Miracle-dz/UniFAR-FTUs huggingface dataset:
GRO J1750–27: A neutron star far behind the Galactic Center switching into the propeller regime

We report on analysis of properties of the X-ray binary pulsar GROJ1750-27 based on X-ray (Chandra, Swift, and Fermi/GBM), and near-infrared (VVV and UKIDSS surveys) observations. An accurate position of the source is determined for the first time and used to identify its infrared counterpart. Based on the VVV data we investigate the spectral energy distribution (SED) of the companion, taking into account a non-standard absorption law in the source direction. A comparison of this SED with those of known Be/X-ray binaries and early type stars has allowed us to estimate a lower distance limit to the source at $>12$ kpc. An analysis of the observed spin-up torque during a giant outburst in 2015 provides an independent distance estimate of $14-22$ kpc, and also allows to estimate the magnetic field on the surface of the neutron star at $B\simeq(3.5-4.5)\times10^{12}$ G. The latter value is in agreement with the possible transition to the propeller regime, a strong hint for which was revealed by Swift/XRT and Chandra. We conclude that GROJ1750-27 is located far behind the Galactic Center, which makes it one of the furthest Galactic X-ray binaries known.
Second Life Battery Energy Storage System: Modular Interface and Control

The utilization and adaptation of retired batteries has been emerging recently due to the hiking dependance on batteries in electric mobility industry. Hence, piling more retired batteries and presenting urgent need for innovative solutions to mitigate their impact on the environment. As the used batteries are decommissioned out of service at different conditions and different states of health, grouping them into one application is not trivial. Each battery bank is dealt with as independent source and requires separate interface. The control of retired batteries is more challenging in the sense they have variable charging/discharging characteristics as compared to new batteries, different nominal voltages and residual capacities. This paper presents a distributed power electronic interface that can be used with multiple retried batteries to feed common dc link. Each battery power electronic converter is current controlled while single dc link voltage loop is implemented. The currents references for each converter are derived from the single dc link voltage loop through proposed current sharing mechanism that guarantees the batteries are discharged at different rates such that they are depleted at the same time to maximize the utilization time of the batteries. Proposed topology, control structure and simulation results are presented and verified.
Tsetlin LOB: Realtime Regime Learning and Interpretable Prediction in Financial Limit Orderbooks using Convolutional Tsetlin Machines

We propose a Convolutional Tsetlin Machine approach for the problem of detecting and analysing regime changing in multivariate time series generated by financial limit orderbooks. Such time series comprise of multiple time series exhibiting changing behaviors at different time intervals and contain different nonlinear noise and signal characteristics over time. Learning and predicting these changing regime behaviors has several challenges: (1) failure to take relationships between time series into consideration for discovering regimes in multiple time series; (2) lack of an effective approach that models time-dependent behaviors exhibited by series; (3) difficulties in handling outliers or discontinuities which may be informative. We show that our adapted approach of Convolutional Tsetlin Machines to multivariate time series regime classification enjoys many properties of traditional Tsetlin Machines while providing informative insights to regime detection in limit orderbooks while aiming to overcome these challenges.
Named Entity Recognition Methods in Zero Knowledge Network Traffic Analysis

The article discusses methods for searching address fields in network traffic without a priori information about its structure using named entity search methods from natural language processing algorithms. The urgency of the problem being solved is explained, due to the complexity in meeting the requirements of the law in ensuring information security in the automated control system and IoT networks. The method of generating the initial data, the method of its preprocessing (calculation of statistical characteristics) to improve the accuracy of the methods is described. Two approaches to the search for named entities are described: using hidden Markov models and using recurrent neural networks (with LSTM and GRU cell architectures). For each method, its essence is briefly described and the results of work on test data are shown. In conclusion, the conclusion is made about the applicability of models in real work, and also an assumption is made about the promising directions of development of the described method.
5G Ambient Backscatter Communications: Achievable Range and Power Levels in an Urban Environment

The ambient backscatter communication (AmBC) is a promising approach for wireless data transmission, ensuring the Internet of Things (IoT) growth. This paper presents a study to assess the power budget when considering the 5G as an ambient radiofrequency (RF) source. The paper aims to pore over the feasibility boundaries, namely the power and range levels in the context of 5GAmBC. The investigation deals with an urban macro-cellular outdoor environment. A line of sight (LOS) bi-static scenario was inspected as a case study, in the 3.5 GHz frequency range. The power levels were evaluated based on convenient propagation models. The obtained results are very helpful for a better understanding of the backscatter communication ranges for 5G systems in the considered environment
Variable Traffic Scenario Generation for Testing Automotive Radar Sensors

On the path towards higher automation in vehicles, there are higher demands on the sensor systems in which radar sensors play a central role. In order to validate the functionality of radar-based systems for advanced driver assistance systems, a radar target simulator is used. This publication shows the synthesis of measured and simulated data in order to replicate a real world traffic scenario. This way, it is possible to combine separately measured and simulated signatures into a single scene. Full flexibility is achieved by using target lists, which can be processed further, in order to assign the required properties of targets in a scenario. For the evaluation, the generated scenario is compared to a measured scenario in terms of range, velocity, amplitude and angle.
Deep Learning Methods for Plankton Identification: A Bibliometric Analysis and General Review

This study investigates trends in deep learning research on microscopic images to identify and classify plankton. We performed a bibliometric analysis using VOSviewer to display the visualizations. The first step is to extract the relevant journals from the Scopus database. Extraction from relevant journals found 42 articles, including 21 journals and 21 conferences. We analyzed publication trends and analyzes of co-authorship, co-occurrence, and citations. In addition, it examines the collaborative relationship between authors by country, the emergence of co-author keywords, and the citation of journal sources. We found that the researchers performed plankton classification tasks that achieved high accuracy (>90%). The plankton identification method uses a deep learning architecture based on the Convolutional Neural Network. Among the architectures are AlexNet, GoogleNet, InceptionV3, VGGNet (16.19), ResNet (50.101), DenseNet, NasNet, MobileNetV2, ResidualNet, Shufflenet, EfficienNet, and CapsNet. Some researchers combine CNN with SparseConvNets, LMP, SVM, MorphoCluster, and Quantification Algorithm (CC, AC, PCC, PAC, Hdy). Some methods also implement transfer learning, fine-tuning, and advanced features. In addition to the CNN approach, End-to-End Object Detection with transformers is one of the new approaches in the future to identify plankton.
Fast and resource-efficient CNNs for Radar Interference Mitigation on Embedded Hardware

Recently, Convolutional Neural Networks have demonstrated impressive performance in automotive radar interference mitigation tasks. However, high-performance hardware is typically indispensable for low-latency applications of Convolutional Neural Network models, which prohibits usage in resource-constrained automotive environments. In this paper, we propose a scalable, resource-efficient accelerator for interference mitigation, which enables low-latency and low-memory computations. In extensive experiments we contrast three substantially different implementations: (i) a software implementation in Python, (ii) a firmware implementation on an ARM CPU using either floating-point, or fixed-point arithmetic, and (iii) a Field-Programmable Gate Array implementation. These different implementations are analyzed with respect to latency, memory, and interference mitigation performance. We conclude, that hardware accelerated inference is magnitudes faster than CPU-based inference while increasing power consumption just slightly. The use of integer arithmetic due to quantization accelerates the computation without impairing interference mitigation performance. Furthermore, the parallelization of layers in programmable logic further reduces latency. This parallelization introduces a trade-off with resource requirements and power consumption versus latency, and is therefore a scalable approach for different application and hardware demands.
Fuzlearn: A Fuzzy Clusterization based Machine Learning using Learning Automata

Existing machine learning (ML) algorithms, such as Neural Networks (NNs), typically consist of complex data encoding and preprocessing on real-world data followed by extensive arithmetic. As such when implemented in hardware or software they incur high energy consumption as well as increased data-to-decision latency. Recently, the Tsetlin Machine (TM) was proposed as a promising new alternative which takes advantage of logic propositions founded on the principle of Tsetlin Automata, leading to higher energy efficiency, faster training convergence and lower latency. However, TM interfaces require complex data encoding procedures which can add significant overheads.In this paper, we propose a new machine learning approach that is built on the principle of fuzzy clusterization of decision boundaries directly from analog data. This approach effectively excludes the usual booleanization or analog-to-digital (A2D) conversion necessary in conventional approaches, thus offering low-complexity and potentially energy efficiency gains. Fundamental to this algorithm is a team of Learning Automata constituting a clusterization unit termed Clause that act as an Analog-to-Boolean classifier. Depending on the complexity of the ML dataset, a team of clauses derive the classification rules with minimal algorithmic and arithmetic complexities. The software implementation of the algorithm is tested against several problems. Experiments show that the proposed algorithm manages to achieve comparable accuracy as the Tsetlin Machine with fewer clauses. To study the impact of noise in the input features, we subsequently validate our algorithm with a few variants of noisy XOR datasets. Experiments with these datasets show that our approach is capable of mitigating noise effectively due to fuzzy classification boundaries.
Parameter Selection of an LLC Resonant Converter for PV Application

LLC resonant converters are widely used in highly efficient isolated power conversion applications. Parameters of the LLC converter are usually determined according to the required highest/lowest gain at rated power level for mini-mum/maximum input voltages. However, a PV system does not provide the rated power at the voltage values that are used for determination of the required highest and lowest gains. The voltage and power of the converter depend on atmospheric con-ditions such as temperature and irradiation. This study presents a new approach to determinate parameters of the LLC resonant converters for solar PV applications. The proposed algorithm determines the required maximum and minimum gains and defines the LLC resonant converter parameters. The performance of the method is validated using the MATLAB/Simulink.
Domain Adaptation With Evolved Target Objects for AI Driven Grasping

The recent developments of AI and deep learning, together with increased data availability, have made noticeable progress in the field of robotic object grasping. However, the performance of state-of-the-art models in predicting reliable grasp for a given set of novel industrial objects, especially from a different domain with respect to the training data, still needs improvement. In this regard, we propose a novel approach of applying domain adaptation to the novel object synthesis by generating evolved objects. This proposed approach addresses the use case scenario of grasping a newly introduced set of industrial objects and an associated lack of training data. To realize the aforementioned domain adaptation, we apply genetic algorithm based on the method in Evolved Grasping Analysis Dataset (EGAD). Here we modify it to generate evolved objects from a given complex object rather than generating random objects, and the modified algorithm is referred to as EGAD-COMPLEX (EGAD-CMPLX). Our evaluation results show that for a given test set of novel target domain industrial objects, the grasp prediction model developed using the proposed evolved objects and the EGAD objects have superior performance. Specifically, the proposed model have, on average, 16 percent better grasp success rate than the baseline model.
Points of Interest Identification: A Case Study in Beijing Metropolitan Area

Recent advances in navigation have generated an extensive amount of georeferenced trajectories which contain information about citizen's movement and their behavior. The objective of this article is to explore georeferenced satellite-based trajectories to identify points of interest, like university, restaurant and gym. We proposed a three-step approach, the first step is cleaning the georeferenced satellite-based data, the second step is to identify stay points by checking the distance and time between the points of each trajectory, and finally we cluster these stay points to extract points of interest. We evaluate the proposed method with a real-world dataset recorded over three years by 62 users in Beijing. We worked with trajectories where the users were walking, riding a bike and driving a car. Our approach was able to extract 138 points of interest, in which 40 % were extracted from walking trajectories 33 % from biking and 26% from driving. Our results have demonstrated that taking into consideration, the transportation mode for points of interest identification is an important aspect specially for the parameters estimation.
Networked SSD: Flash Memory Interconnection Network for High-Bandwidth SSD

As the flash memory performance increases with more bandwidth, the flash memory channel or the interconnect is becoming a bigger bottleneck to enable high performance SSD system. However, the bandwidth of the flash memory interconnect is not increasing at the same rate as the flash memory. In addition, current flash memory bus is based on dedicated signaling where separate control signals are used for communication between the flash channel controller and the flash memory chip. In this work, we propose to exploit packetized communication to improve the effective flash memory interconnect bandwidth and propose packetized SSD (pSSD) system architecture. We first show how packetized communication can be exploited and the microarchitectural changes required. We then propose the Omnibus topology for flash memory interconnect to enable a packetized network SSD (pnSSD) among the flash memory – a 2D bus-based organization that maintains a “bus” organization for the interconnect while enabling direct communication between the flash memory chips. The pnSSD architecture enables a new type of garbage collection that we refer to as spatial garbage collection that significantly reduces the interference between I/O requests and garbage collection. Our detailed evaluation of pnSSD shows 82% improvement in I/O latency with no garbage collection (GC) while improving I/O latency by 9.71× when GC occurs in parallel with I/O operation, through spatial garbage collection.
Distributed Caching Control Strategy in Mobile Edge Computing: A Mean Field Game Approach

Mobile edge computing (MEC) is a novel computing paradigm that sinks the computing capacity of cloud servers into edge nodes to reduce network latency. By caching the popular content at small base station (SBS) can reduce the heavy backhaul load and the content retransmission in MEC. However, the dynamic and time-varying of the content requests may increase the network cost. In this paper, we study a distributed edge caching optimization problem in MEC scenario with the spatio-temporal requirements. The considered cache control is described as a stochastic differential game (SDG) in which each SBS defines a caching strategy to reduce the cost in terms of the service delay and backhaul link load. To reduce the computational complexity, the original problem can be transformed into a mean field game (MFG). We propose a caching iterative control algorithm that decouples the information interactions between the general SBS and others with the mean field distribution. In addition, we obtain the optimal caching strategy which achieves the existence and uniqueness of the mean field equilibrium (MFE). Simulation results demonstrate that our proposed algorithm can reduce more storage space and total cost compared to the Kim's approach.
Comparative analysis of sub-THz detection in graphene, GaN HEMT, and FinFET devices

We studied temperature-dependent photo response in the sub-THz regime (0.14 THz) of graphene and AlGaN/GaN-based field-effect transistors (FETs). Instead of measuring the voltage response using a lock-in amplifier, the current induced by the incoming sub-THz radiations was measured directly using the semiconductor parameters analyzer (SPA). Such an approach allows fast and multiple measurements as a function of temperature. We have observed that, while responsivity increase saturates at 50-100K, depending on the device, the noise equivalent power continues to decrease with the temperature decrease. Our results show the advantage of the graphene-based detectors over the GaN-based ones while operating at low temperatures.
Harmonic Modeling Methods for Power Electronic Converters: Case study and Comparison

Large-scale integration of renewable generation interfaced to the network through power electronic converters has led to drastic changes in power system dynamics. In islanded microgrids such as More Electric Aircraft on-board microgrids or airport grids, different control concepts for the synchronization of converters have been proposed in order to provide a virtual inertia and improve their resilience against transient events, ensuring the safety operation without heavy redundant design. The complexity of these power-related control algorithms and their interaction with the inner control loops cause problem in frequency components above the range of traditional studies which calls on modeling techniques with wider bandwidth. This work aims to provide an outline of two well-established harmonic modeling methods for power electronic converters and latest research highlights. Theoretical foundation and derivation process is discussed to show the similarities and differences. Relevant case studies are performed to show the modeling process, simulation results are compared with Plexim PLECS models so as to validate their accuracy.
CU Comae: A New Field Double-Mode RR Lyrae Variable, the Most Metal-poor Discovered to Date

We report the discovery of a new double-mode RR Lyrae variable (RRd) in the field of our Galaxy: CU Comae. CU Com is the sixth such RRd identified to date and is the most metal-poor RRd ever detected. Based on BVI CCD photometry spanning 11 years of observations, we find that CU Com has periods P0 = 0.5441641 days (±0.0000049) and P1 = 0.4057605 days (±0.0000018). The amplitude of the primary (first overtone) period of CU Com is about twice the amplitude of the secondary (fundamental) period. The combination of the fundamental period of pulsation P0 and the period ratio of P1/P0 = 0.7457 places the variable on the metal-poor side of the Petersen diagram, in the region occupied by M68 and M15 RRd's. A mass of 0.83 M⊙ is estimated for CU Com using an updated theoretical calibration of the Petersen diagram. High-resolution spectroscopy (R = 30,000) covering the full pulsation cycle of CU Com was obtained with the 2.7 m telescope of the McDonald Observatory and has been used to build up the radial velocity curve of the variable. An abundance analysis performed on the four spectra taken near minimum light (0.54 < ϕ < 0.71) confirms the metal-poor nature of CU Com, for which we derive [Fe/H] = -2.38 ± 0.20. This value places this new RRd at the extreme metal-poor edge of the metallicity distribution of the RR Lyrae variables in our Galaxy.
Probability-based, Risk-adjusted Energy Consumption Optimisation in Industrial Applications

Production planning is not normally concerned with energy costs, but with the increasing share of volatile renewable energy, energy prices are becoming volatile, too. Production planning will ultimately have to take this into account. The problem here is in the uncertainly of price predictions and the associated risk. The present work investigates how to integrate dynamic energy price prediction uncertainty into the optimization of energy consumption in an industrial production setting. To that end, we incorporate the cost of prediction errors into the predicted energy prices up to the planning horizon. Such risk-adjusted prices are then used as the basis for energy-aware production planning. First results show that this concept can handle different error distributions and can be assumed feasible.
Differential Coupler Topologies for Built-In Self-Test of SiGe Automotive Radar Transceivers

This paper presents two differential directional coupler topologies for millimeter-wave built-in self-test (BIST) in the 76 GHz to 81 GHz automotive radar band. One structure is weakly-coupled with a coupling factor (CF) of 21.5 dB, which corresponds to a minimal insertion loss of only 0.3 dB in the signal path under test. The second structure features a tighter coupling of 9.6 dB with an insertion loss around 2 dB. Measured sample-to-sample and frequency variation of the coupling factor are below 0.8 dB for the weakly-coupled and 0.2 dB for the tightly-coupled topology, facilitating accurate power measurement in the BIST. To minimize BIST overhead, the area footprint is kept compact. A generic approach to layout ensures that the couplers can be re-used in various parts of the circuit under test to minimize design time.