Miracle-dz/UniFAR-FTUs huggingface dataset:
MRAC-MU Online Learning

In this paper, we apply the method of control theory to machine learning, proposing a new multiplication update algorithm combined with adaptive control theory, we name it MRAC-MU algorithm. A new parameter updating law is obtained according to Lyapunov stability theorem. Using the same object function as the exponential gradient (EG) algorithm, which is the key online learning method to multiplicative updates algorithm, Experiments are used to validate the proposed algorithm has a better result than EG algorithm in prediction accuracy.
Crosstalk Noise Reduction Through Adaptive Power Control in Inter/Intra-Chip Optical Networks

In recent years, optical interconnection networks have been proposed in order to achieve the ultrahigh bandwidth and low latency requirements for inter/intra-chip communication. In these optical interconection networks, series of basic optical elements are employed. Via these series of optical elements, the intrinsic crosstalk noise is generated. With a large scale of these optical elements, the signal-to-noise ratio (SNR) of an optical interconnect can be reduced by this crosstalk noise. In this paper, we utilize the adaptive power control (APC) to enhance the SNR under the crosstalk noise constraints. APC has been known to save energy and reduce power consumption. We apply this technique in one of the inter/intra-chip optical interconnect called I2CON. A new cluster design, namely the Beam cluster, is also introduced. Results have demonstrated that the APC can help to reduce crosstalk noise, hence, the overall SNR is improved. Comparison results have also indicated the further improvement of SNR in I2CON using Beam cluster when APC is applied.
Multi-Agent Cross-Platform Detection of Meltdown and Spectre Attacks

Modern processors use out-of-order and speculative execution to maximize performance. However, these types of optimization executions may leak users' confidential information to another malicious party through a side channel, as can be seen from the notorious Meltdown and Spectre poC attacks. Although some big companies have released software patches and updates to work around the hardware problems, however, they might inadvertently cause some stability or performance issues, especially for Windows systems. Furthermore, some legacy systems are never going to be patched, such as XP systems. Therefore, we provide a more proactive method, a multi-agent way of detecting Meltdown and Spectre on Windows (including the XP System), Linux and OS X Systems. Experiments show that our detection mechanism is very effective in detecting these attacks for all the major existing operating systems.
Performance Comparison of Multichannel MAC Protocols for Low-power and Lossy Networks: TSCH versus EM-MAC

The Medium Access Control (MAC) protocol proposed by the evolving IEEE 802.15. 4 standard for Low-power and Lossy Networks (LLNs) includes a multichannel mode known as Time-Slotted Channel Hopping (TSCH). TSCH, published in 2015, is part of the most recent version of the IEEE 802.15. 4 standard. In this paper, we present a performance comparison between TSCH (with Orchestra scheduling algorithm) and EM-MAC, a contention-based multichannel MAC protocol for LLNs, in terms of average packet delivery ratio, power consumption and latency after network start-up phase. Results obtained from experiments on a testbed with Z1 nodes sending traffic to the sink in an environment with negligible external interference, follow the same trend as the ones obtained through a simulation study. TSCH/Orchestra outperforms EM-MAC or performs equally well for all considered loads. A point of attention though for TSCH, is the significant time required by newly joining nodes to associate to the TSCH network and to join the tree created by the IPv6 Routing Protocol for LLNs (RPL).
Design of 28/38 GHz Dual-Band Triangular-Shaped Slot Microstrip Antenna Array for 5G Applications

Nowadays, evolving an advanced technology for the next generation of wireless cellular communication networks has put a great interest for researchers over the world. This aim is to accomplish the growing demand for advanced data rates. The fifth generation (5G) promises more advantages and benefits to the world. It will create an essential difference over 4G. The design of 5G antenna providing broad bandwidth is very important to ensure the performance of 5G networks. In this paper, microstrip antennas array of single, two, four and six elements dual-band (28/38GHz) for 5G applications are discussed. The proposed antennas are printed on 1.575 mm-thick Rogers Duroid 5880 substrate with dielectric constant of εr= 2.2 and loss tangent (tan δ) of 0.0009. They consist of a triangular shaped radiating patch fed by a 50 Ω microstrip line. The antenna bandwidth is furthered by etching the triangular shaped slot on the ground plane. The highest gain is provided by the antenna with six elements array for both frequencies. The maximum gain of 7.47 dBi with return loss of −30.70 dB at 28 GHz and 12.1 dBi with return loss of −34.5 dB at 38 GHz are obtained.
Online Semantic Segmentation and Manipulation of Objects in Task Intelligence for Service Robots

Task Intelligence is the capacity of a robot to learn, reason and execute specific behaviours based on its environment. In this paper, the Task Intelligence problem formulated by the Robot Intelligence and Technology Laboratory at KAIST is researched further: specifically the proposed contribution is a brand new perceptual pipeline in which the recognition, detection, segmentation and grasping of objects is achieved assuming no prior knowledge of the environments arrangement nor the objects appearance. A Convolutional Neural Net (CNN) is used to detect, recognize and semantically label those objects that need to be interacted with. 3D point clouds, corresponding to the objects model, are extracted after several segmentation procedures and registered over time. Dimensional and positional information of the object is acquired. Additional grasping pose data is calculated. All of the collected knowledge is parsed so that the Task Intelligence system is able to deal with previously unknown objects in dynamic environments. This system is formed by an Episodic Memory (Deep-ART), an action sequence generator (FF-planner) and a trajectory warping module for pre-learnt behaviours. The proposed approach has been tested using the Webots simulator.
Wideband 39 GHz Millimeter-Wave Channel Measurements under Diversified Vegetation

This paper presents a measurement-based analysis of 39 GHz millimeter wave (mm-wave) radio propagation through vegetation using an ultra-wideband (UWB) channel sounder. The measurement campaign was conducted in a suburban area on campus of Tongji University, Shanghai, China. Two different kinds of measurements were performed to investigate the foliage influence on the characteristics of radio propagation in the mm-wave bands. A direction-scan sounding (DSS) approach was used in the measurements aiming to explore the vegetation attenuation as well as dispersive characteristics of the propagation channel. Approximately 6.7 to 8.7 dB single-tree attenuation and 1.86 to 2.23 dB/m vegetation attenuation were observed at the so-called boresight aligned direction (BAD) of a transmitter (Tx) and a receiver (Rx). The presence of vegetation leads to dispersion in delay and angular domains. Both delay spread and azimuth of arrival spread were proportional to the number of trees existing between the Tx and the Rx. Measurements with the purpose of investigating the relationship between the foliage loss and Tx/Rx distances away from trees were also conducted.
A Convolution Neural Network-Based Speckle Tracking Method for Ultrasound Elastography

Accurate tracking of tissue motion is critically important for several ultrasound elastography methods including strain elastography and shear wave elastography. In this study, we investigate the feasibility of using a convolution neural network (CNN)-based speckle tracking method for elastographic applications. Additional data sets produced by finite element simulations were used to train an existing Flownet 2.0 model. After training, the improved network was evaluated using computer-simulated and tissue-mimicking phantoms, and in vivo breast ultrasound data. Our preliminary results were compared to a published 2D high-quality speckle tracking method by our group. Our preliminary results showed that the improved CNN outperformed the original Flownet 2.0. More specifically, in a tissue-mimicking phantom and one set of in vivo breast ultrasound data, the proposed CNN-based method achieved higher contrast-to-noise ratios (0.64 vs 1.05 and 1.16 vs. 1.40, respectively), as compared with the original Flownet 2.0 model. However, the improved CNN was still inferior to the coupled tracking algorithm. Currently, the inference process after the training of the proposed CNN can achieve approximately 60 frames/second for 2D speckle tracking under the NVIDIA TensorRT™ framework. Overall, we conclude that applying the proposed CNN-based speckle tracking method is feasible and good-quality strain elastography data can be obtained in TM phantoms and in vivo breast data. Our future work includes applying this technique to in vivo 3D whole breast ultrasound data.
Optimization of Antenna Beamwidth under Blockage Impact in Millimeter-Wave Bands

In this paper, beam expansion and optimization approach is proposed as a solution to alleviate blockage impact at millimeter wave (mm-wave) frequency bands. Directional communication in mm-wave bands provides opportunities to combat with high-level propagation loss. However, initiating communication over a certain direction makes the link vulnerable against blockage impact. In this study, an investigation on antenna directionality is conducted through indoor measurements and statistical analysis to elaborate directional link performance under blockage effect. Measurements are conducted at 60 GHz frequency band by using 15° half power beamwidth (HPBW) antennas at transmitter (TX) and receiver (RX). Beam expansion approach is evaluated by power azimuth spectrum (PAS) analysis. The proposed approach is also statistically analyzed using mm-wave channel simulator. It is shown that the beam optimization approach provides up to 13 dB gain (2 dB in average) on the received signal strength (RSS).
A Flexible Virtual Capacitance Control Strategy for DC Micro-Grid

Compared with AC grid, DC micro-grid has little inertia. Therefore, when the system suffers from transient disturbance, it is easy to cause DC voltage mutation and even system instability. In the AC system, the inertia can be developed by applying virtual synchronous generator(VSG) control to the converter. A flexible virtual capacitance(FVC) control strategy for DC micro-grid is proposed by analogizing the correspondence of variables between AC and DC systems. Potential “inertia” in DC micro-grid can be developed with the proposed control strategy, and the system stability can be improved by restraining the mutation of DC voltage.
Effect of Downlink Traffic on Performance of LoRaWAN LPWA Networks: Empirical Study

Today the LoRaWAN is among the most widely adopted Low Power Wide Area Network (LPWAN) technologies. In contrast to the other alternatives, LoRaWAN enables deploying both continent-wide public networks, and private networks composing one or several gateways. In this paper, we empirically investigate how does the presence of the downlink traffic affects the performance of uplink for LoRaWAN operating in 868 MHz EU bands. Our results show that in real-life the downlink transmissions can compromise performance of uplink and must be accounted for when planning a network. Also we demonstrate the effects that the selection of secondary frequency channels and data rates have on the performance of a LoRaWAN network. The reported results reveal new dependences and provide the ground truth reference for the future analytical works. For these reasons, the paper can be of interest to both practitioners, planning and deploying LoRaWAN networks, and the researchers performing analytical work on LoRaWAN.
Joint Design of Training and Hardware Towards Efficient and Accuracy-Scalable Neural Network Inference

The intrinsic error tolerance of neural network (NN) presents opportunities for approximate computing techniques to improve the energy efficiency of NN inference. Conventional approximate computing focuses on exploiting the efficiency-accuracy trade-off in existing pre-trained networks, which can lead to suboptimal solutions. In this paper, we first present AxTrain, a hardware-oriented training framework to facilitate approximate computing for NN inference. Specifically, AxTrain leverages the synergy between two orthogonal methods-one actively searches for a network parameters distribution with high error tolerance, and the other passively learns resilient weights by numerically incorporating the noise distributions of the approximate hardware in the forward pass during the training phase. Then, we incorporate AxTrain framework in an accuracy-scalable NN accelerator designed for high energy efficiency. Experimental results from various data sets with different approximation strategies demonstrate AxTrain’s ability to obtain resilient neural network parameters for approximate computing and to improve system energy efficiency. And with AxTrain-guided NN models our proposed accuracy-scalable NN accelerator could achieve significantly higher energy efficiency with limited accuracy degradation under joint approximation techniques.
Assessing Strength of Multi-Infeed LCC-HVDC Systems Using Generalized Short-Circuit Ratio

In this paper, a generalized short-circuit ratio (gSCR) is proposed for assessing the grid strength of multi-infeed LCC-HVDC (MIDC) systems by the theoretical analysis of the relationship between static voltage stability and grid strength. The gSCR can resolve the ambiguity when applying the concept of short-circuit ratio (SCR) for single-infeed LCC-HVDC (SIDC) systems to MIDC systems. Moreover, the gSCR is the generalized presentation of SCR in MIDC systems. Based on the gSCR, our defined critical gSCR (CgSCR) and boundary gSCR (BgSCR) for MIDC systems have the same functions as the critical SCR (CSCR) and boundary SCR (BSCR) used in grid strength analysis for SIDC systems. Also, the CgSCR and BgSCR have the same values as those of CSCR and BSCR (i.e., <inline-formula><tex-math notation="LaTeX">${\text{CgSCR}}= {\text{CSCR}\approx 2}$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">${\text{BgSCR}}= {\text{BSCR}\approx 3}$</tex-math></inline-formula>) to characterize the stability boundaries of MIDC systems. This allows the analysis experience of using the SCR, CSCR, and BSCR for SIDC systems can be easily transplanted to the application of the gSCR, CgSCR, and BgSCR for MIDC systems. The efficacy of the proposed gSCR along with CgSCR and BgSCR is demonstrated by comparing them with other indices of grid strength assessment for MIDC systems.
Experimental Verification of Triple Lobes Generation in Fractional Memristive Circuits

Recently, the triple-lobe behavior is found in the <inline-formula> <tex-math notation="LaTeX">$I$ </tex-math></inline-formula>–<inline-formula> <tex-math notation="LaTeX">$V$ </tex-math></inline-formula> characteristics of some memristive devices generating another non-zero pinchoff point. In this paper, a flux-controlled memristive model is developed to generate the triple-lobe behavior (double pinchoff points) based on a fractional second-order model. The conditions for observing triple lobes are derived besides the coordinates of the pinchoff points. Different scenarios have been considered by changing the model parameters and fractional order. Furthermore, the minimum and maximum achievable conductances are analyzed and mathematically derived. In addition, a floating emulation circuit is introduced and analyzed, which is able to generate the triple-lobe behavior and mimic the dynamics of the proposed fractional modeling equation, showing good matching with the mathematical model. Finally, the obtained triple-lobe behaviors have been verified using experiments with fractional orders: 0.3 and 0.4.
Grid Matching Localization on Evidential SLAM

The self-localization of a vehicle is still an ongoing and challenging task for the autonomous driving development. At the same time, the correct understanding of the vehicle's surroundings and the creation of a map with the traversed trajectory is essential for driving in complex urban scenarios. This paper proposes a solution to create an enriched global map of the environment while localizing the vehicle within it. We use the Evidential framework based on the Dempster-Shafer theory to create a map able to distinguish between static and dynamic obstacles and to keep the information from the entire traversed path. Additionally, we propose a method that at each current map creation estimates the vehicle's position by a grid matching algorithm based on image registration techniques. The method transforms the grid maps into images using the evidential data to extract relevant information to perform a better localization. The proposed method was tested on the KITTI dataset and shows that the solution is able to reduce the drift and improve the localization compared to other methods using similar configurations.
Model-free Approach for Sensor Network Localization with Noisy Distance Measurement

A model-free localization method with noisy distance measurement is proposed for estimating a moving robot in 3D space. Considering that the traditional filter-based sensor network localization algorithms can not provide acceptable estimation accuracy in altitude in 3D space, the proposed method utilizes not only current measurements but also previous measurements to localize a robot. This character adds more constraints to localization to avoid local minimum. In addition, different from the traditional filter-based localization methods which need kinetic model for localization, our proposed method is model-free and converts the localization problem to graph optimization problem. The advantage is that we avoid the possible estimation error caused by inaccurate or simplified kinetic model. Considering that the communication limitation in application makes many graph optimization theories such as distributed localization theory and trilateration difficult to be realized, our method proposes to add constrained equation between adjacent positions to solve this problem. Experiments under a variety of scenarios verify the stability of this method and show that the algorithm achieves better localization accuracy than filter-based methods.
Enhanced Frequency Regulation Using Multilevel Energy Storage in Remote Area Power Supply Systems

Frequency support from renewable power generators is critical requirement to ensure the frequency stability of remote area power supply (RAPS) systems with high penetration of renewable power generation. However, traditional control strategies and the stochastic nature of wind resource constrain wind energy conversion system (WECS) such as permanent magnet synchronous generator (PMSG) from participating in frequency regulation. This work proposes to integrate hybrid energy storage including ultracapacitors (UCs) and lead-acid batteries (LABs) into a PMSG to provide frequency support. The UCs deal with fast changing frequency by emulating conventional inertial response, whereas the LABs mimic automatic governor response (i.e., primary frequency response). The mechanical power reserved in wind turbine using suboptimal maximum power point tracking strategy is utilized to restore system frequency (i.e., secondary frequency response). Moreover, supplementary control strategies are proposed to enable the UCs and LABs to assist primary frequency response and secondary frequency response, respectively. Simulation study and experimental test are carried out to validate the effectiveness of frequency response provided by the multilevel energy storage. The multilevel energy storage solution can effectively regulate RAPS system frequency while avoiding abrupt and frequent charging/discharging of the LABs and significant mechanical/electromagnetic stress on the WECS.
Integrated Deep Model for Face Detection and Landmark Localization From “In The Wild” Images

The tasks of face detection and landmark localisation are a key foundation for many facial analysis applications, while great advancements have been achieved in recent years there are still challenges to increase the precision of face detection. Within this paper, we present our novel method the Integrated Deep Model (IDM), fusing two state-of-the-art deep learning architectures, namely, Faster R-CNN and a stacked hourglass for improved face detection precision and accurate landmark localisation. Integration is achieved through the application of a novel optimisation function and is shown in experimental evaluation to increase accuracy of face detection specifically precision by reducing false positive detection’s by an average of 62%. Our proposed IDM method is evaluated on the Annotated Faces In-The-Wild, Annotated Facial Landmarks In The Wild and the Face Detection Dataset and Benchmark face detection test sets and shows a high level of recall and precision when compared with previously proposed methods. Landmark localisation is evaluated on the Annotated Faces In-The-Wild and 300-W test sets, this specifically focuses on localisation accuracy from detected face bounding boxes when compared with baseline evaluations using ground truth bounding boxes. Our findings highlight only a small 0.005% maximum increase in error which is more profound for the subset of facial landmarks which border the face.
Quantitative Ul Trasound- Based Detection of Cancerous Thyroid Nodules

Thyroid cancer is the most-common endocrine malignancy in the world. The objective of this study was to investigate the suitability of quantitative ultrasound (QUS)for detecting thyroid cancer. Radio-frequency (RF)data were acquired from 53 patients using a GE Logiq E9 research ultrasound system equipped with a lO-MHz, ML6-l5 linear probe. QVS-estimate values, such as effective acoustic concentration (EAC), effective scatter diameter (ESD), and spectral parameters (i.e., midband fit (Mf), slope (SS), and intercept (I0))were derived using a reference-phantom method. Additional QUS estimates (i.e., μ and Ω)were derived from the envelope statistics of a Nakagami distribution. Estimate values were used to train a linear classifier, and performance was assessed using area-under-the-curve (AUC)values obtained from receiver operating characteristic (ROC)analyses based on lO-fold cross validation. Eight patients had pathology-confirmed thyroid cancer. A combination of EAC, I0, and μ produced an AUC value of 0.96. TI-RADS used alone by clinical thyroid sonographic experts produced an AUC of 0.94. A combination of TI-RADS, EAC, andμ produced an AUC value of 0.97. By achieving AUC values of 0.97, our initial results are very encouraging for developing a new objective and non-invasive tool for thyroid-cancer detection. These results suggest that QUS has promising potential to improve non-surgical evaluation of thyroid nodules.