Miracle-dz/UniFAR-FTUs huggingface dataset:
ECML: Improving Efficiency of Machine Learning in Edge Clouds

Edge cloud data centers (Edge) are deployed to provide responsive services to the end-users. Edge can host more powerful CPUs and DNN accelerators such as GPUs and may be used for offloading tasks from end-user devices that require more significant compute capabilities. But Edge resources may also be limited and must be shared across multiple applications that process requests concurrently from several clients. However, multiplexing GPUs across applications is challenging. With edge cloud servers needing to process a lot of streaming and the advent of multi-GPU systems, getting that data from the network to the GPU can be a bottleneck, limiting the amount of work the GPU cluster can do. The lack of prompt notification of job completion from the GPU can also result in poor GPU utilization. We build on our recent work on controlled spatial sharing of a single GPU to expand to support multi-GPU systems and propose a framework that addresses these challenges. Unlike the state-of-the-art uncontrolled spatial sharing currently available with systems such as CUDA-MPS, our controlled spatial sharing approach uses each of the GPU in the cluster efficiently by removing interference between applications, resulting in much better, predictable, inference latency We also use each of the cluster GPU's DMA engines to offload data transfers to the GPU complex, thereby preventing the CPU from being the bottleneck. Finally, our framework uses the CUDA event library to give timely, low overhead GPU notifications. Our evaluations show we can achieve low DNN inference latency and improve DNN inference throughput by at least a factor of 2.
A 120GHz Frequency Tripler with Improved Output Power in 40nm CMOS

This paper introduces a frequency tripler with improved output power in 40nm CMOS process. Low insertion loss and high balanced Baluns are designed for the input and output impedance matching. Meanwhile, LC series resonance is used to suppress the fundamental wave and improve the power of third harmonic wave. When the input power is 10dBm, the peak output power is 3.7dBm, the frequency conversion loss is 6.3dBm and the fundamental suppression ratio is 41dBc. The total DC power consumption is 9.6mW. The 3-dB bandwidth is 12GHz (115.5GHz~127.5GHz).
Optimized Electro-Optical Detection System for a FEL-THz Source

A detection system based on electro-optical(EO) sampling is designed for the established high-power, wide-band and wave-tunable FEL-THz source at Huazhong University of Science and Technology (HUST). The phase mismatch and dispersion propagation of the femtosecond laser pulse in the crystal are the main reasons that cause the distortion of the EO signal. We theoretically calculate and analyze the mixing process of the probe pulse and THz pulse in the crystal. Calculating the coherence length to find the suitable center wavelength probe for different detected crystals (ZnTe, Gap, GaAs). In addition, our simulation results show that the broadband laser can eliminate the effect caused by phase mismatch between the THz phase velocity and the group velocity of the laser in different crystals (ZnTe, Gap, GaAs). Moreover, the experiment results of 3-mm-thick ZnTe with sampling laser of 100nm bandwidth agree the simulation results well. The research is meaningful for optimizing the EO sampling system in the future.
UCA-Based OAM Non-Orthogonal Multi-Mode Multiplexing

Electromagnetic waves carrying orbital angular momentum (OAM) can improve the spectral efficiency of communication systems by multiplexing a number of OAM modes. However, since being not able to perform water-filling power allocation and adaptive modulation for each sub-channel like closed-loop multiple-input multiple-output (MIMO) systems, the traditional orthogonal multi-mode OAM multiplexing system usually has poor bit error rate (BER) performance due to the inherent large divergence angle of high-order orthogonal OAM beams. Therefore, in this article we propose a non-orthogonal OAM (NO-OAM) multi-mode multiplexing scheme based on uniform circular array (UCA), which regulates the divergence angles of all non-orthogonal OAM beams to be the same, circumventing the problem that large beam divergence of high-order orthogonal OAM modes results in low received signal-to-noise ratio (SNR) at the receive UCA with a fixed aperture. Mathematical analysis and numerical simulations show that in contrast to the existing uniform concentric circular array (UCCA) beam adjustment scheme, the proposed NO-OAM scheme has slightly better beam adjustment effect but with only one UCA. Moreover, in contrast to the traditional orthogonal OAM multi-mode transmission, the proposed NO-OAM multi-mode multiplexing scheme has asymptotically equivalent channel capacity and much lower BER.
A Full E-Band Single-Channel SiGe Transceiver MMIC for Monostatic FMCW Radar Systems

In this paper a fully integrated monostatic single-channel SiGe transceiver chip is presented for applications in the whole E-Band. The architecture consists of a fundamental E-Band VCO, a direct down-conversion mixer, a static divider chain, and coupling/matching networks at the output for a good chip-to-PCB interface via bond wires. The chip is fabricated in Infineon's BiCMOS production technology B11HFC which offers HBTs with an fT of 250GHz and fmax of 370GHz. The peak output power of the chip is -1 dBm while the receive mixer offers a -0.2 dBm input referred compression point to keep it from being saturated. The chip has a power consumption of 254mW and uses an area of 1.35mm2, A complete FMCW radar system prototype is also presented with a power consumption below 2.1 W and an effective chirp bandwidth of 31.1 GHz in the E-Band. The output power at the antenna flange is around -5 dBm. The in-loop phase noise at 10 kHz offset is better than -78 dBc/Hz in a wide frequency range of 32.1 GHz.
Overload Clutch Design for Collision Tolerant High–Speed Industrial Robots

Industrial robots can collide due to malfunctioning, wrong user input or unforeseen changes in the environment in which they operate. Different solutions exist to minimize the damage caused by a collision, either absorbed by the environment or by the robot itself. The specific solution depends on the type, size and dynamics of the robot. For high–speed industrial robots, the challenge is in dealing with the large kinetic energy upon impact without compromising the dynamic response during normal operation. The Combined Friction Cam Clutch (CFCC) is proposed as a solution for this problem. We provide a custom mechanical design and derive equations to calculate the threshold and residual torque which are identified as key design metrics. The concept is validated experimentally on a test bench using a prototype of the set-up.
Automotive Radar Interference Mitigation Based on a Generative Adversarial Network

This paper introduces a novel automotive radar interference mitigation approach using a generative adversarial network (GAN). Instead of tackling the mutual interference in the time domain, a generative adversarial network is trained and used to recover the complex signal in the frequency domain, namely on the complex range profile obtained after the fast Fourier transform of fast-time samples (RFFT spectrum). It is shown that by employing the gated convolution and an attention mechanism, the generator network has the ability to learn the amplitude and phase information for missing data from the remaining signal. Experimental results show that the proposed method can provide a remarkable improvement in signal-to-interference-plus-noise ratio (SINR) and preserves its robustness in severely disturbed scenarios that are much more complex than the training dataset.
Community Detection for Mobile Money Fraud Detection

This paper presents an overview of a first-year PhD thesis. The main idea can be expressed as a community detection issue in a real network containing data from a telecommunications operator. More precisely, mobile money transactions will be studied, with a view to detecting fraudulent ones. For this purpose, we propose to address the problem by studying different community detection approaches. Indeed, in a network, communities are constituted between users sharing common habits, therefore fraudulent transactions may appear as unusual behaviors. The originality of this work is to process real data, gathered by a telecom operator. Complex network organization is expected, making this thesis quite promising.
A 3D-Printed K/Ka-Band Dual Circularly Polarized Feed for Offset-Fed Reflector Antennas

According to the International Telecommunication Union (ITU), 19.6 GHz – 21 GHz and 29.4 GHz – 31 GHz are allocated for fixed-/mobile-satellite communications. To facilitate high throughput payload date, this paper presents a dual-circularly polarized (CP) feeding antenna operating on K-/Ka-band for offset-fed reflector antennas. Horn corrugations are placed parallelly with the central axis so that 3D-printing technology is easily employed to holistically fabricate the feed, which includes a horn, a polarizer and two matching adapters for standard waveguides. The simulated results reveal that an aperture efficiency of 74% and 68% is achieved for K-/Ka-band, respectively.
Time-controlled Regular Language Search over Encrypted Big Data

The rapid development of cloud computing and the arrival of the big data era make the relationship between users and cloud closer. Cloud computing has powerful data computing and data storage capabilities, which can ubiquitously provide users with resources. However, users do not fully trust the cloud server’s storage services, so lots of data is encrypted and uploaded to the cloud. Searchable encryption can protect the confidentiality of data and provide encrypted data retrieval functions. In this paper, we propose a time-controlled searchable encryption scheme with regular language over encrypted big data, which provides flexible search pattern and convenient data sharing. Our solution allows users with data’s secret keys to generate trapdoors by themselves. And users without data’s secret keys can generate trapdoors with the help of a trusted third party without revealing the data owner’s secret key. Our system uses a time-controlled mechanism to collect keywords queried by users and ensures that the querying user’s identity is not directly exposed. The obtained keywords are the basis for subsequent big data analysis. We conducted a security analysis of the proposed scheme and proved that the scheme is secure. The simulation experiment and comparison of our scheme show that the system has feasible efficiency.
A Method to Approximate the Resonance Frequencies of a Coaxial TEM-Cell

TEM-cells can be used for EMC-measurements or field sensor calibration. For a practical application, the TEM-cell’s resonance behavior over a broad range of frequencies is essential. This paper presents two methods to compute the resonance frequencies of an ideal (perfect electrical conductor) coaxial TEM-cell based on the generalized telegraphist equations. We compare our numerical results with commercially available software and measurements on a coaxial TEM-cell with similar geometric dimensions.
Joint Behavioral Cloning and Reinforcement Learning Method for Propofol and Remifentanil Infusion in Anesthesia

Total intravenous anesthesia using propofol and remifentanil is widely used. However, a rapid elimination property of the control of the drug infusion rate leads to problems. Recently, it has been shown that machine learning algorithms are able to more accurately predict drug effects than traditional prediction models. Based on this trend, this paper proposes a machine learning method that can simulate the transition dynamics of patient data during anesthesia to conFigure the interactive environment. In addition, we propose deep reinforcement learning with behavior cloning-based initialization to train the propofol and remifentanil control policies. As shown in the dataintensive performance evaluation, the proposed method achieves a desirable performance in terms of average bispectral index and blood pressure during the surgery simulation.
Fiber-Optic Surface Waveguide Modes Excited by Inter/Intra Mode Transition for Refractometric Sensitivity Enhancement

The generation of surface waveguide modes in telecommunication-grade optical fibers is highly desirable, since it would enable the development of novel all-fiber devices with high performance. Herein, we carry out a comprehensive theoretical investigation on the intrinsic excitation mechanism and sensing performance of fiber-optic surface waveguide modes supported by a tilted fiber grating (TFG) with nanometric-scale gold and <inline-formula><tex-math notation="LaTeX">$ \operatorname{TiO_{2}}$</tex-math></inline-formula> films. The results reveal that both P- and S-polarized surface waveguide modes originate from the mode transition tuned by the Au-<inline-formula><tex-math notation="LaTeX">$ \operatorname{TiO}_{2}$</tex-math></inline-formula> composite, including inter mode transition associated with two adjacent modes and intra mode transition only related to the mode itself. During the mode transition, the power carried by cladding modes is absorbed by the Au-<inline-formula><tex-math notation="LaTeX">$ \operatorname{TiO}_{2}$</tex-math></inline-formula> composite, thereby yielding a strong surface-localized evanescent field that is sensitive to external medium. It is found that for both polarizations the surface waveguide modes induced by the inter/intra mode transition present almost the same refractometric sensitivity, but with the P-polarization (<inline-formula><tex-math notation="LaTeX">$ 605.6 \, nm/RIU$</tex-math></inline-formula>) being more sensitive than the S-counterpart (<inline-formula><tex-math notation="LaTeX">$ 339.2 \, nm/RIU$</tex-math></inline-formula>), the state-of-the-art surface plasmon resonance (<inline-formula><tex-math notation="LaTeX">$ 539.7 \, nm/RIU$</tex-math></inline-formula>), and also the approach enhanced by high-refractive-index film (<inline-formula><tex-math notation="LaTeX">$ 7.4 \, nm/RIU$</tex-math></inline-formula>). The results attained here represent a breakthrough since they would open new pathways toward single molecular detection.
Wind Turbine Gearbox Fault Detection based on Dilated Convolutional Neural Networks

Nowadays, wind energy has become one of the most concerning renewable energy sources due to its low damage to nature and low cost. Wind turbines are usually deployed in remote areas and harsh environments, so they (especially gearboxes) are more likely to suffer faults. Therefore, it is necessary to vigorously develop wind turbine gearbox fault detection method to provide more accurate fault alarms. This paper proposes a fault detection method based on dilated convolutional neural network(DCNN). Compared to the traditional convolutional neural network, the dilated convolution can maintain a larger receiving field to speed up the monitoring speed, and is more suitable for real-time fault monitoring. Finally, an experimental wind turbine gearbox is used to verify the effectiveness of the method.
Service Migration in Multi-domain Cellular Networks based on Machine Learning Approaches

The number of mobile subscribers has increased drastically with the deployment of high-performance mobile cellular networks such as Long Term Evolution (4G) (LTE), and the upcoming 5th Generation Mobile Cellular Network (5G). Seamless connectivity is an important factor to provide better Quality-of-Service (QoS) as well as Quality-of-Experience (QoE) in cellular networks. In this regard, the utilization of Mobile Edge Computing (MEC) technology allows to bring required mobile services close to the end users to reduce latency, and increase service quality. However, switching between MECs nodes needs to be optimized in order to satisfy the expected quality of service as well as to guarantee service continuity SC. This paper addresses this problem by proposing a methodology and a prediction algorithm to boost SC in a MEC configuration.
A Review of Corner Detection Algorithms Based on Image Contour

Corner detection is an important basic research content in the fields of image processing and computer vision. Corner detection algorithm based on image contour is widely used in image registration, target recognition and shape analysis. In this paper, the existing corner detection algorithms based on contour are classified in detail, the main ideas of the algorithms are elaborated, the advantages and disadvantages of each algorithm are summarized, and the existing problems and further research directions are pointed out. It is expected to provide reference for the research of corner detection based on image contour.
Double Deep Q-Network-Based Energy-Efficient Resource Allocation in Cloud Radio Access Network

Cloud radio access network (CRAN) has been shown as an effective means to boost network performance. Such gain stems from the intelligent management of remote radio heads (RRHs) in terms of on/off operation mode and power consumption. Most conventional resource allocation (RA) methods, however, optimize the network utility without considering the switching overhead of RRHs in adjacent time intervals. When the network environment becomes time-correlated, mathematical optimization is not directly applicable. In this paper, we aim to optimize the energy efficiency (EE) subject to the constraints on per-RRH transmission power and user data rates. To this end, we formulate the EE problem as a Markov decision process (MDP) and subsequently adopt deep reinforcement learning (DRL) technique to reap the cumulative EE rewards. Our starting point is the deep Q network (DQN), which is a combination of deep learning and Q-learning. In each time slot, DQN configures the status of RRHs yielding the largest Q-value (known as state-action value) prior to solving a power minimization problem for active RRHs. To overcome the Q-value overestimation issue of DQN, we propose a Double DQN (DDQN) framework that obtains optimal reward better than DQN by separating the selected action from the target Q-value generator. Simulation results validate that the DDQN-based RA method is more energy-efficient than the DQN-based RA algorithm and a baseline solution.
A Probabilistic Approach to Radiated Electromagnetic Interference

Radiated electromagnetic interference is an ubiquitous problem that needs to be accounted for in the design of electronic devices. Typically, restrictions on the maximum permissible field level for a device to cause over a certain frequency range are imposed, based on characterizing the device's radiations in the far-field. We present a probabilistic approach to characterize and model radiated interference, based on stochastic electromagnetic fields. This allows for computationally predicting the probability for exceeding the emission threshold, set by international electromagnetic compatibility regulations.
Exploring Replacement Policy for Memcached

Caching efficiency is critical to performance when an in-memory database system acts as a cache for a disk-oriented database system. Memcached, one of the widely used in-memory database that stores key-values, roles as a cache for high-speed data storage layer. It replaces items using LRU replacement policy when memory capacity is used up. Other than LRU policy for maximizing temporal locality, in this paper, we confirm the impact of replacement policies for workloads with random pattern operations. We analyze hit counts of workload by applying BIP and SRRIP replacement policies to Memcached. Experimental results show that hit counts of Memcached to which SRRIP is applied is improved by up to 12%.
An Ensemble Classification Technique of Neurodegenerative Diseases from Gait Analysis

Neurodegenerative diseases (NDDs) lead to extreme locomotion disorders, which stem from the impairment of motor function caused by these diseases. They alter the gait rhythms and gait dynamics of their patients, which can be reflected in their time-series recordings of footfall contact times. Studies has shown that human gait can be used to identify NDDs. In this paper, an ensemble classification-based diagnostic system that uses patients’ gait data to diagnose NDDs is introduced. The diagnostic system is an ensemble of four binary classifications. The proposed technique is applied to gait dynamics data recorded from 64 subjects representing three NDDs: Amyotrophic Lateral Sclerosis (ALS), Parkinson’s Disease (PD), and Huntington’s Disease (HD), in addition to healthy subjects. Our results demonstrate the ability of the proposed technique to recognize gait dynamics of ALS, PD and HD with an accuracy of 96.8, 86.8% and 85.5%, respectively. These results demonstrate the efficacy of the proposed approach in diagnosing NDDs from gait analysis.
Roadblocks of I/O Parallelization: Removing H/W Contentions by Static Role Assignment in VNFs

Achieving 100 Gbps+ throughput with commodity servers is a challenging goal, even with state-of-the-art Data Plane Development Kit (DPDK). Fundamental performance of CPU/Memory is now the bottleneck and simple code optimization of Network Functions (NFs) cannot be the solution. Hardware accelerators including FPGA are getting attentions for performance boost; however, relying on specific features degrades manageability of NFV-nodes. Common Receive Side Scaling (RSS) provides a means of H/W-level parallelization, but per-flow throughput is not accelerated. Existing software-based approaches distribute processing load of NFs, but I/O is still serialized for each datapath. We tackled I/O parallelization and uncovered encounterd certainly misty contentions in our previous study. Specifically, per-thread CPU cycle consumptions proportionally grew as increasing parallelization level, although the overhead of conceivable mutual executions (e.g. CAS operations) was trivial. In this paper, we pursue the cause of the issue and upgrade our I/O parallelization scheme. Our careful investigation of NFV-node internals ranging from application to device driver layers indicates that hidden H/W-level contentions involving DMA heavily consume CPU cycles. We propose a contention avoidance design of thread role assignment and prove our design can flatten per-thread CPU cycle consumptions.
Smoothness Priority Approach Based Epileptic Seizure Classification Using ANN

Seizures due to epilepsy are futile, normal activities of patients are hampered and motor along with sensorial function becomes enfeeble. The Neurologist diagnoses epileptic seizures by visual or manual inspection of Electroencephalogram (EEG) signals. This paper provides a simple classification of epileptic seizures through the smoothness priority approach (SPA) de-trending technique. The SPA technique removes very low frequency, time-varying trending interference from the EEG signal. DWT MATLAB toolbox is used for decomposing the EEG signal. Four important time-frequency domain features termed as relative energy, index of fluctuation, entropy, recursive energy efficiency is extracted. The final classification is performed by an artificial neural network (ANN) and 98% accuracy, 100% sensitivity, 96.2% specificity, 100% precision is obtained.
Comparison between Voltage and Current Control methods for Power Factor Correction in Modified Dual Output Cuk Converter-Fed Switched Reluctance Motor Drive

Switched reluctance motors (SRM) are increasingly used these days because they do not use rare earth permanent magnet material. A dual output Cuk converter is used to control the SRM drive. The modified Cuk converter produces balanced voltages across two output capacitors which feeds the machine side midpoint converter of the SRM drive and also acts as a power factor correction (PFC) converter. A comparison between voltage and current control approaches for power factor correction is performed. They are performed on a high power and low power machine to check the suitability of the methods for both high and low power applications. Simulations are done on MATLAB/Simulink environment. The power factor (pf) and total harmonic distortion(THD) of input current are observed.