Miracle-dz/UniFAR-FTUs huggingface dataset:
Functional Split Architecture for Energy Efficiency in 5G Backhaul

As a new promising of higher data rates and to enable the Internet of Things (IoT), the thirst of energy efficiency in communication networks has become an important milestone in the design and operation. With the emergence of the 5G of wireless networks and the deployment of billions of base stations to the connected devices, the requirement for system design and energy efficiency management will become more attractive. In addition, in the next era of cellular, the energy efficiency is the most important requirement determined by the needs in reducing the carbon footprint of communications, and also in extending the life of the terminal battery. Nevertheless, the new challenge has emerged especially in the backbone of the networks. Therefore, the aim of this paper is to present the potential of 5G system to meet the increasing needs in devices and explosive capacity without causing any significant energy consumption based on functional split architecture particularly for 5G backhaul.
Localization of UAV in Urban Scenario Using Multipath Exploiting TDoA Fingerprints

This paper presents a localization procedure to locate UAV in an urban scenario and improve the Time Difference of Arrival (TDoA) performance in Non-line of Sight (NLoS) situation. The idea is to use ray-tracing simulation and Time Difference of Arrival (TDoA) to build a geographic information database that reproduces the “site-specific” multipath patterns that enhance the position estimation performance in multipath scenarios. The conventional emitter location systems based on TDoA sensors mitigate errors trying to avoid NLoS measurements, while the proposed method, uses the multipath information to extract the time of arrival characteristic from the environment. A measurement campaign was done to validate the technique and showed an enhancement in the localization processing, depending on the precision of the buildings and obstacles information.
Deadline-Aware Scheduling and Flexible Bandwidth Allocation for Big-Data Transfers

Big data is becoming a major focus for both industry and academia, requiring drastic changes in all aspects of computer systems in order to store, process, and transfer big data. In networks, a fundamental problem is how to efficiently transfer big data since the performance is affected by several factors such as path, bandwidth, and scheduled start time. Best-effort algorithms are no longer applicable as they may not satisfy the deadline requirement of the requests. In this paper, we consider the problem of scheduling and flexible bandwidth allocation for big-data transfers with deadline constraints. With flexible bandwidth allocation, the bandwidth allocated to a request can be dynamically adjusted any time during its transfer. We develop an optimization programming formulation that provides admission and scheduling decisions, bandwidth allocation, and path selection for each accepted request. The formulation aims at maximizing the acceptance while guaranteeing the deadline constraints of transfer requests. Due to the complex nature of the optimization problem, we develop a two-phase heuristic algorithm namely deadline-aware flexible bandwidth allocation for big-data transfers (DaFBA). We develop two scheduling approaches for DaFBA using batch scheduling to be used for every time interval and dynamic scheduling to be used upon every request arrival. We evaluate the performance of the proposed algorithm through comprehensive simulations with two routing scenarios: pre-computed path scenario and load-based routing scenario. The results show that the proposed algorithm performs close to the optimal solution and outperforms baseline algorithms in terms of rejection ratio and the amount of data transferred.
Calibration for Kinematic Parameters of Industrial Robot by a Laser Displacement Sensor

The equipment used in kinematic calibration schemes is generally expensive and time-consuming, and requires skilled engineers. This paper proposes a new measurement method for identifying kinematic errors on a six degree of freedom industrial robot with a single laser displacement sensor. The three-dimensional deviations of the end-effector are calculated by an algorithm with data collected from the scanning procedure. Then, the kinematic parameters are identified by combining the joint angles, the deviations involved error information and an error model. The accuracy and efficiency are improved by the laser displacement sensor and the simple scanning procedure. The method presented in this paper can also be expanded to other serial industrial robots. The experimental results validate the effectiveness of the proposed method.
Implications of Small Geometry Effects on  $g_m/I_D$  Based Design Methodology for Analog Circuits

Small geometry effects have become increasingly important in analog circuits as transistors continue to shrink. As a result, transconductance-to-drain current (<inline-formula> <tex-math notation="LaTeX">${g_{m}/I_{D}}$ </tex-math></inline-formula>) transistor parameters are no longer width-independent. In this brief, a procedure to develop “unit-sized” transistors with minimal sensitivity to small geometry effects is proposed. It is shown that by using the unit-sized transistors, the impact of small geometry effects on <inline-formula> <tex-math notation="LaTeX">${g_{m}/I_{D}}$ </tex-math></inline-formula> dependent parameters such as current density and self gain can be reduced to 3.6% and 1.5%, respectively.
Area and Energy Efficient Series Multilevel Cell STT-MRAMs for Optimized Read–Write Operations

With the inception of perpendicular magnetic anisotropy-based magnetic tunnel junction (PMTJ) devices, spin-transfer torque (STT) magnetic random access memory (MRAM) is considered as a promising candidate for low power high-density embedded memory applications. However, the single-level cell STT-MRAM did not create much impact due to its energy/area inefficiency, higher cost per bit, and unoptimized read/write operations in sub-50 nm regime. In this paper, a series multilevel cell (sMLC) STT-MRAM is proposed that offers higher array density with reliable and energy efficient read–write operations with minimum error rates at the lower supply voltage. A high-k dielectric metal gate-based gate-all-around vertical silicon nanowire is used to drive the sMLC. A unified model based on Verilog-A and HSPICE is employed to imitate the PMTJ device. The results of 2 bit sMLC designs demonstrate switching error probability much below $10^{-9}$ , and average write energy <2 pJ at the pulsewidth of 20 ns with a footprint area of 2F2 per bit (F is the feature size).
Miniature Multi-Band Absorptive Bandstop Filter Designs Using Bridged-T Coils

In this paper, miniature absorptive bandstop filters (ABSFs) with one to three stopbands are demonstrated. First, the proposed single-band ABSF is realized by the introduction of one lossy resonator to a conventional stub bandstop filter, while the circuit size is largely reduced by employing bridged-T coils to replace the transmission line sections. Next, by employing the dual-band design of bridged-T coil, a miniature dual-band ABSF can be achieved. The proposed tri-band ABSF is then implemented through the cascade of the proposed single- and dual-band ABSFs. As a demonstration, three proposed single-, dual-, and triple-band ABSFs are implemented in a commercial GaAs pHEMT process. Much smaller circuit size than the conventional multi-band ABSFs and good measured performance are achieved.
Experimental phantom-based evaluation of Physical Layer Security for Future Leadless Cardiac Pacemaker

Next generation of cardiac pacemakers are expected to be completely wireless bringing along new security threats. Thus, it is critical to secure the pacemaker transmissions between legitimate nodes from a third party or an eavesdropper. This work explores the potential of securing leadless cardiac pacemaker by using physical layer security methods. In this work, we perform phantom experiments to replicate the dielectric properties of human heart and measure the path loss models for in body to in body scenario and in body to off body scenario. These scenarios reflects the channel between legitimate nodes and that of channel between legitimate node and eavesdropper in frequency band ranging between 1.7–2.5 GHz. In our case, legitimate nodes are leadless cardiac pacemaker implanted in right ventricle of human heart transmitting to a legitimate receiver, which is subcutaneous implant beneath the collar bone under the skin, whereas third party outside the body trying to eavesdrop the communication. By using these models, potential of positive secrecy capacity has been shown along with its probability by varying eavesdropper distances. It has been seen that even if eavesdropper is at the same distance as of legitimate node, still we have about 45% probability of positive secrecy rate. The randomness in channel measurements is observed because of measurements at different angles from source, both for legitimate link and as well as eavesdropper link, which can reflect to propagation through different parts/organs of human body.
A Currentless Sorting and Selection-Based Capacitor-Voltage-Balancing Method for Modular Multilevel Converters

This letter proposes a currentless sorting and selection (SAS)-based capacitor-voltage-balancing method for modular multilevel converters. Without the knowledge of arm-current signals, this method has almost the same performance as the conventional SAS method while reducing the sampling signals, compacting the control system, and saving the overall cost. In this letter, the derivative of the total capacitor voltage of an arm, instead of the arm current, is employed to determine which submodules should be inserted or bypassed. Furthermore, the efficacy of the proposed method is verified by experimental results.
Next-App Prediction by Fusing Semantic Information With Sequential Behavior

Next-app prediction is the task of predicting the next app that a user will choose to use on the smartphone. It helps to establish a variety of intelligent personalized services, such as fast-launch UI app, intelligent user-phone interactions, and so on. Since app names only provide limited semantic information, the intrinsic relation among apps cannot be fully exploited. Meanwhile, next-app to be used is largely determined by a sequence of apps that a user used recently. To address these challenging problems, this paper first enriches the semantic information of apps by extracting descriptive text of each app from the app store and thus proposes a topic model to transform apps as well as user preferences into latent vectors. Then, a set of nearest neighbors can be constructed based on the similarity of latent vectors and it is employed for training the prediction model. Furthermore, our prediction scheme is built on the temporal sequential data and is modeled by using the chain-augmented Naive Bayes model. Experimental results with a real smartphone application log data have demonstrated that our method achieves higher recall and DCG values compared with several baseline next-app prediction methods.
Decentralized Computation Offloading and Resource Allocation for Mobile-Edge Computing: A Matching Game Approach

In this paper, we propose an optimization framework of computation offloading and resource allocation for mobile-edge computing with multiple servers. Concretely, we aim to minimize the system-wide computation overhead by jointly optimizing the individual computation decisions, transmit power of the users, and computation resource at the servers. The crux of the problem lies in the combinatorial nature of multi-user offloading decisions, the complexity of the optimization objective, and the existence of inter-cell interference. To overcome these difficulties, we adopt a suboptimal approach by splitting the original problem into two parts: 1) computation offloading decision and 2) joint resource allocation. To enable distributed computation offloading, two matching algorithms are investigated. Moreover, the transmit power of offloading users is found using a bisection method with approximate inter-cell interference, and the computation resources allocated to offloading users is achieved via the duality approach. Simulation results validate that the proposed framework can significantly improve the percentage of offloading users and reduce the system overhead with respect to the existing schemes. Our results also show that the proposed framework performs close to the centralized heuristic algorithm with a small optimality gap.
Hybrid Beamforming for mmWave MIMO-OFDM System with Beam Squint

In this paper, we study the hybrid beamforming for the wideband mmWave MIMO-OFDM system with observation of beam squint. Firstly, we present the beam squint effect in the wideband mmWave system. We further characterize the mmWave wideband channel from Saleh-Valenzuela model. Secondly, in the full-connected hybrid architecture, we seek for the optimal hybrid precoder in the mmWave MIMO-OFDM system, which aims to maximize the spectral efficiency. The precoder design problem is formulated as the matrix factorization in this paper. To find the optimal precoder, wideband hybrid precoding (WHP) algorithm is proposed by using the manifold optimization. Finally, simulation results show the proposed algorithm could approximate to the optimal digital precoder in term of spectral efficiency for the wideband mmWave MIMO-OFDM system.
Hand Tracking-based Motion Control for Robot Arm Using Stereo Camera

In this paper, we designed an application and implemented the robot arm control by hand tracking using the stereo camera. The camera captured and tracked the hand motion and saved the coordinate position or the hand motion position in 3D. Our system was designed to track pitch, yaw and roll motion of the hand. Besides that, the system could also be tracked the hand motion of forwarding and backward motion. The hand tracking data were then saved and processed in our system. By using TCP/IP communication, those data were sent to the arm robot and be transformed become the movement of the arm robot. Our system could move the arm robot by the tracking of the hand motion. The experimental results showed that our system successfully reduces the error of the arm robot motion for each motion such as pitch, yaw and roll motion with the motion error 7.28°, 4.70°, 5,37°, respectively.
Two-Stage Noise Filtering for DFT-Based Synchrophasor Measurement in Distribution Systems

To improve the operation security and stability of power systems, the distribution-level synchrophasor measurement techniques have been developed rapidly. However, the signals in distribution systems are heavily corrupted by noise which will deteriorate synchrophasor measurement accuracy. This paper preliminarily investigates the noise in distribution systems according to the field measurement results. Then the two-stage noise filtering method is proposed based on the median filter and mean filter. According to the additive problem caused by median filter, the corresponding compensation coefficients are derived. Finally, numerical tests verify the effectiveness of the proposed filtering and compensation method under different conditions.
DECODING EMOTIONAL EXPERSSIONS: INTERACTION OF PERCEIVED RACE AND IMPLICIT RACIAL PREFERENCES

Two experiments investigated whether negative attitudes towards an out-group interferes with perception of emotional expressions on faces of differing ethnicity. selectively process either emotional or racial information was assessed using the Garner Interference Task (GIT). The stimuli consisted of photographs of Scandinavian-appearing (s) or Middle-Eastern-appearing (ME) men expressing either happiness or anger. Participants either classified the faces according to emotional expression, or according to apparent race. To assess attitudes towards Middle-Easterners and Scandinavians, participants performed a Swedish-Moslem IAT. The emotion-GIT revealed interference from ethnicity, which also interacted with the IAT such that those with low IAT scores (ME preference) showed greatest interference for angry ME faces whereas those with high IAT scores showed a facilitation for S happy faces. Emotional expression generally did not interfere with processing in the race- GIT, although individuals with low IAT scores showed facilitation for angry ME faces.
RL and ANN Based Modular Path Planning Controller for Resource-Constrained Robots in the Indoor Complex Dynamic Environment

Traditional Reinforcement Learning (RL) approaches are designed to work well in static environments. In many real-world scenarios, the environments are complex and dynamic, in which the performance of traditional RL approaches may drastically degrade. One of the factors which results in the dynamicity and complexity of the environment is a change in the position and number of obstacles. This paper presents a path planning approach for autonomous mobile robots in a complex dynamic indoor environment, where the dynamic pattern of obstacles will not drastically affect the performance of RL models. Two independent modules, collision avoidance without considering the goal position and goal-seeking without considering obstacles avoidance, are trained independently using artificial neural networks and RL to obtain their best control policies. Then, a switching function is used to combine the two trained modules for realizing the obstacle avoidance and global path planning in a complex dynamic indoor environment. Furthermore, this control system is designed with a special focus on the computational and memory requirements of resource-constrained robots. The design was tested in a real-world environment on a mini-robot with constrained resources. Along with the static and dynamic obstacles’ avoidance, this system has the ability to achieve both static and dynamic targets. This control system can also be used to train a robot in the real world using RL when the robot cannot afford to collide. Robot behavior in the real ground shows a very strong correlation with the simulation results.
Using SDN and Slicing for Data Offloading over Heterogeneous Networks Supporting non-3GPP Access

The foreseen exploding number of devices connected to the mobile network has been pushing operators to look for mechanisms to transparently offload their data. However, such procedures need to consider the different demands from users' traffic and involved access networks. Towards that end, Network Slicing has been proposing a logical partition of the network to better serve different verticals requirements, while providing isolation, achieved by using Software Defined Networking (SDN) and Network Function Virtualisation (NFV) mechanisms. In this paper, we explore the realization of a mobile traffic offloading framework that is able to dynamically instantiate a non-3GPP slice and use SDN mechanisms to seamlessly handover existing flows into the new connection point. For this, we virtualized both the mobile Core Network components as well as the User Equipment, allowing the mobility procedure to be handled in the cloud and become transparent to the physical endpoints. A prototype of our proposal was deployed in a physical testbed, and evaluated in a mobile video offloading scenario where the operator strategically deploys Wi-Fi access points around the city, with results showing that the user is able to continuously visualize the video while switching access technology.
Recognition of Multi-scale Multi-angle Gestures Based on HOG-LBP Feature

Gesture rotation and zooming have significant impact on the gesture recognition system and can greatly reduce the recognition rate. In this paper, we propose a novel recognition method for the multi-scale multi-angle gestures in skin-like noise backgrounds based on HOG-LBP feature extraction. The proposed gesture recognition system consists of pretreatment, feature extraction and classification. First, the single Gaussian model (SGM) and K-means algorithm was used to extract gesture images from a skin-like noise background region. Then, a HOG-LBP feature descriptor is proposed to represent multi-scale multi-angle gesture information. The HOG component provides the gesture edge gradient information and the LBP component provides the texture feature information, which can compensate for the lack of rotation invariance of a single feature and improve the recognition rate of gestures at multiple scales and multiple angles. Finally, the SVM classifier is utilized to realize the gesture classification. Experiment results on the home-made data sets show that the proposed method can achieve 99.01% recognition rate. Experiments on the NUS database and the MUGD database also demonstrate the performance of the proposed method.
General Control Scheme for a Dual-Input Three-Level Inverter

This paper proposes a general control scheme for the dual-input three-level inverter, which comes from a neutral point clamped three-level inverter. It can be used for photovoltaic (PV) systems or battery storage systems with two dc sources. The dual-input three-level inverter can be connected with two PV arrays, which are able to realize maximum power point tracking independently. First, the dynamic model of the dual-input three-level inverter is established, which describes the relationship between the dual-input voltages and the duty cycle. After discussing the control theory of the dual-input voltages, a typical control scheme is proposed. The design of a control loop is introduced in details. Afterward, modulation and restriction of the proposed method are discussed. Finally, experiment on a 125 kW dual-input three-level PV inverter is implemented to verify the validity and feasibility of the proposed method.