Miracle-dz/UniFAR-FTUs huggingface dataset:
Limits on Hysteresis-Free Sub-60 mV/Decade Operation of MFIS Nanowire Transistor

In this work, we present purely device-dependent conditions for achieving hysteresis-free sub-60 mV/decade (HF-sub-60) current transition in metal–ferroelectric–insulator–semiconductor (MFIS) nanowire transistor. The proposed bias-independent conditions for HF-sub-60 operation are also verified through conventional bias-dependent capacitance matching. Optimal ranges of ${T} _{\text {fe}}$ for achieving HF-sub-60 current transition are examined for 1) varying radii of nanowire; 2) three ferroelectric materials (Al-HfO2, hafnium zirconium oxide (HZO) and Y-HfO2); 3) nanowire and planar architectures; and 4) coercive field and remnant polarization variations. Furthermore, the impact of gate length scaling is incorporated into the developed model. The proposed methodology in this article provides new and detailed guidelines into the selection of device and process parameters, ferroelectric materials, and device topologies for facilitating high internal voltage amplification factor, improved subthreshold swing and hysteresis-free operation in MFIS transistors.
Real Time Target Detection for Infrared Images

The paper focuses on the real time detection and identification of human in IR images. Regular RGB cameras cannot capture images well in different weather conditions as dense fog, rain and complete darkness. IR objects due to its heat signature properties are well captured by IR cameras. YOLOv3 model is used as base model for IR detection as it delivers high frames per second.YOLOv3 is fine tuned for enhancing the performance and generalization ability of the model with IR images. The fine-tuned YOLO model is trained with customized dataset of IR images. The model achieves good accuracy of 89.28 mAP with validation set of IR images. The performance of tuned model is compared with the original YOLOv3 model. The original YOLOv3 model (without fine tuning) provides poor accuracy with 1.3 mAP with same validation set of IR images.
Compressed Sensing for Physical Layer Security in Single Relay Cooperative System

In this paper, we investigate the physical layer security of single amplify-and-forward (AF) relay communication system based on compressed sensing (CS). Different to the traditional method by compressing the source data with a specific measurement matrix (e.g. Gaussian matrix)to achieve security, we first demonstrate that the combination matrix of the compression measurement matrix and cooperative AF channel matrix between legitimate nodes (source, relay, and destination) can be considered as a secure measurement matrix for physical layer security. Compared with CS scenario without AF (CS Non-AF), the eavesdropper in the proposed scheme can hardly decode the original data correctly even when it has a perfect knowledge of eavesdropper channel state information (ECSI). The robustness of the scheme in the presence of one eavesdropper is also investigated.
Fuzzy CMAC-Based Adaptive Scale Force Control of Body Weight Support Exoskeletons

The body weight support method of exoskeletons is useful for the rehabilitation of patients with lower limb dyskinesia and performance augmentation of elders. Rather than enhancing the human joint strength directly, the body weight support exoskeleton provides assistance by supporting part of the human body weight and thus releasing the burden experienced by human legs. This paper proposes a two-level hierarchical control method aiming to achieve task-free and strong system robustness. A scale force control method, which can be simply described as making the supporting force of the robot several times the magnitude of a human leg, is developed as the high-level controller. The low-level controller is designed as a fuzzy cerebellar model articulation controller (CMAC)-based adaptive control to enhance the system robustness. The proposed method is applied to a squat movement and a single leg stance movement and is simulated in a combined simulation environment. The results show that the exoskeleton can always provide improved support according to the foot reaction force that is controlled by the user and that the system handles model errors and external disturbances well.
Automated Walking Guide to Enhance the Mobility of Visually Impaired People

This paper has shown the implementation detail of a spectacle prototype to assist the visually impaired people with safe and efficient walking in the surrounding’s environment. The walking guide uses three pieces of ultrasonic sensors to identify the obstacle in three directions: front, left and right. In addition, the system can detect potholes on the road surface using another ultrasonic sensor and convolutional neural network (CNN). The CNN runs on an embedded controller to identify obstacles on the surface of the road. Images are trained initially using a CNN on a host computer and are then classified on the embedded controller in real-time. The experimental analysis reveals that the proposed system has 98.73% accuracy for the front sensor with an error rate of 1.26% when the obstacle is at 50 cm distance. In addition, the proposed system obtains the accuracy, precision and recall of 92.67%, 92.33% and 93% respectively for image classification. The experimental study also demonstrates how the developed device outperforms prominent existing works.
Service Placement for Real-Time Applications: Rate-Adaptation and Load-Balancing at the Network Edge

Mobile Edge Computing may become a prevalent platform to support applications where mobile devices have limited compute, storage, energy and/or data privacy concerns. In this paper, we study the efficient provisioning and management of compute resources in the Edge-to-Cloud continuum for different types of real-time applications with timeliness requirements depending on application-level update rates and communication/compute delays. We begin by introducing a highly stylized network model allowing us to study the salient features of this problem including its sensitivity to compute vs. communication costs, application requirements, and traffic load variability. We then propose an online decentralized service placement algorithm, based on estimating network delays and adapting application update rates, which achieves high service availability. Our results exhibit how placement can be optimized and how a load-balancing strategy can achieve near-optimal service availability in large networks.
An Adversarial Machine Learning Method Based on OpCode N-grams Feature in Malware Detection

Machine learning has become an important method in malware detection. However, due to the weakness of machine learning models, a large number of researches related to adversarial machine learning has emerged. At present, the researches about adversarial machine learning mainly focus on the image and speech recognition. In the field of malware detection, because the feature modification can easily damage the integrity and functionality of the code, it is usually through adding noise such as garbage instructions to achieve fooling the malware detection model. In this paper, we propose an adversarial machine learning method on malware detection model based on OpCode n-grams feature. We first collect a large number of malicious code and normal code data sets, and use TF-IDF to extract OpCode n-grams with different n values from the data set. Then we train three malware detection models based on OpCode n-grams. Under the premise of comprehensive consideration of efficiency, accuracy and interpretability, we select XGBoost as the adversarial feature extraction model and extract adversarial features. Finally, in order to verify the accuracy of the extracted features, we conduct an adversarial machine learning experiment. The experimental results show that the adversarial method proposed in this paper can completely fool the machine learning detection model.
Changes in gene expression during development and thermogenesis in Arum

Thermogenesis in Arum species is induced by salicylic acid (SA) and caused by activation of the alternative respiration pathway and the alternative oxidase (AOX), resulting in heat production. The enzymes phosphoenolpyruvate carboxylase (PEPCase) and NAD-dependent malic enzyme (NAD-ME) also show dramatic increases in activity during thermogenesis and are essential for heat generation. In this current study, we characterized the timing and localization of changes in levels of AOX, NAD-ME, and PEPCase polypeptide accumulation, and changes in Ppc and Me mRNA accumulation, in various Arum tissues during prethermogenic development and during thermogenesis. In addition, changes in ribulose-1,5-bisphosphate carboxylase/oxygenase (Rubisco) gene expression were analysed at the level of rbcL protein and mRNA accumulation. AOX, PEPCase, and NAD-ME all increased only in clubs during development, increasing 5–6-fold by the latest prethermogenic stage and remained at this level. The induction of thermogenesis did not cause any changes in levels of AOX, indicating that SA does not affect levels of the enzyme itself, but instead must act to stimulate activity. Reported increases in NAD-ME activity during club development correlated closely with mRNA and protein accumulation, whereas PEPCase activity appears to be determined by post-transcriptional and post-translational processes. Interestingly, Rubisco protein and mRNA were found in relatively abundant amounts in clubs during early developmental stages, and disappeared rapidly as the thermogenic enzymes began to increase in abundance. The induction of thermogenesis by a synthetic inducer of plant pathogen resistance, 2,6-dichloroisonicotinic acid, as well as the involvement of SA and AOX in both processes, reinforces evidence for a link between mechanisms controlling disease resistance in all plants and thermogenic induction in Arum.
Shared Leadership and Just Culture: Tools to Promote SMS Hazard Reporting

Human error accounts for approximately 80% of Federal Aviation Regulation (FAR) Part 121 aviation accidents in the United States. Of that 80%, 10% can be traced to human error in aviation maintenance. To combat this well- known and established challenge in the industry, the Federal Aviation Administration has provided air carriers with tools to assist in the tracking and reporting of potential safety violations specific to the maintenance environment; Safety Management Systems (SMS) and Maintenance Human Factors (MxHF) Training (AC 120-72A). In theory, these implements provide a well-rounded approach to managing safety and human error in aviation maintenance. However, cultural influences and other barriers (e.g., poor communication) often hamper successful use of such tools. The purpose of this research study was to determine if leveraging human capital through shared leadership and the promotion of hazard reporting via a Just Culture improved the likelihood of hazard reporting. To address the problem of insufficient reporting resulting from communication and cultural barriers a survey was conducted. It was found that the presence, real or perceived, of shared leadership and a Just Culture minimized these barriers and promoted safety hazard reporting. It was also found that the inclusion of a reporting system which addressed the presence of human error in an aviation maintenance environment, such as the REPAIRER method, further supported an SMS program when paired with Shared Leadership and a Just Culture environment.
FCN-Based DenseNet Framework for Automated Detection and Classification of Skin Lesions in Dermoscopy Images

Skin Lesion detection and classification are very critical in diagnosing skin malignancy. Existing Deep learning-based Computer-aided diagnosis (CAD) methods still perform poorly on challenging skin lesions with complex features such as fuzzy boundaries, artifacts presence, low contrast with the background and, limited training datasets. They also rely heavily on a suitable turning of millions of parameters which often leads to over-fitting, poor generalization, and heavy consumption of computing resources. This study proposes a new framework that performs both segmentation and classification of skin lesions for automated detection of skin cancer. The proposed framework consists of two stages: the first stage leverages on an encoder-decoder Fully Convolutional Network (FCN) to learn the complex and inhomogeneous skin lesion features with the encoder stage learning the coarse appearance and the decoder learning the lesion borders details. Our FCN is designed with the sub-networks connected through a series of skip pathways that incorporate long skip and short-cut connections unlike, the only long skip connections commonly used in the traditional FCN, for residual learning strategy and effective training. The network also integrates the Conditional Random Field (CRF) module which employs a linear combination of Gaussian kernels for its pairwise edge potentials for contour refinement and lesion boundaries localization. The second stage proposes a novel FCN-based DenseNet framework that is composed of dense blocks that are merged and connected via the concatenation strategy and transition layer. The system also employs hyper-parameters optimization techniques to reduce network complexity and improve computing efficiency. This approach encourages feature reuse and thus requires a small number of parameters and effective with limited data. The proposed model was evaluated on publicly available HAM10000 dataset of over 10000 images consisting of 7 different categories of diseases with 98% accuracy, 98.5% recall, and 99% of AUC score respectively.
Influence Based Community Detection Over Social Media

With the drastic increase of data which is generated by user, it's usage in research work becomes very common. This gives rise to the Community detection as an emerging field in social media mining. Also, researchers become interested and started working in this area. But there are certain constraints in this area like scalability and quality of the community. There are a few research algorithms which have done work in this area and are also efficient in terms of scalability and accuracy. We have done the comparison of various algorithms with our proposed algorithm on the social data of the twitter. And with the help of the results we got, we have shown that our algorithm is more efficient in comparison of previous algorithms.
A Novel Fog-Based Multi-Level Energy-Efficient Framework for IoT-Enabled Smart Environments

The Internet of Things (IoT) has emerged as a promising paradigm to enhance the living standard of human life by employing varied smart devices including smart phones, smart watches, sensors, on-board units and other networking equipment. However, these devices consume a considerable amount of energy to perform their operations that has a significant impact on the environment, product cost and life of the device. Given this fact, energy-efficient solutions for smart environments have gained great attention from researchers and the industrial community. In this context, a novel fog-based multi-level energy-efficient framework for IoT-enabled smart environments has been proposed. To achieve this, the proposed framework adds additional two layers in the existing IoT-fog-cloud architecture – sensors-based energy-efficient hardware layer and policy layer, to monitor the energy consumption and to enable the energy-aware decision making. Initially, the main sources of energy consumption in an IoT-enabled smart environment are identified. Further, the energy requirements of a device to perform a specific task are estimated. Moreover, the alternative devices to perform the same task using less energy are searched out. Finally, a device or a set of devices, to process the job consuming lower energy while ensuring the job requirements, is selected. To validate the proposed framework, four case studies are considered – smart parking, smart hospital specifically ICU, smart agriculture and smart airport. Simulations are conducted using iFogsim toolkit and results show that a significant amount of energy can be conserved by employing the proposed framework.
Robust Energy-Based Control of a Swash Mass Helicopter Subject to Matched Disturbances

We consider a helicopter structure that comprises a double blade coaxial shaft rotor and swash masses to control the orientation and therefore maneuvering the vehicle. It is referred to as a swash mass helicopter (SMH). The SMH is subject to disturbances so that the problem addressed in this paper is the robust control under the presence of matched and unknown disturbances. We first present the dynamical system of the SMH. Then, under the presence of highly-coupled inputs, an interconnection and damping assignment passivity-based Control (IDA-PBC) mechanism is derived. The problem of increasing robustness in the presence of disturbances is addressed. Finally, several numerical results are reported to show the performance of the proposed control design.
Parallel Multi-threaded Gridrec Algorithm for Computer Tomography on GPU for Edge Computing

Tomography reconstruction is the process of quickly reconstructing the original image form the projection obtained by X-ray radiation. At present, the high-resolution detector of the Shanghai Synchrotron Radiation Facility (SSRF) can scan more than 4GB of tomographic data every 1.5 seconds, and the transmission speed is increased to more than 100GB s-1. With the upgrade of high-resolution detectors and the increase of data transmission volume, the reconstruction computation on cloud has become a bottleneck in improving the speed of tomography reconstruction even if the fastest Gridrec algorithm is adopted. In this paper, we propose an improved serial Gridrec algorithm and a parallel Gridrec algorithm by improving the convolution kernel to optimize the speed of existing image reconstruction algorithms on low cost GPUs for edge computing. On these GPUs, the multi-threaded tomography reconstruction algorithm not only guarantees high-quality results, but also improves the reconstruction speed over original Gridrec algorithm by more than 11x, and over the classic FBP algorithm by more than 234x. Besides the significant speedup, our work would be the first parallel implementation of Gridrec algorithm on GPU for edge computing.
Anomaly-based IDS to Detect Attack Using Various Artificial Intelligence & Machine Learning Algorithms: A Review

Cyber-attacks are becoming more complex & increasing tasks in accurate intrusion detection (ID). Failure to avoid intrusion can reduce the reliability of security services, for example, integrity, Privacy & availability of data. The rapid proliferation of computer networks (CNs) has reformed the perception of network security. Easily accessible circumstances affect computer networks from many threats by hackers. Threats to a network are many & hypothetically devastating. Researchers have recognized an Intrusion Detection System (IDS) up to identifying attacks into a wide variety of environments. Several approaches to intrusion detection, usually identified as Signature-based Intrusion Detection Systems (SIDS) & Anomaly-based Intrusion Detection Systems (AIDS), were proposed in the literature to address computer safety hazards. This survey paper grants a review of current IDS, complete analysis of prominent new works & generally utilized dataset to evaluation determinations. It also introduces avoidance techniques utilized by attackers to avoid detection. This paper delivers a description of AIDS for attack detection. IDS is an applied research area in artificial intelligence (AI) that uses multiple machine learning algorithms.
A Comprehensive Survey on Mobility-Aware D2D Communications: Principles, Practice and Challenges

Device-to-device (D2D) communication proposes a new epitome in mobile networking to avail data exchange between physically proximate devices. The exploitation of D2D communication enables mobile operators to harvest short range communications for improving network performance and corroborating proximity-based services. In this paper, we investigate mobility aspects of D2D communication, which are indispensable for the adoption and implementation of D2D communication technology. We present an extensive review of the state-of-the-art problems and the corresponding solutions for encouraging the exploitation of mobility to assist D2D communication. Specifically, by identifying the mobility models, traces, problems, requirements, and features of different proposals, we discuss the lessons learned and summarize the advantages of mobility-aware D2D communication. We also present open problems and highlight future research directions concerning D2D communication applications in real-life scenarios. To the best of our knowledge, this is the first comprehensive survey to address mobility-aware D2D communication, which offers insight to the underlying problems and provides the potential solutions.
Monostatic MIMO Backscatter Communications

Backscatter communications have two major antenna configurations: the bistatic configuration, in which the reader employs two different sets of antennas to transmit and receive, and the monostatic configuration, in which the reader employs one set of antennas for both transmitting and receiving. In this paper, we provide a comprehensive study on the MIMO techniques for the $M \times L$ monostatic channel. Particularly we study on the joint design of the query and the coding matrices. We show that the maximum achievable diversity order of the monostatic channel is the diversity order achieved by the block-lever unitary query and orthogonal space-time block code (BUTQ-OSTBC) design pair, which is $\frac {ML}{2}$ , exactly the half of the diversity order of the conventional MIMO channel. Then we show that uniform query, the simplest query approach, cannot achieve the maximum achievable diversity order in the monostatic channel. We generalize BUTQ-OSTBC to the general augmenting approach, and show that unitary matrix is optimal in terms of SER performance among all possible query matrices, when OSTBC is employed. The above results further indicate that in the backscatter channel, additional diversity can be obtained by varying the query signals over time slots within the channel coherent time, which is quite different from the results from the conventional MIMO channels. We verify our results by Monte Carlo simulations.
Comparison and Performance Evaluation of Software-Defined Networking Controllers

Software-Defined Networking (SDN) is a prominent networking prototype that has removed the limitations of the traditional network framework by moving all the controllable functionalities to a much focused centralized region. SDN allows the detachment of data/physical plane as well as the network/control plane, as an outcome, switches are implemented as the forwarding gadgets and the control plane become a centralized consolidated controller. In SDN, controllers are the brain of the network which is having a power of performing control decisions during the routing of data packets. This paper presented the prior work done in the field of SDN controllers along with the architectural overview of the controller. Also, discuss the design aspects, productivity, modularity and many other factors of each SDN controller. In addition, this paper estimates the performance of the controller using several metrics like response time, average response time, throughput and average throughput.