Miracle-dz/UniFAR-FTUs huggingface dataset:
Characterization of Model Uncertainty Features Relevant to Model Predictive Control of Lateral Vehicle Dynamics

The information about a system’s dynamics represented by measurement data sets are often confined to regions of restricted operations where the system is not sufficiently excited for model identification purposes. Experiments performed in closed-loop with safety constraints allow only for reduced order modeling. In the paper, a set of low order models are identified from real experimental data of the lateral dynamics of an electric passenger car. Low order models are advantageous for on-line computation in model-based control, though uncertainty due to neglected dynamics may deteriorate control performance and constraint satisfaction. The effect of uncertainty is analyzed by controller cross-validation where a controller designed based on one model is evaluated on other models playing the role of the true system. This method allows us to qualify not only model-controller pairs, but to determine the properties of input data and model uncertainty, which lead to more useful data sets, more robust and better performing controllers than the others.
MobiLattice: A Depth-wise DCNN Accelerator with Hybrid Digital/Analog Nonvolatile Processing-In-Memory Block

Nonvolatile Processing-In-Memory (NVPIM) architecture is a promising technology to enable energy-efficient inference of Deep Convolutional Neural Networks (DCNNs). One major advantage of NVPIM is that the vector dot-product operations can be completed efficiently by analog computing inside a Nonvolatile Memory (NVM) crossbar. However, its inference efficiency is severely downgraded when processing depth-wise convolution layers, which have been widely employed in many lightweight DCNNs. One major challenge is that the cell utilization is extreme low when mapping the depth-wise convolution layer to a crossbar. To overcome this problem, we propose a novel hybrid mode NVPIM architecture, namely, MobiLattice. With moderate hardware overhead, Mobi-Lattice enables both analog and digital mode operations on NVM crossbars. While conventional convolution layers are computed efficiently using the analog mode, the computation efficiency of depth-wise convolution layers are substantially improved using the digital mode by mitigating the redundant memory space in the NVM crossbars. Experimental results show that, compared to prior approaches where only the analog mode is supported by the NVPIM architecture, MobiLattice can speedup the processing of typical depth-wise DCNNs by 2 ~5× on average and up to 30× by combining with some extreme quantization schemes.
A Specular Removal Algorithm Based on Improved Specular-free Image and Chromaticity Analysis

Aiming at the problem of removing specular in the process of detecting foreign matter in packaging of straw production, an efficient specular removal algorithm based on improved specular-free image (MSF) and chromaticity analysis (SIRBMSFC) is proposed in this paper. Based on the MSF image, diffuse reflection and diffuse reflection plus specular reflection pixel set are separated, and the highlight separation is realized on the single pixel level through chromaticity error analysis and the least-square method of dichromatic reflection model. This algorithm does not require image segmentation or local interaction between adjacent pixels. Experimental results show that the proposed algorithm is robust and fast, and can meet the needs of real-time scenes.
Design of a Functional Observer for Non-Linear Singular Delayed Systems

In this paper, we propose a new approach to design a functional observer for delayed non-linear singular systems. Delay functions are considered in both state and input vectors. Using the Takagi-Sugeno multiple model principle, we propose to extend observer design for linear singular delayed systems to the non-linear singular delayed representation. Sufficient conditions of stability are given to develop the designed observer gain. Such proposed observer proves its effectiveness on a numerical example.
Position-Aware Communication via Self-Attention for Multi-Agent Reinforcement Learning

Multi-agent reinforcement learning is important for real-world applications but is still a challenging problem. A feasible way is to share information among all agents via a communication channel. In recent years, attentional communication emerged in order to differentiate valuable information especially with a large number of agents. However, existing attentional communication, which relies on long short-term memory (LSTM) units with attention mechanism, makes parallelization difficult. Another problem is that the output of LSTM is dependent on the sequence of agents, but the relationship between agents, in general, is not sequential. In this paper, we proposed using multi-head self-attention layer, which is proposed from a well-known net “Transformer”, as a communication channel for parallelization. Attention mechanism is independent of the sequence of agents. In addition, we also incorporate position information via positional encoding. In our experiments, the proposed method achieves improvements in terms of reward compared with existing approaches.
PSION 2: Optimizing Physical Layout of Wavelength-Routed ONoCs for Laser Power Reduction

Optical Networks-on-Chip (ONoCs) are becoming increasingly attractive for intra-chip communications due to their low power-per-bit requirements and high bandwidth. Wavelength-Routed ONoCs (WRONoCs), a subtype of ONoCs, further reduce network latency. Recently, tools to design WRONoCs have been developed, but these tools are still incomplete as they do not yet consider key design aspects such as the type of laser source used and the impact of the laser Power Distribution Network (PDN) on the laser power consumption. In this work we propose the first design automation tool to combine awareness of both on-chip and off-chip lasers with optimization of both the logical topology and the physical layout of WRONoCs for application-specific designs. Compared to previous works, the incorporation of the type of laser and the PDN into the optimization process combined with a new Generic Routing Unit (GRU) placement method leads to a laser power reduction of up to 20%.
Borderline high serum calcium levels are associated with arterial stiffness and 10‐year cardiovascular disease risk determined by Framingham risk score

Recent epidemiological data indicate that the concentration of circulating calcium is related to cardiovascular disease (CVD) mortality. We determined whether serum calcium level is related to arterial stiffness and 10‐year CVD risk calculated by Framingham risk score (FRS). We examined the association of normal‐range serum calcium level with arterial stiffness and FRS in 565 Korean adults participating at the Health Promotion Center of Gangnam Severance Hospital between March 2016 and May 2017. High brachial‐ankle pulse wave velocity (baPWV) was defined as >1460 cm/s, and high FRS was defined as ≥10 percent for 10‐year CVD risk. Odds ratios (ORs) and 95% confidence intervals (95% CIs) for high baPWV and high FRS were calculated using multiple logistic regression analysis after adjusting for confounding variables. The OR (95% CI) for high baPWV was 3.91 (1.15‐7.36) per 1 mg/dL increment of serum calcium after adjusting for age, sex, body mass index, smoking status, exercise regularity, alcohol consumption, mean blood pressure, fasting plasma glucose, triglyceride, HDL‐cholesterol, C‐reactive protein, γ‐glutamyltransferase, uric acid level, phosphate level, potassium level, and presence of hypertension, diabetes and dyslipidemia medications (P = 0.024). A positive association between serum calcium level and high FRS was also observed after adjusting for the same covariables (OR, 3.54 [95% CI, 1.01‐12.44], P = 0.048). Serum calcium level was independently and positively associated with baPWV and 10‐year CVD risk estimates. Early detection of higher serum calcium level may be important for the assessment of arterial stiffness and future risk of a cardiovascular event.
Comparision of Classification Algorithims for Survival of Breast Cancer Patients

Breast cancer has become one of the most common diseases, especially among women, increasing the importance of predicting survival. In this study, the successes of machine learning algorithms on survival prediction were compared using the Surveillance, Epidemiology, and End Results (SEER) breast cancer data set. Used machine learning algorithms are: Naive Bayes, J48, Multiobjective Evolutionary Fuzzy Classifier (MEFC), Support Vector Machines (SVM). The most successful algorithm is J48 algorithm. The most successful algorithm is J48 algorithm according to the tests.
Statistical Analysis for EEG Patterns Comparison Between Real Motion and Imagery Motion

Electroencephalogram (EEG) is a brain signal which widely used in the Brain-Computer Interface (BCI) system, where the data collection of EEG signals is done by a non-invasive method. Motor imagery recently has been an interesting issue since the development of many cognitive studies including neuromuscular disorder rehabilitation. This research is focusing on comparing two motions, one with real motion, and the other is with no motion, but by using imagery motion. C3 and C4 channels are used to record the EEG data. The data processing is done by filtering the EEG band by using Finite Impulse Response (FIR) to limit the low pass and high pass frequency. The raw data is then cleared from the artifact by using the Automatic Subspace Reconstruction (ASR) method. The clean EEG is then decomposed into sub-band alpha and beta using the Butterworth filter. For comparing the two kinds of motions, time-domain feature extraction is used with parameters: Mean, Mean Absolute Value (MAV), and Standard Deviation (STD). The analysis is carried out by comparing the results of extraction parameters of real motor motion with imagery motion. The experimental results show that STD produces the highest number of data frames with a similar value between the real motion and imagery motion compared to other parameters. The value of the STD extraction on the alpha band (mu-rhythm) frequency in C3 channel is 654 data frames (85.16%), and C4 channel is 672 data frames (87.45%), while the beta band frequency in C3 channel is 528 data frames (68.75%), and in C4 channel is 536 data frames (69.82%). The conclusion is standard deviation shows the best parameter with a similar pattern between real motion and imagery motion.
VEM-SLAM - Virtual Environment Modelling through SLAM

Creating a virtual environment for virtual reality applications can be an expensive task, especially when we aim to build a virtual environment based on a real one. In this work, we integrate solutions for 2D object detection and monocular visual SLAM to map a real indoor environment in order to recognize the static objects contained in this environment, estimate their poses, and build a similar virtual environment. The problem of mapping a real environment has received attention with the advances of SLAM solutions and robust object detection solutions in Computer Vision area. The problem with Simultaneous Localization and Mapping (SLAM) in robotics is to create a (generally geometric) map of the scene while estimating the viewer’s pose. The solutions to this problem are used in several areas where a map of the environment is desirable and extracts geometric information from it. Object detection allows us to identify the object in the scene according to the object classes of the reference database. For detection in 2D images, the best solutions are based on convolutional neural networks. Multiple methods are necessary to create a virtual environment by extracting the 3D geometric information of the objects in the scene, and they vary according to the model of the reference 3D object. We also propose a new integration between an object detector and a monocular SLAM solution based on keyframes. As a result, we obtained an improvement in the estimation of the camera’s trajectory when compared to the original SLAM method and we demonstrate the use of our system creating virtual environments analogous to the real ones.
Heuristic Algorithms Testbed for Flying Ad-Hoc Network Systems

UAVs in military and commercial applications; It plays several important roles in situational awareness, search and rescue, environmental monitoring, and environmental surveillance in natural disasters. Working with a small group of unmanned aerial vehicles has far more advantages than working with a single larger unmanned aerial vehicle. In this study, it is proposed to develop a testbed in which smart algorithms can be tested for mesh UAV network systems outside the communication range of the ground control station. Unlike other multi-UAV systems, it has fully autonomous and ultra-low power calculation features. The hardware design and communication algorithms of the Mesh UAV network system are discussed.
A Split and Merge Based Plane Segmentation Method with Octree Data Structure for Unorganized Point Cloud Data

In recent years, point cloud data have been used frequently in robotic applications. It is considered that the segmentation of that data is the first step in solving several problems such as scene and object classification. In this study, it is aimed to the segmentation of planar surfaces such as ceiling, floor, and walls via unorganized point cloud data captured with 3D laser scanner in indoor environments. To achieve that, firstly, the unorganized point cloud data is converted to organized point cloud data with octree data structure and the relation between neighbor points is defined. Then, a split and merge based segmentation method, which uses that relation, is proposed. The Fukuoka indoor laser dataset with different noise levels obtained with a 3D laser scanner was used to test the efficiency of the proposed methods. The proposed method was compared with the RANSAC and region growing methods in terms of segmentation accuracy and robustness against the noise.
Blood Flow Lateralization and Collateral Compensatory Mechanisms in Patients With Carotid Artery Stenosis

Background and Purpose— Four-dimensional phase-contrast magnetic resonance imaging enables quantification of blood flow rate (BFR; mL/min) in multiple cerebral arteries simultaneously, making it a promising technique for hemodynamic investigation in patients with stroke. The aim of this study was to quantify the hemodynamic disturbance and the compensatory pattern of collateral flow in patients with symptomatic carotid stenosis. Methods— Thirty-eight patients (mean, 72 years; 27 men) with symptomatic carotid stenosis (≥50%) or occlusion were investigated using 4-dimensional phase-contrast magnetic resonance imaging. For each patient, BFR was measured in 19 arteries/locations. The ipsilateral side to the symptomatic carotid stenosis was compared with the contralateral side. Results— Internal carotid artery BFR was lower on the ipsilateral side (134±87 versus 261±95 mL/min; P<0.001). BFR in anterior cerebral artery (A1 segment) was lower on ipsilateral side (35±58 versus 119±72 mL/min; P<0.001). Anterior cerebral artery territory bilaterally was primarily supplied by contralateral internal carotid artery. The ipsilateral internal carotid artery mainly supplied the ipsilateral middle cerebral artery (MCA) territory. MCA was also supplied by a reversed BFR found in the ophthalmic and the posterior communicating artery routes on the ipsilateral side (−5±28 versus 10±28 mL/min, P=0.001, and −2±12 versus 6±6 mL/min, P=0.03, respectively). Despite these compensations, BFR in MCA was lower on the ipsilateral side, and this laterality was more pronounced in patients with severe carotid stenosis (≥70%). Although comparing ipsilateral MCA BFR between stenosis groups (<70% and ≥70%), there was no difference (P=0.95). Conclusions— With a novel approach using 4-dimensional phase-contrast magnetic resonance imaging, we could simultaneously quantify and rank the importance of collateral routes in patients with carotid stenosis. An important observation was that contralateral internal carotid artery mainly secured the bilateral anterior cerebral artery territory. Because of the collateral recruitment, compromised BFR in MCA is not necessarily related to the degree of carotid stenosis. These findings highlight the importance of simultaneous investigation of the hemodynamics of the entire cerebral arterial tree.
Real-Time Maintaining of Social Distance in Covid-19 Environment using Image Processing and Big Data

Recent research in computer vision is increasingly focusing on developing systems to understand people's appearance, movements and activities, provide advanced interfaces for interacting with people, create human models. For any of these systems to work, they need methods to identify people from a particular input image or video. Today, real-time object detection and sizing of objects is an important issue in many areas of the industry. This is a vital issue of computer vision problems. With Covid-19's healing process, it will be very important to maintain social distance. In this research and development, it is aimed to maintain social distance with proposed big data architecture. This article provides an advanced technique to detect objects in video streams in real time and calculate their distance. The system composed research and developments to perform a stream from the camera, such as video stream, distance and object detection model, incoming data stream, data stream collection and report generation. The video stream from the camera is processed with GStream. The frames from the video stream are taken by OpenCV, YOLOV3 is trained by distance and object detection model and developed by Python. Video streaming data trained with Kubeflow is published with Apache Kafka and Apache Spark. It uses HDFS used to store published data. It is used to query and analyze data in Hive, Impala, Hbase HDFS. After that Analytical reports are created. E-mail notifications can be created according to the data in the database using by Apache Oozie. Through the proposed real time big data architecture, people can be safe in closed areas.
Analysis of Secondary Electron Yield and Energy Spectrum of Metal Materials based on Furman Model

In the study of secondary electron emission, this paper first uses the Furman model to classify secondary electrons into three types of electrons and model them separately. Second, the Monte Carlo simulation process of the model is realized. In the process of establishing Monte Carlo simulation, a single particle model is used, and the energy spectrum distribution of the entire model is presented based on the superposition of a large number of single-particle distributions. Thirdly, several sets of experiments on the corresponding energy spectrum distribution data obtained under different incident energies of one electron and different incident angles were carried out. Then, discuss the relationship between the secondary electron yield and the incident angle and energy of the primary electron, and quantitatively analyze the relationship between the secondary electron yield and the incident electron angle and incident energy. Finally, the relationship between the incident electron energy and incident angle and the secondary electron emission yield was compared and verified.
Cordic Based Hardware Implementation for Object Region Layer of Yolo V2

This paper proposes the hardware architecture design and implementation for the region layer of Tiny Yolo V2 by calculating Softmax and Bounding Box to get the classification and the location of the object. The proposed architecture is composed of three units: Sigmoid unit, Softmax unit, and Bounding Box unit. To reduce the calculation complexity, data reordering of thresholding confidence score is provided to reduce the numbers of input of the Sigmoid unit. Moreover, reducing class and coordinate as the input of the Softmax unit and Bounding Box unit to calculate the accurate classification and location of the object is also presented. Furthermore, Coordinate Rotation Digital Computer (Cordic) based operation is adopted to the process of Exponential and Natural Log to reduce the complexity of the operation and speed up the operation time in our design. Experiment results show the proposed architecture can achieve real-time operation in Yolo-v2.
Development of an Efficient 3D Reconstruction Solution from Permissive Open-Source Code

3D reconstruction is one of the main topics in computer vision and is heavily applied for creating virtual environments. Photogrammetry is a technique for obtaining 3D information by mapping objects and scenarios using only images. However, this process can take a long time when using large datasets. In this paper, a permissive open-source pipeline is proposed focusing on robustness, efficiency, and low execution time in batch processing. The permissive license allows commercial use without the need of keeping the code open. We mixed an enhanced structure from motion algorithm and a recurrent multi-view reconstruction. We also use Point Cloud Library for normal estimation, surface reconstruction, and texture mapping. We compared our results with COLMAP and MVE techniques using the DTU MVS dataset and real-world scenarios with our own datasets. The results showed a decrease of 69.4% on average time (compared to the best result of other techniques), but also demonstrated the need for more images to generate a complete reconstructed model.
Task-Driven Approach for Deadline Based Scheduling Across Sensor Networks

Tremendous evolution towards cloud computing provides changing demands to work in virtualized environment, i.e. applications such as Internet of Things (IoT), healthcare system, video streaming that are transferred to cloud, that needs responses in certain time period, that is, deadline. Moreover, cloud computing acquires substantial amount of energy whilst offering services to meet applications need, that leads to higher operational cost. In specific, bulky task to offer services to deadline based task while reducing energy consumption. However, effectual task scheduling is an attractive way to reduce energy utilization whilst guaranteeing fulfilled services for cloud users. Task scheduling in cloud is measured as multi-objective minimization crisis which comprises of make-span and reduction of energy consumption. Initially, novel learning based scheduling approach for deadline tasks has been proposed. This model is an adaptive decision making approach assists schedulers to recognize finest responses Then, decision based scheduling (DSA) algorithm is provided which investigates tasks’ heterogeneity by guaranteeing time needed for scheduling tasks. Extensive simulation was carried out in MATLAB simulation environment to show applicability and effectiveness of DSA for deadline scheduling in cloud environment.
Design of a low power, high speed self calibrated dynamic latched comparator

A Dynamic Comparator is an important part of the analog-to-digital converter (ADC). The performance and accuracy of ADC depends on the design of comparator. Moreover, comparator is the most power-hungry among all other parts of ADC. To satisfy speed and accuracy within a low power budget requirements, a PMOS based Double tail latch type Comparator circuit has been presented in this work. Detail analysis of offset voltage of the dynamic latched comparator has been carried out in this work. A Foreground calibration technique is used in this work to minimize the offset. The entire architecture is designed and simulated in CADENCE Virtuoso using UMC 180nm PDK with 1.8V power supply. Transient and statistical measurements have been carried out to test the performance of the circuit under the impact of process variations, temperature and mismatches. Post-layout simulation shows that the comparator achieves 200µV resolution with 13 bit precision at a frequency of 50 MHz while dissipating 6. 68µW from 1.8V supply with 0.074mm2 area consumption.