Miracle-dz/UniFAR-FTUs huggingface dataset:
Maximum Information Capacity Modulation for Peak Power Limiting

Modern encoding methods made it possible to obtain the information-interchange rate over communication channels closely approaching the Shannon limit. Issues connected with technological limitations that define the value of the information capacity for the used signals, which set out the value of Shannon rate, are clearly entering into the foreground of practical problems. Traditional approach to calculating the Shannon limit based on the model that envisages a limitation of the average signal power in the case of transmission over the communication channel with additive Gaussian noise has lost its universality at the current stage. Methodology is proposed for the synthesis of optimal amplitude/phase structures of continuous and discrete signal modulations that ensure the maximum rate of information transmitting in the case of the peak power limiting as it is typical for modern communication systems. Numerical results were obtained for Shannon limits under these conditions. Adjustments were found to the results that obtained on the basis of the traditional methodology. It was shown that in some cases these adjustments might be up to 3.5. 4.0 dB in the view through the reduction of effective signal-to-noise level in the channel.
Region-based deformation transfer

Mesh deformation is a fundamental technique for geometric modeling which is applied successfully in a wide range of applications from shape design to computer animation. Normally, the deformation transferred from one actor to another actor is based on all vertices and triangles of a mesh, leading to time-consuming in terms of a 3D sequential model. To address this problem, we propose a region-based deformation transfer that automatically identifies several regions with the largest displacement in time series, and then exploits those deformations of such regions. Our experimental results demonstrate that we can obtain the similar deformed mesh in spite of using approximately 50% – 60% of the racial area, therefore the time decrease significantly for deformation transfer step.
Imaging a Range's Stray Signals with a Planar Scanner

The fundamental purpose of absorber treatment in an anechoic chamber is to ensure that only the direct-path signal is coupled between the range antenna(s) and the device under test. For many simple and standard geometries, this is readily accomplished with conventional processes and procedures. When the geometry and/or stray-signal requirements deviate from the norm, however, it can be very beneficial to have an easy and reliable way to locate and quantify sources of stray signals. This paper discusses a straightforward algorithm for creating images of those stray signals in a range when a planar scanner and broad-beamed probe are available in the test zone. Measured data from multiple facilities are evaluated, along with absorber-treatment improvements made based on some of the images produced.
Energy-Efficient Dynamic Computation Offloading and Cooperative Task Scheduling in Mobile Cloud Computing

Mobile cloud computing (MCC) as an emerging and prospective computing paradigm, can significantly enhance computation capability and save energy for smart mobile devices (SMDs) by offloading computation-intensive tasks from resource-constrained SMDs onto resource-rich cloud. However, how to achieve energy-efficient computation offloading under hard constraint for application completion time remains a challenge. To address such a challenge, in this paper, we provide an energy-efficient dynamic offloading and resource scheduling (eDors) policy to reduce energy consumption and shorten application completion time. We first formulate the eDors problem into an energy-efficiency cost (EEC) minimization problem while satisfying task-dependency requirement and completion time deadline constraint. We then propose a distributed eDors algorithm consisting of three subalgorithms of computation offloading selection, clock frequency control, and transmission power allocation. Next, we show that computation offloading selection depends on not only the computing workload of a task, but also the maximum completion time of its immediate predecessors and the clock frequency and transmission power of the mobile device. Finally, we provide experimental results in a real testbed and demonstrate that the eDors algorithm can effectively reduce EEC by optimally adjusting CPU clock frequency of SMDs in local computing, and adapting the transmission power for wireless channel conditions in cloud computing.
Lightweight Hardware Implementation of R-LWE Lattice-Based Cryptography

Lattice based cryptography (LBC) is one of the most promising post-quantum cryptographic candidates. Ring-learning with errors (R-LWE) is an encryption scheme of LBC. In this paper, a lightweight hardware implementation is presented including key generation, encryption, and decryption. The R-LWE encryption scheme consists of a Gaussian sampler and polynomial multiplication. This paper uses cumulative distribution table (CDT) as the Gaussian sampler and schoolbook approach for the polynomial multiplication. The purpose of this architecture is to achieve small area consumption with high frequency. The hardware implementation results on the Xilinx Kintex-7 FPGA show that the design consumes 808 slices and the frequency can be up to 288.35MHz.
ALARS - Automated Launch And Recovery System for AUVs

Underwater sensing and mapping operations using autonomous vehicles are becoming widely used. This article describes an automated system to launch and recover an AUV. It can operate in any host platform and can transport any torpedo-shaped vehicle with 0.2 meters of diameter, length up to 3 meters and weight up to 1000 N. The system ensures a restrained transportation of the vehicle and guarantees that it performs a smooth entrance in the water. It was instrumented for continuous status remote monitoring, using linear and angular motion sensors, as well as enables to remotely take control over the operation. Experimental results carried out within the XPRIZE competition demonstration scope are presented.
Classification of 3D Archaeological Objects Using Multi-View Curvature Structure Signatures

We propose a generalized 3D shape descriptor for the efficient classification of 3D archaeological artifacts. Our descriptor is based on a multi-view approach of curvature features, consisting of the following steps: pose normalization of 3D models, local curvature descriptor calculation, construction of 3D shape descriptor using the multi-view approach and curvature maps, and dimensionality reduction by random projections. We generate two descriptors from two different paradigms: 1) handcrafted, wherein the descriptor is manually designed for object feature extraction, and directly passed on to the classifier and 2) machine learnt, in which the descriptor automatically learns the object features through a pretrained deep neural network model (VGG-16) for transfer learning and passed on to the classifier. These descriptors are applied to two different archaeological datasets: 1) non-public Mexican dataset, represented by a collection of 963 3D archaeological objects from the Templo Mayor Museum in México City that includes anthropomorphic sculptures, figurines, masks, ceramic vessels, and musical instruments; and 2) 3D pottery content-based retrieval benchmark dataset, consisting of 411 objects. Once the multi-view descriptors are obtained, we evaluate their effectiveness by using the following object classification schemes: $K$ -nearest neighbor, support vector machine, and structured support vector machine. Our object descriptors classification results are compared against five popular 3D descriptors in the literature, namely, rotation invariant spherical harmonic, histogram of spherical orientations, signature of histograms of orientations, symmetry descriptor, and reflective symmetry descriptor. Experimentally, we were able to verify that our machine learnt and handcrafted descriptors offer the best classification accuracy (20% better on average than comparative descriptors), independently of the classification methods. Our proposed descriptors are able to capture sufficient information to discern among different classes, concluding that it adequately characterizes the datasets.
Analysis of Switching Network Based on Third-order Circuit with a Memristor

A switching network based on third-order circuit with a memristor is studied in this paper. Through existing literatures, a practical model of memristors is selected and applied in a third-order standard circuit. The Routh-Hurwitz condition and Lyapunov approach with upper Din i derivative are used in the analysis of the circuit and switching network to help obtain the stable conditions and parameters. The simulation results under the stable parameters proved the existence of the equilibrium point of the network and the steady state trajectory.
Optimising Cloud-Based Hadoop 2.x Applications

Hadoop is an open-source MapReduce implementation widely adopted in industry and academia. However, achieving effective trade-offs between the cost and execution time of Hadoop applications is challenging due to the numerous Hadoop parameters that need to be configured properly. Running Hadoop applications on public clouds compounds this challenge because cloud parameters such as the number and types of virtual machines used to run the application also need to be decided. We will develop an approach for the multi-objective optimisation of Hadoop applications running on public clouds, enabling users to make the best of Hadoop processing when deployed in a cloud environment. The approach will provide those responsible for the configuration of a Hadoop application with a set of Pareto-optimal configurations, allowing them to run the application optimally within their time and/or budgetary constraints.
Zone-Based Cooperative Content Caching and Delivery for Radio Access Network With Mobile Edge Computing

Mobile edge computing (MEC) is a promising solution to meet the latency requirement for delay-sensitive services in a 5G radio access network (RAN). Its key idea is to deploy computing and storage capacities at the edge of the RAN to quickly provision content and processing capacities as required by the users. Efficient content caching and delivery are key issues to ensure the success of this technique. This paper proposes a zone-based cooperative content caching and delivery scheme for a RAN supporting MEC (MEC-RAN), where the RAN is modelled as a zone and is further sub-divided into multiple sub-zones. Content items are cooperatively cached and delivered among multiple sub-zones. The caching problem is formulated as a mixed integer linear programming model. We also develop a heuristic cooperative content caching strategy to decide the content items to be cached in each MEC server. This novel strategy divides the storage space in each MEC server into two parts. The first part caches locally popular contents and the second part is used to cooperatively cache zone-wide popular items. We study the proposed scheme both through simulations and implementation on a testbed that consists of a subnetwork on our campus and commercial cloud service from Ali-Cloud. Both of these show that the proposed zone-based scheme performs better than other typical caching strategies in terms of average content delivery latency and balanced loading of the MEC servers.
Smart Parking Using Wireless Sensor Network System

This paper presents a wireless sensor networking smart parking system that can provide information about parking slots located in the parking area. In this study, to find out the parking slots available in the parking area, a wireless communication that can be monitored through the monitor screen is proposed. The monitor serves as a monitoring of the parking slot area to be displayed, so that visitors know whether the parking slot is still available or even fully charged. The HC-SR04 ultrasonic sensor is installed in each parking slot area, the sensor will detect the parking slot, which will be filled by the car. The data obtained from the ultrasonic sensor will be managed by arduino mega 2560 located in monitor area and then sent to the monitoring area using the Wireless Sensor Network Xbee-Pro S2 communication. The parking slot area available or already filledby car will be displayed using Visual Studio software display 2013.
Real Time Detection on Face Side Image with Ear Biometric Imaging Using Integral Image and Haar-Like Feature

There are no CCTV that accurately record human movement from the front of camera. Sometimes the recording shows the figure of a human face from the side. Face detection is usually used to identify facial data with different expressions. Various studies have been developed on Facial Recognition and Expression. However, most of the analysis is about the face from the front side. To deal with the problem, it is necessary to develop research on the side face using ear biometric. This research uses Integral Image, Adaboost and Haar-Like techniques. Direct test via laptop webcam with the object facing from the side face. The experiment result of this research is that the system successfully classifies 33 ears that can be detected from 60 ear database which has been labeled manually. From 33 ear detected there are 29 ears that can be classified correctly. So the accuracy is 88% as false positive. Fault tolerance of ear position is 45.07/345384 or 0.01% in data testing. The use of Haar-Like Feature has been successfully used as a biometric ear detector on the side of the face images. Ear detection takes place at 60 frames in 5 seconds. Or about 12 frames per second. For testing via webcam, the ear can be detected in about 0.5776376 seconds.
Multi-Frequency Averaging (MFA) Model of Electric-Hybrid Powertrain Suitable for Variable Frequency Operation Applied in Geographically-Distributed Power Hardware-in-the-Loop (GD-PHiL) Simulation

Project aims to develop capability for OEMs and suppliers to 'virtually-connect' multiple prototype powertrain components (engine, drive etc.) and engage in real-time system simulation, thereby reducing cost by eliminating co-location dependency. Technical challenge faced is to duplicate test-hardware state & performance at remote location(s) real-time via an indeterminate internet connection characteristic of latency and jitter. MFA-based remote model is a potential solution but applied research till date has been for fixed-frequency operation. This paper explores variable frequency operation of MFA and outlines the implementation of generic electric powertrain intended for remote control/operation, indicating key performance improvements over time-domain switching models.
Graph Signal Processing-Based Network Health Estimation for Next Generation Wireless Systems

In this letter, we propose a novel network health estimation technique for wireless cellular networks. The proposed scheme makes use of graph signal processing techniques to estimate network health over the entire coverage area with sparse availability of measured data. To achieve this objective, we solve an optimization problem on graph using proximal splitting method. The results show that the proposed technique outperforms existing methods such as kriging for network health estimation with an improved accuracy of more than 60% along with a reduced time complexity of <inline-formula> <tex-math notation="LaTeX">$\mathcal {O}(n)$ </tex-math></inline-formula> compared with <inline-formula> <tex-math notation="LaTeX">$\mathcal {O}(n^{4})$ </tex-math></inline-formula> for kriging. Thereafter, coverage holes present in the network are found with an extremely high detection rate and extremely low false positive rate. The results, unlike kriging, are devoid of any spatial bias present in the training data.
Design and Implementation of Trajectory Tracking Motion in Mobile Robot Skid Steering Using Model Predictive Control

This research aims to design and implement the motion planning and trajectory tracking on a mobile robot four-wheel skid steering system using Model Predictive Control (MPC). Motion planning is one of the fundamental problems in the navigation of autonomous robots. The main concern of this research is to find the paths that can guide the robot running from the initial position to the destination. The first thing to do was create a map of the environment that would be used, then the map result was used to find the path that was free from obstacles. The searching process used A* (A-star) algorithm, then the path was used as the robot reference path. The Design of Predictive Model Control aims to track the reference path by the robot, in designing this MPC used the dynamics of the Pioneer 3-AT robot model based on the current robot position against the reference robot position. Model Predictive Control could accommodate the limitation on control signals, while also predicting the subsequent system behavior along the specified horizon. Error tracking errors are a combination of MPC which was derived through the quadratic function of the error tracking system and advanced feed control. Designed control would be simulated first to see the system behavior. This simulation process used MobileSim software that was integrated with MATLAB to obtain appropriate system behavior. The results of the implementation showed that the Model Predictive Control could track to reach the destination on the reference path in the form of the number “8”, the path to the end of the LSKK Lane, and the path to the JCC LSKK ITB Room with an average tracking error of less than 10 cm (x, y) and 0,2 degree.
Optimizing Resource Allocation in the Short Blocklength Regime for Ultra-Reliable and Low-Latency Communications

In this paper, we aim to find the global optimal resource allocation for ultra-reliable and low-latency communications (URLLC), where the blocklength of channel codes is short. The achievable rate in the short blocklength regime is neither convex nor concave in bandwidth and transmit power. Thus, a non-convex constraint is inevitable in optimizing resource allocation for URLLC. We first consider a general resource allocation problem with constraints on the transmission delay and decoding error probability, and prove that a global optimal solution can be found in a convex subset of the original feasible region. Then, we illustrate how to find the global optimal solution for an example problem, where the energy efficiency (EE) is maximized by optimizing antenna configuration, bandwidth allocation, and power control under the latency and reliability constraints. To improve the battery life of devices and EE of communication systems, both uplink and downlink resources are optimized. The simulation and numerical results validate the analysis and show that the circuit power is dominated by the total power consumption when the average inter-arrival time between packets is much larger than the required delay bound. Therefore, optimizing antenna configuration and bandwidth allocation without power control leads to minor EE loss.
Continuous Human Learning Optimizer based PID Controller Design of an Automatic Voltage Regulator System

In this paper, an intelligent design and tuning method for the proportional-integral-derivate (PID) controller of an automatic voltage regulator (AVR) system using a novel heuristic algorithm termed as the continuous human learning optimizer (CHLO) is presented. The CHLO is inspired by human learning mechanisms wherein a well-defined rule based probabilistic procedure of random learning, individual learning, and social learning leads the search process. The CHLO is implemented in Matlab and identification of the PID parameters for said AVR system is done by stating the design task as an optimization problem. The problem statement is formulated to minimize the integral of absolute error (IAE) criterion with in-built weighted preferences for transient response characteristics. The simulation experiments are devoted both towards the application as well as in exploring the behavioral parameters of the CHLO optimizer. The performance measures such as transient respone indices, root locus analysis, and bode analysis are carried out. The obtained results are compared with other heuristic approaches in terms of percentage improvement in transient response indices. The numerical simulation results endorse competitiveness and better optimization potential of the proposed method than the biography based optimization (BBO), particle swarm optimization (PSO), differential evolution algorithm (DEA), and artificial bee colony (ABC) algorithm in parameter identification of the AVR system.
A Bandgap Reference Using a Novel Soft Self-start Bias Circuit

A bandgap reference with a novel self-start bias circuit is presented in this paper. The self-start bias circuit uses the principle of negative feedback to achieve soft start-up of the voltage reference. Simulation results obtained in 0.35 μm BCD process demonstrate that the minimum supply voltage can be as low as 1.2V. The generated reference voltage is 1.13V, with a temperature coefficient (TC) of 6.86 ppm/°C for the −55°C to 125°C temperature range. The line sensitivity is 1.014mV/V. The start-up time is 1.15 μs. The circuit occupies an active area of 580×230 μm<sup>2</sup>
Comparison of Bidirectional Quasi Z-Source- and Bidirectional Conventional Two-Stage-Inverter for Electric Traction System

This paper presents a comparative study between the bidirectional conventional two-stage inverter (Topology A) and the called bidirectional quasi-Z- source inverter (Topology B) for electric vehicle (EV) applications. The comparison is conducted in terms of the voltage gain, current ripple, the transient and the steady state performances, and the inverters eﬃciency. The modeling and the control design for both inverters are presented and discussed. Simulation investigations are conducted to verify the inverter. It is shown that the Topology B has a higher voltage gain than the Topology A for some small duty cycle. The results demonstrate that the transient and the steady state performances of Topology A are comparable with the ones of Topology B under the same operating conditions. However, the Topology B shows lower inductor current ripple than Topology A. The results prove that the Topology B shows higher eﬃciency than the conventional solution. With these observations, the Topology B proposes an attractive alternative for EV applications.
Frontalization with Adaptive Exponentially-Weighted Average Ensemble Rule for Deep Learning Based Facial Expression Recognition

Automatic Facial Expression Recognition (FER) is an important technique in human-computer interfaces and surveillance systems. It classifies the input facial images into one of the basic expressions (e.g., anger, sad, surprise, happy, disgust, fear, and neutral) and has attracted significant attention in pattern recognition and computer vision. In this paper, we proposed an advanced convolutional neural networks based FER system. It applies the techniques of face frontalization and feature positioning to reduce the effects of background noise and non-prominent parts. Moreover, the hierarchical structure together with the adaptive exponentially weighted average ensemble are adopted to further improve the accuracies. Simulations on several datasets show that the proposed system outperform state-of-the-art FER methods and can well identify the expression of a person.