Miracle-dz/UniFAR-FTUs huggingface dataset:
Investigating ventilation system performance in large space building: A nozzle primary air supply with secondary airflow-relay system

In large space buildings, nozzle is usually used for ventilation and air conditioning. A secondary airflow-relay system is proposed for situations that building span is larger than the primary jet flow range. A field measurement was conducted to investigate the performance of primary airflow nozzle and secondary jet fan system; a computational fluid dynamics model was built up to research the secondary airflow velocity and relative location of primary and secondary terminal on the ventilation performance. The performance of different computational fluid dynamics turbulence models in predicting the primary jet trajectory path was evaluated by comparing predicted result with field measurements. The temperature field predicted by computational fluid dynamics method was also validated by experimentally measured vertical temperature distribution data in certain measurement points. Various indices including Air Diffusion Performance Index, percentage dissatisfied, and coefficient of variation of temperature and velocity at breathing level were used to quantitatively evaluate the ventilation performance of different configurations. It is found that re-normalization group k-ϵ turbulence model gives the best overall prediction on the primary air nozzle jet trajectory path and field temperature distribution. In a certain large space building utilizing secondary airflow system, the relative location of primary and secondary equipment and air velocity should be properly specified in order to improve the ventilation performance of a large space building. The secondary airflow-relay may potentially increase the cooling energy consumption as indicated by computational fluid dynamics result.
RNN with Improved Temporal Modeling for Speech Emotion Recognition

Emotions are a natural part of daily human activities and play a key role in human decision-making, interactions, and cognitive processes. In recent years, an automatic speech emotion recognition has aroused great interest due to its wide potential use in environments where machines need more natural human interaction or monitoring (i.e., detection of stress levels in pilots). In this work, we focused on exploring new ways of temporal modeling of recurrent neural network (RNN) architectures. The proposed solution leverages contextual information from the speech sequence using recurrent model inspired by Siamese RNN. Compared to a baseline LSTM model, the proposed solution achieved a relative improvement of accuracy of approx. 5% on the IEMOCAP database.
Model-Aided Drone Classification Using Convolutional Neural Networks

Classifiers using convolutional neural networks (CNNs) often yield high accuracies on samples that come from the same distribution as the training data. In this study we evaluate a CNN classifier's ability to discriminate drones from non-drone targets, such as birds, when they are not represented in the training data. We found that the mean accuracy on such out-of-distribution drones was 78%. By introducing a synthetic drone class, generated from a mathematical model, the out-of-distribution drone accuracy was improved to 86%. When trained on all drone types the mean accuracy over all classes was 90%. The data was collected with a 77 GHz mechanically scanning radar with only 9 ms dwell time.
Performance of Massive MIMO Systems Considering Three Dimensional Array-based Spatially Correlated Channel

This work deal with the uplink phase of the pilot-based channel estimation (CE) process for a Massive multiple-input multiple-output (M-MIMO) network operating under the time-division duplex protocol and considering spatially correlated channels. Additionally, this work investigates and examines uncorrelated channels and CC. In addition, the channels’s spatial correlation is investigated using two models, namely, the local scattering model (LSM) and the exponential correlation model (ECM), where each model is examined for two array configurations (i.e., uniform linear array (ULA) and uniform planar array (UPA)). Furthermore, we propose a UPA configuration for each model based on the Kronecker product (KP) of the ULA configuration, i.e., we propose the ECM and LSM for the UPA configuration, respectively, based on the KP and using either the ECM or the LSM for the ULA configuration. A comparison and evaluation of these models and their influence on the CE process for a M-MIMO network is analyzed. Numerical results are offred to assert our theoretical expressions.
Recent Progress of Air/Water Cross-Boundary Communications for Underwater Sensor Networks: A Review

The next-generation of wireless network 6G is envisioned to achieve seamless global full coverage, and provide ultra-high-speed and extreme low delay network to connect the Internet of Everything (IoE) on the Earth. To this end, as the space-air-ground-underwater integrated network is a solid foundation of 6G, it is critical to develop reliable and robust cross-boundary communication techniques to connect the sky and the ocean. However, due to the harsh environment-induced channel complexity of different transmission media, penetrating through the air/water interface seamlessly faces significant challenges. In this paper, we present a comprehensive review of recent progress of air/water cross-boundary communications, and group the existing works into three categories, such as 1) optical direct communication, 2) relay-based communication, and 3) non-optical direct communication, and then summarize them respectively. The three types of cross-boundary approaches have their own pros and cons which are suitable for different application scenarios. Among them, the optical direct communication has made tremendous progress recently. However, the volatile wavy surface and depth-temperature-dependent parameters make it a great challenge to build robust communication links. Moreover, the misalignment caused by the dynamics of the channel severely degrades the performance and limits their practical applications. To tackle these challenges, we summarize the complex channel characteristics, analyze the causes of the misalignment and provide potential solutions. In the end of this paper, we also discuss future research directions and hope to inspire expanding research on the air/water cross-boundary communications to further propel towards the space-air-ground-ocean integrated network in 6G.
LTrack: A LoRa-Based Indoor Tracking System for Mobile Robots

The robot’s mobility and intelligence have expanded its application in recent years. Specifically, indoor tracking is a fundamental function of public service robots in nursing homes, hospitals, and warehouses. Existing vision-based tracking requires visual information, which may be unavailable and introduce privacy issues in practical deployment. To this end, in this paper, we propose <inline-formula><tex-math notation="LaTeX">$\mathsf {LTrack}$</tex-math></inline-formula>, a long-range tracking system based on LoRa, an emerging low-power wide-area networking (LPWAN) technology, with a single transceiver pair. Note that commodity LoRa devices cannot estimate the angle of arrival (AoA) of signals due to hardware limitations. We design a virtual circular antenna array in the mobile rotating anchor via a lightweight hardware modification to multiplex the only RF channel in the low-cost LoRa device. The difference of time of flight (TDoF) measured in the circular antenna array is fused with the rotating orientation to estimate the target AoA. We also redesign and optimize the primitive LoRa ranging engine based on systematic analysis. Further, we present a real-time mobile target tracking algorithm based on the Doppler frequency shift to combat the uncertainty introduced by the target movement. We have developed the prototype of <inline-formula><tex-math notation="LaTeX">$\mathsf {LTrack}$</tex-math></inline-formula>, which consists of a mobile rotating anchor, a LoRa tag, and a commercial robot. The system is evaluated in both LOS and NOLS indoor scenarios. Experiments show that <inline-formula><tex-math notation="LaTeX">$\mathsf {LTrack}$</tex-math></inline-formula> supports robust tracking with a median error of 0.12 m and 0.45 m in a <inline-formula><tex-math notation="LaTeX">$\text{137}\,\text{m}^2$</tex-math></inline-formula> lab space and a <inline-formula><tex-math notation="LaTeX">$\text{600}\,\text{m}^2$</tex-math></inline-formula> corridor, respectively.
Broadband Ka-band SPDT switch with 15 GHz bandwidth and 15.4 dBm IP1dB

This paper introduces the use of a pass-gate switch configuration to design and implement a 25-to-40 GHz single-pole double-throw (SPDT) switch in 130 nm BiCMOS technology. Also, biasing pass-gate transistor terminals to 0 V through 20 $\mathbf{K}\Omega$ resistance is introduced to minimize the variation in device parasitics. Performance improvements due to these design techniques have been benchmarked in the EM simulation of the proposed pass-gate switch configuration based differential SPDT switch. This SPDT switch has demonstrated minimum insertion-loss of 1.29 dB, maximum isolation of 41.2 dB and $\mathbf{S}_{11}/\mathbf{S}_{22}$ better than 12 dB across the frequency band of operation. This SPDT switch has also shown input PldB (IP1dB) of 15.4 dBm with a layout area of 0.11 mm2
A Novel Positioning and Orientation System Based on Three-Axis Magnetic Coils

The positioning and orientation system consists of three-axis generating coils and three-axis sensor coils in quasi-static magneticfield. The three-axis generating coils arefi xed orthogonally and excited by the alternating current (AC) signals with different frequencies. They create the magnetic fi eld that is equivalent to that generated by three orthogonal magnetic dipoles. Using the amplitude and phase information of the sensing signals in the sensor coils, the position an d orientation parameters of the sensor coils can be computed by using an appropriate algorithm. In this paper, a n ovel algorithm is proposed to determine the po sition of the sensor coil object by some equa-tions directly, so that its position and orientation parameters can be calculated mucheasier and faster.Basedon this method,asystem with support circuitry is designed with some special signal acquisition and sampling methods. Especially, a signal extraction (function fitting) method is proposed to pick up the coupling AC signal magnitude of the sensor coils, which simpli fi es the hardware circuitry and improves the signal acquisition accuracy. The simulation and real ex perimental results show that the system works satisfactorily with good accuracy. Index Terms—Equation-based algorithm, functionfi tting signal extraction, positioning and orientation, three-axis magnetic dipoles.
Deployable Long Range Passive Radar for Space Surveillance

A deployable long-range passive radar system has been used to observe satellites in low Earth orbit utilising distant transmitters in real time. This paper details the experimental radar's design, including its array, configuration and software. Methods to improve satellite detections and initial orbit determination are also described. The radar is capable of detecting objects the size of small rocket bodies, in real time, utilising a transmitter 300 km away. Additionally, during post-processing, satellites were detected when utilising a separately collected reference, from a transmitter 1,600 km away. Such a system enables passive long-range detection at considerable standoff ranges from transmitters of opportunity.
SAR image formation with embedded QPSK via LFM waveform guardbands

Quadrature phase shift keying (QPSK) modulation is embedded to transmit data to a communications receiver via the guardbands (and perhaps beyond) of a linear frequency modulated (LFM) signal for synthetic aperture radar (SAR). We show that the combined signal sharing the spectrum can be used to form SAR images, i.e., there's no need to filter out the communications signal in the radar receiver. The communications signal itself is used in the image formation. We investigate the effect of parameterizing radar signal to noise power ratio (SNR), radar power to communications power ratio (RCR), and LFM to communications bandwidth ratio (LCBR) to the images. We also look at the effect of shifting the communications carrier away from the LFM spectrum on the SAR image.
New Method to Perform TDDB Tests for Hybrid Bonding Interconnects

Hybrid Bonding (HB) is progressing as the major solution for 3D integrated-circuit with pitch reduction becoming the key. Reliability needs to be studied with HB pitch reduction for possible evaluation of new failure mechanisms and modes. In this paper, we developed a new methodology to study Time-Dependent Dielectric Breakdown (TDDB) at HB level that accounts wafer-to-wafer (W2W) overlay variations. Application of this method to a 1.44 µm-pitch 3D stacked test vehicle demonstrates its accuracy. TDDB at Cu/SiO2 HB interface follows the 1/E model at low electric fields for all studied temperature suggesting that the role of Cu in breakdown is negligible. The acceleration parameter and the activation energy dependence on the electric fields are studied. The cuprous oxide layer which may act as a barrier to Cu diffusion previously highlighted at the Cu/SiO2 HB interface does not exist for Cu/SiN interfaces as evidenced by EELS study. This might explain the difference in the TDDB acceleration models between HB level and BEoL ones.
A New Framework for Multi-Hop ABS-Assisted 5G-Networks With Users’ Mobility Prediction

Unmanned Aerial Vehicles (UAVs) have gained momentum as one of the potential solutions in 5 G networks. Indeed, using UAVs as Aerial Base Stations (ABS) to assist the traditional cellular infrastructure and/or extend its coverage represents a high-potential flexible and cost-effective approach to provide on-demand communications in these new generations of networks. Yet, their effective deployment brings out many challenges, including their optimal 3D placement, coverage optimization, backhaul constraints, etc. Hence, we tackle these challenges by proposing a new framework for 3D positioning of an ABS swarm in order to extend the coverage of ground base stations (GBS). Unlike existing work that ignore ABS’s limited backhaul capacity and how it restraints the number of users that can be served, we consider a multi-hop backhaul scheme that allows ABSs to remain connected to the core network through the GBSs directly, or other deployed ABSs. Under this setup, we aim to maximize the number of served UEs, by optimizing the ABS’s placement, jointly with their association with UEs and backhauling base stations. Besides, our framework, named ASF for Adaptive Social Spider Optimization based framework, uses machine learning techniques to predict the users’ mobility and enable ABSs to adjust their position according to their spatial distribution. Simulations show that ASF framework significantly improves the cellular network’s coverage and provides access to 92% of mobile users, as compared to other benchmark schemes that only serve 79% and 58% of users.
GraFin: An Applicable Graph-based Fingerprinting Approach for Robust Indoor Localization

Wi-Fi fingerprinting using the received signal strength (RSS) of the access point (AP) as a physical signal feature is widely studied in the indoor localization area with various applications. One main problem with fingerprinting based approach is the uncertainty of RSS measurements, which often leads to instability and decline of localization performance. In this work, we propose GraFin, a graph-based fingerprinting approach, to provide accurate and robust indoor localization without tedious site surveys and extra assistant information. The key idea lies in the insight that despite the RSS measurement of one AP at one reference point (RP) can be noisy, the proximity pattern, which describes one AP's relative position to other APs and RPs, is usually more stable. Specifically, GraFin models APs and RPs on a graph based on limited RSS measurements and provides position-aware fingerprints for APs and RPs based on an inductive deep graph model. We evaluate GraFin on a public indoor localization dataset, and the results demonstrate the effectiveness and robustness of our approach. Furthermore, we apply our approach to the arrival-departure time estimation task for instant delivery service. Experiment results on the enterprise dataset from one of the largest instant delivery platforms in China show that GraFin outperforms baseline approaches with significantly lower time estimation error.
Smart Health Technology Model for Adoption of IoT in Automobiles for Driver's Safety

Evolution of transport systems is going side by side with the human history. Nowadays, it has become hard to imagine a life without a vehicle. To accommodate the enormous growth in population, the number of vehicles also have been increased rapidly. Apart from providing convenience, this has also resulted in increased number of road accidents and deaths. This research is focused on adoption of IoT in automobiles for driver's safety to overcome the number of road accidents. This research proposes a smart health technology model that consists of an IoT based steering wheel equipped with the sensors and cameras as well as the architectural description of implementing it. The architectural model provides a communication framework between driver, service provider, patrolling police, ambulance service and the mobile application users in the nearby cars. Authors have used the literature review and expert interviews as the basis of collecting input and applied tool-based data analysis techniques to reach conclusions. Authors have also performed the validation of the model using expert review to ensure that the proposed architectural model is generally acceptable and can provide the reflection of real-world requirements.
Novel methodology for temperature-aware electromigration assessment in on-chip power grid: simulations and experimental validation (Invited)

A novel methodology for electromigration (EM) failure assessment in power/ground nets of integrated circuits, which is based on analysis of IR drop degradation, has been enhanced by considering non-uniform temperature distribution in interconnects. Temperature gradient along an interconnect tree can affect void nucleation due to the divergency of atomic flux, as well as due to development of thermal stress. Compact models for resistance increase of voided metal have been developed, using results of FEM simulations of voiding kinetics in via-metal structures. The simulation approach has been validated using measurements of node voltages in a specially designed test power grid. Increase of anode-cathode voltage drop above a threshold value was adopted as a failure criterion determining time-to-failure (TTF) of the grid and mean-time-to-failure (MTTF) was obtained, assuming random distributions of the critical stress and atomic diffusivity. In the studied cases of temperature distributions, a good agreement of simulated and measured TTF distributions has been obtained.
Automatic earthquake recognition and timing from single traces

A computer program has been developed for the automatic detection and timing of earthquakes on a single seismic trace. The program operates on line and is sufficiently simple that it is expected to work in inexpensive low-power microprocessors in field applications. In tests with analog tapes of earthquakes, the program correctly identified and timed to within 0.05 sec about 70 per cent of the events which would normally be timed in operation of a network. The program evaluates the accuracy of its picks, and its estimates appear to be quite reliable. The algorithm is working at present in a 16-bit minicomputer and appears to be compatible with presently available microprocessors.
Hybridization drives mitochondrial DNA degeneration and metabolic shift in a species with biparental mitochondrial inheritance

Mitochondrial DNA (mtDNA) is a cytoplasmic genome that is essential for respiratory metabolism. While uniparental mtDNA inheritance is most common in animals and plants, distinct mtDNA haplotypes can coexist in a state of heteroplasmy, either because of paternal leakage or de novo mutations. MtDNA integrity and the resolution of heteroplasmy have important implications, notably for mitochondrial genetic disorders, speciation and genome evolution in hybrids. However, the impact of genetic variation on the transition to homoplasmy from initially heteroplasmic backgrounds remains largely unknown. Here, we use Saccharomyces yeasts, fungi with constitutive biparental mtDNA inheritance, to investigate the resolution of mtDNA heteroplasmy in a variety of hybrid genotypes. We previously designed 11 crosses along a gradient of parental evolutionary divergence using undomesticated isolates of Saccharomyces paradoxus and Saccharomyces cerevisiae. Each cross was independently replicated 48 to 96 times, and the resulting 864 hybrids were evolved under relaxed selection for mitochondrial function. Genome sequencing of 446 MA lines revealed extensive mtDNA recombination, but recombination rate was not predicted by parental divergence level. We found a strong positive relationship between parental divergence and the rate of large-scale mtDNA deletions, which lead to the loss of respiratory metabolism. We also uncovered associations between mtDNA recombination, mtDNA deletion, and genome instability that were genotype-specific. Our results show that hybridization in yeast induces mtDNA degeneration through large-scale deletion and loss of function, with deep consequences for mtDNA evolution, metabolism and the emergence of reproductive isolation.
Hippocampal cAMP-dependent synaptic potentiation, ERK-dependent immediate-early gene activation, and context-dependent fear conditioning in mice are linked through dependence on the guanine nucleotide exchange factor RapGEF2

The MAP kinase ERK is important for neuronal plasticity underlying associative learning, yet specific molecular pathways for ERK activation in hippocampus are still largely undetermined. RapGEF2 has emerged as a neuron-specific cAMP sensor that mediates ERK activation. We investigated whether RapGEF2 might also be required for cAMP-dependent ERK activation leading to synaptic potentiation, and how this involvement might be penetrant to hippocampus-dependent learned behavior. We demonstrate that conditional knockout of Rapgef2 in forebrain neurons, specifically in dentate gyrus and CA1 of the hippocampus, leads to an attenuation of context-dependent fear conditioning, but not of cue-dependent fear conditioning, in mice. RapGEF2 knockout is associated with a reduction in cAMP-dependent synaptic potentiation at two central hippocampal synapses-the entorhinal cortex-granule cell synapse and the CA3-CA1 synapse. Furthermore, cAMP-induced postsynaptic potentiation requires both RapGEF2 and activation of ERK. Induction of Egr-1/Zif268 (and pERK), but not of c-Fos, immediately following fear conditioning, was abolished in CA1 and detate gyrus, in the absence of RapGEF2 expression in these hippocampal regions, thus revealing a link between learning (conditioning) and molecular pathways activated during conditioned fear memory formation. Hence, we suggest that contextual fear conditioning is mediated via RapGEF2-dependent ERK activation and downstream induction of Egr-1, via an underlying mechanism of cAMP-dependent long-term potentiation at hippocampal synapses. Cyclic AMP-dependent GEFs have been genetically associated as risk factors for schizophrenia, a disorder associated with cognitive deficits. This study provides a functional link between one of these cAMP-dependent GEFs, RapGEF2, and cognitive processes involved in associative learning.