Miracle-dz/UniFAR-FTUs huggingface dataset:
The effect of boundary conditions on the efficiency of heat or contaminant removal from a ventilated room

This paper presents numerical simulations of the convection and diffusion of heat or a contaminant released from a source in a ventilated two-dimensional room with one inlet and one or two outlets. Besides the influential factors on the air flow characteristic such as the Reynolds number ( Re ) and Grashof number ( Gr ) as well as the geometrical arrangement of the inlet and outlets, the effect of imposing boundary conditions particularly the condition at the outlet upon the air flow is studied numerically in this paper. By imposing an adequate condition for heat/contaminant on the outlet, the steady and near steady solutions of the problem are firstly computed. These solutions allow to evaluate the removal efficiency as well as show the different influence of the zero-value condition from that of the zero-flux one on the air flow characteristic. As shown in this paper, these two boundary conditions have quite different effects on the heat/mass transfer capability of the air flow.
Three-dimensional Visualization Technology for Ultrasonic Detection of Partial Discharges in Power Transformers

Partial discharges are crucial sources for insulation deterioration in power transformers, and precise detections of partial discharge is of great significance for transformer condition monitoring. Recently, visualized ultrasonic detection technology has been increasingly used as a live detection method for locating partial discharges inside transformer. It has the advantages of strong penetrating ability and fast response, but still has the problems of poor detection sensitivity and low positioning accuracy when the atmosphere noises are high. Further, the sonography indicating the region of partial discharges measured by visualized ultrasonic technology is only the two-dimensional projection of the region of interest, and the precise spatial positioning of the partial discharge cannot be evaluated. This paper proposes an improved visualized ultrasonic technology for panoramic visualization of partial discharge voiceprint on power transformers. The refined power transformer model is established through 3-D laser scanning and virtual reconstruction technology. Ultrasonic sensors with center frequency of 100 kHz were used to collect ultrasonic signals presented on the surface of power transformer. A self-developed data extraction algorithm was used to obtain ultrasonic signal digital matrix from the original collected data. In the following, 2D to 3D geometric transformation is applied to perform digital ultrasonic data on the meshed grid of transformer model. Finally, the ultrasound image on the entire hologram can be displayed holographically, which can be used to indicate partial discharge defects that occur inside the transformer in the 3-D space. Experimental verification on a converter transformer with a capacity of 382MVA confirmed that using this new visualization method, the location of the ultrasonic hot spots has a higher accuracy.
Verification of Error-Increasing Factors by Sensor Response-Based Localization Technology Through Real Device Experiments

A sensor response-based localization technology that uses the non-geotagged responses of environmental object detection sensors to estimate the positions of sensors in a defined observation area was proposed in a previous study. That method is based on the concept that the proximity relationships among sensors obtained from the closeness of the sensor response times or collected data similarities. However, since that study was limited to simulation-based experiments, this study extends the scheme to experiments involving real devices, simulated environments, and ideal environments to ascertain the accuracy of localization estimates produced using real devices. We also aimed at extracting the problems that must be resolved before the proposed technology can be put into practical use. Even if clock synchronization between the sensors was impossible, the proximity relationships among them could be estimated using before-after detection time relationships, thus confirming that sensor locations could be estimated based on these proximity relationships. In a real device experiment using 20 sensors indoors at 20.25 $m^{2}$ , we confirmed that the average localization error was 0.84 m. Based on those results and other findings resulting from this study, we determined that the primary factor related to increased sensor localization errors was the number of incorrectly constructed sensor pairs. The results of this study are expected to facilitate the realization of physical world sensing using sensor response-based localization technology.
Performance of PON Dynamic Bandwidth Allocation Algorithm for Meeting xHaul Transport Requirements

Passive optical networks (PONs) use dynamic bandwidth allocation (DBA) algorithms to assign upstream bandwidth for different types of traffic to transmission containers (T-CONTs) used by optical network units (ONUs). In this paper, we show that the performance of a PON DBA algorithm that incorporates a colorless grant can ensure that the average upstream delay and delay distribution of T-CONT frames fall within the latency requirements for different traffic types in an xHaul transport network. We evaluate the performance of two deployment scenarios for a 10-Gigabit-capable PON (XGS-PON) system using the OMNeT++ network simulator. The simulation results show that, when the delay distribution of frames is analyzed, more than 99% of fronthaul traffic at 80% load in both scenarios are below the 250 µs latency requirement. Overall, the results indicate that the latency requirements of fronthaul traffic can be met by over-allocating bandwidth to it relative to other types of traffic in the network.
Pharmaceutical Blister Package Identification Based on Induced Deep Learning

Prescription dispensing accuracy is of paramount importance for all hospitals. However, human errors are inevitable due to multiple reasons, such as fatigue, stress, heavy workload, lack of effective verification measures, mismanagement. Such human errors pose serious safety and health concerns on the part of patients and may as well lead to a series of medical disputes. Based on induced deep learning, this paper proposes a real-time Blister Package Identification System (BPIS) to assist pharmacists’ drug verification and dispensing. Under the guidance of the induction strategy, image preprocessing is introduced to form a standardized image containing the front and back side of the blister package, which is subsequently sent to CNN-based object identification network for feature extraction and identification. This preprocessing method allows the identification system to promote the deep learning system to focus on feature learning to obtain more information about the appearance of the package ruling out confounding factors such as background noise, size, shape or positioning. In addition, this article collects and establishes an image dataset of adult lozenges. Under this dataset, this paper verifies the enhancement of Induced Deep Learning (IDL) on YOLO v2, ResNet, and SENet. By optimizing the deep learning identification network with the help of the embedded technology and a two-side extraction mechanism, a real-time BPIS is built. Long-term tests in hospitals prove the effectiveness of the proposed system.
Modeling Communication Reliability in LoRa Networks with Device-level Accuracy

Long Range (LoRa) is a low-power wireless communication technology for long-range connectivity, extensively used in the Internet of Things. Several works in the literature have analytically characterized the performance of LoRa networks, with particular focus on scalability and reliability. However, most of the related models are limited, as they cannot account for factors that occur in practice, or make strong assumptions on how devices are deployed in the network. This article proposes an analytical model that describes the delivery ratio in a LoRa network with device-level granularity. Specifically, it considers the impact of several key factors that affect real deployments, including multiple gateways and channel variation. Therefore, the proposed model can effectively evaluate the delivery ratio in realistic network topologies, without any restrictions on device deployment or configuration. It also accurately characterizes the delivery ratio of each device in a network, as demonstrated by extensive simulations in a wide variety of conditions, including diverse networks in terms of node deployment and link-level parameter settings. The proposed model provides a level of detail that is not available in the state of the art, and it matches the simulation results within an error of a few percentage points.
Crossbar-Level Retention Characterization in Analog RRAM Array-Based Computation-in-Memory System

The reliability issues bring great challenges in the performance maintenance of computation-in-memory (CIM), especially based on large-scale resistive random access memory (RRAM) arrays. In this article, we directly characterize the retention of output differential/accumulation current for analog RRAM-based CIM applications. Different from the conventional concerns on the device-level conductance time-dependent fluctuation, this work focuses on the influence of crossbar-level weighted-sum currents on the accuracy loss over time in the general convolutional and fully connected (FC) networks. This is the first Mb-level long-term retention characterization and evaluation in analog RRAM arrays. Comparing with the simulation accuracy based on the short-term device-level test, the computing accuracy values based on crossbar-level characterizations are improved for about 16.8% and 31.3% at 500 and 1000 min at 125 °C and match well with the measured accuracy, indicating that the crossbar-level retention evaluation is more accurate. This work provides new insights for developing RRAM-based CIM systems with excellent reliability.
Comparison of His-Purkinje and Biventricular pacing in patient-specific computer models

Biventricular pacing (BiV) is a conventional form of cardiac resynchronization therapy (CRT) setting. Unfortunately, about 30% of patients undergoing CRT do not show clinical improvements. Direct His-Purkinje system stimulation (HPS) is a promising method to restore the physiological sequence of ventricular activation and synchronize contraction. In this study, we evaluate the effects of HPS on several indices of ventricular activation and electrical dyssynchrony in 27 patientspecific computer models of ventricles utilizing the CT imaging and ECG data from the patients. We compared simulation results of HPS pacing with standard BiV pacing using CT data on the location of ventricular leads (BiV CRT). Moreover, using personalized ventricular models we predicted an optimal position of LV lead in terms of minimization of QRS complex durations (QRSd) and compared outputs of this BiVopt model with other modes of ventricular pacing. We showed that the majority of indices demonstrated that HPS is superior to both BiV pacing.
S, C and Extended L-Band Transmission with Doped Fiber and Distributed Raman Amplification

We transmit 690, near continuous, 25 GHz spaced channels in 138 nm of S, C and extended L-bands over 54 km of standard single-mode fiber. Erbium and Thulium doped-fiber amplifiers with distributed Raman amplification enable a 206.1 Tb/s GMI estimated data-rate.
Instrument Design for Detecting the Inner Damage of Casing

Detecting and localizing the inner damage of the downhole casing is crucial for oil and gas production. The current methods for damage detection suffer from polarization effect, capacitance effect, and difficulty in measuring weak direct current signals. As an alternative, we propose a new instrument design based on a six-electrode array in this study. The basic detection principle is introduced, and the key factors that affect the detection performance are studied through modeling and simulation. Besides, the entire system of the instrument design consisting of several modules is presented. In addition, an experiment is conducted to demonstrate the detection capability of the proposed design. It is found that the detection dynamic of the instrument is 93 dB and a signal-to-noise ratio is 73 dB. It can detect damage when salinity is lower than 120 g/l and can identify the location of damage with high resolution in the longitudinal and radial directions.
Network Intrusion Detection on the IoT Edge Using Adversarial Autoencoders

Network intrusion detection systems have received a lot of attention in the computer security literature. As the number of IoT devices grows exponentially, intrusion detection on the back-end servers or indeed even the fog will become intractable. Consequently, there is a need to move intrusion detection closer to the IoT edge. Doing so will have a significant impact on the network as well as the compute required on the server-side. In this paper, we show how deep learning can be used to build state-of-the intrusion detection algorithms that can be executed on small routers near the IoT edge. Adversarial autoencoders with the K nearest neighbor algorithm were trained on the NSL-KDD intrusion data set to yield state-of-the-art results. The model had an accuracy of 99.991% and an F1-Score of 0.9990. On a Raspberry PI 3B (RPI) device, using TensorFlow Lite, the model achieved an average per-packet latency of less than 16ms which is sufficient for many IoT sensors on the edge giving a worst-case bandwidth of 3kibts/second.
Curing behavior and physical properties of an epoxy nanocomposite with amine-functionalized graphene nanoplatelets

Abstract Amine-functionalized graphene nanoplatelets (AGNPs) were prepared via an easy simple one-step process, treating graphite powder with 4-aminobenzoic acid in polyphosphoric acid, and then the effects of the AGNPs on the curing and physical properties of an epoxy resin were studied. The formation of the AGNPs was confirmed by scanning electronic microscopy (SEM), Fourier transform infrared spectroscopy, and thermogravimetric analyzer. Curing behavior of the epoxy/AGNPs nanocomposite was investigated by differential scanning calorimeter. The AGNPs made the epoxide curing reaction with amine groups slightly faster. The physical properties of the epoxy/AGNPs nanocomposite were investigated by dynamic mechanical analyzer, thermomechanical analyzer, and impact test. The AGNPs improved Tg by 21.4 °C, and storage modulus and impact strength of the epoxy resin 23 and 73%, respectively, much more effective than the graphite powder at the same filler loading of 1 phr. SEM images for the fracture surfaces of the epoxy/AGNPs nanocomposite showed improved interfacial bonding between the epoxy matrix and the nanofillers due to the amine functional groups of the AGNPs.
High-Performance Fiber Optic Interferometric Hydrophone Based on Push–Pull Structure

A high-performance fiber optic interferometric hydrophone based on a push–pull mandrel structure, with high sensitivity and wide-frequency response, is designed and developed. Each arm of the Michelson interferometer is a fiber coil wrapped onto a separate but concentric thin elastic tube as the sensing arm to detect acoustic pressure wave. The fiber optic hydrophone is fabricated and examined experimentally, demonstrating its capacity to detect very low-frequency underwater acoustic pressure variation. The theoretical design is validated experimentally, with results showing that the push–pull fiber optic hydrophone has an average sensitivity of −134.39 dB re rad/<inline-formula> <tex-math notation="LaTeX">$\mu$ </tex-math></inline-formula>Pa within the frequency range of 1 Hz–2 kHz, which is 5.67 dB higher than that of the analogous one-sensing-arm fiber optic interferometric mandrel hydrophone that we made for comparison. The push–pull fiber optic hydrophone has a minimum detectable pressure (MDP) of 25.37 <inline-formula> <tex-math notation="LaTeX">$\mu {\mathrm {Pa}}/\sqrt {{\mathrm {Hz}}}$ </tex-math></inline-formula> at 1 kHz, about 15 dB lower than the deep-sea state zero (DSS0) level. At 10 Hz, the MDP is ~1.71 mPa/<inline-formula> <tex-math notation="LaTeX">$\sqrt {{\mathrm {Hz}}}$ </tex-math></inline-formula>, which is nearly 7 dB lower than the acoustic ambient noise in the ocean at low frequency. The sensitivity and the MDP of the push–pull fiber optic hydrophone have been doubled and improved by about 20% at 1 kHz, respectively, compared to other state-of-the-art flat response fiber optic hydrophones.
Vocal-Accompaniment Compatibility Estimation Using Self-Supervised and Joint-Embedding Techniques

We propose a learning-based method of estimating the compatibility between vocal and accompaniment audio tracks, i.e., how well they go with each other when played simultaneously. This task is challenging because it is difficult to formulate hand-crafted rules or construct a large labeled dataset to perform supervised learning. Our method uses self-supervised and joint-embedding techniques for estimating vocal-accompaniment compatibility. We train vocal and accompaniment encoders to learn a joint-embedding space of vocal and accompaniment tracks, where the embedded feature vectors of a compatible pair of vocal and accompaniment tracks lie close to each other and those of an incompatible pair lie far from each other. To address the lack of large labeled datasets consisting of compatible and incompatible pairs of vocal and accompaniment tracks, we propose generating such a dataset from songs using singing voice separation techniques, with which songs are separated into pairs of vocal and accompaniment tracks, and then original pairs are assumed to be compatible, and other random pairs are not. We achieved this training by constructing a large dataset containing 910,803 songs and evaluated the effectiveness of our method using ranking-based evaluation methods.
A 3.3-4.5GHz Fractional-N Sampling PLL with A Merged Constant Slope DTC and Sampling PD in 40nm CMOS

In this work, we present a novel fractional-N sampling PLL architecture where the functions of a constant slope digital-to-time converter (CS-DTC) and a sampling phase detector (SPD) are merged into one block. This simplifies the PLL's critical phase detection path, saves power, reduces noise sources and improves the overall linearity. The 3.3-4.5GHz PLL implemented in 40nm CMOS achieves a 292fs rms jitter, integrated from 10kHz to 10MHz while consuming 2.4mW, leading to a PLL FoM of - 246.9dB. The in-band fractional spur is −54dBc and the PLL occupies a core area of 0.36mm2.
Online Sequential Task Assignment With Execution Uncertainties for Multiple Robot Manipulators

In order to let multiple robot manipulators cooperatively complete a sequence of tasks in a shared workspace under task execution uncertainty, this letter proposes a multi-robot task allocation framework for constantly assigning tasks to robots, while the interference among concurrent robot motions is account for. An online sequential task assignment method is presented, which decouples the time-extended problem into a sequence of synchronous and asynchronous instantaneous assignment sub-problems. This renders the approach capable of reacting to task execution uncertainties in real-time. A one-step-ahead simulation method is employed to reduce the idle time of robots and improve task completion efficiency. Each instantaneous assignment sub-problem is modeled as an optimal assignment problem with variable utility and solved by a branch-and-bound algorithm, with which multi-robot motion coordination is integrated. Experimental results conducted with three Franka-Emika Panda arms show that these can cooperatively complete all tasks without collision and little waiting time. Simulations with larger multi-robot systems show that the approach scales linearly with the number of robots.
Demixed Sparse Principal Component Analysis Through Hybrid Structural Regularizers

Recently, the sparse representation of multivariate data has gained great popularity in real-world applications like neural activity analysis. Many previous analyses for these data utilize sparse principal component analysis (SPCA) to obtain a sparse representation. However, $\ell _{0}$ -norm based SPCA suffers from non-differentiability and local optimum problems due to non-convex regularization. Additionally, extracting dependencies between task parameters and feature responses is essential for further analysis while SPCA usually generates components without demixing these dependencies. To address these problems, we propose a novel approach, demixed sparse principal component analysis (dSPCA), that relaxes the non-convex constraints into convex regularizers, e.g., $\ell _{1}$ -norm and nuclear norm, and demixes dependencies of feature response on various task parameters by optimizing the loss function with marginalized data. The sparse and demixed components greatly improve the interpretability of the multivariate data. We also develop a parallel proximal algorithm to accelerate the optimization for hybrid regularizers based on our method. We provide theoretical analyses for error bound and convergency. We apply our method on simulated datasets to evaluate its time cost, the ability to explain the demixed information, and the ability to recover sparsity for the reconstructed data. Finally, we successfully separate the neural activity into different task parameters like stimulus or decision, and visualize the demixed components based on the real-world dataset.
An Adjustable-delay SSHC Method for Inductorless Load Regulation for Piezoelectric Energy Harvesting

This paper presents an adjustable-delay SSHC method to regulate the load for piezoelectric energy harvesting. The load regulation usually requires a second-stage independent DC-DC converter. The proposed SSHC circuit intends to address the problem. It adjusts the pulse-width (i.e., the delay in this paper) of the switching pulse signal for the SSHC circuit to regulate the power flowing into the load. The proposed circuit has the capability of load regulation by using only one stage without an inductor. The operation of the proposed circuit and simulation results verified the effectiveness of the proposed method.
Gather Interface for Freezing Pages in Flash Storage

It is usual in many applications that a large fraction of data pages is, once created, actively updated for a while, and then eventually no longer updated. In flash storage, these frozen pages tend to spread over physical flash blocks and thus are repeatedly relocated upon garbage collection, exacerbating the write amplification. To address the problem of scattered frozen pages, we propose the interface, which allows users or applications to inform the flash storage of frozen pages explicitly. Upon receiving the call, FTL clusters and isolates frozen pages into a physically separate space so that they are no longer relocated upon garbage collection. Experimental results confirm that can reduce the write amplification factor from 25% to 40% of the conventional SSDs. The scheme is novel in the way it opens up a model case of physical tuning knobs in flash storage.
Autonomous Aerial Vehicle Vision and Sensor Guided Landing

The use of autonomous landing of aerial vehicles is increasing in demand. Applications of this ability can range from simple drone delivery to unmanned military missions. To be able to land at a spot identified by local information, such as a visual marker, creates an efficient and versatile solution. This allows for a more user/consumer friendly device overall. To achieve this goal the use of computer vision and an array of ranging sensors will be explored. In our approach we utilized an April Tag as our location identifier and point of reference. MATLAB/Simulink interface was used to develop the platform environment.