Miracle-dz/UniFAR-FTUs huggingface dataset:
Energy-Efficient Application-Aware Mobile Edge Computing with Multiple Access Points

The offloading decision making of multiple mobile units in a mobile edge computing (MEC) scenario with multiple access points, each with an attached cloudlet server, is investigated. This scenario bears a joint problem to be solved: the assignment of mobile units to access points, the allocation of the shared communication and computation resources and the offloading decision of each mobile unit. Additionally, the offloading decision is influenced by the availability of the required software for the computation of the task of the mobile unit that may not be available at the cloudlet and has to be downloaded before starting the computation. The proposed offloading decision making and resource allocation problem is formulated as a global optimization problem. To handle higher numbers of mobile units in the network and also achieve a greatly faster convergence, a game theoretic algorithm for the individual offloading decisions is introduced. In numerical simulations, the game based algorithm is shown to deliver results close to the optimum solution, while requiring only a small number of iterations until convergence to a Nash equilibrium.
VTacArm. A Vision-based Tactile Sensing Augmented Robotic Arm with Application to Human-robot Interaction

Endowing artificial sense of touch comparable to human’s has been challenging, yet significant to enabling adaptive and collaborative interaction in contact-rich tasks. This work is dedicated to proposing a novel vision-based tactile sensor augmented robot arm (VTacArm) design with full surface coverage and developing algorithms to retrieve contact information which is essential for down-stream feedback control. We first introduce the robot arm design and its accompanying fabrication process. Then, to convert the contact signals in the image space to the arm coordinate system, a calibration procedure and method are proposed. Finally, the tactile robot arm and the contact information extraction algorithm are integrated into a control system for collaborative interaction tasks. Bumping detection/reaction and contact motion following experiments are presented to justify that the designed tactile robot arm and proposed contact sensing method are beneficial and give robot capabilities to adapt to human contacts, which is vital for workers’ safety. Our work can be informative for developing novel full-body vision-based tactile sensing on robots as a new concept with significantly lower cost and manageable fabrication complexity.
Robust Localization of a Self-Driving Vehicle in a Lane

Localization is still one of the most challenging tasks in autonomous driving on city roads. Further development and improvement of automatic functions of vehicles in urban conditions are not possible without overcoming the problem of significant degradation of GNSS signal quality. The proposed approach to localization can provide information about vehicle position on the road in different operational conditions. Desired stability and quality are achieved by using the combination of conventional computer vision, neural networks and Kalman filtering.
On the Performance Improvement of OTA in Sub-Threshold Region with Dual Supply

Performance improvement of symmetrical OTA (Operational Transconductance Amplifier) designed with DTMOS transistors in sub-threshold region is presented in this work. The improvement of Gm, noise and dynamic range is realized with double supply voltage. On the other hand, double supply improves f-3dB (cut-off frequency), Gm (transconductance), THD (total harmonic distortion) of designed OTA-C filter. The proposed method is tested through four cases. The proposed technique has been simulated with 0. 18μm TSMC process in Cadence environment.
Fast Angle-of-Arrival Estimation via Virtual Subarrays in Analog Antenna Array

Angle-of-arrival (AoA) estimation is a challenging problem for analog antenna arrays. Typical schemes use time-consuming beam scanning, and the resolution is limited to the scanning beam width. In this paper, we propose a virtual-subarray based AoA (ViSA) estimation scheme, which divides an analog array into two virtual subarrays and exploits phase difference between one pair of measurements for AoA estimation. The basic ViSA algorithm can obtain a direct AoA estimate from every two temporal measurements. We propose different subarray constructions which can lead to different accuracy of estimation. We provide closed-form expressions for the statistics of the estimation error. Based on the basic ViSA estimator, we develop two methods to combine multiple pairs of measurements, when they are obtained via sequential and multi-resolution scanning, respectively. Near-optimal estimators are derived for both methods, employing the maximum likelihood principle. Novel techniques are also proposed to address the typical phase ambiguity problem due to the periodic phase function. Simulation results demonstrate that the proposed scheme significantly outperforms existing ones.
Performance of Underlay Cooperative Hybrid OMA/NOMA Scheme with User Selection

In this paper, we analyze the performance of an underlay downlink non-orthogonal multiple access (NOMA) network with multiple near and far-users. Different from cognitive radio (CR) inspired CR-NOMA where cognitive principles are used to guarantee performance of one selected user, all nodes here are in the underlay mode, and re-use the spectrum of the primary network. Use of NOMA is counter-intuitive in an underlay network since the transmit powers are constrained to ensure that the interference temperature limit is met. We show in this paper that switching carefully between NOMA, NOMA with relaying, and orthogonal multiple access (OMA) allows good performance to be attained in such networks. We further propose a user selection scheme, and analyze its performance. We derive approximate closed-form expressions for the near and far-user throughput. Simulations are carried out to verify the derived results.
Architecture of a Public Transport Supervision System Using Hybridization Models Based on Real and Predictive Data

Management of a multimodal transport network is a challenging task and operators regularly have to deal with different types of disturbances that affect the quality of service of the transport system. To support them in their decision-making, we present the architecture of a multimodal supervision system that implements monitoring, prediction of affluence, disturbances, impacts and evaluation of functionalities focusing on one or multiples lines of the network. Our system uses the real-time data of transport operators to compute several key performance indicators (KPI) in order to monitor the network status and to detect disturbances. The prediction function is used to predict passengers’ attendance at stations, incident duration and the evolution of the whole network, in particular, phenomena emerging from the interconnections linked to the affected line. Finally, we present a use case of the supervision system applied to a real-time control of a bus line belonging to a very dense and busy segment of the multimodal transport network in Ile-de-France area.
On the Spectral Efficiency of Oversampled 1-Bit Quantized Systems for Wideband LOS Channels

In this work, we investigate the spectral efficiency (SE) of a system where the transmitter utilizes zero crossing modulation (ZXM), which was implemented by combining faster-than-Nyquist signaling with runlength-limited transmit sequences, and the receiver employs 1-bit quantization and temporal oversampling. The SE is evaluated numerically with respect to a fractional power containment bandwidth, which allows for a fractional amount of out-of-band emissions. In contrast to most existing works, the SE is evaluated for a practical transmitter and receiver implementation under a wideband line-of-sight channel model. The studied system achieves SEs above 3 bit/s/Hz, which corresponds to an increase of more than 50 % as compared to standard QPSK. Furthermore, the studied system outperforms a related system by Deng et al., which also employs 1-bit quantization and temporal oversampling, by approx. 15 dB at a SE of 3 bit/s/Hz.
High Gain and Low-Profile Stacked Magneto-Electric Dipole Antenna for Phased Array Beamforming

A high gain stacked antenna based on a planar magneto-electric dipole structure is proposed. The main radiator is configured by a probe-fed patch with a symmetrically arranged pair of dipole radiation elements. Further, an additional air-gapped radiator with multiple patch elements is integrated for gain enhancement. Since both the main and stacked radiators are planar structures, the overall volume can remain low-profile regardless of the airgap. To verify the performance of the proposed structure, a single magneto-electric dipole antenna and three different types of stacked radiators were implemented at 5.8 GHz. The magneto-electric dipole antenna showed measured 10-dB impedance bandwidth and gain of 5.2% and 8.0 dBi, respectively with the overall size of <inline-formula> <tex-math notation="LaTeX">$0.96\,\,\lambda _{0} \times 0.96\,\,\lambda _{0}$ </tex-math></inline-formula> including a ground plane. With the additional stacked radiator having the airgap of <inline-formula> <tex-math notation="LaTeX">$0.1~\lambda _{0}$ </tex-math></inline-formula>, the maximum measured gain was increased to 9.6 dBi. Further, to verify the beamforming performances, three types of <inline-formula> <tex-math notation="LaTeX">$1\times 8$ </tex-math></inline-formula> phased array stacked structures were fabricated with a volume of <inline-formula> <tex-math notation="LaTeX">$0.96\,\,\lambda _{0} \times 6.38\,\,\lambda _{0} \times 0.14\,\,\lambda _{0}$ </tex-math></inline-formula> at 5.8 GHz. The measurements showed a maximum peak gain of 18.1 dBi and a half-power-beamwidth scan angle of 49° with a side-lobe level less than −8 dB.
Learning Engineering Properties with Bag-of-Tricks. For the Automated Evaluation of a Piping Design

This paper presents a text based methodology to evaluate the piping design of a ship, which is usually in the form of a Piping and Instrumentation Diagram. The method starts with describing a piping layout or a design in the form of a paragraph of text, describing the essential properties of the design, including but not limited to the connectivity and the attributes. Next, vectorisation takes place to obtain a mathematical or quantitative representation of the text-based description. Technically, the piping design is represented as a point in d-dimensional space. Next, leveraging on the recent development in natural language processing and based on the vectorised representations, a bag-of-tricks based classifier is trained to evaluate if the design complies with the applicable rules of design. After the process of learning, the trained classifier can be used to evaluate if the piping layout or design of a ship is compliant. The developed method has demonstrated encouraging performance on a challenging dataset of 144 compliant and 1836 non-compliant piping designs collected, based on an existing case study around Regulation 12, Annex I Prevention of Pollution by Oil, International Convention for the Prevention of Pollution from Ships by learning in the space of 100 and 300 dimensions.
Passive Optical Phase Stabilization on a Ring Fiber Network

Optical frequency transfer provides the means for high-fidelity frequency transfer across thousands of kilometers. A critical step in the further developing optical frequency transfer is its capability to transfer a high spectral purity feature from ultrastable lasers or optical clocks to any remote locations and, at the same time, its adaptability to incorporate the optical frequency transfer technique into any existing communication networks with different topologies. Here we for the first time report a technique that delivers optical-frequency signals to multiple independent remote hubs along a ring optical-fiber network with passive phase stabilization. The technique automatically corrects optical-fiber length fluctuations of arbitrary hubs along the loop by mixing and shifting optical signals. Without the help of an active phase tracker and a compensator, it could significantly mitigate some technical problems such as the limited compensation speed and phase recovery time, the phase jitter contamination caused by the servo bump in conventional phase noise cancellation. Moreover, by transmitting optical signals along both directions using the same optical source, it can improve the signal-to-noise ratio at each hub. This technique maintains the same delay-limited phase noise correction capability as in conventional techniques and, furthermore, improves the phase jitter by a factor of 3, opening a way to a broad distribution of an ultrastable frequency reference with high spectral purity and enabling a wide range of applications beyond metrology over a ring fiber network with the naturally impressive reliability and scalability.
A 22.4-to-40.6-GHz Multi-Ratio Injection-Locked Frequency Multiplier with 57.7-dBc Harmonic Rejection

This paper presents a multi-ratio injection-locked frequency multiplier (MR-ILFM) targeting millimeter-wave (mm-wave) multiband 5G communications. With only low-frequency narrow-bandwidth input signals required, difficulties in generating low-phase-noise mm-wave wideband signals can be solved by switching the multiplication ratio of the MR-ILFM for bandwidth extension. Issues on suppressing undesired harmonics in multi-ratio multipliers are considered, and an impulse sensitivity function (ISF) based analysis is adopted to show the superior harmonic rejection properties of injection locking. With the use of injection locking and ILFM cascading, the harmonic rejection reaches to 57.7 dBc. The proposed MR-ILFM has × 5 and ×7 multiplication ratio, and 24 switchable frequency bands for ratio switching. Fabricated in 65 nm CMOS process, the MR-ILFM consumes 10.0-mW power and can operate from 22.4 to 40.6 GHz with 4.3-to-5.8-GHz inputs.
Imagery based Parametric Classification of Correct and Incorrect Motion for Push-up Counter Using OpenPose

This paper presents a real-time approach to count push-ups using 2D video imagery. The proposed method uses OpenPose in each frame to extract multiple joints and links of a human body. Then, it analyzes key motion features linked to counting the push-ups. Taking in consideration the push-up rules of the Republic of Korea Army, five criteria are defined and used parametrically to discriminate both correct and incorrect push-ups. A total of 147,840 samples have been collected from 220 push-up videos each in two different viewpoints: half of the videos for modeling the proposed method and the other half for testing its performance. Finally, the results shows 90.00%, 87.82%, 97.86%, and 92.57% for accuracy, precision, recall, and F-measure, respectively, demonstrating its reliability in military physical tests.
60 GHz variable Gain & Linearity Enhancement LNA in 65 nm CMOS

This paper reports on the design of a 60 GHz low-noise amplifier (LNA) with a linear and power efficient gain tuning mechanism in a 65 nm CMOS process. The transformer-based (TF) matching topology enables a compact LNA design which requires a total chip area of 0.26 mm2 including all probing pads. The designed LNA has a maximum gain of 25 dB with a 3-dB bandwidth of 8 GHz and a minimum measured noise figure (NF) of 4.8 dB. By means of the proposed gain tuning concept, a tuning range of 17 dB with an input referred 1-dB compression point, P1dB, of –7 dBm can be achieved. The power consumption is adapted to the LNA gain and ranges from 26 mW to 47 mW.
A Statistical Characterization of SINR Coverage and Network Throughput with Macro-Diversity

In this paper, we consider a cellular network in which the user equipments (UEs) connect to multiple base stations (BSs) for augmenting their signal to interference plus noise ratio (SINR) coverage. In literature, the improvement in the SINR performance with multi-connectivity, or macrodiversity has been well-investigated for several network scenarios, e.g., millimeter wave (mm-wave) networks, networks with random blockages, etc. However, the impact of such multi-connectivity schemes on the network throughput has relatively sparse literature. We bridge this gap by jointly characterizing the effect of multi-connectivity on the SINR coverage and the throughput coverage of the UEs in the network, leveraging tools of stochastic geometry. Such a characterization for generic n connections to the typical user is necessary for 5G and beyond cellular networks, precisely since the degree of multi-connectivity should necessarily be dynamic and self-organizing in nature. Our results show that although the SINR coverage of the UEs always improves with more number of connections, the per-UE throughput in the network may increase or decrease depending on the density and data-rate requirement of the UEs. Interestingly, if the data-rate requirement of the UEs increases, our results show that the UEs should connect to a fewer number of BSs to maximize the per-UE throughput.
Development of the approach to collaborative BCI

In the present study we aim to find specific characteristic based on brain activity, that can be used to evaluate attention and, thus, can be used in brain-computer interface. We propose an approach to collaborative BCI aimed to enhance human-to-human interaction while performing shared visual task. We also describe the setup for such BCI and its possible application in long task of classifying ambiguous visual stimuli with varying degrees of ambiguity by a group of people.
Variable Speed Limit Control Based on Deep Reinforcement Learning: A Possible Implementation

Today’s urban motorways cannot fulfill their purpose to simultaneously serve transit and local urban traffic with a high Level of Service. In the case of urban motorway infrastructure, the traditional “build only” approach is not always possible due to the lack of space. This study is focused on the Variable Speed Limit Control (VSLC) as one of the traffic control methods applicable for any type of motorway and Q-learning as one commonly used approach for designing learning based VSLC algorithms. The drawback of this methodology is the representation and exploration of the large state-action space as it is the case in its application for VSLC. This study introduces a Deep Q-Network to approximate the Q-function and presents a novel learning approach for the VSLC application with possibility to track vehicles on the microscopic level. The proposed reward function steers the learning towards the improvement of reward and prevention of oscillation among consecutive speed limits.
Beam Shape Optimization Method for Low Outage Beamforming Training with Limited Number of Beams

This paper considers beamforming training methods for mobile access network systems in high frequency bands such as millimeter wave bands. A two-stage beamforming training method is an attractive one to find the desired beam quickly, where the initial training stage is performed roughly, with a limited number of beams. The set of beams for the initial training should be designed carefully to reduce outage probability, because a base station cannot communicate with a mobile terminal if the terminal cannot receive any beams during the initial training. Therefore, we propose a beam shape optimization method for the initial beamforming training that maximizes the minimum received power in the coverage area with a limited number of beams. In the proposed method, the beam direction and beam width of each of the beams are optimized in the optimization process based on an envelope pattern of all the beams. The results of computer simulations where the free space propagation loss is assumed demonstrate that better outage performance can be achieved by beams optimized by the proposed method when compared with a method to optimize each beam separately based on a given beam width.
IoT-based Recommendation Systems – An Overview

Internet of Things (IoT) has emerged in many industries, such as health care, transportation, agriculture, manufacturing, smart homes, to name a few. It paves the path for massive applications on the user level to enhance the quality of life or service, and on the decision-makers’ level to provide a sustainable increase in revenue. IoT principally connects different physical objects (e.g., sensors) and enables them to communicate, collect, and share data. In the Era of IoT, Recommendation systems provide personalized recommendations based on the user's historical datasets collected from the IoT devices. These recommendations enable an efficient decision-making process by suggesting relevant products, resources, and information. This paper provides an overview of various multi-layers IoT architectures, and IoT-based recommendation systems with an emphasis on their advantages, disadvantages, application domains, and validation metrics for quality assessment.
Robust Interference Exploitation for Multi-Cell Transmission

In this paper, we investigate power-efficient constructive interference (CI) exploitation in multi-cell coordination systems. By only sharing channel state information (CSI) among the coordinated base stations (BS)s, we propose a CI-based coordinated beamforming (CBF) scheme to judiciously exploit multiuser interference as a beneficial element rather than strictly mitigating it, while simultaneously suppressing inter-cell interference as a destructive element. Then taking imperfect channel state information (CSI) into consideration, we minimize the total transmission power consumption with multiple users’ probabilistic signal-to-interference-and-noise ratio (SINR) requirements, where the users’ SINR requirements are guaranteed in a statistical manner. Finally, under the presence of CSI error, simulation results demonstrate that the proposed CI-based CBF scheme consumes much lower transmission power compared to the classical CBF benchmarks, where both intra-cell multi-user and inter-cell interference need to be strictly cancelled as destructive elements. Last but not least, the incurred overhead and computational complexity of the proposed scheme are analytically analyzed, confirming its practicality as a new dimension on multi-cell coordination.
Cross-Site Edge Framework for Location-Awareness Distributed Edge-Computing Applications

In an edge computing application, cloud-side process and edge-side process run individually. This distributed process of an application fulfilling one common goal results in low readability of codes and additional maintenance cost. To address this issue, we propose a new edge-computing application framework, called ``cross-site edge framework'' (CEF). The key approach of CEF is a messaging middleware layer on which function calls are converted into messages that are exchanged between regions behind the application code. Developers can implement all processes in all environments into a single file or project as if the application runs in one environment. Using this approach, developers can add an annotation that specifies the location where the function is performed to a function in the application. This approach reduces the number of operation tasks required for developing edge-computing applications by a half compared to the number required by an edge-computing service of cloud vendor.