Miracle-dz/UniFAR-FTUs huggingface dataset:
2D MIMO Radar with Coprime Arrays

In this contribution, a new class of planar coprime MIMO radar systems based on quadratic integers is proposed where the antenna locations are represented by lattice points generated by prime integers in quadratic number fields. By exploiting the coprimality of certain quadratic integers, the virtual coarrays of proposed structures enjoy a quadratic gain in parameter identifiability according to the Chinese Remainder Theorem (CRT). To avoid holes in the coarray, we present Hole-free CRT arrays with guaranteed full-rank autocorrelation matrices for subspace-based target estimation. The ring of Gaussian integers and the ring of Eisenstein integers are chosen as examples of designing coprime MIMO radar systems. It is shown that the proposed arrays achieve enhanced angular resolutions and improve the side lobe suppression. In the context of target estimation, simulations show the superior performance of the coprime MIMO structures based on CRT.
Human Action Recognition Based on Selected Spatio-Temporal Features via Bidirectional LSTM

Recently, many deep convolutional networks approaches have been proposed for human action recognition. The challenge is to capture complementary information from still and motion frames. In contrast to previous models, which use holistic clips to model spatial information and traditional temporal information for sequential processing, in this paper, we propose a novel framework that can select the discriminative part in the spatial dimension and enrich the modeling action of motion in the temporal dimension. We utilize part selection within clips and consider the bidirectional temporal information when modeling the temporal pattern using multiple layers of a long short-term memory framework, which can learn compositional representations in space and time. Our results are evaluated on the standard benchmarks UCF101 and HMDB51 and show that the proposed architecture achieves state-of-the-art results.
A Network Compatibility Condition For Compressed Sensing Over Complex Networks

This paper continues our recently initiated line of work on analyzing the network Lasso (nLasso, which has been proposed as an efficient learning algorithm for massive networkstructured data sets (big data over networks). The nLasso extends the well-known Lasso estimator to network-structured datasets. In this paper we consider the nLasso using squared error loss and provide sufficient conditions on the network structure and available label information such that nLasso accurately recovers a clustered (piece-wise constant) graph signal (representing label information) from the information pro-vided by the labels of a few data points.
WiDMove: Sensing Movement Direction Using IEEE 802.11n Interfaces

The accurate detection of people in indoor environments requires high-cost devices, while low-cost devices, in addition to low accuracy, offer little information about the monitored events. The perturbations that result from indoor movements affect the signals received by 802.11 interfaces. Hence, an 802.11 device becomes a widely available, low-cost, and reasonably accurate solution for several applications. This paper presents WiDMove, a proposed technique to detect the entry and exit of persons, within an indoor environment, using the channel state information (CSI) measurements, which is provided by the IEEE 802.11n compliant devices. Based on the gathered CSI measurements, we utilized frequency-time analysis methodology to build an efficient features vector based on Short-Time Fourier Transform (STFT) and Principal Component Analysis (PCA). We used the extracted features to train and develop a Support Vector Machine (SVM) classifier, which provided very promising initial results. Our initial results have an accuracy near 80 %.
Membrane Electrolysis Assisted Gas Fermentation for Enhanced Acetic Acid Production

Gas fermentation has rapidly emerged as a commercial technology for the production of low-carbon fuels and chemicals from (industrial) CO and/or CO2-rich feedstock gas. Recent advances in using CO2 and H2 for acetic acid production demonstrated that high productivity and substrate utilization are achievable. However, the costly constant addition of base and the energy-intensive nature of conventional recovery options (e.g. distillation) need to be overcome to drive organic acid production forward. Recently, membrane electrolysis has been presented as a technology that enables for the direct extraction of carboxylates across an anion exchange membrane into a clean and low pH concentrate stream. Continuous in-situ extraction of acetate directly from the catholyte of a microbial electrosynthesis reactor showed that membrane electrolysis allows pure product recovery while improving productivity. Here we demonstrate that the system can be further enhanced through additional input of electrolytic hydrogen, produced at higher energetic efficiency while improving the overall extraction efficiency. A gas-lift reactor was used to investigate the hydrogen uptake efficiency at high hydrogen loading rates. During stable operation acetate transport across the membrane accounted for 31% of the charge balancing, indicating that the use of external H2 can lead to a more efficient use of the extraction across the membrane. By coupling membrane electrolysis with the gas fermentation reactor the pH decrease associated with H2/CO2 fermentations could be prevented, resulting in a stable and zero-chemical input process (except for the CO2). This now enables us to produce more than 0.6 M of acetic acid, a more attractive starting point towards further processing.
An Approach to Embodiment and Interactions with Digital Entities in Mixed-Reality Environments

The advances in mixed reality (MR) technologies provide an opportunity to support the deployment and use of MR for training and education. We describe an approach that extends the functionality of the Microsoft HoloLens device to support a wider range of embodied interactions by making use of the Microsoft Kinect V2 device. The embodied interactions can support novel interaction scenarios, especially within the context of training and skills development, thereby removing or reducing the need for training equipment.
Collision Avoidance in Fixed-Wing UAV Formation Flight Based on a Consensus Control Algorithm

This paper addresses a collision avoidance problem for multiple unmanned aerial vehicles (UAVs) in the process of high-speed flight, thereby enabling UAV cooperative formation flight and effective mission completion. The main contribution is to propose a collision avoidance control algorithm for a multi-UAV system based on a bi-directional network connection structure. To effectively avoid collisions between UAVs and between UAVs and obstacles, the proposed consensus-based algorithm and a “leader-follower” control strategy are simultaneously applied for UAV formation control to ensure the convergence of the formation. Each of the UAVs has the same forward velocity and heading angle in the horizontal plane, and they maintain a constant relative distance in the vertical direction. This paper proposes a consensus-based collision avoidance algorithm for multiple UAVs based on an improved artificial potential field method. Simulation tests involving multiple UAVs were performed to validate the proposed control algorithm and to provide a reference for engineering applications.
A High Performance Contingency Planning System for VAVs with Lost Communication

Lost link (LL) in unmanned air vehicles (UAVs) can cause safety hazards to other air vehicles and can also significantly increase air traffic controllers' (ATC) workload. To the best of our knowledge, there still does not exist a systematic and automatic software tool that can generate contingency plans to deal with LL. We developed a new, comprehensive, fast, automated, and standardized lost link plan (LLP) generation framework and detailed procedures that can satisfy all the FAA regulations with respect to lost link situations. Extensive simulations were performed to demonstrate the efficacy of the proposed framework.
A New Method for Handling Lockout Constraints on Controlled TCL Aggregations

Thermal loads are recognized as a valuable source of flexibility in face of the increasing variability caused by the large shares of renewable production. Lockout constraints can significantly reduce the flexibility of thermostatically controlled loads (TCLs). We propose a novel way of modifying the loads' lockout durations to achieve non-intrusive centralized control without relying on local computations and estimations. We derive analytical expressions for the flexibility reduction and validate them via simulations, which show that the proposed method describes the TCLs flexibility accurately. We further show that a simple stochastic centralized controller, which does not rely on local temperature measurements, outperforms the commonly used priority-stack controller in terms of system robustness against infeasible trajectories.
Multi-Objective Probabilistic Power Resources Planning for Microgrids with Ancillary Services Capacity

Microgrids have become an attractive strategy to guaranty a reliable, efficient and sustainable electricity delivery to the distribution network, as well as to offer effectively ancillary services to the power utility. Therefore, special features have to be introduced to the planning methodology, while the optimization problem has also to be re-evaluated to contemplate the large number of variables, multiple objectives, and different constraints and uncertainties. Hence, a probabilistic multi-objective microgrid planning methodology based on the optimal size and location of distributed generation is proposed. In this methodology, the mismatch power in islanded mode and the annual energy losses in grid-connected mode are minimized, while the residual power for reserve provision is maximized. For that purpose, probabilistic models, distributed storage units, and constructive constraints are contemplated in the methodology. The methodology is tested in an adapted PG&E 69-bus distribution system and the NSGAII optimization method is used to solve the optimization problem.
Vital Sign Localization and Measurement Using an LFMCW MIMO Radar

Non-contact vital sign monitoring offers several advantages over contact based methods in particular regarding user-friendliness. Measurements relying on the radar principle differentiate from other contactless techniques in its robustness. In order to allow convenient assessment of cardiopulmonary activity a separation of the respiratory and cardiac components in the time domain is useful as shown by means of a signal model. A signal processing method to achieve this is developed by utilizing the empirical mode decomposition. Experiments were carried out to verify the proposed technique. Comparing the processed radar results with measurements of a gold standard reference, a successful heart rate estimation is demonstrated.
Fitting Software Execution-Time Exceedance into a Residual Random Fault in ISO-26262

Car manufacturers relentlessly replace or augment the functionality of mechanical subsystems with electronic components. Most such subsystems (e.g., steer-by-wire) are safety related, hence, subject to regulation. ISO-26262, the dominant standard for road vehicles, regards software faults as systematic, while differentiating hardware faults between systematic and random. The analysis of systematic faults entails rigorous processes and qualitative considerations. The increasing complexity of modern on-board computers, however, questions the very notion of treating the violation of execution-time envelopes for software programs as a systematic fault. Modern hardware in fact reduces the user's ability to delve deep enough into the fabric of hardware–software interaction to gage its extent of contribution to the worst-case execution time (WCET). Changing the nature of the WCET-analysis problem may help address that challenge effectively. To this end, we propose a solution that should allow ISO-26262 to quantify the likelihood of execution-time exceedance events, relating it to target failure metrics employed in support of certification arguments, similarly to random faults in hardware. To this end, we inject randomization in the timing behavior of the computer hardware to relieve the user from the need to control hard-to-reach low-level parts, and use measurement-based probabilistic timing analysis to quantify, constructively, the failure rates resulting from the likelihood of execution-time exceedance events.
Towards Privacy-Preserving and Practical Image-Centric Social Discovery

Images are becoming one of the key enablers of user connectivity in social media applications. Many of them are directly exploring image content to suggest new friends with similar interests. To handle the explosive volumes of images, one common trend is to leverage the public cloud as their robust service backend. Despite the convenience, exposing content-rich images to the cloud inevitably raises acute privacy concerns. In this paper, we propose a privacy-preserving architecture for image-centric social discovery services, designed to function over encrypted images. We first adopt the effective Bag-of-Words model to extract the “visual content” of users’ images into respective image profile vectors. We then model the core problem as similarity retrieval of encrypted high-dimensional vectors. To achieve scalable services over millions of encrypted images, we design a secure and efficient index structure, which enables practical and accurate social discovery from the cloud, without revealing any image profile or image content. For completeness, we further enrich our service with secure updates, facilitating user's image update. Our implementation is deployed at an Android phone and Amazon Cloud, and extensive experiments are conducted on a large Flickr image dataset which demonstrates the desired quality of services.
Data Collection Strategy for Magnetic Induction Based Monitoring in Underwater Sensor Networks

Underwater wireless sensor networks (UWSNs) based on magnetic induction (MI) have been recently proposed as a promising candidate for underwater networking due to its benefits, such as small transmission delay, low vulnerability to environment changes, multipath fading negligibility, and high bandwidth. Most of the UWSN applications are location dependent and, thus, localization plays an important functionality for obtaining sensor positions. In this paper, we first study an MI-based monitoring network in shallow sea, then focus on how to design an optimal node deployment strategy and a clustering algorithm to prolong network lifetime for a 3D-UWSN by reducing the network energy consumption. Using the Voronoi diagram, we propose a high-energy node priority clustering algorithm, in which a cluster head would be selected according to the remaining energy of sensor nodes and the geometry distance among them. Moreover, in order to improve the efficiency of data collection, we use the ant colony optimization to find the shortest path for autonomous underwater vehicle. The simulation results show that the proposed approach outperforms other conventional protocols in some certain scenarios.
Changes in Executive Function Over Time in Bilingual and Monolingual School-Aged Children

We examined the development of 3 executive function (EF) components—inhibition, updating, and task shifting—over time in monolingual and bilingual school-age children. We tested 41 monolingual and 41 simultaneous bilingual typically developing children (ages 8–12) on nonverbal tasks measuring inhibition (the Flanker task), updating (the Corsi blocks task), and task shifting (the Dimensional Change Card Sort task; DCCS) at 2 time points, 1 year apart. Three indexes of task shifting (shifting, switching, and mixing costs) were derived from the DCCS task. The 2 groups did not differ in their development of updating, but did demonstrate distinct patterns of development for inhibition. Specifically, while the bilingual group demonstrated a steep improvement in inhibition from Year 1 to Year 2, the monolingual group was characterized by stable inhibition performance over this time period. The 2 groups did not differ in their developmental patterns for shifting and switching costs, but for mixing costs, the bilingual children outperformed the monolingual children in both years. Together, the findings indicate that bilingual experience may modulate the developmental rates of some components of EF but not others, resulting in specific EF performance differences between bilinguals and monolinguals only at certain developmental time points.
6G Vision and Requirements: Is There Any Need for Beyond 5G?

To address the question in the subtitle of this article, we start by discussing earlier mobile communication systems, beginning with the first analog wireless cellular standards, then moving on to second generation (2G) [or Global System for Mobile Communications (GSM)], passing third generation (3G) and fourth generation (4G), and proceeding to fifth generation (5G). First, we present each generation's key achievements in terms of user services, each generation's technologyrelated factors of success (called innovations) as well as its relation to regulation, and each generation's potential deficiencies.
Revisiting the Taxonomy of the Genus Arcobacter: Getting Order From the Chaos

Since the description of the genus Arcobacter in 1991, a total of 27 species have been described, although some species have shown 16S rRNA similarities below 95%, which is the cut-off that usually separates species that belong to different genera. The objective of the present study was to reassess the taxonomy of the genus Arcobacter using information derived from the core genome (286 genes), a Multilocus Sequence Analysis (MLSA) with 13 housekeeping genes, as well as different genomic indexes like Average Nucleotide Identity (ANI), in silico DNA–DNA hybridization (isDDH), Average Amino-acid Identity (AAI), Percentage of Conserved Proteins (POCPs), and Relative Synonymous Codon Usage (RSCU). The study included a total of 39 strains that represent all the 27 species included in the genus Arcobacter together with 13 strains that are potentially new species, and the analysis of 57 genomes. The different phylogenetic analyses showed that the Arcobacter species grouped into four clusters. In addition, A. lekithochrous and the candidatus species ‘A. aquaticus’ appeared, as did A. nitrofigilis, the type species of the genus, in separate branches. Furthermore, the genomic indices ANI and isDDH not only confirmed that all the species were well-defined, but also the coherence of the clusters. The AAI and POCP values showed intra-cluster ranges above the respective cut-off values of 60% and 50% described for species belonging to the same genus. Phenotypic analysis showed that certain test combinations could allow the differentiation of the four clusters and the three orphan species established by the phylogenetic and genomic analyses. The origin of the strains showed that each of the clusters embraced species recovered from a common or related environment. The results obtained enable the division of the current genus Arcobacter in at least seven different genera, for which the names Arcobacter, Aliiarcobacter gen. nov., Pseudoarcobacter gen. nov., Haloarcobacter gen. nov., Malacobacter gen. nov., Poseidonibacter gen. nov., and Candidate ‘Arcomarinus’ gen. nov. are proposed.
Linear approximation of shortest superstrings

We consider the following problem: given a collection of strings <italic>s<subscrpt>1</subscrpt></italic>,…, <italic>s<subscrpt>m</subscrpt></italic>, find the shortest string <italic>s</italic> such that each <italic>s<subscrpt>i</subscrpt></italic> appears as a substring (a consecutive block) of <italic>s</italic>. Although this problem is known to be NP-hard, a simple greedy procedure appears to do quite well and is routinely used in DNA sequencing and data compression practice, namely: repeatedly merge the pair of (distinct) strings with maximum overlap until only one string remains. Let <italic>n</italic> denote the length of the optimal superstring. A common conjecture states that the above greedy procedure produces a superstring of length <italic>O(n)</italic> (in fact, 2<italic>n</italic>), yet the only previous nontrivial bound known for any polynomial-time algorithm is a recent <italic>O(n</italic> log <italic>n</italic>) result. We show that the greedy algorithm does in fact achieve a constant factor approximation, proving an upper bound of 4<italic>n</italic>. Furthermore, we present a simple modified version of the greedy algorithm that we show produces a superstring of length at most 3<italic>n</italic>. We also show the superstring problem to be MAXSNP-hard, which implies that a polynomial-time approximation scheme for this problem is unlikely.
Tagspin: High Accuracy Spatial Calibration of RFID Antennas via Spinning Tags

Recent years have witnessed the advance of RFID-based localization techniques that demonstrate high precision. Many efforts have been made locating RFID tags accurately with a mandatory assumption that the RFID reader's position is known in advance. Unfortunately, calibrating reader's location manually is always time-consuming and laborious in practice. In this paper, we present Tagspin, an approach using COTS tags to pinpoint the reader (antenna) quickly and easily with high accuracy. Tagspin enables each tag to emulate a circular antenna array by uniformly spinning on the edge of a rotating disk. We design an SAR-based method for estimating the angle spectrum of the target reader. Compared to previous AoA-based techniques, we employ an enhanced power profile modeling the relative signal power received from the reader along different spatial directions, which is more accurate and immune to ambient noise as well as measurement errors caused by hardware characteristics. Besides, we find that tag's phase measurements in practice are related to its <italic>orientation</italic>. To the best of our knowledge, we are the first to point out this fact and quantify the relationship between them. By calibrating the phase shifts caused by orientation, the positioning accuracy can be improved by <inline-formula><tex-math notation="LaTeX">$3.7\times$</tex-math><alternatives> <inline-graphic xlink:href="duan-ieq1-2796092.gif"/></alternatives></inline-formula>. We have implemented Tagspin with COTS RFID devices and evaluated it extensively. Experimental results show that Tagspin achieves mean accuracy of <inline-formula><tex-math notation="LaTeX">$7.3\; \mathrm{cm}$</tex-math><alternatives> <inline-graphic xlink:href="duan-ieq2-2796092.gif"/></alternatives></inline-formula> with standard deviation of <inline-formula><tex-math notation="LaTeX">$1.8\; \mathrm{cm}$</tex-math><alternatives> <inline-graphic xlink:href="duan-ieq3-2796092.gif"/></alternatives></inline-formula> in 3D space.
SysMART Indoor Services: A System of Smart and Connected Supermarkets

Smart gadgets are being embedded almost in every aspect of our lives. From smart cities to smart watches, modern industries are increasingly supporting the Internet-of-Things (IoT). SysMART aims at making supermarkets smart, productive, and with a touch of modern lifestyle. While similar implementations to improve the shopping experience exists, they tend mainly to replace the shopping activity at the store with online shopping. Although online shopping reduces time and effort, it deprives customers from enjoying the experience. SysMART relies on cutting-edge devices and technology to simplify and reduce the time required during grocery shopping inside the supermarket. In addition, the system monitors and maintains perishable products in good condition suitable for human consumption. SysMART is built using state-of-the-art technologies that support rapid prototyping and precision data acquisition. The selected development environment is Lab Viewwith its world-class interfacing libraries. The paper comprises a detailed system description, development strategy, interface design, software engineering, and a thorough analysis and evaluation.