Miracle-dz/UniFAR-FTUs huggingface dataset:
Two-Side Coalitional Matching Approach for Joint MIMO-NOMA Clustering and BS Selection in Multi-Cell MIMO-NOMA Systems

Resource management in multi-cell multiple-input multiple-output non-orthogonal multiple access (MIMO-NOMA) is challenged by computational complexity, flexible clustering, and potential channel correlation. In this paper, we focus on a combined resource allocation problem: NOMA mobile user (MU) clustering and the base station (BS) selection, to improve system data rate. Different from sum data rate maximization and max-min fairness, we introduce a new objective function, i.e., relative fairness, which integrates MU fairness into system data rate optimization to overcome the domination effect of BS in advantaged situations of sum data rate improving. Moreover, we derive the closed form solution of MIMO-NOMA resource allocation for a single cluster, and it can be employed for any size of cluster. Furthermore, we propose a new two-side coalitional matching approach to jointly optimize MIMO-NOMA clustering and BS selection, which is able to balance the tradeoff between MUs’ individual benefits and the overall network performance. The proposed approach is core stable. Pauta-criterion is employed on system performance evaluation to provide a judgement on win-win solutions. In simulation, extensive comparisons provide insightful understanding of our proposed MIMO-NOMA clustering strategy, relative fairness, and the proposed two-side coalitional matching approach.
Identification of Wild Species in Texas from Camera-trap Images using Deep Neural Network for Conservation Monitoring

Protection of endangered species requires continuous monitoring and updated information about the existence, location, and behavioral alterations in their habitat. Remotely activated camera or “camera traps” is a reliable and effective method of photo documentation of local population size, locomotion, and predator-prey relationships of wild species. However, manual data processing from a large volume of images and captured videos is extremely laborious, time-consuming, and expensive. The recent advancement of deep learning methods has shown great outcomes for object and species identification in images. This paper proposes an automated wildlife monitoring system by image classification using computer vision algorithms and machine learning techniques. The goal is to train and validate a Convolutional Neural Network (CNN) that will be able to detect Snakes, Lizards and Toads/Frogs from camera trap images. The initial experiment implies building a flexible CNN architecture with labeled images accumulated from standard benchmark datasets of different citizen science projects. After accessing satisfactory accuracy, new camera-trap imagery data (collected from Bastrop County, Texas) will be implemented to the model to detect species. The performance will be evaluated based on the accuracy of prediction within their classification. The suggested hardware and software framework will offer an efficient monitoring system, speed up wildlife investigation analysis, and formulate resource management decisions.
Adaptive Integral Inverse Kinematics Control for Lightweight Compliant Manipulators

In this letter, an adaptive to unknown stiffness algorithm for controlling low-cost lightweight compliant manipulators is presented. The proposed strategy is based on the well-known transpose inverse kinematics approach, that has been enhanced with an integral action and an update law for the unknown stiffness of the compliant links, making it valid for soft materials. Moreover, the algorithm is proven to guarantee global task-space regulation of the end effector. This approach has been implemented on a very low-cost robotic manipulator setup (comprised of 4 actuated and 3 flexible links) equipped with a simple Arduino board running at 27 Hz. Notwithstanding, the strategy is capable of achieving a first-order-like response when undisturbed, and recover from overshoots provided by unforeseen impacts, smoothly returning to its nominal behaviour. Moreover, the adaptive capabilities are also used to perform contact tasks, achieving zero steady-state error. The tracking performance and disturbance rejection capabilities are demonstrated with both theoretical and experimental results.
Electricity Usage Monitoring and Alerting System

Electricity is one of the inevitable sources of energy in our day to day life. Almost every single device we use from the moment we wake up until going to bed is electrically powered. Meanwhile, there is always a problem of power scarcity in our country. It mainly depends on hydraulic power, and we don’t have much rainfall nowadays due to climate change issues. Our government is trying to overcome it and also to ensure power supply to all the consumers of the country without interruption. Reduced power consumption will lead to overcome electricity scarcity. Many pieces of research to monitor and reduce electricity usage is done in the past. However, most of them help to reduce electricity by displaying the total electricity consumption for a month. The best solution for this problem is to find the total electricity units consumed by each device for a given period of time rather than calculated as a whole. We have developed a gadget that will sense the electricity units consumed by a particular device being plugged in and will wirelessly transmit the units consumed in real-time and stored on a database. The total units consumed by the particular device per month will be calculated and displayed. Two prototypes are designed for this research and were tested over a two-week period of time. These gadgets can be plugged into any sockets and will help to detect the amount of power consumption, used through the gadget.
Adaptive Clustering Strategy Based on Capacity Weight

With the rapid development of Internet of Things (IoT) technology, the number of nodes in wireless sensor networks (WSNs) is explosively increasing, and the scale of network is increased gradually. Traditional single-layer non-clustering network is no longer suitable for current WSNs, which results in high maintenance cost and fast deterioration of network performance. By analyzing the impact of existing static and dynamic clustering schemes on network performance, it is concluded that additional factors need to be considered to improve the overall performance of the network, such as residual energy of nodes, number of neighbor nodes and load balancing. Therefore, an adaptive multi-layer clustering networking strategy based on capability weights is proposed. Based on the real-time changes of each cluster density, node load and residual energy, the node capacity weights are updated dynamically according to the actual network performance, then the cluster heads are renewed adaptively. By comparing the performance metrics in the experiments, proposed strategy can effectively reduce the load of key nodes such as cluster head, and improves the network performance metrics such as average transmission delay, average transmission hops and load balancing.
Towards the Identification of Profiles based on the Understanding of Programming Concepts: the Case of the Variable

This Research Full Paper presents a longitudinal study aimed at identifying profiles describing freshers’ understanding of the programming concepts. The variable was chosen as a first case study. 108 students enrolled in a university introductory programming course were submitted to a concept inventory at four different times according to a precise schedule. This sample presents an interesting diversity: 64 students with a major in computer science and 44, in business engineering; 19 are females. Among these students, seven profiles were identified. At first glance, it appears that these profiles could be related to the consistency of students in terms of used mental models to solve variable assignment problems.
Direction Finding Using Coprime Array with Sensor Gain and Phase Errors

This paper investigates the issue of direction finding of multiple signals using coprime array with sensor gain and phase errors. As the sensor errors decrease the performance of direction estimation, it proposes a calibration method with one signal, where one accurate direction signal enables to obtain high precise result. By building the rough direction signal in real system as usual, it introduces the gain self-calibration and iterative self-calibration to enhance the performance of error estimation, which is based on the rough error estimate by one signal calibration. Considering the analysis of computational complexity and performance simulation, the gain self-calibration improves the performance in low signal to noise ratio compared with no self-calibration and the iterative self-calibration gets the best performance with the most computational complexity. With known error values, it can be found more signals than the sensor number with coprime array.
3D Object Recognition and Pose Estimation From Point Cloud Using Stably Observed Point Pair Feature

Recognition and pose estimation from 3D free-form objects is a key step for autonomous robotic manipulation. Recently, the point pair features (PPF) voting approach has been shown to be effective for simultaneous object recognition and pose estimation. However, the global model descriptor (e.g., PPF and its variants) that contained some unnecessary point pair features decreases the recognition performance and increases computational efficiency. To address this issue, in this paper, we introduce a novel strategy for building a global model descriptor using stably observed point pairs. The stably observed point pairs are calculated from the partial view point clouds which are rendered by the virtual camera from various viewpoints. The global model descriptor is extracted from the stably observed point pairs and then stored in a hash table. Experiments on several datasets show that our proposed method reduces redundant point pair features and achieves better compromise of speed vs accuracy.
Decentralized Radial Segregation in Heterogeneous Swarms of Robots

This article proposes a decentralized control strategy to reach radial segregation in heterogeneous robot swarms. The approach is based on a consensus algorithm applied to virtual points attached to each robot and a heuristics to compute the distance between the robots and the virtual point. Two scenarios are considered: when robots have access to a global reference point and when robots can communicate through a fixed underlying topology. A convergence proof is presented. Simulations and experimental results show that our approach allows a swarm of multiple heterogeneous robots to segregate radially using local information.
A Nonlinear Distributed Control Strategy for a DC MicroGrid using Hybrid Energy Storage for Voltage Stability

DC MicroGrids with the integration of renewable energy sources (renewables) and energy storage systems can improve the performance of power systems, ensuring safe operation of a local network, where the MicroGrid control is responsible to assure stability and power quality of the system. This paper proposes a distributed nonlinear control strategy for a DC MicroGrid composed of a PV, a battery storage and a supercapacitor to properly supply a local AC load, where a complete stability analysis in conducted using Lyapunov techniques. The MicroGrid is connected to an AC grid by a VSC converter, which injects the right amount of active and reactive power according to the AC load demand. The supercapacitor’s duty is to control DC bus voltage dealing with transient stability while the battery fulfills long term power flow, where the energy is controlled to assure power flow balance, ensuring proper operation for the MicroGrid. Simulation results illustrate the performance of the proposed control and the system’s operation.
Coverage control and distributed consensus-based estimation for mobile sensing networks in complex environments

In this paper we jointly discuss two problems: coverage control for a set of agents and distributed estimation of an observed quantity. Our coverage solution does not require convexity of the environment and it can be used for static coverage. The idea hinges on the application of a Llyod algorithm with the probability functions manipulated in order to avoid obstacles and adapt to non-convex sets. Our control approach operates hand-in-hand with distributed estimation. We provide ample simulation evidence of the advantages of this combination.
Mental Stress Level Detection using Physiological Signals and Evolutionary Algorithm

Stress can be defined as the sum of the physical, mental and emotional strains or tensions on a person. Providing a method for measuring individual’s stress levels under different conditions can pave the way for early diagnosis and efficient treatments of mental related diseases. Recent studies show that some of the biological signals such as Galvanic Skin Response (GSR) and Photoplethysmogram (PPG) are associated with stress and therefore, can be used for non-invasive monitoring of stress. In this study, in addition to GSR and PPG, Abdominal Respiratory (AR) and Thoracic Respiratory (TR) signals were used as well. Various temporal and spectral domain features together with some statistical and semantic characteristics were extracted from signals of 30 volunteers who participated in a five-phase mental task. Based on data extracted, stress levels were classified into five different groups in terms of severity. In this process, an optimal feature selection procedure was performed by the NSGA-II evolutionary algorithm, and the classification step was performed by subspace discriminant classifier which resulted in 68.79% accuracy and 83.82% Interclass correlation (ICC).
On Improving the Robustness of Reinforcement Learning-based Controllers using Disturbance Observer

Because reinforcement learning (RL) may cause issues in stability and safety when directly applied to physical systems, a simulator is often used to learn a control policy. However, the control performance may be easily deteriorated in a real plant due to the discrepancy between the simulator and the plant. In this paper, we propose an idea to enhance the robustness of such RL-based controllers by utilizing the disturbance observer (DOB). This method compensates for the mismatch between the plant and simulator, and rejects disturbance to maintain the nominal performance while guaranteeing robust stability. Furthermore, the proposed approach can be applied to partially observable systems. We also characterize conditions under which the learned controller has a provable performance bound when connected to the physical system.
A Barrier Function Based-Adaptive Super-Twisting Controller for Wind Energy Conversion System

The specific feature of the wind energy conversion system (WECS) is that its model is a nonlinear system with uncertainties whose upper bound is also unknown. To control WECS, barrier function based-adaptive Super-Twisting controllers are proposed, ensuring the convergence of the extracted power to the maximum value obtained by maximum power point tracking (MPPT) controller, and the convergence of the reactive power to the reference reactive value with predefined errors independent of the upper bound of the uncertainties derivatives.
A Third-Order Super-Twisting Extended State Observer for Dynamic Performance Enhancement of Sensorless IPMSM Drives

In practice, sensorless drive systems of interior permanent magnet synchronous motors (IPMSMs) are usually working in highly utilized conditions where fast changing uncertain loads, inverter losses, magnetic saturation, and other disturbance exist. These issues can reduce the performance and stability, which become the main vulnerability of sensorless drives. Linear extended state observer (LESO) or quadrature-phase-locked loop is conventionally adopted to estimate the speed and position. Without compensation or adaptive methods, fast changing uncertainties cannot be observed thoroughly. In this article, a third-order super-twisting extended state observer (STESO) is proposed to enhance the dynamic performance of position and speed estimation for IPMSM. Utilizing the high-order extended state and super-twisting algorithm, fast convergence and disturbance estimation can be achieved in STESO. The effectiveness and stability of STESO are analyzed and an optimized parameter selection method is presented. Comparative experimental results between the LESO and STESO verify the effectiveness and improvement of the proposed STESO against rapid speed and load variations.
Event Causality Detection in Tweets by Context Word Extension and Neural Networks

Twitter has become a great source of user-generated information about events. Very often people report causal relationships between events in their tweets. Automatic detection of causality information in these events might play an important role in prescriptive event analytics. Existing approaches include both rule-based and data-driven supervised methods. However, it is challenging to identify event causality accurately using linguistic rules due to the unstructured nature and grammatical incorrectness of social media short text such as tweets. Also, it is difficult to develop a data-driven supervised method for event causality detection in tweets due to insufficient contextual information. This paper proposes a novel event context word extension technique based on background knowledge. To demonstrate the effectiveness of our event context word extension technique, we develop a feed-forward neural network based approach to detect event causality from tweets. Extensive experiments demonstrate the superiority of our approach.
Sensorless Explicit Model Predictive Control for IPMSM Drives

The present study proposes a novel sensorless model predictive control (MPC) scheme for interior permanent magnet synchronous motor (IPMSM) drive systems. The control scheme consists of a Proportional Integral (PI)-based speed controller cascade with an explicit MPC-based current controller. A rotor speed and position observer is employed to estimate the motor motion states on the rotor flux without a position sensor. The current controller is obtained by combining the explicit MPC with a torque-current lookup table (LUT). The nonlinearities of the IPMSM dynamics resulting from the cross coupling and the back electromotive force (EMF) are considered for deriving the current controller. Moreover, the possibility of combining explicit MPC with a sensorless observer is implemented at a two-level voltage source inverter for demonstration. The experimental results of the present study demonstrate the effectiveness of the proposed method.
A Data-Driven Policy Iteration Scheme based on Linear Programming

We consider the problem of learning discounted-cost optimal control policies for unknown deterministic discrete-time systems with continuous state and action spaces. We show that a policy evaluation step of the well-known policy iteration (PI) algorithm can be characterized as a solution to an infinite dimensional linear program (LP). However, when approximating such an LP with a finite dimensional program, the PI algorithm loses its nominal properties. We propose a data-driven PI scheme that ensures a certain monotonic behavior and allows for incorporation of expert knowledge on the system. A numerical example illustrates effectiveness of the proposed algorithm.
Fault isolation based on online sparse optimization of streaming faulty data

This paper proposes an online sparse optimization algorithm for fault isolation, which is of a great demand to ensure the normal operation of industrial processes. The proposed method can identify faulty variables without resorting to the historical normal process data. The task of faulty variable location is achieved via performing a sparse matrix decomposition technique on the streaming faulty data, from which a sparse matrix containing fault information is generated and can be further used for pinpointing faulty variables. Additionally, given that process characteristics will change as time goes by, the above decomposition is realized in an online recursive fashion. The efficacy of the proposed method is verified by the Tennessee Eastman benchmark process.
Bufferless Network-on-Chips With Bridged Multiple Subnetworks for Deflection Reduction and Energy Savings

A bufferless network-on-chip (NoC) can deliver high energy efficiency, but such a NoC is subject to growing deflection when its traffic load rises. This article proposes Deflection Containment (DeC) for the bufferless NoC to address its notorious shortcomings of excessive deflection for performance improvement and energy savings. With multiple subnetworks bridged by an added link between two corresponding routers, DeC lets a contending flit in one subnetwork be forwarded to another subnetwork instead of deflected. Microarchitecture of DeC routers is rectified to shorten the critical path and lift network bandwidth. Its Cadence RTL implementations with a <inline-formula><tex-math notation="LaTeX">$15-nm$</tex-math><alternatives><mml:math><mml:mrow><mml:mn>15</mml:mn><mml:mo>-</mml:mo><mml:mi>n</mml:mi><mml:mi>m</mml:mi></mml:mrow></mml:math><inline-graphic xlink:href="sigdel-ieq1-2959307.gif"/></alternatives></inline-formula> process are conducted respectively for mesh-based NoCs and torus-based NoCs. Additionally, different sized DeC-NoCs are evaluated extensively and compared with previous bufferless designs (BLESS and MinBD), uncovering that DeC with two bridged subnetworks (dubbed DeC2) for 8X8 mesh-based NoCs can lower deflection drastically by some 90 percent and energy consumption by upto 51 percent under real benchmark traffic loads, in comparison to BLESS. Under various synthetic traffic models and workloads, 16X16 torus-based DeC2-NoC sustains up to 2.33X loads when compared with its mesh-based counterpart, exhibiting the same clock rate and taking only negligible more power and area according to our full layout results.
A Score Level Fusion Scheme for Iris Recognition

The main aim of this paper is to analyse the iris texture which becomes the basis of feature extraction. In Iris Recognition System (IRS), feature extraction is one of the significant steps which is followed by a matching process. The proposed method takes into account three techniques: DSIFT, HOG and DCT. These techniques are individually employed on the blocks that correspond to the regions of interest, not occluded by noise. A block-wise matching of obtained feature vectors is carried out using a standard measure. The true match of the system relies on the three aforementioned techniques which make the system more robust. The experimental results are reported on CASIA-IrisV1 database. The obtained outcomes indicate that the performance of the system increased significantly due to the score level fusion of the matching that makes it suitable for the iris recognition related applications.
Tiny Electronic Component Detection Based on Deep Learning

Tiny electronic component detection mostly suffers from compact size and multiple interferences of environment. To solve this problem, this paper presents an object detection method based on convolutional neural networks (CNNs). This method combines residual blocks in residual networks with CNNs. The CNNs can reduce the dimensions of the image, and the residual block in the residual network can train deeper neural networks, and then use the successful application of CNNs in computer vision to improve the recognition rate of components and improve the efficiency of automated processing of tiny electronic components. After the hyper-parameter adjustment, this method achieved 95.63% accuracy on our test set.