Miracle-dz/UniFAR-FTUs huggingface dataset:
A Framework for Radio Resource Allocation and SDMA Grouping in Massive MIMO Systems

This work proposes a framework for multiuser massive Multiple Input Multiple Output (MIMO) systems which is composed of three parts – clustering, grouping, and scheduling – and aims at maximizing the total system data rate considering Quality of Service (QoS) constraints. We firstly use a clustering algorithm to create clusters of spatially correlated Mobile Stations (MSs). Secondly, in the grouping part, we select a set of Space-Division Multiple Access (SDMA) groups from each cluster. These groups are used as candidate groups to receive Scheduling Unit (SU) in the scheduling part. In order to compose a group, we employ a metric that takes into account the trade-off between the spatial channel correlation and channel gain of MSs. In this context, it is proposed a suboptimal solution to avoid the high complexity required by the optimal solution. Thirdly and finally, we use the candidate SDMA groups from the grouping part to solve the data rate maximization problem considering QoS requirements. The scheduling part can be solved by our proposed optimal solution based on Branch and Bound (BB). However, since it has high computational complexity, we propose a suboptimal scheduling algorithm that presents a reduced complexity. In the simulation results, we evaluate the performance of both optimal and suboptimal solutions, as well as an adaptation of the Joint Satisfaction Maximization (JSM) scheduler to a massive MIMO scenario. Although the suboptimal solution presents a performance loss compared to the optimal one, it is more suitable for practical settings as it is able to obtain a good performance-complexity trade-off. Furthermore, we show that the choice of a suitable trade-off between the spatial channel correlation and channel gain improves the system performance. Finally, for a low number of available SDMA groups, the suboptimal solution presents near optimal outage and a throughput loss of only 10% in comparison to the high-complexity optimal solution while it outperforms the JSM solution in terms of outage and system throughput.
Automatic Planning Algorithm of 300 GHz Backhaul Links Using Ring Topology

In the future, the base stations are expected to be deployed with higher density. To save cost and man forces, wireless links operating at 300 GHz can replace conventional cable-based backhaul connections. One of the goal of Horizon 2020 EU-Japan project ThoR is to design automatic planning tools for 300 GHz wireless backhaul network. To accomplish this task, a novel planning tool has been developed to plan backhaul links with the usage of a ring topology. To evaluate the proposed tool’s functionality, system-level simulations are conducted with an in-house developed mobile network simulator. In this paper, the algorithm of the new planning tool for 300 GHz wireless backhaul network is introduced and the SINR characteristics of the planned THz links are evaluated using 3D ray optical path loss predictions. For that, a realistic scenario of an ultra-dense cellular network in Hanover, Germany, is used with the diverse tropospheric climate conditions.
Closer: Scalable load balancing mechanism for cloud datacenters

Cloud providers (e.g., Google, Alibaba, Amazon) own large-scale datacenter networks that comprise thousands of switches and links. A load-balancing mechanism is supposed to effectively utilize the bisection bandwidth. Both Equal-Cost Multi-Path (ECMP), the canonical solution in practice, and alternatives come with performance limitations or significant deployment challenges. In this work, we propose Closer, a scalable load balancing mechanism for cloud datacenters. Closer complies with the evaluation of technology including the deployment of Clos-based topologies, overlays for network virtualization, and virtual machine (VM) clusters. We decouple the system into centralized route calculation and distributed route decision to guarantee its flexibility and stability in large-scale networks. Leveraging In-band Network Telemetry (INT) to obtain precise link state information, a simple but efficient algorithm implements a weighted ECMP at the edge of fabric, which enables Closer to proactively map the flows to the appropriate path and avoid the excessive congestion of a single link. Closer achieves 2 to 7 times better flow completion time (FCT) at 70% network load than existing schemes that work with same hardware environment.
Predicting Students Final Academic Performance using Feature Selection Approaches

Data analytics and data mining techniques are increasingly being used to assist managers, business leaders, and policymakers in their decision-making. These techniques help them distill the useful information hidden in their ever-growing databases that are distributed across various data stores. Another area in which these techniques are utilized is the education domain, wherein data analytics can be leveraged to predict students’ academic performance. Teachers will therefore be better informed in providing their students with additional support if required. Learning Management Systems (LMS) captures student online learning data and are extensively used by researchers to gather data-driven insights on students’ overall academic performance. This study aims to predict students’ academic performance with the LMS data from an online training course using various machine learning algorithms. The study further highlights that selection of features by using feature selection methods will enable early prediction of student academic performance. Early predictions can benefit those students who are considered to be at risk of failing a course with targeted early interventions to help improve their performance throughout the course.
Performance Optimization and Scalability Analysis of the MGB Hydrological Model

Hydrological models are extensively used in applications such as water resources, climate change, land use, and forecast systems. The focus of this paper is performance optimization of the MGB hydrological model, which is widely employed to simulate water flows in large-scale watersheds. The optimization strategies that we selected include AVX-512 vectorization, thread-parallelism on multi-core CPUs (OpenMP), and data-parallelism on many-core GPUs (CUDA). We conducted experiments for real-world input datasets on state-of-the-art HPC systems based on Intel's Skylake CPUs and NVIDIA GPUs. In addition, a Roofline model characterization for these datasets confirmed performance improvements of up to 37.5x on the most time-consuming part of the code and 8.6x on the full MGB model. The work proposed herein shows that careful optimizations are needed for hydrological models to achieve a significant fraction of the performance potential in modern processors.
Application of Deep Residual Network in Fault Diagnosis of Wellbore

Aiming at the shortcomings of general CNN wellbore fault identification models that require a large number of samples and low recognition accuracy, this paper proposes a wellbore fault diagnosis model based on the combination of ResNet34 and transfer learning. Migrate the ResNet34 pre-training model to the image data set of four kinds of wellbore faults for training, extract the features of the wellbore fault images, use the feature values as the input of the fully connected neural network classifier, and then classify and identify the data set, which improves the accuracy and training speed of this model. The experimental data in this paper uses the wellbore fault images of an oil field in Shandong obtained by the VideoLog visualization logging platform. The experimental results show that the recognition accuracy of ResNet is 99.2%, which is 8.9% higher than that of MobileNet, indicating ResNet34 has high accuracy in wellbore fault identification.
Performance Evaluation of a Collaborative IoT Framework for Energy-Efficient Communities

Smart communities of consumers have the chance to cooperate and reach coordinated behaviour within the Internet-of-Things (IoT) towards the achievement of common goals. A pilot testbed of a collaborative IoT-based framework for energy-efficient and sustainable communities is deployed and evaluated. The framework is centralised on a community aggregator, which implements a fair scheduler in response to both, the consumers’ demand time preference and the utility’s day-ahead supply from renewables. In this paper, we present the architecture design and analyse the performance cost as to validate the feasibility of the proposed collaborative architecture. Home Controllers and the community Aggregator are implemented on Raspberry Pi 4 boards throwing feasible performance results: overheads at the Aggregator lie within suitable ranges (19 seconds for 52 appliances) even in scenarios of rigid consumers’ demand. Scenarios are configured from Living Labs’ datasets containing appliances’ consumption and operation time frames. Communication overhead imposed by the tested network protocols (sockets-UDP, MQTT, MQTT/TLS, and CoAP/DTLS), and links (WiFi, ZigBee and Z-Wave) result in CoAP/DTLS behaving more efficiently (90 ms) than MQTT/TLS (160 ms).
Analysis and Efficient Design of Sub-THz Transmitarrays with Three Anisotropic Layers

In this paper, we study the transmission behavior of a three-layer anisotropic frequency selective surface with applications to the optimal design of transmitarray antennas. We theoretically investigate the depolarizing properties of the middle layer and derive a condition to achieve any transmission phase and at the same time zero insertion loss. Lastly, a 3-bit 40×40 transmitarray is designed at 300 GHz, based on the proposed approach. The antenna attains a maximum gain of 35.1 dBi with an aperture efficiency of 64.8%, which is only 0.5 dB less than the ideal case of a transmitarray having perfect transmission and phase compensation.
Vector Field Model for Trajectory Data and Its Application in Similarity Query

With the increasing number of mobile global positioning system (GPS) devices, more and more trajectory data are collected, stored, and analyzed for various applications. One of the basic operations in trajectory analysis is the similarity query, which retrieves similar trajectories of a given one. In this paper, we model trajectory data as vector fields, by which the similarity between two trajectories can be measured in the vector space that they are transformed to. Using such model, trajectory queries can be performed efficiently using Locality Sensitive Hashing (LSH) to filter out the dissimilar trajectories. Experiments using Geolife dataset demonstrate that LSH can filter out nearly 70% dissimilar trajectories while maintaining the recall rate close to 100%. Meanwhile, experiments show that our method is 30 times faster than the tradition Longest Common Subsequence (LCSS) method when querying ten thousands of trajectory data.
Predicting potential customers of 5G services via ADTree

Using customer history data and data mining methods to discover potential customers of new business has become an important means of precise marketing for telecommunication operators. In this paper, we use ADTree for potential 5G customers prediction. Firstly, the training data is preprocessed, including data cleaning, discretization and data transformation. Secondly, in order to improve the modeling speed and classifier performance, the information gain method is used to select attributes. Thirdly, ADTree is used to model the training data, and the accuracy rate of the model and AUC value are evaluated by 10 fold cross validation. Finally, this model is used to classify the test data, and general guidance rules are given. Experiments show that the prediction accuracy and AUC value of ADTree method are superior to other classification methods.
Extensive Electrical Characterization Methodology of Advanced MOSFETs Towards Analog and RF Applications

This review paper assesses the main approaches in the electrical characterization of advanced MOSFETs towards their future analog and RF applications. Those approaches are shown to be different from the traditionally used ones for the assessment of the device perspectives for digital applications. Based on the original research realized by our group over the last years, advantages and necessity of those techniques will be demonstrated on different study cases of various advanced MOSFETs, such as Fully Depleted Silicon-on-Insulator (FDSOI), FinFETs and NanoWires (NW) in a wide temperature range (from cryogenic, 4 K up to 250°C). A wide frequency band characterization (from DC up to hundred GHz range) will be positioned as a key element enabling a fair device assessment towards analog and RF applications. Importance of the “extrinsic” parasitic elements in the advanced devices is enormous, sometimes even dominating the device performance. Therefrom arises the need for a proper separate extraction and discussion of “intrinsic” versus “extrinsic” parameters.
A Frequency Tunable MIMO Antenna Cluster with Transmitter IC

This paper demonstrates the feasibility of an transmitter system where a handset MIMO antenna is fed by a 0.5–4.5 GHz integrated transmitter front-end to implement a frequency-tunable antenna cluster. The transmitter implementation performs on-chip amplitude and phase tuning to provide compatible MIMO performance over a wide frequency range in the low bands (700 MHz–960 MHz) and the high bands (1.5 GHz–4.5 GHz). The antenna is built with 8 elements comprising 4 clusters, two for low-band frequencies and two for higher frequencies. The transmitter-IC driving the antenna cluster is implemented in a 28-nm CMOS process with an active chip area of only 0.2 mm2. The proof-of-concept implementation demonstrates enhanced antenna efficiencies from -6.5 dB to -1.5 dB across the frequency bands, and envelope correlation coefficient below 0.4. The measurement results indicate that the antenna cluster technique can be applied with a real integrated transmitter to enhance MIMO operation of mobile antennas.
A Blind Beamforming Algorithm Based on Time-Frequency Analysis Technology

In order to achieve the signal fusion of Doppler radar array under the condition of low signal-to-noise ratio, a blind beamforming algorithm based on time-frequency analysis technology is proposed. The algorithm first performs a fractional Fourier transform on the received signal to estimate its parameters, and then estimates the time-frequency curve of the received signal, calculates Steering vector by the time-frequency point based on the time-frequency estimation curve on the spectrum, and finally use the Capon method for beamforming. Theoretical analysis and experiments show that the performance of the algorithm in this paper is stable, and it can acheive the reception of the desired signal and the suppression of interference noise.
Robust Rigid Body Localization Under Position Error

Rigid body localization (RBL) technology, which can obtain both the position estimation and direction information of a target, has been incorporated into location based services (LBS). However, most existing works on the rigid body based LBS assume that there are no sensor position errors, which can lead to misleading results. This paper proposes a semi-definite relaxation method, in which both rigid body sensors and anchor nodes position errors are taken into account. Since the original problem is non-convex, we transform it into the auxiliary variable based semi-definite programming via squaring both sides of range measurement. Simulation results demonstrate that the proposed algorithm outperforms the existed works in terms of root mean square error (RMSE).
SAR Evaluation for an Inductive Wireless Low Power Charger by a Worst-Case Approach

This paper presents Specific Absorption Rate (SAR) calculations by electromagnetic simulations (EM). A worst-case analysis is proposed to evaluate the compliance of Wireless Power Chargers (WPC) installed within a vehicle with the existing recommendations of exposure levels to Electro Magnetic Fields (EMF). This analysis is intended for low power wireless chargers (up to 15 W) operating at 127 kHz but the method could be extended to any kind of wireless power chargers. In addition, this method could be retained for assessing RF exposure compliance during the radio homologation process.
The Role of Conserved Water Molecules in Allosteric Modulation of Caspase-3

MACIAG, JOSEPH JOHN. The Role of Conserved Water Molecules in Allosteric Modulation of Caspase-3. (Under the direction of Dr. A. Clay Clark). Caspases mediate apoptosis, a programmed cell death cascade which is a vital component of homeostatic maintenance among metazoans. The activation of the apoptotic caspases results in the irreversible obligation to cell suicide. Additionally, caspases demonstrate regulatory roles in a multitude of processes including: cellular proliferation, cellular differentiation, cytokine processing, macrophage infectious response, neuronal pruning and tissue development, referred to as the adaptive responses. Due to the involvement of caspases in disease it is important to understand how they are regulated. Investigation into the specific mechanisms governing caspase inhibition and activation may provide novel insights during design and rationale for potential regulators. Cells utilize two general mechanisms to modify caspase activity, through modulating levels of active caspase or through allosteric mechanisms that change the distribution of conformations in the native ensemble. Caspase activity is also affected by posttranslational modifications (PTMs), most notably by phosphorylation, where several sites on mature caspases are modified. Provided herein is the examination of the role of conserved water molecules in the allosteric regulation of conformational states in the native ensemble. The positioning of the conserved waters correlates with regions of the protein that are also modified, suggesting that PTMs may fine-tune activity by disrupting the conserved water networks. We used saturation mutagenesis in an allosteric site to test this hypothesis, and we show that the activities of the mutants vary over four orders of magnitude. Our database of 37 high-resolution structures of caspase-3 variants shows that de-solvating the enzyme decreases activity by repartitioning states within the native ensemble. Overall, the database establishes a means to fine-tune caspase-3 activity over several orders of magnitude through conformational selection that is facilitated by globally dehydrating the native state ensemble. Importantly, the ensemble may provide the cell with the means to reversibly fine-tune caspase activity by utilizing combinations of common and/or unique sites of PTMs. Recent innovations in genomic editing tools provides a reliable and efficient means to mutate caspaes-3 genes in a cellular or animal model context. These tools provide researchers with the ability to determine how cells modulate a threshold level of activity. Regulation of caspase-3 provides the ability to discern specific caspase involvement in disease and is vital when striving for limited off target effects during disease treatment. It is vital to understand caspase regulation so we may rebalance caspase activity. The Role of Conserved Water Molecules in Allosteric Modulation of Caspase-3
Printed score detection based on deep learning

Optical Music Recognition (OMR) is an important way to realize the digitization of musical score images. It has wide application prospects in computer music, digital music library, computer-assisted music teaching and other fields. Recognition of musical symbols is an important research content of optical score recognition. To solve the problems of OMR recognition process is complicated and recognition accuracy is not high, the end-to-end printed music score recognition model based on YOLOv3 is designed. The model adopted multi-task learning, and learns the pitch and duration classification task and note coordinates task, which improved the generalization ability of the model. Finally, the model was tested on the test set generated by MuseScore. The experimental results show that the accuracy of note recognition is high, which can reach the accuracy of duration value of 0.92 and the accuracy of pitch of 0.96, which is higher than the accuracy of note recognition of other score recognition models.
Ka-Band Circularly-Polarized Antenna Array with Wide Gain and Axial Ratio Bandwidth

In this paper, the design, simulation and measurement results of a 2×2 circularly polarized microstrip antenna sub-array at Ka-band are presented. The proposed antenna sub-array employs the sequential rotation technique for improvement of AR bandwidth with high circular polarization purity without degrading the circularly polarized gain. The 3-dB gain reduction due to using sequential rotation technique is compensated by employing an antenna element with wide axial ratio bandwidth. The antenna array has a wide impedance bandwidth (S11< -10 dB) of 6.5 GHz from 26-32.5 GHz. The antenna has a wide axial ratio bandwidth (AR< 3 dB) of 6.2 GHz from 25.4-31.6 GHz with high circular polarization purity. The 3-dB gain bandwidth of the antenna is from 25.5-31.2 GHz and the peak gain is 12.1 dBic. The antenna is fabricated through multilayer printed circuit board process and has a size of 18.5 mm × 18.5 mm × 1.02 mm.
Research on improved nucleic acid amplification temperature control system with single neuron adaptive PID control

In nucleic acid amplification technology, the influence of temperature on the reaction process is one of the most critical factors. This paper first describes the requirements and overall architecture of the temperature control system, followed by a detailed explanation of the principle of the temperature detection circuit and temperature control circuit in the process of temperature control. Combined with the improved single neuron adaptive PID algorithm, the temperature reaction process is accurately regulated. The simulation results and experimental data show that the system meets the design needs.
A Decoupling and Matching Network With Harmonic Suppression for MIMO Antennas

This article presents a decoupling and impedance matching network with harmonic suppression for two-element antenna arrays, including a resistor-loaded decoupling part and a slotline-based impedance matching and harmonic suppression part. By integrating with the proposed network, the mutual coupling between the elements at the center frequency can be well suppressed. Moreover, the second-order harmonic is also suppressed to a low level. For verification purposes, a 2×2 dual-polarized antenna array centered at 3.5 GHz is employed and integrated with the proposed networks. A 3-D model is constructed and full-wave simulations are carried out. The results denote that the strongest in-band mutual coupling is suppressed from -16.6 dB to less than -25 dB, and a good out-of-band rejection level up to 10 GHz is obtained.
Performance Improvement of a Water-Based Transparent and Flexible Unidirectional Antenna

In this paper, a study has been devoted to design a water-based flexible, robust and transparent antenna having unidirectional radiation pattern. Simulation analysis is presented to exhibit the determining factors affecting antenna performance and further a method is demonstrated to improve the performance. The proposed antenna is designed by utilizing pure water, polymer and transparent-flexible e-textile sheet. The design concept of the antenna is based on the utilization of a reflector surface on one side of a dipole radiator to transform the omnidirectional radiation of the dipole to unidirectional pattern. The reflector is constructed from pure water enclosed inside a rectangular hollow cavity made from polydimethylsiloxane (PDMS) and the dipole radiator is made from transparent-flexible e-textile sheet. The impacts of the reflector’s properties on antenna performance are investigated in this research to identify the optimum design parameters for performance improvement.