Miracle-dz/UniFAR-FTUs huggingface dataset:
A Differential Optical Receiver With Monolithic Split-Microring Photodetector

This paper presents a high-sensitivity, fully differential (FD) optical receiver (RX) for high-density system-on-chip applications. The high digital activity prominent in such applications creates common-mode and power-supply noise, which degrades the optical RX sensitivity. To suppress these noise sources and enhance the RX sensitivity, an optical RX is proposed that realizes FD operation by using a new resonant split-microring photodetector. The complete RX is implemented in a 45-nm SOI CMOS with no process changes. When aptly biased, the detector outputs FD photocurrent with <±0.3% splitting error. It has 0.52-A/W responsivity and a bandwidth of 5.0 GHz. The RX circuit is optimized to achieve BER < 10−12 at 12 Gb/s with an optical modulation amplitude (OMA) sensitivity of −18.2 dBm while consuming 7 mW. Compared with a conventional single-ended RX on the same test-chip, the proposed RX improves sensitivity from 13 to 7.9 $\mu \text{A}_{\mathrm {pp}}$ without reducing the bandwidth or significantly impacting the RX energy efficiency.
Energy-Efficient Approximate Squaring Hardware for Error-Resilient Digital Systems

In recent years, there has been a high demand for executing digital signal processing and machine learning applications on energy-constrained devices. Squaring is a vital arithmetic operation used in such applications. Hence, improving the energy efficiency of squaring is crucial. In this paper, a novel approximation method based on piecewise linear segmentation of the square function is proposed. An energy-efficient 32-bit approximate hardware for squaring was implemented using this method. The proposed hardware achieved a mean relative error of 0.43% and delivered up to 47% energy saving when compared with state-of-the-art approximate multipliers. The comparison also revealed that the proposed hardware is the most efficient design in terms of error-area-delay-power product.
A CMOS-based Resistive Crossbar Array with Pulsed Neural Network for Deep Learning Accelerator

A CMOS-based resistive computing element (RCE), which can be integrated in a crossbar array, is presented. The RCE successfully solves the hardware constraints of the existing memristive devices such as dynamic ranges of conductance, I-V nonlinearity, and on/off ratio without increasing hardware complexity compared to other CMOS implementations. The RCE has been designed using a 65nm standard CMOS process and SPICE simulations have been performed to evaluate feasibility and functionality of the RCE. In addition, a pulsed neural network employing an RCE crossbar array has also been designed and simulated to verify the operation of the RCE.
A Kernel Rootkit Detection Approach Based on Virtualization and Machine Learning

OS kernel is the core part of the operating system, and it plays an important role for OS resource management. A popular way to compromise OS kernel is through a kernel rootkit (i.e., malicious kernel module). Once a rootkit is loaded into the kernel space, it can carry out arbitrary malicious operations with high privilege. To defeat kernel rootkits, many approaches have been proposed in the past few years. However, existing methods suffer from some limitations: 1) most methods focus on user-mode rootkit detection; 2) some methods are limited to detect obfuscated kernel modules; and 3) some methods introduce significant performance overhead. To address these problems, we propose VKRD, a kernel rootkit detection system based on the hardware assisted virtualization technology. Compared with previous methods, VKRD can provide a transparent and an efficient execution environment for the target kernel module to reveal its run-time behavior. To select the important run-time features for training our detection models, we utilize the TF-IDF method. By combining the hardware assisted virtualization and machine learning techniques, our kernel rootkit detection solution could be potentially applied in the cloud environment. The experiments show that our system can detect windows kernel rootkits with high accuracy and moderate performance cost.
Scalable DDoS Mitigation System

Distributed Denial of Service attacks (DDoS) are used by attackers for their effectiveness. This type of attack is one of the most devastating attacks in the Internet. Every year, the intensity of DDoS attacks increases and attackers use sophisticated multi-target DDoS attacks. In this paper, a modular system that allows to increase the filtering capacity linearly and allows to protect against the combination of DDoS attacks is designed and implemented. The main motivation for development of the modular filtering system was to find a cheap solution for filtering DDoS attacks with possibility to increase filtering capacity. The proposed system is based on open-source detection and filtration tools.
Generalized Stochastic Petri Nets (GSPN) for Analysis of Microgrid under Uncertainities

Petri Nets (PNs) are extensively used to illustrate and analyze industrial systems performance under both discrete and continuous-time event conditions. Endeavor to utilize Petri Nets for building a smart grid, specially a microgrid system leads to major challenges for secure, automated and energy competent operations. This is due to uncertain function of energy resources and loads integrated into microgrid. This work proposes to promote microgrid modeling by considering variability in renewable energy resources and stochastic load profile showing indicative results depicted as under parallel computing task by Petri Nets. With the knowledge of economic green energy application, a comprehensive model has been prepared using Generalized Stochastic Petri Nets (GSPN) to reflect deliverable features of microgrid. A reachability graph is generated by useful marking to analyze the entire system properties.
Edge and Fog Computing Enabled AI for IoT-An Overview

In recent years, Artificial Intelligence (AI) has been widely deployed in a variety of business sectors and industries, yielding numbers of revolutionary applications and services that are primarily driven by high-performance computation and storage facilities in the cloud. On the other hand, embedding intelligence into edge devices is highly demanded by emerging applications such as autonomous systems, human-machine interactions, and the Internet of Things (IoT). In these applications, it is advantageous to process data near or at the source of data to improve energy & spectrum efficiency and security, and decrease latency. Although the computation capability of edge devices has increased tremendously during the past decade, it is still challenging to perform sophisticated AI algorithms in these resource-constrained edge devices, which calls for not only low-power chips for energy efficient processing at the edge but also a system-level framework to distribute resources and tasks along the edge-cloud continuum. In this overview, we summarize dedicated edge hardware for machine learning from embedded applications to sub-mW “always-on” IoT nodes. Recent advances of circuits and systems incorporating joint design of architectures and algorithms will be reviewed. Fog computing paradigm that enables processing at the edge while still offering the possibility to interact with the cloud will be covered, with focus on opportunities and challenges of exploiting fog computing in AI as a bridge between the edge device and the cloud.
Normally-Off Tri-Gate GaN MIS-HEMTs with 0.76 mΩ·cm2 Specific On-Resistance for Power Device Applications

A GaN metal–insulator–semiconductor high electron mobility transistor (MIS-HEMT) using tri-gate architecture and hybrid ferroelectric charge trap gate stack is demonstrated for normally-off operation. Compared with the conventional planar device, the tri-gate device has the 2-D electron gas (2-DEG) channel exposed on the nanowire sidewalls, so that the trapped charges in the HfON charge-trapping layer can easily deplete the channel from the sidewalls, leading to a high positive threshold voltage (<inline-formula> <tex-math notation="LaTeX">${V}_{\text {th}}$ </tex-math></inline-formula>) to realize the normally-off operation. Moreover, through this electrostatic control on the sidewall, a high density of negative charge caused by hybrid ferroelectric charge trap gate stack with the optimized tri-gate structure, the tri-gate device can achieve normally-off GaN device with both low on-resistance (<inline-formula> <tex-math notation="LaTeX">${R}_{ \mathrm{ON}}$ </tex-math></inline-formula>) and high positive <inline-formula> <tex-math notation="LaTeX">${V}_{\text {th}}$ </tex-math></inline-formula>. The designed tri-gate device exhibits a high <inline-formula> <tex-math notation="LaTeX">${V}_{\text {th}}$ </tex-math></inline-formula> of +2.61 V at current density (<inline-formula> <tex-math notation="LaTeX">${I}_{\text {DS}}) = 1\,\,\mu \text{A}$ </tex-math></inline-formula>/mm, a high maximum current density (<inline-formula> <tex-math notation="LaTeX">${I}_{\text {DS, MAX}}$ </tex-math></inline-formula>) of 896 mA/mm, a low <inline-formula> <tex-math notation="LaTeX">${R}_{ \mathrm{ON}}$ </tex-math></inline-formula> of <inline-formula> <tex-math notation="LaTeX">$5.0~\Omega \! \! \cdot \! \! \text {mm}$ </tex-math></inline-formula> and a high breakdown voltage (BV) of 788 V. To the best of our knowledge, the proposed tri-gate device shows the lowest specific on-resistance (<inline-formula> <tex-math notation="LaTeX">${R}_{ \mathrm{ON}, \text {SP}}$ </tex-math></inline-formula>) among reported normally-off GaN device results with BV >650 V.
Detection of QRS Complexes Using Convolutional Neural Network

This paper deals about electrocardiogram beat detection using the Convolution Neural Network model. The Convolutional Neural Network is modelling three classes. Each class represents normal, abnormal and another QRS like signal. We used the MIT-BIH Arrhythmia Database. This database consists of 48 electrocardiogram records of patients. Each record length is approximately 30 minutes. We split the records into four subsets. Each subset has records of 12 patients. The cardiovascular experts annotate this database also each record have more than 2000 labels. In our experiments, we trained four models With similar architecture. Difference between models is the selection of the training and testing subset.
Development and Validation of Pneumatic Muscle based Back Assistance Exoskeleton*

We propose a design of a back assistance exoskeleton for workers powered by pneumatic artificial muscle and validated the efficiency of the system using sEMG data based evaluation index (sEMG peak, Integrated EMG, RMS values). The selection of commercial artificial muscle for instantaneous compression force (~ 6000N) with developed power transmission structure of minimized level of friction could effectively transmitted the assistance power. The level of air pressure from the compressor were determined from the human feedback experiment, ranges from 0.65Mpa~0.75Mpa. The sEMG based indices on the hamstring and erector spinae muscles represented the decreased level of 20% to 30% in muscle activation when lifting the loads. We found the feasibility of using lumbar support hardware in weight lifting, also the validity of evaluation index based on sEMG processing technique.
Drive-Cycle Optimized 99% Efficient SiC Boost Converter Using Planar Inductor with Enhanced Thermal Management

This paper presents a boost dc-dc converter design that maximizes drive-cycle weighted efficiency based on the CAFE standard. A drive-cycle simplification approach is introduced to enable faster yet accurate CAFE-based optimization. The optimization is based on calibrated loss models, including soft switching operation in boundary conduction mode (BCM) using a compact planar inductor. An enhanced thermal management strategy based on additional thermally-conductive layers is proposed to address design limitations related to the planar inductor realization. The design approaches are verified on an experimental 400 V, 11 kW SiC boost converter prototype, which achieves 99% measured efficiency over a wide operating range, and a CAFE-weighted efficiency of 99.1%.
Dynamic Whole-Body Control of Unstable Wheeled Humanoid Robots

Control of two-wheeled humanoid robots poses several challenges due to the unstable dynamics of their mobile base and the coupling between the upper and lower body dynamics. The problem is often faced in the literature with methods based on the linearized or simplified models that fail in exploiting the whole body dynamic capabilities of the platform in use. In this letter, we tackle the problem of whole-body dynamic control for a mobile wheeled unstable humanoid robot. Compared to the existing approaches based on online optimization to guarantee respect of the constraints, we propose a control method that takes into account the nominal constrained dynamics of the robot in the quasi-velocities through an internal model, thus reducing the computational burden. A computed-torque control law in the quasi-velocities is used to stabilize the robot around the upper position. We report on preliminary experimental results and on the method effectiveness in rejecting unknown external disturbances.
Optimization Method of IR Thermography Facial Image Registration

So far, there have been many types of researches subject to technical requirements due to image registration. Using image registration can lower deviation from sequential images and make it possible to analyze the information variation of the particular area subsequently. This study provides a procedure creating fixed image based on the data of facial IR thermography, where its methods include the visual saliency map by detected image, as well as cluster algorithm. Comparison is also made here to solve the merits and demerits by affine parameter to reach the optimum measure among Genetic Algorithm, Particle Swarm Optimization, and Simulated Annealing Algorithm, where there are two control parameters concerning this experiment: one is the calculation of time confined each alignment, while the other one is to use parallel computing toolbox or not. The optimum method will be chosen by the values of the objective function based on the control parameters. Afterward, the optimal internal parameter is to be verified through the Taguchi experiment and the validity of this procedure in this study will be built following the parameter result as above. Therefore, the difference of images before and after alignment can be validated by overlapping the images before and after alignment as well as the image quality measurements, where its results reveal that the alignment procedure of IR thermography in this study is capable of performing human face alignment precisely, and subsequently, do help data statistics and analysis concerning temperature area interdependence.
A Review of Demand Response Techniques and Operational Limitations

Electricity is an essential commodity which modern civilization is heavily dependent on. It is therefore imperative for electric utilities to closely monitor demand levels and ensure that supply always satisfies demand. Demand Response (DR) has in recent times been used as a tool to balance electricity demand and supply and is a useful check to mitigate ever increasing electricity demand. This paper presents an overview of related literatures on Demand Response programs, its techniques, and the deployment of Distributed Energy Resources (DER) with DR. Also, it highlights the challenges and the difficulties facing DR which has now becomes an innovative research topic for researchers to investigate and proffer solutions.
DropOut and DropConnect for Reliable Neuromorphic Inference under Energy and Bandwidth Constraints in Network Connectivity

DropOut and DropConnect are known as effective methods to improve on the generalization performance of neural networks, by either dropping states of neural units or dropping weights of synaptic connections randomly selected at each time instance throughout the training process. In this paper, we extend on the use of these methods in the design of neuromorphic spiking neural networks (SNN) hardware to improve further on the reliability of inference as impacted by resource constrained errors in network connectivity. Such energy and bandwidth constraints arise for low-power operation in the communication between neural units, which cause dropped spike events due to timeout errors in the transmission. The DropOut and DropConnect processes during training of the network are aligned with a statistical model of the network during inference that accounts for these random errors in the transmission of neural states and synaptic connections. The use of DropOut and DropConnect during training hence allows to simultaneously meet two design objectives: maximizing bandwidth, while minimizing energy of inference in neuromorphic hardware. Simulations of the model with a 5-layer fully connected 784-500-500-500-10 SNN on the MNIST task show a 5-fold and 10-fold improvement in bandwidth during inference at greater than 98% accuracy, using DropOut and DropConnect respectively during backpropagation training.
Message Forwarding with Ferries in Delay-Tolerant Networks

Routing in delay-tolerant networks exploits node mobility to distribute messages among inter-contact of mobile nodes in intermittently connected environments. However, only depending on node movements to deliver messages between any two nodes in a network could result in lower successful delivery ratio, longer latency and high transmission overhead. Recent research intends to add ferries which can itinerate around some strategic places in a network to expedite message distribution and improve network performance. This paper designs a ferry-travel scheduling approach for efficiently message forwarding in delay-tolerant networks. In this approach, multiple ferries attempt to sense the intensity of message exchanges in geographic proximity, discover hotspot areas by contact history and then autonomously plan travel routes to go through those hot spots. To examine the network performance elevated by ferries, this paper incorporates this ferry-aided approach into several typical delay-tolerant routing schemes, including Epidemic, Spray and Wait, and PRoPHET. Relative comparison among these combined schemes is shown under synthetical Random Waypoint and real NCUTrace mobility models.
Optimized Availability-Aware Component Scheduler for Applications in Container-Based Cloud

Cloud Computing is a popular platform for providing online computing services to the tenants. With migrating large number of the applications to the cloud, application availability is raised as a concern issue. Availability is a non-functional requirement that refers to the percentage of time the application service is available for the use. Many factors can impact the application availability especially in the cloud environment. In this paper, we show the impact of scheduling (placement) of the application’s components inside the Data Center of the cloud on the application availability. We propose an optimized Availability- Aware strategy to schedule the components with the goal to maximize the availability of the application while satisfying several constraints. We map the scheduling problem to Integer Linear Programming (ILP) optimization model. We compare the Availability-Aware strategy with other scheduling strategies. The results show that the Availability-Aware strategy achieves higher application availability compared to the other strategies.
Low-Cost Planar RF MEMS-Based Attenuator

In this paper, the design and implementation of a low-cost planar RF MEMS-based attenuator is presented. The attenuation is realized by moving a low-resistivity silicon over a meandered grounded coplanar waveguide (GCPW) line. The actuation mechanism is achieved by magnetic forces between a planar spiral coil and a highly magnetized permanent magnet. The attenuator is designed, fabricated, and tested. An insertion loss of less than 1.20 dB and an attenuation range of 16 dB with flat response over the frequency band of 21.5-26.5 GHz are demonstrated.
A Dual-Phase Evolution Model of Emergency Myelopoiesis

The immune system is one of the progressive Complex Adaptive Systems (CASs) that generates cellular response against the foreign disturbances and also exhibits prominent properties such as emergence and self-organization. In a steady state, where a localized infection is caused by the disturbances, the myeloid immune cells are continuously created in the bone marrow. Nevertheless, if the infection gets distributed throughout the body, then these immune cells are created in a larger quantity due to the myelopoiesis process to fulfill the increased demand. This demand leads to the state change in the system from its steady state to the demand adapted emergency myelopoiesis state causing cellular emergence. After the infection, it returns to the steady state due to the self-organization. This immune response results in the phase transitions from steady state to emergency myelopoiesis state and back. Dual-phase evolution (DPE) is a process that brings emergence and self-organization in the CASs due to the phase transitions. DPE allows the immune system to rest in one of the phases such as a steady phase (i.e., the local or poorly connected phase of the DPE) or an emergency phase (i.e., the global phase of the DPE due to the increased number of cells), although it is predominantly the steady phase. In this paper, we propose an integrated model of the emergency myelopoiesis, DPE, and network theory. This model helps to understand the cellular interactions during the phase transitions along with its prominent properties. It is concluded that during the global phase, the immune network exhibits scale-free property. This network further follows the power-law for the degree distribution of its nodes.
Blind Signal-to-Noise Ratio Estimation of Real Sinusoid in Additive Noise

We consider the problem of estimation of signal-to-noise ratio (SNR) with a real deterministic sinusoid with unknown frequency, phase and amplitude in additive Gaussian noise of unknown variance. The method of moments, a general method to derive estimators based on high-order moments, is used to derive a blind SNR estimator that does not require the knowledge of the instantaneous frequency of the sinusoid, through separate estimation of signal and noise power. Cramer-Rao lower bounds (CRLBs) are also derived for estimators of signal and noise power and then, for SNR estimators. We show through numerical simulations the statistical performances of the estimators, that we compare to the corresponding CRLBs, and discuss their use in practical applications.