Miracle-dz/UniFAR-FTUs huggingface dataset:
A Protein Embedding Model for Drug Molecular Screening

Application of machine learning method in new drug development can greatly shorten the process of experimental discovery and reduce the risk of clinical failure. However, the feature extraction of proteins sequence is very difficult due to the large dimension. To this end, we propose a Protein Embedding Model(PEM) for drug molecular screening to predict the interaction between proteins and small molecules. Specifically, PEM first classifies 20 kinds of amino acids into 6 categories to reduce the dimension and learns the representation of protein borrowing the idea of word embedding. Then the model uses multiple imputation to fill the physical and chemical properties of small molecule compounds. Finally, the model uses LightGBM model to predict the affinity value Ki between proteins and small molecules. Experiments show that the model can effectively extract the features of proteins and small molecules and outperforms other traditional methods on the data provided by a drug discovery and development company.
Pilot Study on Objective Evaluation of Human Auditory Ability using Hybrid EEG and FNIRS Acquisition

The multimodal brain-computer interface is very useful for identifying brain states of the human. It can leverage its strengths to improve objective evaluation capabilities, such as increased spatial resolution and temporal resolution. In this paper, we use EEG signals and functional near-infrared signals (fNIRS) to describe responses of the brain region to sounds. Subjects were first trained in simple adaptation and then were asked to make judgments on five types of sound. During the test, EEG and hemodynamic information of the subjects were collected synchronously in real time. Finally, collected information was preprocessed, feature extraction for example, and analyzed. Through the analysis of the results of this paradigm, it can be concluded that the subjects' brain responses are different to different sound types, which means the EEG information and the hemodynamic parameters could be used as objective evaluation indexes of the subject's auditory ability. We hope to conduct many experiments in the future, verify our conjectures and be able to propose an objective evaluation system for auditory assessment, especially for those with impaired hearing.
Improved Feature Selection Model for Big Data Analytics

Although there are many attempts to build an optimal model for feature selection in Big Data applications, the complex nature of processing such kind of data makes it still a big challenge. Accordingly, the data mining process may be obstructed due to the high dimensionality and complexity of huge data sets. For the most informative features and classification accuracy optimization, the feature selection process constitutes a mandatory pre-processing phase to reduce dataset dimensionality. The exhaustive search for the relevant features is time-consuming. In this paper, a new binary variant of the wrapper feature selection grey wolf optimization and particle swarm optimization is proposed. The K-nearest neighbor classifier with Euclidean separation matrices is used to find the optimal solutions. A tent chaotic map helps in avoiding the algorithm from locked to the local optima problem. The sigmoid function employed for converting the search space from a continuous vector to a binary one to be suitable to the problem of feature selection. Cross-validation K-fold is used to overcome the overfitting issue. A variety of comparisons have been made with well-known and common algorithms, such as the particle swarm optimization algorithm, and the grey wolf optimization algorithm. Twenty datasets are used for the experiments, and statistical analyses are conducted to approve the performance and the effectiveness and of the proposed model with measures like selected features ratio, classification accuracy, and computation time. The cumulative features picked through the twenty datasets were 196 out of 773, as opposed to 393 and 336 in the GWO and the PSO, respectively. The overall accuracy is 90% relative to other algorithms ’ 81.6 and 86.8. The total processing time for all datasets equals 184.3 seconds, wherein GWO and PSO equal 272 and 245.6, respectively.
An Enhanced Step-Size Gaussian Damped Least Squares Method Based on Machine Learning for Inverse Kinematics of Redundant Robots

An existing problem in the robotic field is to solve the inverse kinematics (IK) problem of redundant robot with high speed and high precision. A novel IK optimization method based on the Gaussian Damped Least Squares (GDLS) is proposed in this paper. A significant contribution of this method is to make the iteration converge in a faster and more accurate way by introducing an optimal enhanced step-size coefficient. The machine learning model can be trained with 106 data points in the reachable region of the robot, and the optimal enhanced step-size coefficient in each solving process can be predicted by the model. The accuracy and stability of the algorithm proposed are verified through an example of an arbitrary 7R redundant robot. The average number of iterations is less than 10, with super high solving speed. Furthermore, the algorithm also has better convergence, which can reach 96.23% when the error threshold is 0.01 mm. The common IK methods are evaluated in this paper, and the results show that the optimized method has good performance in convergence, accuracy and speed.
Survey of Symmetric and Asymmetric Key Management Schemes in the context of IoT based Healthcare System

Internet of Things (IoT) based real-time health monitoring system gains the attention of the researchers as well as the business sectors. This service turns out to be a blessing to the patients as well as the health care professionals. However, without data security, the practical implementation of this vital healthcare service is not feasible. The sensitive nature of the patient’s healthcare data is very crucial and the compromise of such data may compromise one’s life. Therefore, in any IoT based healthcare system, data security is the vital issue. In this direction of IoT based healthcare system, this paper presents a survey of symmetric and asymmetric key management schemes. Considering the cryptographic key (symmetric or asymmetric), we have discussed the relevant studies within a time period of 6 years. A comparison of these schemes is presented with respect to the security features and simultaneously described the pros and cons of the respected schemes.
A Constrained K-Nearest Neighbor Approach for Semantic Indoor Trajectory Extraction

One of the indoor location-based services use cases is giving real-time information to users while they are inside a building. For example, in a museum, they can check next-to-visit recommendations or emergency guidance in real-time. Thus, it is vital to extract their movement as indoor trajectories. However, most indoor positioning methods have some degrees of error due to noisy observations. Those errors may lead to distant consecutive positions, which is impossible in a real-world case. Thus, we propose the semantic space-based movement constraints to produce indoor semantic trajectory without distant consecutive positions. The proposed approach is designed to extract semantic trajectory effectively in a real-time manner.
Libera for Programmable Network Virtualization

Current network virtualization allows tenants to have their own virtual networks. However, new demands to "program" virtual networks at a finer granularity have arisen as tenants want the ability to provision and control switches and links in their virtual networks. This study proposes a new concept called the p-NIaaS model. The p-NIaaS model enables tenants to program their own packet processing logic and monitor network status from any virtual network infrastructure, which is not possible with current network virtualization. This article presents the Libera network hypervisor that implements the p-NIaaS model. Libera overcomes the shortcomings of existing network hypervisors such as scalability, VM migration support, and flexibility. The evaluation shows that the Libera hypervisor is highly scalable and effectively supports VM migration. We also present the overheads of Libera. Libera incurs up to 11 percent overhead in comparison with a non-virtualized network, which we believe is promising in the first prototype of the p-NIaaS model.
Design of a Microstrip Three-State Switchable and Fully Tunable Bandpass Filter With an Extra-Wide Frequency Tuning Range

In this study, a new method and structure are proposed to design of a switchable and fully tunable microstrip bandpass filter (BPF). The central frequency and bandwidth (or the resonance frequency of the resonators, coupling coefficient between resonators, and external quality factor of the I/O resonator) of the proposed BPF can be efficiently controlled using varactor diodes as tuners. In addition, the proposed BPF is designed to have three operating states, each with a different frequency tuning range. The three operating states can be switched between independently by introducing three pairs of switching diodes in the input and output feeding structures. Because of the ability to switch among the three operating states, an extra-wide frequency tuning range can be achieved. For demonstration, a two-pole switchable and tunable BPF was designed and implemented using microstrip technology. The experimental and simulation results were consistent and indicate that a wide frequency tuning range from 0.54 to 2.4 GHz, corresponding to a fractional tuning range of 127%, and a nearly constant absolute bandwidth of 82 MHz are achieved. In addition, the proposed switchable and tunable BPF is small ( $0.24\lambda _{g} \times 0.15\lambda _{g}$ ).
A Miniaturized Antipodal Vivaldi Antenna for 5G Communication Applications

This paper reports the design of a miniaturized 1 X 4 Antipodal Vivaldi Antenna (AVA) for 5th generation (5G) communication applications. The size of proposed AVA is 28.8 mm X 24 mm x 0.254 mm and it is designed by using Rogers RT5880 substrate. Initially, 1 x 4 AVA array (AVA-A) is designed with slots in between two antenna elements. This designed AVA-A operates over three frequency bands which are 24.91 GHz to 33.18 GHz, 34.95 GHz to 36.58 GHz, and 38.34 GHz to 39.38 GHz and provides gain in the range of 8.47 dB to 12.63 dB. The performance parameters of AVA-A is enhanced by incorporating corrugations in it. The AVA-A with corrugations(AVA-AC) enhances gain, bandwidth and front to back ratio of an antenna. The proposed AVA-AC provides enhanced and nearly constant gain of 8.2 dB to 13.2 dB over 24.04 GHz to 40.85 GHz frequency range which includes three bands of 5G communication spectrum (24.25 GHz to 27.5 GHz, 31.8 GHz to 33.4 GHz, and 37 GHz to 40.5 GHz). Also, results shows that, AVA-AC provides stable radiation pattern over its complete operating range which makes AVA-AC suitable for integration in 5G communication devices.
Mutual Authentication in Body Area Networks Using Signal Propagation Characteristics

Developments in wireless communications and wearable devices have facilitated the emergence of a network of tiny sensors embedded in, on or around human body called Wireless Body Area Network (WBAN). Over the last decade, WBAN has increasingly been playing a vital role in modern medical systems because of its potential to revolutionize healthcare delivery. The data collected by the sensors contain sensitive information and are transmitted via wireless channels. However, the openness of these channels makes WBAN vulnerable to attacks by unauthorized users. Therefore, secure authentication and data encryption schemes in WBAN are essential. The resource constraint nature of the sensors makes traditional cryptographic schemes unsuitable. Consequently, authentication schemes based on channel characteristics are proposed, which are more suitable with fewer requirements. However, existing approaches do not consider mutual authentication as well as passive/active attacks. Motivated by these limitations, we propose in this paper, a mutual authentication and data encryption scheme based on signal propagation characteristics and enhanced butterfly algorithm. To validate the effectiveness of our scheme, we conducted an extensive real-world experiment involving 5 volunteers in indoor and outdoor areas, under distinct scenarios. We further conducted security and performance analyses to validate the effectiveness of our scheme in terms of resources and its resilience to various attacks. The results of the experiments and the analyses show that our scheme could mutually identify legitimate users and protect user data against active/passive eavesdropping attacks with minimal overhead.
Asymmetrical 7-Level Neutral Point Clamped Converter for Switch Reluctance Motors

Torque ripple has been a major challenge in the control of Switched Reluctance Motor. Various control techniques have been proposed to find the optimum current profile for reducing the torque ripple. The ability of the control strategy to impose the desired current depends on the power converter that drives the SRM. A 7-level converter is proposed in this paper to reduce the current ripple. A PWM control strategy with fixed switching frequency is used to generate the switching pulses instead of hysteresis control that leads to varying switching frequency. The 7-level converter and the PWM control is simulated in Matlab and the performance of the proposed converter is compared with the performance of conventional converter topology.
A Universal Control System for Self-Driving Car Towards Urban Challenges

This paper focuses on the design of an efficient hierarchical control system for a self-driving car in order to navigate safely in the urban environment. The proposed technique is composed of motion planning, local path planner, and control. Firstly, the decision making is designed by applying two-stages finite state machine (FSM) which manipulates the mission planning and control states on the road. Then, the local path planner is conducted to generate a safe and comfortable trajectory. Moreover, an optimization problem with nonlinear constraints is defined and solved to optimize the jerk. Besides, we perform a real-time hybrid A* algorithm based on a probabilistic occupancy grid map to avoid obstacles. Finally, controllers are designed by using the adaptive-purse pursuit controller for lateral control and the scheduled feed-forward PID controller for longitudinal direction. The experimental results show that our algorithms can work efficiently in a practical scenario.
Optimizing the Computational Offloading Decision in Cloud-Fog Environment

Internet of Things is set to revolutionize our lives and it has already penetrated every sphere of our lives. However, IoT applications are in need of various resources like compute, storage, networking, etc. IoT devices being resource- constrained are mainly dependent on Cloud services for their resource requirements. But as the number of IoT devices is increasing rapidly, Cloud resources are not growing proportionally and in near future demands of IoT devices will exceed the capabilities of the Cloud resources. Keeping this shortcoming of Cloud into mind, Fog computing was introduced as a complementary system paradigm for Cloud computing. Fog computing uses the resources of the geographically closer devices to serve the demands of IoT devices. Our work focuses on the efficient utilization of the Cloud-Fog resources by distributing the application modules among Fog devices and cloud data centers. Placing the application modules on Fog devices improves performance parameters like response time, latency, energy consumption, etc. We have proposed a load balancing algorithm whose performance has been evaluated on the iFogSim simulator and has achieved a 21% reduction in network consumption when compared to the default policy of iFogSim. Our approach is generic which can be used in the majority of IoT applications.
Towards a Smart Parking Management System for Smart Cities

Smart parking management aims to leverage advanced sensing and monitoring technologies to optimize the usage of parking spots within large car parks and ease parking for drivers by directing them to available parking spots, thereby reducing unnecessary stress and delay that further worsen the traffic conditions. The idea of smart parking development began to make parking easier, faster, with less of a drain on resources which are slowly disappearing. The aim of the Smart Parking Management System (SPMS) proposed in this paper is to assist drivers in alleviating several issues linked to parking. Our SPMS provides a sensing platform supported by a mobile app to enable real-time interaction of drivers with the car park infrastructure in a way that minimizes drivers’ search time for an empty parking spot within large multi-storey car parks and facilitates the localization of their parked cars. To validate the efficiency of our SPMS, a small scale test-bed was developed as a proof of concept and tested under five different scenarios. The obtained results highlight its effectiveness and call for its potential integration in real world car parks.
Unmanned Aerial Vehicle Relay System: Performance Evaluation and 3D Location Optimization

The deployment of unmanned aerial vehicle (UAV) in wireless communication as a flying base station (BS) or relay is expected to be dominant in the following years to enhance wireless network performance in terms of coverage and capacity owing to their ability to change altitude, easy 3D movement, low cost, and easy deployment in wireless networks. In this paper, we study the performance of a wireless network in which a UAV is employed as a decode-and-forward (DF) relay linking a ground base station (BS) with multiple users in area where direct terrestrial path between the ground BS and the users is assumed to be blocked. The channel between the BS and the UAV is assumed to follow Rician channel model, while the links between the UAV and the end users are assumed to follow Rayleigh fading model with opportunistic scheduling scheme for user selection. Closed-form expressions for the outage probability and average symbol error rate (ASER) are derived. Due to complexity of the derived closed-form expressions and in order to get more insights at the system behavior in terms of system coding gain and diversity order, an asymptotic expression is derived for the outage probability at high signal-to-noise ratio (SNR) values. Furthermore, an optimization of the UAV 3D location that minimizes the asymptotic outage probability is achieved. Our numerical results show that increasing the UAV transmit power much more than the ground BS transmit power does not improve the system performance. The increase in the UAV transmit power is positive only if the ground BS transmit power is higher than the UAV transmit power. We also show that the increase of the Rician $K$ -factor leads to improving the system performance when the ground BS transmit power is less than or close to the UAV transmit power. Furthermore, the proposed optimization scheme shows superior performance gain in minimizing the outage probability compared to the conventional scenarios, where the UAV is located at a fixed altitude over the center of the coverage region.
3D pose estimation of UAVs using Stereovision

Pose estimation for Unmanned Aerial Vehicles (UAV) is essential for critical maneuvers such as landing and docking. In this work we propose a simple stereovision based pose estimation algorithm using salient points detection. We propose a modular pipeline consisting of 3 major blocks: Image Segmentation (IS), Perspective Transform (PT) and Pose Determination (PD). IS block uses segmentation to detect salient points from the image which the PT block transforms from image coordinates to world coordinates. PD block uses camera parameters and object dimensions to provide Attitude and Translation matrix of the UAV. The proposed approach is highly dynamic and can adjust with change in environmental parameters.
Mathematical Model Extraction and State Space Construction for Nylon Based Smart Force Sensor

Twisted and Coiled Actuator has become widespread in recent years. It has lots of merits in comparison to classical actuators, especially in biomechanical applications. However, the most significant challenge which limits the use of this actuator is the unknown behavior of it for actuating and also self-sensing application. In this paper, we will represent a model for Twisted and Coiled Actuator by means of theoretical concepts and experimental data. First, the preparation process will be explained, then the mathematical model for Voltage-Current and Current-Force will be driven via LS identification method. In the result section, the temperature of this actuator will be estimated using measured current and heat transfer equation. Then it will be validated by experiment which shows that this model can be exploited for future applications in which control of the temperature is necessary.
Human-Robot Interaction based on Facial Expression Imitation

Mimicry during face-to-face interpersonal interactions is a meaningful nonverbal communication signal that affects the quality of the communications and increases empathy towards the interaction partner. In this paper we propose a facial expression imitation system that utilizes a convolutional neural network (CNN). The model was trained by means of the CK+ database. , which is a popular benchmark in facial expression recognition. Then, we implemented the proposed system on a robotic platform and investigated the method's performance via 20 recruited participants. We observed a high mean score of the participants, viewpoints on the imitation capability of the robot of 4.1 out of 5.
KL-EEM: A Novel Kullback-Leibler Distance Based EEM Clustering Model for Breast Cancer Identification

Breast cancer is one the most common malignancies among women, along with the development of machine learning, clustering models have been utilized in this scenario. We focus on the exemplar-based clustering model in this paper. Based on the probability version of EEM clustering model, the choice of distance measurement could be more extensive. Kullbacek-Leibler distance is one of the most effective distances in Information theory, and could naturally measure the similarity between two probability distributions. Thus, this paper newly proposed a KL-distance based EEM clustering model for breast cancer by introducing KL-distance to keep target data similar to the training data into the probability version of EEM model. Deeply analyzing KL-EEM and EEM clustering models, we can conclude that the similarity based on KL-distance can be embedded into the calculation of similarity matrix. Thus, the optimization framework of EEM can be directly used to optimize the new target function of KL-EEM, which has been proved to be more effective and efficient compared with that in AP clustering model. Experiments with two real-world breast cancer datasets have been applied, and verified KL-EEM clustering model.