Miracle-dz/UniFAR-FTUs huggingface dataset:
Pitch Control of an Active Omni-wheeled Unicycle using LQR

Unicycle is a complex control system, it is unstable in both the pitch and roll directions. This paper presents a unique design for the unicycle using an active omni-wheel. This design makes the unicycle more versatile as it can easily move in any direction. The dynamic model of the unicycle in pitch direction is simplified to a moving-cart inverted pendulum model. Linear Quadratic Regulator (LQR) is used to control the pitch stability. Simulations and experimental results are presented to evaluate control performance of LQR.
Wirelessly Control for RGB Lamp End-device: Design and Implementation

In this work, an RGB light-emitting diode (LED) lamp was designed for a smart home system. The ultimate aim of this research is to produce an energy-efficient as well as an environmentally-friendly lamp that is able to be utilized for primarily home activities. The RGB lamp device is made-up of an STM32L100 microcontroller, which controls an SMD-5050 RGB LED lamp using pulse-width modulation (PWM). The RGB lamp device itself is controlled by the user using Android-based application, which communicates using Bluetooth with a central host that passes the command to the lamp device through Zigbee protocol. The end-result of this work is an RGB lamp that uses 42.8 mA current consumption in an idle condition and 603.1 mA in active condition for 5 VDC supply voltage.
Detection of Neuromuscular Activity Using New Non-Invasive and Flexible Multimaterial Fiber Dry-Electrodes

A new non-invasive and flexible sensor was fabricated to monitor the muscular activity of upper body muscles. It consists of a dry electrode made of multimaterial metal-polymer-glass hollow-core fiber electrodes connected to a custom made signal acquisition platform. This fiber electrode only adds a negligible contribution to the recording system’s internal noise (<inline-formula> <tex-math notation="LaTeX">$7.626~\mu \text{V}$ </tex-math></inline-formula> peak-to-peak and <inline-formula> <tex-math notation="LaTeX">$1.234~\mu \text{V}$ </tex-math></inline-formula> RMS; recording system noise: <inline-formula> <tex-math notation="LaTeX">$6.901~\mu \text{V}$ </tex-math></inline-formula> peak-to-peak and <inline-formula> <tex-math notation="LaTeX">$1.205~\mu \text{V}$ </tex-math></inline-formula> RMS). Surface electromyograms (sEMGs) of the biceps bracci and trapezius muscles were recorded during maximal voluntary contractions using the new sensor and compared to that obtained with medical grade commercial sensors. Frequency content and time domain analysis show that the new flexible sensor performs similarly to commercial devices in terms of sEMG signal amplitude discrimination and frequency shift evaluation during the development of muscle fatigue. The advantage of the new sensor is the high flexibility of the fibers allowing their easy integration into stretchable garments without compromising the user’s comfort. This will lead to the development of a new generation of smart textile with large application for telemedicine and assistive devices.
A Speed-Based and Traffic-Based Handover Algorithm in LTE Heterogeneous Networks

With the exponential explosion of high traffic demand in wireless networks, the heterogeneous network (HetNet) is considered as one promoting method for improving the throughput of network and quality of service (QoS) of users. The HetNet consists of different cells, which operate at different transmit power. The small cells can be used to offload traffic from macro cells. Nevertheless, the uses of small cells also bring some problems. One problem is how efficiently an handover (HO) of small cells is performed in mobility management, when small cells are deployed in coverage area of a macro cell. In this condition, the various problems faced by users include frequent and unnecessary HO, ping-pong effect, which severely degrade the quality of traffic and users experience. Many existing works propose optimal solutions to improve the performance of long-term evolution (LTE) HetNets, which only consider the one of users’ speeds and traffic classifications. In this paper, we propose an optimal HO algorithm to meet the margin coverage problems. Our proposed algorithm considers the different conditions of user speed and traffic classification. Our proposed scheme avoids users with high speed in handing off to home-evolved nodeB (HeNB) and makes them limited to evolved nodeB (eNB). Simulation results show that our proposed algorithm improves the performance in in terms of the downlink throughput, physical downlink shared channel (PDSCH) utilization, system delay, voice end-to-end delay and voice jitter.
Residual Syntax Elements Analysis and Design Targeting High-Throughput HEVC CABAC

Video processing is a topic of major interest due to the demand for real-time delivery of increasing resolution videos. Moreover, Internet traffic related to high-quality video processing tends to be the dominant type of information transmitted. The high-efficiency video coding (HEVC) standard is a suitable and efficient choice to overcome the challenges regarding the matter presented. In HEVC, the final encoding step is the entropy encoding, in which the context-adaptive binary arithmetic coding (CABAC) algorithm is the sole option used. Many recent research efforts have been successful in enhancing CABAC throughput for a hardware approach, which is a remarkable difficulty due to the intrinsic dependencies generated by the algorithm. The throughput improvement happens due to the capability of these entropy-encoding designs to consume multiple input data coming from the previous encoding steps. Nevertheless, the same care in terms of data generation shall occur for the stage right before the CABAC block. Otherwise, a transfer of the potential processing bottleneck would move to the input data fabrication. Therefore, this paper proposes a novel scheme, named multi residual syntax element treatment (MRSET), to be compliant with these recent high-throughput CABAC designs, being able to supply the entropy coding with enough input data and thus it would not starve. A complete analysis involving HEVC reference software and recommend video sequences occurred to drive MRSET inception. Different multi-core MRSET hardware architectures results are provided, and the best suitable option is defined based on the characteristics of the state-of-the-art CABAC architectures.
Interference Limit based Resource Allocation for Low Density Signature OFDM in Underlay Cognitive Radio Networks

In underlay cognitive radio, cognitive users (CU) or secondary users (SU) are allowed to use a licensed spectrum band simultaneously with primary users (PU) as long as the overall interference generated is still below PU’s interference limit. To accommodate the large number of SU users and manage the interference in each subcarrier, CR system used low density signature orthogonal frequency division multiplexing (LDS– OFDM) as a multiple access scheme. LDS–OFDM allows each user to spreading their symbols on a small set of subcarriers and set the number of users that using the same resources. This paper proposes a radio resource allocation scheme for SU with respect to dc as a number of users that allowed to access subcarrier and the interference threshold required by PU. The simulation results show that the outage probability not only depend on the interference limit (IL) determined by PU but influenced by the number of subcarriers that can be accessed by SU (dv) and the number of users allowed to access a resource (dc) as well.
Automated Generation of User-Tailored and Time-Sensitive Music Playlists

Streaming music platforms have changed the way people listen to music. Today, we can access to millions of songs with a simple internet-connected device. The drawback is that the selection of what to listen is a long, tedious, ant time-consuming process. This is why, nowadays, we choose playlists instead of songs. Unfortunately, since there are thousands of playlists, the selection process can once again be long, tedious, and time-consuming. In this paper, we design a system to facilitate the listening and discovering of new music. The system automatically generates user-tailored and time-sensitive music playlists and proposes a single playlist to play when the user accesses to a music platform. The system understands the user’s listening habits by analyzing the low-level features of songs recently played by the user and by using two different clustering algorithms. A novel designed method uses these data to produce a playlist that expands the user’s musical knowledge keeping in mind that a good playlist must contain a mix of new and known music and artists. An implementation based on the Spotify API proved the effectiveness of the approach and showed that the proposal might provide benefits to both users (no time wasted to select what to play) and to music platforms (playing of music that otherwise would remain unknown to users).
Volume-Based Assessment of Erosion Patterns around a Hydrodynamic Transparent Offshore Structure

The present article presents results of a laboratory study on the assessment of erosion patterns around a hydrodynamic transparent offshore foundation exposed to combined waves and currents. The model tests were conducted under irregular, long-crested waves in a scale of 1:30 in a wave-current basin. A terrestrial 3D laser scanner was used to acquire data of the sediment surface around the foundation structure. Tests have been conducted systematically varying from wave- to current-dominated conditions. Different volume analyzing methods are introduced, which can be related for any offshore or coastal structure to disclose physical processes in complex erosion patterns. Empirical formulations are proposed for the quantification of spatially eroded sediment volumes and scour depths in the near-field and vicinity of the structure. Findings from the present study agree well with in-situ data stemming from the field. Contrasting spatial erosion development between experimental and in-situ data determines a stable maximum of erosion intensity at a distance of 1.25 A, 1.25 times the structure’s footprint A, as well as a global scour extent of 2.1–2.7 A within the present study and about 2.7–2.8 A from the field. By this means, a structure-induced environmental footprint as a measure for erosion of sediment affecting marine habitat is quantified.
Applications of Artificial Intelligence in Agriculture: A Review

The application of Artificial Intelligence (AI) has been evident in the agricultural sector recently. The sector faces numerous challenges in order to maximize its yield including improper soil treatment, disease and pest infestation, big data requirements, low output, and knowledge gap between farmers and technology. The main concept of AI in agriculture is its flexibility, high performance, accuracy, and cost-effectiveness. This paper presents a review of the applications of AI in soil management, crop management, weed management and disease management. A special focus is laid on the strength and limitations of the application and the way in utilizing expert systems for higher productivity.
Multi-period Market Operation of Transmission-Distribution Systems Based on Heterogeneous Decomposition and Coordination

The integration of shiftable/curtailment distribution generators (DGs) along with quick-response storage has not only increased the transaction’s flexibility but also puzzled the bidding willingness of transmission-connected market players (TMPs). In this paper, the method of heterogeneous decomposition and coordination (HGDC) is applied to decompose the integrated transmission-distribution market framework into a bi-level problem with a transmission wholesale market master problem and several distribution retail market subproblems in a decentralized organization structure. The price-based bidding willingness of demand-side resources’ (DSRs’) aggregator is simulated considering the relation between distribution system operator’s (DSO’s) operation modes and its equivalent market transactive price. Besides the traditional mixed-integer linear programming (MILP) model, the active reconfiguration model of DSOs based on mixed-integer second-order conic programming (MI-SOCP) is proposed to rearrange its operation switch status and elaborate its operation cost according to the market transaction. Multi-period optimal operation modes could be obtained through an HGDC-based iteration process by coordinating the transmission system operator (TSO) with DSOs and considering the market energy equilibrium and reserve requirements for security considerations. Karush-Kuhn-Tucker (KKT) conditions are used to testify the optimality and convergence of the bi-level model in theory. The T5-3D33 case is employed to illustrate the efficiency of the proposed model and method.
Camera Self-Calibration for Unmarked Human Motion Capture System

An unmarked human motion capture system typically requires camera calibration. In this paper, we present a method for calibrating multiple cameras and set up a suitable three-dimensional coordinate system for the calibration as we want. Camera calibration is achieved by using the trajectory of a moving point. After recording the videos of point, we can get several groups of 2D coordinates respective to each camera. Then camera projective matrices are calculated with the data of 2D pixel coordinates via rank-4 factorization. The three-dimensional coordinate system which we want is determined by the position of another three points which constitute a non-isosceles right triangle. We demonstrate our calibration method by the experiments on real unmarked human motion capture system. The biggest reprojection error is less than 1.1 pixels, which is accurate enough for the motion capture.
Impact of Vacancies on Thermal Transport of Defected Zigzag Stanene Nanoribbon: A Molecular Dynamics Simulation Study

Stanene, a two-dimensional buckled honeycomb material, exhibits interesting thermal, mechanical, and electrical properties suitable for electrical and thermoelectric applications. In this study, we have performed an equilibrium molecular dynamic simulation to investigate the impact of different types of vacancies on thermal transport of zigzag stanene nanoribbon using the modified embedded-atom method (MEAM) potential. Thermal conductivity is found to decrease as defect concentration is increased. The decay rate is higher at low defect concentration but gradually decreases at higher defect concentration. Moreover, a comparative study shows that zigzag chirality displays a higher thermal conductivity than its armchair counterpart at all defect concentration. Furthermore, thermal conductivity of defected stanene nanoribbon is observed to decrease with increase of temperature. Such study would further encourage optimized thermal transport in stanene nanostructure based thermoelectric devices.
Kuznets Revisited: What Do We Know about the Relationship between Structural Transformation and Inequality?

This paper revisits the Kuznets postulate that structural transformation will be associated with increasing inequality using comparable time series data for 32 developing and recently developed economies for the post-1950 period. We find that structural transformation in the majority of our economies has resulted in the movement of workers from agriculture to services, and not to manufacturing. Economies show different paths of structural transformation that cut across geographical regions, being either structurally underdeveloped, structurally developing, or structurally developed. We see clear differences in the structural transformation–inequality relationship depending on the stage of structural transformation that a particular economy is in, as well as across regions. We do not see a Kuznets-type relationship between manufacturing employment share and inequality when we take into account the different paths of industrialization that economies in our dataset have followed. On the other hand, inequality unambiguously increases with structural transformation if the movement of workers from agriculture is to services.
A New Adaptive Sliding-Mode Control and Its Application to Robot Manipulators

This paper proposes a new adaptive law based on sliding-mode control(SMC) control. The proposed adaptive law adjusts the switching gain near the sliding manifold to have the appropriate attractivity property. This appropriate attractivity property prevents unexpected errors caused by the excessive or insufficient adaptation of switching gain. Furthermore, this adaptive law ensures the fast adaptation speed of switching gain, and this yields the reduction of chattering. For the more practical implementation based on a model-free controller, the proposed adaptive SMC (ASMC) works together with a time-delay controller(TDC). To validate the proposed adaptive law, the results of simulations are carried out and comparisons are made with existing ASMC control schemes.
Fabrication of Phase Change Microstring Resonators via Top Down Lithographic Techniques: Incorporation of VO2/TiO2 Into Conventional Processes

The electronic, optical, and mechanical properties of vanadium dioxide (VO2) can be significantly altered by external stimuli such as heat, electric field, and so on, due to its metal-insulator transition (MIT). While the properties and potential applications of this material have been widely studied, minimal efforts were pursued to integrate VO2 into existing microfabrication processes. Here, in this paper, we present a process for fabricating VO2/TiO2-coated microstring resonators, where the stability of both films was monitored during the entire process. RIE, vapour HF, ablation, and ICP etching were used throughout the process and their effects on the VO2/TiO2 films, as well as the structural and masking layers was studied. Certain processes required the use of a metal hard mask rather than photo resist. VO2 shows excellent etch selectivity over the metal mask layer with RIE. Of all metals examined, Cr was found to be the most stable as a hard mask. Once fabricated, the mechanical and electrical characterizations of the microstring were carried out by measuring resonance frequency (fr) and electrical resistance which show over 6 % alteration in fr and two orders of magnitude change in resistance during the MIT, with a reversible transition and minimal hysteresis. This report provides a foundation for future processing of VO2 based MEMS/MOEMS devices.
An Energy Efficient Smartphone Sensors’ Data Fusion for High Rate Position Sampling Demands

Many smartphone-based traffic safety applications have been proposed in literatures. These applications demand very high position sampling to safeguard vulnerable road users. European Telecommunication Standards Institute (ETSI) has defined time interval between Cooperative Awareness Messages for collision risk warning to be between Is and 0. 1\s. This implies that geographical awareness information has to be sampled between the frequencies 1Hz and 10Hz inclusive. However, an investigation we made depicts that current smartphones can’t support such high rate location sampling. Even though they meet the aforementioned sampling requirements, high rate sampling of position data is an energy hungry process. In light of this, we have proposed an energy efficient position prediction method that fuses GPS and Inertial Navigation Systems (INS) sensors data to estimate pedestrians’ positions at high rate. INS based dead reckoning is performed to extrapolate positions from last known location when GPS reading is unavailable and when GPS fix is realized the reading is used to correct dead reckoning parameters in addition to serving as a location fix. The proposed solution is compared to a position prediction method that relies solely on GPS data on two selected pedestrian trajectories. The result demonstrates that fusing GPS and INS position data has an average improvement of 30% and 61.4% in error in distance and direction respectively. The proposed position prediction algorithm is also applied to sensors data that are obtained by relaxing sampling rates with the objective of sparing smartphone’s energy. In this regard, first energy efficiency of different position sampling rates of GPS and INS sensors are evaluated and then the algorithm is applied to the sampling frequencies that are proven to husband energy. The outcome of the evaluation is that the battery life of smartphones can be doubled by compromising accuracy of estimated distance and direction by only 11.5% on average.
Machine Learning with partially labeled Data for Indoor Outdoor Detection

This paper demonstrates the feasibility of an hybrid/semi-supervised classification method for detecting the environment of an active mobile phone, based on both labeled and unlabeled cellular radio data. Precisely, we provide answers to the following question: what is the environment of the mobile user when it is/was experiencing a mobile service/application: indoor or outdoor? Implementing this method within the mobile network is interesting for mobile operators since it has low complexity, is less human intrusive (minimal intervention of mobile users) and more accurate. The semi-supervised classification algorithm learns to identify the environment using large and real collected 3GPP signals measurements. As compared to existing work, in addition to existing parameters used for classification, we propose to also use a radio metric called Timing Advance. It is computed within the mobile network. We empirically validate the innovative semi-supervised algorithm using new real-time radio measurements, with partial ground truth information, gathered daily, weekly, monthly, from indoor and outdoor locations and from multiple typical and diversified environments crossed by mobile users. The study confirms the effectiveness of the proposed scheme compared to the existing supervised classification methods including SVM and Deep Learning.
Combination of Image Quality Scores Based on Particle Swarm Optimization

In this paper, we propose a new combination technique for full-reference image quality assessment (IQA) by utilizing three better-recognized IQA methods. It gives the best performance for various databases. The parameter values employed in the new IQA score are optimized using the particle swarm optimization algorithm. In the combination approach, we firstly pick up the most appropriate IQA index for image quality databases and then add other two indices which have the most dissimilar features with the first index. By experiments, it is validated that the proposed method outperforms the other previous combination methods.
Adaptive Non-linear Diffusion Based Local Binary Pattern for Image Denoising

Non-linear diffusion approaches are an effective way to reduce noise and preserve the edge information. In this paper, the work is extended to integrate the non-linear diffusion and local binary pattern (LBP) textons, where the diffusivity function is adapted to pixels type after LBP classification. This allows smoothing on homogenous and noisy regions but not on edges. The proposed method preserves edges better because the diffusion is controlled taking into account the difference of diagonal neighbors in addition to four nearest neighbors. Experimental results on synthetic and real images illustrate the effective performance of the proposed method.