Miracle-dz/UniFAR-FTUs huggingface dataset:
A Decentralized Control Architecture Applied to DC Nanogrid Clusters for Rural Electrification in Developing Regions

DC microgrids built through a bottom-up approach are becoming popular for swarm electrification due to their scalability and resource-sharing capabilities. However, they typically require sophisticated control techniques involving communication among the distributed resources for stable and coordinated operation. In this work, we present a communication-less strategy for the decentralized control of a photovoltaic (PV)/battery-based highly distributed dc microgrid. The architecture consists of clusters of nanogrids (households), where each nanogrid can work independently along with provisions of sharing resources with the community. An adaptive I–V droop method is used, which relies on local measurements of state of charge and dc bus voltage for the coordinated power sharing among the contributing nanogrids. PV generation capability of individual nanogrids is synchronized with the grid stability conditions through a local controller, which may shift its modes of operation between maximum power point tracking mode and current control mode. The distributed architecture with the proposed decentralized control scheme enables 1) scalability and modularity in the structure, 2) higher distribution efficiency, and 3) communication-less, yet coordinated resource sharing. The efficacy of the proposed control scheme is validated for various possible power-sharing scenarios using simulations on MATLAB/Simulink and hardware-in-the-loop facilities at the Microgrid Laboratory, Aalborg University.
Resource-Efficient Parallel Tree-Based Join Architecture on FPGA

The offloading and acceleration of database operations on field programmable gate arrays (FPGAs) have been extensively studied for a long time. Architectures of join, a key database operation, have been proposed and optimized on FPGAs. However, these join architectures are either resource-intensive or have low-throughput. In this brief, we focus on the equi-join and propose a resource-efficient join architecture based on a tree model. The architecture needs two phases: the build phase, in which a binary tree is built by using the first database table, and the probe phase, in which the architecture searches the tree to find matching solutions through a second database table. In addition, we propose a parallel implementation for this architecture to improve its performance. The proposed design was implemented on a Xilinx FPGA, and the results were compared with the most recent works on hardware join. The experimental results demonstrate that for a range of parallelism and dataset sizes, our design achieves a data throughput of 8–100 million tuples per second, which is compatible with the bus rate, and performs well in balancing resource utilization and data throughput.
Review of Multiport Converters for Solar and Energy Storage Integration

This paper presents a comprehensive review of multiport converters for integrating solar energy with energy storage systems. With recent development of a battery as a viable energy storage device, the solar energy is transforming into a more reliable and steady source of power. Research and development of multiport converters is instrumental in enabling this transformation in an efficient manner. The high efficiency of conversion in comparatively smaller footprint makes a multiport converter very attractive in this application. Most of the recently reported multiport converter topologies are discussed here. A breakdown of isolated and nonisolated topologies is presented along with the comparison of converter architectures and features such as operating conditions, device count, efficiency, etc. Each group of multiport converters is subdivided into different smaller groups based on their architecture. Detailed specifications are presented for important topologies. Finally, a performance comparison is carried out featuring the advantages and disadvantages of the various topologies leading to suggestions for the direction of future research.
Critical Communications Over Mobile Operators’ Networks: 5G Use Cases Enabled by Licensed Spectrum Sharing, Network Slicing and QoS Control

Commercial mobile operators’ networks will be used for public safety communications due to demand for wireless broadband services, new applications, and smart devices. Existing dedicated professional mobile radio networks, such as terrestrial trunked radio, Tetrapol, and project 25, are based on narrowband technologies and hence their data bandwidth is limited. This paper studies how critical communications needed, e.g., by ambulance personnel, rescue squads, and law enforcement agencies can be implemented over a 5G network. The most important technology enablers are described and test network architectures used in our project given. We focus on two different use cases. First, how to enable priority communications over a commercial mobile network. Second, how to create rapidly deployable networks for emergency and tactical operations. Tests done with the implemented systems in real networks show that both approaches are very promising for future critical users. Techniques such as network slicing and licensed shared access provide means to support mission critical applications in any environment.
Learning Mobility Aid Assistance via Decoupled Observation Models

This paper presents an active assistance framework for mobility systems, such as Power Mobility Devices (PMD), with the distinctive goal of being able to operate within a local moving window, as opposed to the common reliance upon persistent global environments and objectives. Demonstration data from able experts driving a simulated mobility aid in a representative indoor setting is used off-line to build behavioral models of navigation postulated separately upon user joystick inputs and on-board sensor data. These models are built respectively via Gaussian Processes for the joystick signals, and a Deep Convolutional Neural Network for the sensor data; in this case a planar LIDAR. Their combined outputs form a continuous distribution of estimated traversal likelihood within the user's immediate space, allowing for real-time stochastic optimal path planning to guide a user to its intended local destination. Moreover, the computational efficiency of the decoupled models permits rapid replanning on-the-fly for a smooth assistive action. On-line and off-line evaluations substantiate the advantages of the framework in generalising intelligent navigational assistance, of particular relevance for users who experience difficulty in safe mobility.
A new look to 275 to 400 GHz band: Channel model and performance evaluation

In this paper, we present a novel two-path channel model for wireless terahertz (THz) systems operating in the range of 275 to 400 GHz, which accommodates both the channel particularities and the transceivers parameters. The channel particularities include the frequency selectivity, path-loss, as well as the atmospheric conditions, namely temperature, relative humidity and pressure, while the transceiver parameters, which are taken into account, are the antenna gains as well as the power of the transmitted signal. Finally, we evaluated the THz system performance in terms of average signal to noise ratio and ergodic capacity.
Fast One-Shot Learning for Identity Classification in Person Re-identification and Tracking

For video analytics and surveillance applications, person re-identification across multiple camera views remains an open problem. The challenge of being able to determine that two images of people are the same person based solely on their appearance can be difficult, even for human observers. Many recent re-identification methods use deep learning with supervised learning to discriminate between the identity classes. However, the requisite training data is generally not available in real-world scenarios. In this paper, we compare a number of fast classification methods for the purposes of re-identification, taking extracted and pre-processed feature vectors and classifying them into identity classes, focusing on one-shot and unsupervised learning algorithms. We present two novel one-shot learning methods, including Sequential K-means, a computationally efficient algorithm with competitive accuracy. We demonstrate this on an indoor person tracking dataset, and discuss parameter tuning in order to further improve the accuracy of the algorithm.
Tracking based Multipath Clustering in Vehicle-to-Infrastructure Channels

Reliable and accurate multipath clustering results are essential to design and parameterize geometry based (stochastic) channel models proposed by organizations such as 3GPP, COST, WINNER and others. Clustering within wireless channel models assumes that multipath components a rrive/depart with similar properties in the considered parameter domains. We propose a new approach combining tracking and initialization of the classic K-Means clustering algorithm. The results show a significantly improved consistency for cluster estimation, which subsequently allows for analyzing the lifetime of these clusters. Finally, we apply the proposed approach on two urban macro channel sounding datasets and compare them to the current 3GPP standard. We found that the angular parameters are rather similar to the standard whereas the cluster delay spreads and the number of rays per cluster differ significantly.
Learning Features for Writer Retrieval and Identification using Triplet CNNs

This paper presents a method for writer retrieval and identification using a feature descriptor learned by a Convolutional Neural Network. Instead of using a network for classification, we propose the use of a triplet network that learns a similarity measure for image patches. Patches of the handwriting are extracted and mapped into an embedding where this similarity measure is defined by the L_2 distance. The triplet network is trained by maximizing the interclass distance, while minimizing the intraclass distance in this embedding. The image patches are encoded using the learned feature descriptor. By applying the Vector of Locally Aggregated Descriptors encoding to these features, we generate a feature vector for each document image. A detailed parameter evaluation is given which shows that this method achieves a mean average precision of 86.1% on the ICDAR 2013 writer identification dataset, but future work has to be done to improve the performance on historic datasets. In addition, the strategy for clustering the feature space is investigated.
On the Achievable Rates of Virtual Full-Duplex Relay Channel

We study a multihop “virtual” full-duplex relay channel as a special case of a general multiple multicast relay network. For such a channel, quantize-map-and-forward (QMF) [and its generalization of noisy network coding (NNC) and short message NNC] achieves the cut-set upper bound within a constant additive gap, where the gap grows <italic>linearly</italic> with the number of relay stages <inline-formula> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula>. This gap, however, may not be acceptable for practical communication systems with multihop transmissions (e.g., a wireless backhaul operating at high frequencies). Recently, we improved the capacity scaling by using a forward sliding-window (SW) decoding and by optimizing the quantization level at each relay, obtaining the gap that grows <italic>logarithmically</italic> as <inline-formula> <tex-math notation="LaTeX">$\log {K}$ </tex-math></inline-formula>. Furthermore, the improved scheme has lower decoding complexity and delay than the general QMF and NNC approaches. In this paper, we further improve the performance by presenting a <italic>mixed</italic> scheme in which each relay can perform either decode-and-forward (DF) or the improved QMF (with SW decoding) and can choose to perform rate-splitting to enable partial interference cancellation. In general, the optimization of the relay DF/QMF configuration is combinatorial. Nevertheless, we provide that a simple greedy algorithm finds an optimal configuration under some practically reasonable assumptions. We derive an achievable rate that is easily computable and show that the proposed mixed scheme outperforms the QMF-only schemes. We demonstrate that the performance improvement increases with <inline-formula> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula>, which indicates that the mixed scheme is indeed beneficial for multihop transmission.
Multi-Sensor Multi-Floor 3D Localization With Robust Floor Detection

Location has become an essential part of the next-generation Internet of Things systems. This paper proposes a multi-sensor-based 3D indoor localization approach. Compared with the existing 3D localization methods, this paper presents a wireless received signal strength (RSS)-profile-based floor-detection approach to enhance RSS-based floor detection. The profile-based floor detection is further integrated with the barometer data to gain more reliable estimations of the height and the barometer bias. Furthermore, the data from inertial sensors, magnetometers, and a barometer are integrated with the RSS data through an extend Kalman filter. The proposed multi-sensor integration algorithm provided more robust and smoother floor detection and 3D localization solutions than the existing methods.
Integer Convolutional Neural Network for Seizure Detection

Outstanding seizure detection algorithms have been developed over past two decades. Despite this success, their implementations as part of implantable or wearable devices are still limited. These works are mainly based on heavily handcrafted feature extraction, which is computationally expensive and is shown to be data set specific. These issues greatly limit the applicability of such methods to hardware implementation, including in-silicon implementations such as application specific integrated circuits. In this paper, we propose an integer convolutional neural network (CNN) implementation, Integer-Net, as a memory-efficient unified hardware-friendly CNN framework. The performance of Integer-Net is evaluated with multiple time-series data sets consisting of intracranial and scalp electroencephalogram (EEG) signals. Integer-Net shows a consistent seizure detection performance across three data sets: Freiburg Hospital intracranial EEG data set, Children’s Hospital of Boston-MIT scalp EEG data set, and UPenn and Mayo Clinic’s seizure detection data set. Our experimental results show that a 4-bit Integer-Net leads to only 2% drop of accuracy compared with a 32-bit real-value resolution CNN model, while offering more than 7 times improvement in memory efficiency. We discuss the structure of the integer convolution to improve the computational gain and reduce the inference time that are crucial for real-time application.
Experimental Validation of a Multirobot Distributed Receding Horizon Motion Planning Approach

This paper addresses the problem of motion planning for a multirobot system in a partially known environment where conditions such as uncertainty about robots' positions and communication delays are real. In particular, we detail the use of a Distributed Receding Horizon Approach that guarantees collision avoidance with static obstacles and between robots communicating with each other. Underlying optimization problems are solved by using a Sequential Least Squares Programming algorithm. Experiments with real nonholonomic mobile platforms are performed. The proposed framework is compared with the Dynamic Window approach to motion planning in a single robot setup. A second experiment shows results for a multirobot case using two robots where collision is avoided even in presence of significant localization uncertainties.
An Approximately-Zero-Gradient-Sum Algorithm for Consensus Optimization

This paper presents a set of distributed second-order methods for addressing unconstrained, smooth convex optimization over fixed networks. The proposed second-order methods, referred to as Approximately-Zero-Gradient-Sum (AZGS) algorithms, allow each node to update by combining the Hessian inverse of its local objective and the estimates of its neighbors, so that the gradient sum of the local objectives can be sufficiently close to zero at each iteration. We show that the AZGS algorithms, with properly selected parameters, enable all the nodes to asymptotically reach a consensus that can be arbitrarily close to the optimal solution. Finally, simulation results demonstrate the effectiveness of the AZGS algorithms.
A Digitally Compensated AMR Magnetometer

This paper considers the possibilities of using digital feedback for precise anisotropic magneto-resistance (AMR) magnetometers using commercial off-the-shelf (COTS) components. Requiring only a few analog parts, most of the signal processing is done digitally within an STM32 microcontroller. Because most of the precision is made by the feedback circuit, the analog-to-digital converter (ADC) can be a low-cost type. The compensation source is made with a pulsewidth modulation-driven H-bridge sourced from a voltage reference, so the cost reduction when compared to a “full-analog” design is large. The demodulation of a flipped-AMR signal is done with software after the AD conversion because it improves the offset stability and brings the reduction of the preamp’s and ADC’s LF noise. This paper presents the full characterization of a real instrument, including its noise, linearity, stability, and power consumption.
Delay-limited Protograph Low Density Parity Codes for Space-Time Block Codes

This paper presents a framework to design protograph low density parity check (LDPC) codes for wireless communications where Alamouti space time block code (STBC) scheme is exploited to combat with the fading effect and the number of decoding iterations is limited. The decoding threshold of the resulting protograph code is about 0.7 dB better than that of the state-of-the-art code in the literature. The simulation of the proposed code is reported, and simulation results confirm the analytical advantages of the design. No error floor is found down to frame error rate (FER) of 10−6.
Spurious Free SAW Resonators on Layered Substrate with Ultra-High Q, High Coupling and Small TCF

A new type of surface acoustic wave (SAW)configuration consisting of a very thin single crystal piezoelectric film bonded onto a support substrate (layered substrate)has been actively developed in recent years. This layered SAW structure exhibits superior performance compared to conventional SAW or temperature compensated (TC)SAW, with SiO2over coat, on traditional piezoelectric substrates through a higher quality factor (Q), higher electromechanical coupling factor (k2) and smaller temperature coefficient of frequency (TCF). However, without a careful design of the substrate, the layered SAW potentially has spurious responses in the out-of-band frequencies due to higher order modes guided in the layer. This paper focuses on the design of layered substrate not only to optimize its narrow band characteristics (Q, k2, TCF)but also for eliminating the out-of-band spurious responses. By using a finite element method / boundary element method (FEM/BEM)approach, requirements for the substrate velocity and piezoelectric layer thickness are derived to avoid the presence of spurious modes. Based on the analyses, a new orientation of quartz is proposed as a spurious free support substrate. Sapphire as well as new quartz is selected as a demonstration support substrate and bonded wafers are fabricated using LiTaO3(LT)piezoelectric thin film. Fabricated SAW resonators on LT/sapphire confirm ultra-high Q (>7,000), high k2(9.0%)and small TCF (-2 ppm/K)at 1 GHz, but have several out-of-band spurious responses. LT/new quartz SAW resonators also show ultra-high Q (>6,000), high k2(9.9%)and small TCF (-23 ppm/K)at 1 GHz, and achieve a spurious free out-of-band response as expected.
Channel Estimation for Sparse Massive MIMO Channels in Low SNR Regime

With perfect channel state information, a huge signal to noise ratio (SNR) gain can be obtained in massive multiple input multiple output (MIMO) systems. Therefore, massive MIMO systems are generally assumed to work in low SNR regime. However, channel estimates are contaminated by white noise in practical scenarios, which will induce great performance degradation, especially in low SNR regime. To improve channel estimation quality, we propose a channel estimator to filter out noise in the conventional matched filter-based channel estimates by exploring the spatial sparsity of massive MIMO signals. The viability of this new method is based on the fact that wireless channels are sparse in space domain. To be specific, most energy of the desired signals concentrates on a small number of paths (or directions, equivalently), while the energy of noise is equally spread on all directions. Therefore, we propose an algorithm to identify the desired signals and eliminate most noise. One of the largest advantages of the proposed algorithm is that statistical information concerning the channel vectors is unnecessary. Both theoretical analysis and simulation results justify the efficacy of the proposed channel estimator.
Fashion Culture Database: Construction of Database for World-wide Fashion Analysis

The paper presents a novel concept that analyzes and visualizes worldwide fashion styles. Our goal is to reveal web-based viral fashion styles. To achieve the fashion-based analysis, we have collected fashion culture database (FCDB), which consists of 76 million geo-tagged images in 16 cosmopolitan cities. The database allows us to grasp a trend of mixed fashion styles with a fashion-based descriptor and codeword vector. In order to unveil web-based fashion trends in the FCDB, we applied a simple technique that is a temporal subtraction between consecutive codeword vectors in two different times. In the experiments, we show the analysis of fashion trends and fashion-based city similarity in a social media. As the result of large-scale data collection, we achieved world-level fashion visualization.
A fast calculation for the surface electric field distribution and onset corona voltage of HVDC transmission lines

Understanding and improving the electromagnetic environment of HVDC transmission lines is a basic work for the surface of the electric field distribution and the study of corona initial voltage. It is useful for the design and operation of actual circuit. As a new favorite in the field of power transmission, the shaped line will play an important role in the future line construction. An accurate calculation method for the field intensity distribution on the surface of the shaped wire and round stranded wire was put forward in this paper. This method can obtain the direction of the maximum surface field strength, and apply the corresponding onset corona criterion, the onset corona voltages of the shaped wire are quickly calculated. At the same time, the method is applicable to the calculation of field strength and corona of traditional round stranded wire.
Research on a Cross-Domain Authentication Scheme Based on Consortium Blockchain in V2G Networks of Smart Grid

As an important part of the smart grid, the Vehicle-to-Grid (V2G) can provide various auxiliary services for the power grid and promote the use of renewable resources. However, there are various kinds of attacks in the V2G network. Based on consortium blockchain, this paper proposes a cross-domain authentication scheme for the security threat in the V2G network. This paper designs the trust model and the system architecture, and describes the scheme in detail. The signature and authentication of the scheme adopt the SM9 identity-based cryptographic algorithm. This scheme utilizes the fact that block chain technology is not easy to tamper with. It uses a hash algorithm to verify the certificate, reducing the number of public key algorithm signatures and verifications, making the solution efficient and scalable. The introduction of block chain technology provides new ideas and methods for solving the security problems in the Smart Grid.