Miracle-dz/UniFAR-FTUs huggingface dataset:
Minimizing Thermal Variation in Heterogeneous HPC Systems with FPGA Nodes

The presence of FPGAs in data centers has been growing due to their superior performance as accelerators. Thermal management, particularly battling the cooling cost in these high performance systems, is a primary concern. Introduction of new heterogeneous components only adds further complexities to thermal modeling and management. The thermal behavior of multi-FPGA systems deployed within large compute clusters is little explored. In this paper, we first show that the thermal behaviors of different FPGAs of the same generation can vary due to their physical locations in a rack and process variation, even though they are running the same tasks. We present a machine learning based model to capture the thermal behavior of a multi-node FPGA cluster. We then propose to mitigate thermal variation and hotspots across the cluster by proactive task placement guided by our thermal model. Our experiments show that through proper placement of tasks on the multi-FPGA system, we can reduce the peak temperature by up to 11.50°C with no impact on performance.
DualSLIC: An Automatic Coarse-to-Fine Method on Pancreas Segmentation

Automatic segmentation of organs is a crucial task for computer-aided medical image analysis. Due to the specificity of location and morphology of the pancreas, it is challenging to segment pancreas from other surrounding abdominal organs. This paper introduces DualSLIC, an automatic pancreas segmentation method based on abdominal CT images, combining Simple Linear Iterative Clustering algorithm (SLIC for short) twice and a threshold based corrosion and expansion algorithm to narrow the searching space. In the first SLIC procedure, the coarse model generates large superpixel blocks, and the candidate blocks are selected according to the possible position of the pancreas. Next, a targeted Region of Interest (ROI for short) is selected regarding the gray-level histogram analysis followed by corrosion and expansion elimination of the surrounding tissue. In this way, we achieve the basic outline of the pancreas. Furthermore, the second fine SLIC model is applied on the targeted ROI and the final pancreas contour is hence obtained. In the experiments, DualSLIC is evaluated on a dataset of 187 abdominal CT slices and it outperforms other conventional methods with a (mean±std.dev) Dice similarity coefficient (DSC for short) of 80.5±10.98% and a Jaccard index (JI for short) of 67.05±12.78%. Moreover, it is efficient with an average processing time of 1.05 seconds per slice.
Failure Aware Deployment of Drone Base Stations

The use of drone base stations in wireless networks is studied by many researchers and it is shown that DBSs offer significant potential for meeting user demands in various wireless communication scenarios. However, the scenario where a DBS becomes out of function during the communication operation has not been investigated. This scenario is particulary important when the requirements of public safety and tactical communications networks are considered. In this paper, we propose a failure-aware deployment strategy to alleviate the impact of a DBS loss. We have shown that the proposed failure-aware DBSs positioning algorithm performs better than the comparison algorithm, k-means clustering, in terms of number of users served and the timeout duration where even the basic communication services are not provided to the users.
Repair Schemes with Optimal I/O Costs for Full-Length Reed-Solomon Codes with Two Parities

Network transfer and disk read constitute the two most time-consuming operations in the repair process for node failures in erasure-code-based distributed storage systems. Recent developments on Reed-Solomon codes have demonstrated repair schemes that achieve optimal network bandwidths in the recovery of single failures, although in certain cases at the expense of a it trivially high I/O cost, a term referring to the number of disk reads performed in a repair scheme. We are interested in the lowest $\mathrm {I}/\mathrm {O}$ cost a repair scheme can achieve for Reed-Solomon codes. We establish two repair schemes for a family of Reed-Solomon codes with two parities that achieve the optimal $\mathrm {I}/\mathrm {O}$ cost.
Terahertz Oscillators Using Resonant Tunneling Diodes

A compact source is important for various applications of the terahertz (THz) frequency range. We report our recent works on THz oscillators using resonant tunneling diodes (RTDs). Room-temperature fundamental oscillation up to 1.98 THz has been obtained by reducing the electron delay time in RTDs and conduction loss in antennas. Output power of 0.7 mW was obtained at 1 THz by a large-scale array. Frequency-tunable oscillators, spectral narrowing by the phase-locked loop, and radiation of circular-polarized and vortex waves are reported. For applications, wireless communications with direct modulation, spectroscopy, and ranging system with amplitude-modulated continuous-wave system are demonstrated usinz RTDs.
LMI-based design of dynamic event-triggering mechanism for linear systems

This paper deals with the design of an event-triggering mechanism using local information for a linear system controlled by an observer-based feedback controller. The event-triggered control strategy is based on a new dynamic triggering mechanism, which is introduced through an internal dynamic variable. Sufficient conditions based on linear matrix inequalities are proposed to ensure the asymptotic stability of the closed loop together with the avoidance of Zeno behavior. A convex optimization problem leans on these conditions to determine the parameters of the event-trigger rule aiming at reducing the number of control updates. Discussion with respect to the simpler state feedback case is drawn.
A Low Power Packet Detection Algorithm for FM-UWB PHY for IEEE 802.15.6 WBAN

To maintain quality of life, immediate health care services should be provided. Wireless Body Area Network technologies offer a great improvement in monitoring the patients' medical data and guaranteeing its security. One important technology that serves various medical and non medical applications is the IEEE 802.15. 6 standard. Based on IEEE 802.15. 6, this paper proposes a new algorithm for packet detection which uses only one-third of the preamble length. The functionality of the proposed design is tested through computer simulations. The packet detection algorithm was written in Matlab then prototyped using verilog hardware description language. The design is then synthesized targeting 65 nm CMOS technology. The proposed design consumes $35.18\ \mu W$ in 65 nm technology with a clock frequency of 9 MHz.
A Quasi-Direct Method for the Surface Impedance Design of Modulated Metasurface Antennas

A new approach is presented for synthesizing modulated metasurface (MTS) antennas (MoMetAs) with arbitrary radiation patterns, assumed to be given in amplitude, phase, and polarization. The MTS is defined on a circular domain and is represented as a continuous sheet transition impedance boundary condition (IBC) on the top of a grounded substrate. The proposed method relies on an entire-domain discretization of the electric field integral equation (EFIE). Via the dyadic Green’s function of the grounded substrate, the desired radiation pattern is translated into the visible part of the surface current spectrum, decomposed into entire-domain and orthogonal basis functions, while the invisible part of the spectrum stems from the solution of the unmodulated sheet problem. The EFIE is then inverted to obtain the sheet impedance, which is constrained to be anti-Hermitian, as required for implementation with lossless patches. The efficiency of the method relies on the precomputation of the reaction integrals between three functions: basis functions for currents and impedances and testing functions for fields. The formulation is presented first for the scalar (isotropic) MTS case and then generalized to the tensorial (anisotropic) MTSs. Several radiation patterns are presented and designed successfully. A full-wave method-of-moment code is used to validate the designed MTSs IBC.
Building energy consumption modeling at urban scale: three case studies in Europe for residential buildings

In urban contexts, the use of energy in buildings is one of the main causes of greenhouse gas emissions. The reduction of energy-use in buildings could be one of the main drivers to improve the sustainability, livability and quality of urban environment, together with the production of energy from the available renewable sources. To achieve energy sustainability in the most critical high-density urban contexts, it is necessary to optimize: the energy consumptions compatibility of different users; the distribution of heat, for example through the district heating network; the use of all urban spaces, such as building envelopes and urban surfaces, to produce energy from the available renewable sources. The energy models at urban scale are a complex issue as they should be simplified to be applied on a vast territory. Indeed, detailed information are not given at territorial scale and short time of simulation are preferable; however, information should be detailed enough to describe properly the energy consumption of the whole urban environment from building to urban scale. Therefore, energy models should take into account also the urban morphology, people’s behavior, social and economic conditions, local and national regulation, and the use of outdoor public spaces. The challenge of this work is to present three different energy-use models, to compare their characteristics and to find the best features of an "optimum" model to analyze and represent energy resources, future scenarios, energy efficiency solutions and best energy policies. The aim is to drive a smarter use of energy, matching it with the available and more efficient energy sources to help also public administrations in defining policies adapted to the real buildings heritage. The urban energy models can also be applied in future climatic scenarios, to evaluate the impact of climate change in the energy demand/supply of buildings, as well as in the potential of retrofit scenarios.
Solving the Memory-Based Memoryless Trade-off Problem for EEG Signal Classification

Electroencephalogram (EEG) signals exhibit highly irregular patterns. This irregularity, which arises from i.i. d. measurement noise, has been partially resolved by memoryless classifiers, such as deep convolutional neural networks (CNN). However, there are other major sources of irregularity, including brain network modes, mental states, and various physiological factors. These internal states drift over time, in which case it would be better to use memory-based neural networks, such as long short-term memory networks (LSTM). This paper presents a novel EEG signal classification framework that resolves a trade-off between memoryless and memory-based classification. The proposed method uses deep reinforcement learning (RL) to find a trial-by-trial control strategy for the attention control system that switches between CNN (memoryless) and LSTM (memory-based)—or is a mixture of both. The simulation on the EEG dataset, which was collected while performing a complex cognitive task, shows that the proposed attention control system outperforms other EEG classification methods.
Downsizing: The Effect of Mixed-Reality Person Representations on Stress and Presence in Telecommunication

We study how mixed-reality (MR) telepresence can enhance long-distance human interaction and how altering three-dimensional (3D) representations of a remote person can be used to modulate stress and anxiety during social interactions. To do so, we developed an MR telepresence system employing commodity depth sensors and Microsoft's Hololens. A textured, polygonal 3D model of a person was reconstructed in real time and transmitted over network for rendering in remote location using Hololens. In this pilot study, we used mock job interview paradigm to induce stress in human-subjects interacting with an interviewer presented as an MR hologram. Participants were exposed to three different types of real-time reconstructed virtual holograms of the interviewer, a natural-sized 3D reconstruction (NR), a miniature 3D reconstruction (SR) and a 2D-display representation (LCD). Participants reported their subjective experience through questionnaires, while their biophysical responses were recorded. We found that the size of 3D representation of a remote interviewer had a significant effect on participants' stress levels and their sense of presence. NR condition induced more stress and presence than SR condition and was significantly different from LCD condition.
Architectures and Challenges Towards Software Defined Cloud of Things (SDCoT)

Last decade witnessed a growth on developing of IoT architecture models to cope with various challenges and domains requirements. This leads to rapid increasing the number of the connected devices, which means large amount of data are needed to be stored and processed. Form this context, Cloud Computing is emerged as a solution to enable centrally unlimited storage of data and programs with the ability to access anytime from anywhere. The integration between IoT and cloud brings the concept of Cloud of Things (CoT) to support different types of data and multiple services with data ubiquity, flexibility, security and resilience. CoT facilitates large-scale IoT heterogeneous networks interconnections, but it is valid for static infrastructures. Thus, new challenge opens to handle dynamic configurations and heterogeneous infrastructures over multiple geographical distributions areas. In this paper, we exploit the concept of Software Defined Network (SDN) to present a precursory description called Software Defined Cloud of Things (SDCoT). SDCoT opens new dimensions towards dynamic network resource managements and reconfiguration subject to QoS and performance requirements. This paper summarizes the proposed architectures for IoT and Cloud Computing with the corresponding challenges towards our initiatory attributive SDCoT. Though SDCoT, we can have a live deployment for different platforms and services with customized applications. This will facilitate implementation and configuration of sensors, gateways, switches or controllers based on the programed software defined services. This will rise a new Cloud definition called Device as a Service such as Sensor as a Services, Switch as a Service, Controller as a Service … etc.
A Low-Power and Area-Efficient Digitally Controlled Shunt-Capacitor Delay Element for High-Resolution Delay Lines

A new digitally controlled shunt-capacitor inverter delay element and its design procedure are presented. The digital shunt-capacitors consist of binary scaled transmission gates and have a much smaller total capacitance and resolution than state-of-the-art solutions using separate MOSFETs for switches and capacitors. This results in a lower power consumption and smaller area making the shunt-capacitor inverter an efficient solution for high-resolution delay lines in the GHz range. Different topologies for the MSB and LSB shunt-capacitors further improve the efficiency by reducing the minimum capacitive loading of the CMOS inverter. The proposed delay element is verified in a 65 nm CMOS process implementing a four stage delay-line with a step size of 0.82 ps in a 2.5 GHz all-digital multiphase delay locked loop.
Performance Evaluation of Machine Learning Algorithms in Apache Spark for Intrusion Detection

As the Internet continues to get stronger, so does the potential risk of malicious users trying to harm others. An intrusion detection system (IDS) can be used to alert the appropriate entities when potentially dangerous operations are happening within a host of set of hosts. Nowadays, we need a system that can accurately process large amount of network data quickly. Most of the state-of-the art IDSs apply the traditional machine learning algorithms to classify whether a packet is a part of an attack. However, these algorithms typically aren't implemented with a big data platform. In this research, we will use Apache Spark, a big data processing tool known for handling tasks at fast speeds, to process network packet data. This paper utilizes the Spark libraries to implement Random Forest, Support Vector Machines (SVMs), Logistic Regression, Naïve Bayes, and Gradient Boosted Trees. We also implement a Deep Multilayer Perceptron, which is a Spark implementation of a deep learning algorithm. We compare the results of the traditional machine learning algorithms to the deep learning method. Our results show that the deep learning algorithm produces favorable accuracy, precision, and recall, but takes longer to analyze the data than classical machine learning algorithms.
Image Restoration Based on Deep Convolutional Network in Wavefront Coding Imaging System

Wavefront coding (WFC) is a prosperous technology for extending depth of field (DOF) in the incoherent imaging system. Digital recovery of the WFC technique is a classical ill-conditioned problem by removing the blurring effect and suppressing the noise. Traditional approaches relying on image heuristics suffer from high frequency noise amplification and processing artifacts. This paper investigates a general framework of neural networks for restoring images in WFC. To our knowledge, this is the first attempt for applying convolutional networks in WFC. The blur and additive noise are considered simultaneously. Two solutions respectively exploiting fully convolutional networks (FCN) and conditional Generative Adversarial Networks (CGAN) are presented. The FCN based on minimizing the mean squared reconstruction error (MSE) in pixel space gets high PSNR. On the other side, the CGAN based on perceptual loss optimization criterion retrieves more textures. We conduct comparison experiments to demonstrate the performance at different noise levels from the training configuration. We also reveal the image quality on non-natural test target image and defocused situation. The results indicate that the proposed networks outperform traditional approaches for restoring high frequency details and suppressing noise effectively.
3D Monolithic Stacked 1T1R cells using Monolayer MoS2 FET and hBN RRAM Fabricated at Low (150°C) Temperature

We demonstrate 3D monolithically integrated two-level stacked 1-transistor/1-resistor (1T1R) memory cells, using monolayer MoS2 transistors and few-layer hBN RRAMs, fabricated at temperatures below 150 °C. The stacking process is scalable to an arbitrarily large number of layers and on any substrate material without foreseeable physical limitations. The 1T1R cells can be switched with programming current < $130\ \mu\mathrm{A}$ and voltage < 1 V, close to typical CMOS logic voltages. These cells are promising for in-memory and neuromorphic computing because (1) the hBN RRAM has gradual set and reset switching due to multiple weak-filaments formed along local defects and (2) the MoS2 transistor has low off-current due to the large band gap of monolayer MoS2$(\mathrm{E}_{\mathrm{g}} > 2\ \text{eV})$. We also show that the linearity of RRAM resistance change is well-controlled by the gate voltage of the transistor.
Low-Complexity Distributed Cell-Specific Bias Calculation for Load Balancing in UDNs

The challenge of the fifth generation of wireless systems is to overcome fundamental limits of existing cellular networks by means of advanced technologies such as network densification. Nevertheless, dense and random deployment of the network infrastructure poses new challenges in the network management. In particular, rethinking cell association policies and introducing new specific load balancing strategies will be mandatory to shift traffic from heavy-loaded cells to light-loaded neighbors. Toward this goal, cell biasing is considered one of the most effective way. This letter proposes a low-complexity distributed cell-specific bias algorithm suitable for an actual implementation on highly dense scenarios. This solution takes into account the users’ data rate requests and aims at minimizing the outage rate defined as the amount of unsatisfied data rate requests. Numerical results show that despite its low complexity, the solution achieves performance only slightly lower than the benchmark near-optimum solution.
Lyapunov Functionals for Output Regulation of Exponentially Stable Semigroups via Integral Action and Application to Hyperbolic Systems

The paper deals with output feedback stabilization of exponentially stable systems by integral controllers. We propose appropriate Lyapunov functionals to prove exponential stability of the closed-loop systems. An example of parabolic PDE (partial differential equation) systems and an example of hyperbolic systems are worked out to show how exponentially stabilizing integral controllers are designed. The contribution of the paper is extending the forwarding technique to infinite-dimensional systems by elaborating Lyapunov functionals.
Slit-Slot Line and Its Application to Low Cross-Polarization Slot Antenna and Mutual-Coupling Suppressed Tripolarized MIMO Antenna

A slit-slot transmission line is devised, investigated, and applied to a slit-slot antenna and a co-located tripolarized slit-slot antenna array for 2.45 GHz multipolarization multi-input-multi-output (MIMO) applications. The slit-slot transmission line, with slit array etched on its conductive planes, is analyzed and characterized by using an equivalent circuit model and a graphical method based on simulations. Taking advantage of the features of a slit-slot transmission line, which include a shorter wavelength and a favorable current distribution for antenna applications, a slit-slot antenna, and a co-located tripolarized slot antenna array are presented and investigated by simulations and experiments. The effects of slits on the performances of the slit-slot antenna and the antenna array are explained. The simulated and measured results of fabricated prototypes show that the slits improve the cross-polarization of both the slit-slot antenna element and the antenna array around 5 dB, and that the isolation among the tripolarized slit-slot antenna array is improved by 14 dB, compared to the associated slot antennas. Furthermore, tripolarized slit-slot antenna array has an omnidirectional radiation patterns and low correlation which are favorable for MIMO applications.
Calibration Time Reduction Using Subjective Features Selection Based Transfer Learning For Multiclass BCI

Brain-computer interface (BCI) using machine learning has the requirement for a large number of training data. This requirement makes the long training session inevitable for a new user. Many approaches including transfer learning (TL) already have been reported in the literature to abridge the long training data collection session. One of them is transferring informative instances using active learning (AL) which was approached in our previous attempts for both binary and multiclass BCI. It was associated with the classic common spatial pattern (CSP) feature extraction method. It showed the potential to obtain the benchmark performance using a reduced amount of training data. However, it has subject dependent performance and was not up to the expectation particularly for multiclass BCI. For binary BCI, it is addressed by selecting the best subject-specific features from subjective narrow frequency window using filter bank CSP (FBCSP). Since multiclass BCI has different characteristics in terms of output performance and nature of features, this work investigates the incorporation of FBCSP into informative transfer learning with AL (ITAL) for multiclass BCI. Comparing with existing direct transfer with AL (DTAL) and ITAL with CSP for multiclass BCI, ITAL with FBCSP reaches the benchmark performance for six out of nine subjects using average 42% of the full training set which is significant at 5% (p < 0.05) significance level. For multiclass BCI as well, ITAL combined with discriminating feature extraction ensures better transfer which yields to effective reduction of the training session without sacrificing the benchmark robustness.