BCI) community recognizes the need for systems that make BCI more user-friendly, real-time, manageable and suited for people that are not forced to use them, like clinical patients, and those who are disabled.During this project, we analyzed Electroencephalography signals obtained by Neurosky headset from the brain, acquired by electrode FP1 on the frontal lobe and processed the signals to identify features that subjects were seeing. The signals’ features were analyzed for the purpose to link them to what the subject were seeing.
An Automatic FIR and DCGAN Model-based Fault Detection Framework for Key Components of Planetary Gearboxes under compartively Stable Conditions

An automatic fault detection framework for key components of planetary gearboxes is proposed in this work, in which the finite impulse response (FIR) band-pass filter is adopted to filter out the frequency components of the sun gear, planet gear, planet carrier, and ring gear, respectively, and then train the deep convolutional generative adversarial networks (DCGAN) model with the frequency components of each part separately. Afterwards, when completing the models training, the four FIR filter models and DCGAN models are combined to be a fault detection framework for key components of planetary gearboxes. The input of the framework is the vibration signals measured from a planetary gearbox, and the output are key component health conditions of the monitored planetary gearbox. The key advantage of this framework is to simultaneously and automatically identify faults, namely sun gear, planets, planet carriers and ring gear faults, with one detection.
QFGMOS Based Current Mirror with High Bandwidth and Low Input Impedance

In the proposed circuit, QFGMOS current mirror is used, which allows to operate on low voltage power supply. Also, significantly improves the bandwidth, input impedance and output impedance. Here, bandwidth, input and output impedance becomes 431.94MHz, 4. 55Ω and 43. 1964GΩ respectively. Current mirror with QFGMOS can provide high accuracy with improved gain bandwidth. This work is verified on LTspice XVII in TSMC 0. 18μm CMOS technology. Input voltage is 1V and input current of 15μA is used.
Signal Tracking Control of MEMS Resonators by Fuzzy Terminal Sliding Mode Control Scheme

The object of this study is to propose a fuzzy terminal sliding mode control (FTSMC) scheme to carry out the complete signal tracking control for a MEMS resonator. In the proposed sliding mode control (SMC) scheme, the involved sign function is substituted by the fuzzy inference system. It can be shown that the error states can be reaching into the terminal sliding surface and approaching the original within finite-time. Meanwhile, the chattering effect is reduced by the FTSMC obviously. The stability of tracking control for MEMS resonator is proven according to the Lyapunov theorem. Besides, the numerical simulations are made to illustrate the effectuality of the present scheme.
A VLSI Design of Real-Time Sensor Fusion Unit using Kalman Filters

As the functions of mobile smart devices are diversified with the increase of sensor types, the importance of sensor hubs for sensor data processing is also increasing. One possible application of the mobile sensor hub is to calculate the orientation of the sensor. In this paper, we design the sensor fusion structure as VLSI circuit that can be implemented as a co-processing unit which identifying device orientation using 9axis IMU sensor and Kalman filters. The simulation reported an average error of about 2.27°. The proposed design is fully verified and evaluated through simulation tools for Artix-7 FPGA and 32 nm CMOS standard logic cell library. The average power dissipations of FPGA and 32 nm standard cell technologies were estimated as 7 mW and 0.89 mW, respectively. Also, the gate counts for standard cells are reported as $\sim$ 45,000 in total. The sophisticated design is applicable to the digital IP core for mobile sensor hub architecture.
Dynamic Prediction of the Silicon Content in the Blast Furnace using LSTM-RNN-Based Models

In blast furnace (BF) ironmaking process, the silicon content of hot metal is regarded as an important index to evaluate the stability of blast furnace and the quality of pig iron. In reality, the reactions inside BF with the features of dynamic, nonlinear and hysteretic impede accurate mathematical modeling. Data-driven models predict silicon content variation tendency through the analysis of historical data, which have achieved beneficial results. However, existing data-driven models are inefficient to handle time series specifically. Therefore, this paper proposes a recurrent neural network (RNN) which is apt at processing sequential data for silicon content prediction. A Long short-term memory (LSTM) unit is introduced to solve long-term dependency problems in conventional recurrent neural networks. Experiment results indicate that LSTM-RNN shows stronger adaptability and higher prediction accuracy at 86%-94% than that of other data-driven models, which is of great significance for controling the silicon content of hot metal.