Miracle-dz/UniFAR-FTUs huggingface dataset:
Power Allocation, Relay Selection, and User Pairing for Cooperative NOMA Systems with Rate Fairness

Assuming a cooperative non-orthogonal multiple access (NOMA) system with rate fairness in a scenario with multiple users and arbitrary relays, this paper investigates adaptive power allocation (PA), relay selection (RS), and user pairing (UP) policies. Specifically, two adaptive PA optimization problems are formulated, one at the base station (BS) and another at the selected relays. Closed-form expressions for the power allocation factors are derived as well as an algorithm that provides the optimal solution at the BS. In order to show the superiority of the proposed study, our results are compared with other benchmark schemes in terms of outage probability, Jain’s fairness index, and average sum rate.
How well can your car be tracked: Analysis of the European C-ITS pseudonym scheme

The change of pseudonym certificates for message authentication is the standard approach for privacy-friendly V2X communication. It’s crucial to use an effective and robust pseudonym changing scheme as the location privacy of vehicles relies strongly on it. Therefore, in this work we analyzed a pseudonym change strategy that is recommended by the European C-ITS platform and has good chances to be included in a future European standard. By simulating a realistic urban traffic scenario within Luxembourg, applying and attacking the pseudonym change strategy, we could evaluate the effectiveness of the scheme. Overall, linking pseudonyms with simple traffic statistics in a realistic city traffic scenario is more challenging than related work suggests. However, the consideration of additional static information from the V2X communication, such as length and width of the vehicle, enormously improves the linking of pseudonyms, and thus enables tracking of vehicles and generation of motion profiles. The level of location privacy is thereby particularly influenced by the number of vehicles in the vicinity and especially their properties, as well as, of course, by the observation capabilities of the attacker. Our results suggest that the introduction of VANETs, even with the C-ITS pseudonym scheme, enables the tracking of vehicles, and thus will decrease location privacy in the future.
Distributed Q-Learning Aided Uplink Grant-Free NOMA for Massive Machine-Type Communications

The explosive growth of machine-type communications (MTC) devices poses critical challenges to the existing cellular networks. Therefore, how to support massive MTC devices with limited resources is an urgent problem to be solved. Bursty traffic is an important characteristic of MTC devices, which makes it difficult for agents to learn useful experience and has a negative impact on model convergence. However, most existing reinforcement learning-based literatures assume that devices have saturate data. Towards this end, we propose two distributed Q-learning aided uplink grant-free non-orthogonal multiple access (NOMA) schemes (including all-devices distributed Q-learning (ADDQ) scheme and portion-devices distributed Q-learning (PDDQ) scheme) to maximize the number of accessible devices, where the bursty traffic of massive MTC devices is carefully considered. In order to reduce the dimension of scheduling space and mitigate the impact of bursty traffic, the idea of grouping devices as well as transmission resources and the intermittent learning mode are adopted in our schemes. Extensive numerical results demonstrate the advantages of proposed schemes from multiple perspectives.
Optimum Performance of Nonlinear MIMO-SVD Schemes

Multiple-input, multiple-output (MIMO) techniques usually involve some kind of pre-processing at the transmitter, namely when singular value decomposition (SVD) techniques are employed. However, the precoding operations in general and the ones associated to SVD techniques in particular can substantially increase the envelope fluctuations of the transmitted signal, which leads to an increased sensitivity to transmitter nonlinearities and inherent performance degradation. However, recent research demonstrates that nonlinear distortion can be seen as useful information, provided that an optimum detection is considered. In this work, we present an analytical approach to obtain the asymptotic optimum performance of MIMO-SVD systems with strong nonlinear distortion effects1.
Deep reinforcement learning for hybrid beamforming in multi-user millimeter wave wireless systems

This paper proposes a Machine Learning (ML) algorithm for hybrid beamforming in millimeter-wave wireless systems with multiple users. The time-varying nature of the wireless channels is taken into account when training the ML agent, which identifies the most convenient hybrid beamforming matrix with the aid of an algorithm that keeps the amount of signaling information low, avoids sudden changes in the analog beamformers radiation patterns when scheduling different users (flashlight interference), and simplifies the hybrid beamformer update decisions by adjusting the phases of specific analog beamforming vectors. The proposed hybrid beamforming algorithm relies on Deep Reinforcement Learning (DRL), which represents a practical approach to embed the online adaptation feature of the hybrid beamforming matrix into the channel states of continuous nature in which the multiuser MIMO system can be. Achievable data rate curves are used to analyze performance results, which validate the advantages of DRL algorithms with respect to solutions relying on conventional/deterministic optimization tools.
Efficiency Improvement Scheme for PV Emulator Based on a Physical Equivalent PV-Cell Model

Recently, a photovoltaic (PV) emulator is proposed which is based on a combination of a constant current source and a one-diode photovoltaic model. Its superior dynamic performance is compatible with that of a real PV system. Although it is power efficient at the maximum power point (MPP), it suffers from high power loss around and at the open-circuit voltage (OCV) operation condition. The PV emulator can be used for PV system analysis and testing, such as maximum power point tracking (MPPT). This paper presents a new switching circuit which is placed in parallel with the diode string to minimize the power loss. The switching circuit consists of a two-switch non-inverting buck-boost DC/DC converter. When the operating point of the PV emulator moves from the current source region to the voltage source region, the converter, which is more efficient, switches in to replace the diode string seamlessly to maintain the circuit operation of the emulator. Experimental results show that in the worst case scenario, i.e. OCV condition, the efficiency and temperature of the proposed solution reach 81.47% and 30.1 °C respectively, as compared with 2.8% and 94.2 °C respectively for the diode string only case. In terms of dynamic response, the proposed PV emulator lags the real PV panel by only 3.5 ms as compared with 120 ms by a commercial emulator under the 30% to 60% insolation change test.
Multimodal Representations for Synchronized Speech and Real-Time MRI Video Processing

Representations for data subunits can help with recent data accumulation by enabling efficient storage and retrieval systems. In this paper, we investigate the problem of representation generation for phone classification and cross-modal same-different word discrimination tasks. The benefits of utilizing multimodal data on these tasks are examined together with different data fusion schemes. Mainly, the paper considers two different data modalities, upper airway mid-sagittal plane real-time magnetic resonance imaging (rtMRI) videos and the corresponding speech waveforms, and experiments on USC-TIMIT rtMRI dataset. For the phone classification task, two unimodal neural networks are designed, and these separate systems are merged in two different ways that provide data fusion between two modalities. The proposed networks differ in their stages in which they perform the data fusion. As hypothesized, our results show that data fusion indeed brings a performance improvement over both unimodal approaches, and performing fusion in earlier stages with cross-connections yields better results than fusing the data in later stages. In addition to the proposed phone classification schemes, different unimodal and multimodal systems are designed to obtain phone recognition results on USC-TIMIT rtMRI dataset. Phone representations generated for the phone classification task are also utilized in the phone recognition task, and their representative power is illustrated. Finally, we define a cross-view same-different word discrimination task on USC-TIMIT. We propose two different schemes to tackle this task, and find that for cross-view same-different discrimination, generating representations with the help of cross-modality yields better accuracy than a system employing independently created representations.
Investigation of Dy-Free Hybrid PM Motor Based on Spoke-Type Rotor for Automotive Applications

NdFeB permanent magnets (Nd-PM) can achieve a coercivity of up to 21 kOe without dysprosium due to the development of manufacturing technology. However, due to the high-temperature operation characteristics of vehicle traction motors, Nd-PM over 25kOe is mainly applied. In this paper, 21kOe Nd-PM and 5.5kOe ferrite-PM (Fe-PM) are simultaneously applied, and a hybrid PM motor (HPMM) suitable for environmental temperature conditions of vehicle traction motors is proposed. HPMM is divided into serial configuration and parallel configuration according to the arrangement of each PM. This paper compares the pros and cons of each configuration and presents rotor structures that can improve demagnetization durability and torque characteristics. After comparing the performance characteristics of each rotor structure through 2D-FEM, finally, an HPMM suitable for vehicle traction motors is proposed. Also, the proposed HPMM shows the same torque and power density as the traction motor of TOYOTA PRIUS 4th-generation hybrid electric vehicle (HEV), the target motor. At the same time, the proposed HPMM reduced the usage of Nd-PM by 47% and the total PM cost by 10% compared to the target motor.
SIABR: A Structured Intra-Attention Bidirectional Recurrent Deep Learning Method for Ultra-Accurate Terahertz Indoor Localization

High-accuracy localization technology has gained increasing attention in gesture and motion control and many diverse applications. Due to multi-path fading and blockage effects in indoor propagation, 0.1m-level precise localization is still challenging. Promising for 6G wireless communications, the Terahertz (THz) spectrum provides multi-GHz ultra-broad bandwidth. Applying the THz spectrum to indoor localization, the channel state information (CSI) of THz signals, including angle of arrival (AoA), received power, and delay, has unprecedented resolution that can be explored for positioning. In this paper, a Structured Intra-Attention Bidirectional Recurrent (SIABR) deep learning method is proposed to solve the CSI-based three-dimensional (3D) THz indoor localization problem with significantly improved accuracy. As a two-level structure, the features of individual multi-path rays are first analyzed in the recurrent neural network with the attention mechanism at the lower level. Furthermore, the upper-level residual network (ResNet) of the constructed SIABR network extracts hidden information to output the geometric coordinates. Simulation results demonstrate that the 3D localization accuracy in the metric of mean distance error is within 0.25m. The developed SIABR network has very fast convergence and is robust against THz indoor line-of-sight blockage, multi-path fading, channel sparsity and CSI estimation error.
End-to-end Learning Improves Static Object Geo-localization from Video

Accurately estimating the position of static objects, such as traffic lights, from the moving camera of a self-driving car is a challenging problem. In this work, we present a system that improves the localization of static objects by jointly-optimizing the components of the system via learning. Our system is comprised of networks that perform: 1) 5DoF object pose estimation from a single image, 2) association of objects between pairs of frames, and 3) multi-object tracking to produce the final geo-localization of the static objects within the scene. We evaluate our approach using a publicly-available data set, focusing on traffic lights due to data availability. For each component, we compare against contemporary alternatives and show significantly-improved performance. We also show that the end-to-end system performance is further improved via joint-training of the constituent models. Code is available at: https://github.com/MedChaabane/Static_Objects_Geolocalization.
Sub-Band AGC-Based Interference Mitigation

Interference signals significantly affect the performance of global navigation satellite system (GNSS) receivers. Therefore, different mitigation methods have been proposed in the literature with different mitigation performances according to the type of interference. One method proven more effective against several interference types is the high-rate DFT-based data manipulator (HDDM) pulse blanking (PB). However, it performs poorly in the case of wide-band noise signals. To overcome these limitations, we propose a new HDDM structure where the interference is attenuated instead of removed. It corresponds to automatic gain control (AGC) applied to each fast Fourier transform (FFT) output in the HDDM: the new version is called HDDM-AGC. This paper presents a hardware implementation of the HDDM-AGC algorithm and its evaluation in an exemplary hardware configuration. Results with interference show, in most cases, the same or improved performance as the HDDM-PB. The significant improvements can be seen in continuous interference as wide-band noise or continuous-wave (CW) signals. Only with the frequency hopper and pulsed interferences, we see inferior results than to HDDM-PB.
User-Centric Radio Access Technology Selection: A Survey of Game Theory Models and Multi-Agent Learning Algorithms

User-centric radio access technology (RAT) selection is a key communication paradigm, given the increased number of available RATs and increased cognitive capabilities at the user end. When considered against traditional network-centric approaches, user-centric RAT selection results in reduced network-side management load, and leads to lower operational costs for RATs, as well as improved quality of service (QoS) and quality of experience (QoE) for users. The complex between-users interactions involved in RAT selection require, however, specific analyses, toward developing reliable and efficient schemes. Two theoretical frameworks are most often applied to user-centric RAT selection analysis, i.e., game theory (GT) and multi-agent learning (MAL). As a consequence, several GT models and MAL algorithms have been recently proposed to solve the problem at hand. A comprehensive discussion of such models and algorithms is, however, currently missing. Moreover, novel issues introduced by next-generation communication systems also need to be addressed. This paper proposes to fill the above gaps by providing a unified reference for both ongoing research and future research directions in the field. In particular, the review addresses the most common GT and MAL models and algorithms, and scenario settings adopted in user-centric RAT selection in terms of utility function and network topology. Regarding GT, the review focuses on non-cooperative models, because of their widespread use in RAT selection; as for MAL, a large number of algorithms are described, ranging from game-theoretic to reinforcement learning (RL) schemes, and also including most recent approaches, such as deep RL (DRL) and multi-armed bandit (MAB). Models and algorithms are analyzed by comparatively reviewing relevant literature. Finally, open challenges are discussed, in light of ongoing research and standardization activities.
A Compressed Sensing Approach to Pooled RT-PCR Testing for COVID-19 Detection

We propose ‘Tapestry’, a single-round pooled testing method with application to COVID-19 testing using quantitative Reverse Transcription Polymerase Chain Reaction (RT-PCR) that can result in shorter testing time and conservation of reagents and testing kits, at clinically acceptable false positive or false negative rates. Tapestry combines ideas from compressed sensing and combinatorial group testing to create a new kind of algorithm that is very effective in deconvoluting pooled tests. Unlike Boolean group testing algorithms, the input is a quantitative readout from each test and the output is a list of viral loads for each sample relative to the pool with the highest viral load. For guaranteed recovery of <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> infected samples out of <inline-formula><tex-math notation="LaTeX">$n \gg k$</tex-math></inline-formula> being tested, Tapestry needs only <inline-formula><tex-math notation="LaTeX">$O(k \log n)$</tex-math></inline-formula> tests with high probability, using random binary pooling matrices. However, we propose deterministic binary pooling matrices based on combinatorial design ideas of Kirkman Triple Systems, which balance between good reconstruction properties and matrix sparsity for ease of pooling while requiring fewer tests in practice. This enables large savings using Tapestry at low prevalence rates while maintaining viability at prevalence rates as high as 9.5%. Empirically we find that single-round Tapestry pooling improves over two-round Dorfman pooling by almost a factor of 2 in the number of tests required. We evaluate Tapestry in simulations with synthetic data obtained using a novel noise model for RT-PCR, and validate it in wet lab experiments with oligomers in quantitative RT-PCR assays. Lastly, we describe use-case scenarios for deployment.
Compact Design of Ka-Band antenna for 5G Applications

The Proposed paper proposing a slotted microstrip patch antenna which is designed and simulated using a high-frequency structure simulator(HFSS) for 5G applications in cellular networks particularly at Ka-band. The frequency ranges for Ka-band is (26-40) GHz. The antenna is designed using a rectangular microstrip patch element with a feed line connected to a patch and source. To increase the gain multiple slots are designed in a patch. The patch and ground are made up of a metal surface and a substrate material act as a dielectric material between them. The Electrical length of antenna is 6.7mm. In this antenna), ROGERSRT/DUROID-5880(tm) is used having the dielectric constant value 2.2. The performance of the proposed design is evaluated based on return loss, VSWR, gain, and radiation pattern.
Identifying Sources of Unfairness in Bayesian Logistic Regression

Machine learning processes have been incorrectly perceived as a neutral way of overcoming human bias in decision-making and promoting fairness in social contexts. Instead, algorithmic decisions have been found to reflect and reinforce existing human biases. Since decisions about people can directly affect their chances in live, the question of fairness is inevitable in this context. In recent years, numerous promising approaches have been proposed and discussed to achieve fairness on a group or individual basis. However, to approach fairness in a holistic way, it is clearly not enough to just work on the symptoms. Instead, further investigations are essential to identify the underlying causes and reveal how they relate to observed biases of algorithmic systems. This should happen within the machine learning community, but also requires interdisciplinary discourse and the allocation of responsibility. The first part of this thesis explores existing approaches to achieving fairness and discusses open challenges. Using Bayesian logistic regression on an exemplary data set the relationships between data, classifier and outcomes is then analysed in more detail in the second part. The aim is to identify different sources of unfairness and thus contribute to the fairness debate. For this purpose, we propose two methods that are suitable for a more detailed analysis of the classifier’s decisions. The first is a preprocessing method that modifies the data with regard to fairness criteria. The second method applies after the training of the classifier and makes it possible to examine the classifications and the associated uncertainty more closely. The methods were evaluated on the basis of three fundamental notations of group fairness, but are not limited to these. Through a better understanding of data set and classifier, we hope to uncover potential sources of unfairness in classification tasks and thus contribute to the development of genuinely fair and transparent algorithmic systems.
Interval Type-2 Fuzzy Logic Controlled Shunt Converter Coupled Novel High-Quality Charging Scheme for Electric Vehicles

The deployment of electric vehicle charging stations degrades the quality of power in the distribution grid. This article proposes an interval type-2 fuzzy logic controlled shunt converter coupled novel high-quality charging scheme for electric vehicles. This system includes three-phase bidirectional front-end ac–dc pulsewidth modulation (PWM) converter, backend dc–dc PWM converter and three-phase three-wire distribution static compensator. The bidirectional converters help to perform both grid to vehicle and vehicle to grid mode of operations. The combination of dc-link voltage with decoupled current control technique is exploited for ac–dc converter. A multistep constant current control technique is proposed for the dc–dc converter to charge and discharge the battery. A fuzzy logic controller based instantaneous reactive power theory control method is proposed for shunt converter. The performance of type-1, interval type-2, and real coded genetic algorithm optimized fuzzy logic controllers are evaluated by the shunt converter dc-link voltage and the total harmonic distortion of the source current. Lithium-ion batteries are utilized as an energy storage device for electric vehicles in the proposed system. The entire system is modeled and evaluated in the MATLAB/Simulink environment. An interval type-2 and real coded genetic algorithm optimized fuzzy logic controller affords the better performance during V2G and G2V operations, respectively.
Biosorption of Pb(II)and Cr(III)from aqueous solutions by carboxyl-functionalized durian shell

The adsorption efficiency,isothermal adsorption and kinetics of durian shell modified by citric acid for Pb(Ⅱ)and Cr(Ⅲ)from aqueous solution was investigated.Both modified and unmodified adsorbents were characterized by FT-IR,SEM and XPS,and modification and adsorption mechanism of durian shell was discussed.The results of FT-IR and XPS demonstrated that after modified by citric acid via N,N'-carbonyldiimidazole as a activator,the new peaks at 1 730 and 1 640cm-1 were assigned to ester carbonyl groups and carboxylate stretching band on durian shell and the m(O)/m(C)atom mass ratio increased,which indicated the presence of citric acid grafted onto durian shell.Adsorption results showed that,the adsorption capacity of durian shell modified by citric acid for Pb(Ⅱ)and Cr(Ⅲ)were 3.15 and 2.42 times as those of the natural durian shell,respectively.The adsorption of Pb(Ⅱ)on durian shell modified by citric acid exhibited a stronger competitive effect than Cr(Ⅲ)in the aqueous solution with two coexisting anions.The adsorption kinetics data showed that the adsorption equibrium was reached in 120 min for Pb(Ⅱ)and Cr(Ⅲ)and the data of adsorption rate could be described by pseudo-second-order rate model(R20.9975). At 25 degrees celsius,the isothermal adsorption of Pb(Ⅱ)and Cr(Ⅲ)on durian shell modified by citric acid tended to be fitted well by the Freundlich model(R20.9902). Adsorption-desorption results showed that the CM-CDI-DRS remained high biosorption ability for Pb(Ⅱ)after four-time usage.