Miracle-dz/UniFAR-FTUs huggingface dataset:
A strategy for fault tolerant control of full-state feedback linearisable nonlinear systems

We present a scheme for fault detection and isolation (FDI) and controller reconfiguration (CR) in full–state feedback linearisable nonlinear systems subject to actuator faults. The nonlinear plant is feedback–linearised and then controlled by a linear controller that satisfies a requirement of set–point tracking. Ultimate bound arguments are used to detect and isolate the healthy or faulty system operation. Then, the virtual actuator methodology is applied to reconFigure the control laws after the occurrence of actuator faults. This proposed scheme is illustrated by an example of the Chua circuit.
Assessing Data Traffic Classification to Priority Access for Wireless Healthcare Application

New healthcare applications rely on wearable devices to collect and send patient's physiological and biomedical information to cloud servers, which executes most of the data processing and analysis. However, healthcare applications have strict requirements of packet delivery and traffic latency to ensure accurate information for medical/healthcare purposes. In this paper, we introduce a device management system, called MAESTRO, to improve the Quality of Service (QoS) for healthcare applications. The MAESTRO system combines a machine-learning traffic classification with a prioritization algorithm to provide a required transmission priority for physiological data. We set up the machine learning module in the R language, using the algorithms in the caret package. We implemented and simulated the prioritization algorithm in NS-3, in a scenario where wearable medical devices share network access with generic stations. Results confirmed the machine learning module achieved 91.5% of accuracy when identifying the physiological data and assigning the expected priority. Further, MAESTRO reached 60% of improvement in the packet delivery ratio for physiological data, in a scenario with a variable number of devices and stations.
Continuously Tunable True-Time-Delay Phase Shifter Based on Transmission Lines With Simultaneously Reconfigurable Impedance and Phase Constant

This article reports on the design of continuously tunable true-time-delay (TTD) phase shifter using transmission lines with simultaneously reconfigurable impedance and phase constant. The reconfigurable transmission lines are designed with an independent control of equivalent series inductance and capacitance, which thus can achieve tunable phase constant (i.e., phase shift) while maintaining constant characteristic impedance. With the perfect matching property for all tuning states of phase constant, the proposed reconfigurable transmission-line units are cascaded to construct a tunable TTD phase shifter. For the proof-of-concept demonstration, three TTD phase shifters based on the proposed reconfigurable transmission lines with phase shifts of 45°, 90°, and 180° at center frequency of 1 GHz (<inline-formula> <tex-math notation="LaTeX">$f_{0}$ </tex-math></inline-formula>) are designed and fabricated. The measured 2-unit phase shifter is tuned from −128° to −175° at <inline-formula> <tex-math notation="LaTeX">$f_{0}$ </tex-math></inline-formula> while the insertion loss is better than 0.8 dB. The return loss is better than 15 dB from dc to <inline-formula> <tex-math notation="LaTeX">$1. 28f_{0}$ </tex-math></inline-formula>. For the 4-unit phase shifter, the phase shift tuning range is 93° at <inline-formula> <tex-math notation="LaTeX">$f_{0}$ </tex-math></inline-formula> with insertion loss better than 1.4 dB. The return loss is better than 15 dB from dc to <inline-formula> <tex-math notation="LaTeX">$1.03f_{0}$ </tex-math></inline-formula> for all states. The 8-unit phase shifter shows a measured tuning range of 183° at <inline-formula> <tex-math notation="LaTeX">$f_{0}$ </tex-math></inline-formula> with insertion loss better than 2.0 dB. The return loss is better than 15 dB from dc to <inline-formula> <tex-math notation="LaTeX">$1.03~ f_{0}$ </tex-math></inline-formula> for all states. The measured continuously tunable delay time is 507 ps. The measured 1-dB compression point is better than 12 dBm at <inline-formula> <tex-math notation="LaTeX">$f_{0}$ </tex-math></inline-formula> for all states. As compared with the state-of-the-arts, the main merits of the proposed phase shifter are large phase shift/unit and wide matching bandwidth.
Artificial-Noise-Aided Robust Beamforming for MISOME Wiretap Channels with Security QoS

This paper studies secure communication from a multi-antenna transmitter to a single-antenna receiver in the presence of multiple multi-antenna eavesdroppers, considering constraints of security quality of service (QoS), i.e., minimum allowable signal-to-interference-and-noise ratio (SINR) at receiver and maximum tolerable SINR at eavesdroppers. The robust joint optimal beamforming (RJOBF) of secret signal and artificial noise (AN) is designed to minimize transmit power while estimation errors of channel state information (CSI) for wiretap channels are taken into consideration. The formulated design problem is shown to be nonconvex and we transfer it into linear matrix inequalities (LMIs) along with semidefinite relaxation (SDR) technique. The simulation results illustrate that our proposed RJOBF is efficient for power saving in security communication.
Power Delivery Network (PDN) Modeling for Backside-PDN Configurations With Buried Power Rails and $\mu$ TSVs

In this article, a power delivery network (PDN) modeling framework for backside-PDN configurations is presented. A backside-PDN configuration contains dense microthrough silicon vias ( $\mu $ TSVs) and power/ground metal stack on the backside of the die. This approach separates the PDN from a conventional signaling network of the back-end-of-the-line (BEOL) and improves power integrity and core utilization. We benchmark this technology with conventional front-side BEOL PDN configurations. Owing to the lower resistivity compared with Cu metal lines for advanced technology nodes, we use ruthenium (Ru)-based buried power rail for PDN modeling. Our analysis shows that the steady-state IR-drop reduces by more than $4\times $ in the backside-PDN configuration, and a simultaneous switching noise analysis shows a significant reduction in transient droops. The framework results are validated with a place-and-route (P&R)-based physical implementation flow. We quantify the area improvement in the actual flow and observe 25%–30% improvement in the backside-PDN configuration. From a PDN modeling framework, the PDN results follow a trend similar to the ones obtained from the block-level P&R of the given configurations. Moreover, we investigate the impacts of package-to-die interconnect pitch, metal–insulator–metal cap density, and input pulse on the PDN performance. In addition, we perform thermal modeling to analyze the thermal implications of the backside-PDN configuration. From a thermal modeling perspective, there is negligible influence from a dielectric bonding layer in the backside-PDN configuration.
End-to-End Blood Pressure Prediction via Fully Convolutional Networks

Cardiovascular disease is the leading cause of death in the world. It is vital to prevent it by rapid diagnosis and appropriate management through periodic blood pressure (BP) measurement. Recently, many studies have been conducted on methods to measure BP without a cuff. One of the most common methods of predicting BP without a cuff is to use the correlation between pulse wave velocity (PWV) and BP. Studies that predict BP through PWV have two problems to overcome: 1) Additional efforts are required to extract PWV features manually from various biomedical signals, such as electrocardiogram (ECG) and photoplethysmogram (PPG); and 2) in predicting BP using biomedical signals from other people, individual periodic calibration is required because the correlation between PWV and BP differs from person to person. In this study, we proposed a cuffless BP prediction method based on a deep convolutional neural network (CNN) that can overcome the problems mentioned above. The proposed CNN method 1) can use raw signals for training without PWV feature extraction; and 2) automatically learns the characteristics of biomedical signals from other people to predict BP accurately without calibration. We propose two schemes: extraction through multiple dilated convolution, and concentration through strided convolution with a large kernel, to process sequential ECG and PPG signals through CNN. BP prediction performance was the best when both ECG and PPG signals were used together. To this end, we conducted extensive experiments on the different settings of the proposed method and constructed an effective learning model. The proposed method achieved excellent performance in predicting both systolic blood pressure and diastolic blood pressure over other known approaches. We also verified that the performance of our method fulfills international standard protocols, AAMI, and BHS.
Investigating Cross-Repository Socially Connected Teams on GitHub

Teamwork is very important to software development. There are many studies focusing on different aspects of teamwork in open source software projects, but neglecting the fact that most teams of open source projects are temporary and dependent on the context of one specific project. Whether the collaboration of such teams can extend to different projects is highly doubted. In contrast, we are interested in long-lasting socially connected teams, whose members have steady social connections and have collaborated with each other on multiple projects. Therefore, we mine Cross-Repository Socially Connected (CRSC) teams on GitHub, the largest open-source project hosting platform. Community detection methods are used to mine CRSC teams from the developer network and more than 20,000 CRSC teams are discovered on GitHub. The productivity of such teams and how the hosting repository may influence them are studied. Their preferences for repositories are investigated. Moreover, we study the structures of these teams using complex network analysis methods. Our results indicate that CRSC teams are stable, highly productive and mature. Therefore, open-source project owners and recruiters can pay more attention to such teams.
A 0.58-to-0.9-V Input 0.53-V Output 2.4- $\mu$ W Current-Feedback Low-Dropout Regulator With 99.8% Current Efficiency

This letter presents an output capacitor-less low-dropout regulator (LDO) topology that can operate from 0.58-to-0.9-V supply, and has a minimum dropout voltage of 50 mV. Compared with traditional analog LDOs, the proposed design incorporates a current reference into the regulator loop and uses current feedback to alleviate the design constraints caused by the limited voltage headroom. The LDO is implemented in a 0.13-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> standard-threshold-voltage CMOS process. Measurement results show that depending on the supply voltage the quiescent power consumption is from 2.4 to 3.6 <inline-formula> <tex-math notation="LaTeX">$\mu \text{W}$ </tex-math></inline-formula>, and the current efficiency is 99.8%. The mean value of the output voltage of 16 samples is 0.53 V and the standard deviation is around 4 mV. The load current range of the proposed LDO is from 0 to 3 mA, and it is capable of driving a load capacitor of up to 120 pF.
Face Recognition in Complex Lighting Environment

In view of the non-linear change of face image caused by illumination, the recognition rate is reduced and the ability of single feature expression is limited. Based on local binary mode (LBP) and Gabor wavelet, we propose a face recognition method. By extracting these two local features which are robust to illumination, we use generalized discriminant analysis (GDA) to reduce the dimension and then use discriminant correlation analysis (DCA) to fuse the features. Experiments on the ORL, Extended YaleB, OFD, and CAS-PEAL face databases demonstrate that the proposed method works better than LBP or Gabor alone.
Analysis and Comparison of Several Mitigation Techniques for Middleton Class-A Noise

Impulsive noise is a common impediment in many wireless, power line communication (PLC), and smart grid communication systems that prevents the system from achieving error-free transmission. In this paper, we compare and analyze several impulsive noise mitigation techniques for Middleton class-A noise considering single carrier modulation with low-density parity-check (LDPC) coded transmission. For this, we investigate the widely used non-linear methods such as clipping, blanking, and combined clipping/blanking to mitigate the noxious effects of impulsive noise. Although, the performance of these techniques are widely acknowledged for simple Bernoulli-Gaussian impulsive noise mitigation in case of orthogonal frequency division multiplexing (OFDM)-based multi-carrier communication systems, their mitigation capabilities in regards to Middleton class-A noise remains unknown. We further investigate the log-likelihood ratio (LLR)-based impulsive noise mitigation. Simulation results are provided to highlight the robustness of the LLR-based mitigation scheme over simple clipping/blanking schemes for the considered scenario. Moreover, our results show that while clipping performs better than blanking for Bernoulli-Gaussian noise, the later shows better performance in case of Middleton class-A noise.
Efficient Allocation of Mobile Resources using Fuzzy Systems for a QoS Planning

With mobile data growth and rapid urbanization trends, there will be an extremely high density of wireless communication links in cities, therefore users expect an environment where they can access the Internet to their devices anytime, anywhere. Due to CAPEX/OPEX problems, a small cell deployment is not an economic strategy in scenarios of intense data traffic, so the use of Unmanned Aerial Vehicles (UAVs) to improve network coverage and performance becomes feasible. In order to improve the Quality of Service of the network, a computational system was proposed to perform decision making that receives as input network information such as network throughput, packet loss rate, and delay, and returns the network quality for a specific type of network application. The system as a whole verifies the necessity or not of using UAVs to improve the quality of the network and the coverage of the area of highest demand. If UAVs are required, they are shifted to these areas. Otherwise, the UAVs remain in the base station. Through the proposed method there were significant improvements in the Quality of Service (QoS) of the network, allowing a reduction in packet loss ratio and delay, and an increase in the network throughput rate.
A Method for Extracting Topological Features of Internet Testbeds Oriented to Equivalent Deduction

A critical task in the topological construction of Internet testbeds is to create a cost function consisting of a finite subset of non-trivial topological features. Beyond the traditional creation methods of cost functions based on subjective preference, this paper aims at a specific task, namely multicast routing test, and adopts the equivalent deduction of delay and cost indexes as the standard for extracting topological features. The equivalent deduction means that the results of indexes on small-scale simulated testbeds are consistent with those on large-scale realistic Internet, which is the original intention of the testbed construction. Due to the non-trivial property, this paper only selects twelve commonly-used topological features and studies a ranking method of these features from spatial recognition ability and evolutionary stability. The ranking method presents a basic framework for the topological feature extraction oriented to the equivalent deduction.
Fabrication of Nanopores Using Controlled Dielectric Breakdown

Nanopore manufacturing technology is related to the research process of nanopore detection technology. However, with many traditional methods including FIB, it is still a great challenge to obtain small-scale nanopore with a diameter of about 2 nm. In this work, nanopores of 5 nm or less were fabricated on SiNx membranes using pulsed voltage or constant voltage by controlled dielectric breakdown. The Labview program is designed to judge the fabrication of the nanopore and automatically cut off the voltage in time to limit the nanopore to a small scale. We measured the ion current through the nanopore using a patch clamp amplifier, and plotted the I-V image. Then the diameters of the nanopores were roughly calculated by the formula. Here, it was found that it is easier to fabricate small-scale nanopores using a constant voltage than a pulsed voltage. Probably because the nanopore is fabricated and enlarged in a very short time at the pulse voltage. Therefore, this nanopore manufacturing method has the advantages of fast and high efficiency, low cost, and the achievement of small-scale nanopore.
A Greedy Approach for TDMA Based on Matrix Operation

BSP (Broadcast Scheduling Problem) is a NP-complete problem with ad hoc, and TDMA is a widely used conflict-free scheduling scheme. To reduce the running time of TDMA scheduling for ad hoc, a greedy algorithm based on matrix operation and SVC (Sequential Vertex Coloring) is proposed. Both the minimizing frame length and maximizing channel utilization stages of the algorithm, the running speed is improved by matrix operation. The slot allocation scheme is obtained by the forbidden slot matrix T OR compatibility matrix D. The simulation results show that the running time of this algorithm is significantly lower than that of traditional SVC-based TDMA scheduling algorithm.
Reducing Patient Waiting Time for Radiotherapy Treatments with a Genetic Algorithm

Health care systems have been challenged in recent years to deliver high quality care. One of the reasons is due to limited resources. How to efficiently utilize limited resources has been and still is a research problem. A particular case is radiotherapy patient scheduling in oncology departments. The As Soon As Possible (ASAP) and Just-in-time(JIT) algorithms are commonly used scheduling approaches. However, with these approaches, it is not difficult to see that a long treatment process with an early release time will occupy most of early available time slots and thus cause short treatment processes with slightly late release times being blocked. In this paper, we apply a genetic algorithm to optimize the patient scheduling orders and explore if the patients total waiting time can be reduced. Our experimental studies indicate that using a genetic algorithm to determine a scheduling order can reduce patient total waiting time.
The Optimal Placement for Caching UAV-assisted Mobile Relay Communication

Recently, unmanned aerial vehicles (UAVs)-assisted communication networks have been widely concerned in terrestrial and maritime communications for UAV high mobility. However, capacity-limited wireless backhaul links connected UAVs with the base stations degrade the transmission rate of the links from UAVs to the ground users. In this paper, we focus on a half-duplex decode-and-forward cache-enabled UAV relaying communication system in downlink scenario and obtain a semi closed-form of the optimal UAV placement by maximizing the system average achievable rate. Numerical results validate the accuracy of our theoretical analysis and show the significant effects of caching on optimal UAV placement and the system average achievable rate.
A 1-V 8.1- $\mu$ W PPG-Recording Front-End With > 92-dB DR Using Light-to-Digital Conversion With Signal-Aware DC Subtraction and Ambient Light Removal

This letter reports on a photoplethysmogram (PPG) readout IC (ROIC), comprising an ultralow-power recording front-end and a 2-channel light-emitting diode (LED) driver. The front-end uniquely employs a continuous-time, first-order, incremental delta–sigma modulator (I–<inline-formula> <tex-math notation="LaTeX">$\Delta \Sigma \text{M}$ </tex-math></inline-formula>) for direct light-to-digital conversion in a single stage with duty-cycled operation for power savings. Moreover, the I–<inline-formula> <tex-math notation="LaTeX">$\Delta \Sigma \text{M}$ </tex-math></inline-formula> enables reconfigurable operation in intermittent conversion phases that allows the direct measurement of dc and ambient light-induced components of the photodiode current for signal-aware on-the-fly cancellation to recover the small ac PPG signal. Fabricated in 0.18-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> 1P/6M CMOS, the PPG ROIC features a high dynamic range of 92.7 dB, dissipates 8.1 <inline-formula> <tex-math notation="LaTeX">$\mu \text{W}$ </tex-math></inline-formula> and 1.95 mW in the recording front-end and the LED (with its driver), respectively, with ~10% duty cycling, and is validated by high-fidelity PPG recording from a human subject fingertip using an infrared LED.
Network Coded Feedback-Free Relay Communications over Lossy Coastal Channels

Wtreless coverage in the coastal area will play an important role in the forthcoming space-air-ground integrated networks. Over the long-distance and highly-dynamic coastal channels, the communication can be lossy and much delayed. This paper exploits the fountain erasure-correction property of network coding (NC) to achieve feedback-free relay communications in such a lossy environment. Since the coding coefficient overhead of NC may overwhelm the performance gain, a problem is formulated to optimize the related coding parameters to achieve the fastest completion of a message. We adopt the random linear network coding (RLNC) and one of its sparse variants. The completion times of the schemes are analyzed using a Markov chain model, which relates the time to the key NC parameters. Under the packet size constraint, we devise an efficient algorithm to search for the optimal NC parameters. Based on the algorithm, the finite blocklength coding rates are employed to further jointly optimize the NC and the physical (PHY) layers. Numerical and simulation results show that sparse RLNC can be more beneficial. In addition, the cross-layer design can reveal when NC is inefficient due to carrying coding coefficients. In this case, end-to-end coding would be more efficient.
Automatic Generation of Lymphoma Post-Treatment PETs using Conditional-GANs

Positron emission tomography (PET) imaging is a nuclear medicine functional imaging technique and as such it is expensive to perform and subjects the human body to radiation. Therefore, it would be ideal to find a technique that could allow for these images to be generated automatically. This generation can be done using deep learning techniques, more specifically with generative adversarial networks. As far as we are aware there have been no attempts at PET-to-PET generation to date. The objective of this article is to develop a generative adversarial network capable of generating after-treatment PET images from pre-treatment PET images. In order to develop this model, PET scans, originally in 3D, were converted to 2D images. Two methods were used, hand picking each slice and maximum intensity projection. After extracting the slices, several image co-registration techniques were applied in order to find which one would produce the best results according to two metrics, peak signal-to-noise ratio and structural similarity index. They achieved results of 18.8 and 0.856, respectively, using data from 90 patients with Hodgkin's Lymphoma.