Miracle-dz/UniFAR-FTUs huggingface dataset:
Advancements and Security Issues of IoT & Cyber Physical Systems

We as a human are becoming more dependent on the technologies day by day, if we take a look at our environment then we realize that we are surrounded by so many sizes of electronic devices. These devices have now become basic need that suffice our daily routine activities. Technology is developing so rapidly that year after year a new boom is introduced in the field like, IoT(Internet of Things) which serves the purpose of connecting several electronic and smart devices together to automate the system. In IoT, sensors and actuators are used to send and receive information and perform the specified task. If considering a system larger than what we have done in IoT, connecting smart devices on a large scale will be requiring a huge database and high computing power. Hence, Cyber Physical Systems (CPS) came in with providing massive database and high computation power through Cloud Computing. In this paper, we have reviewed these technologies which have attracted the attention of researchers at present.
Robot Comfort Following based on Extended Social Force Model in Dynamic Environment

Following a person in dynamic environments is one of the hotspots of service robot research. The traditional following model can't meet the requirements of human-robot integration. The new service robot not only has to complete the following tasks but need to consider human body safety and psychological comfort. In this paper, we propose an extended social force model for comfortable follow-up, which is a variant of the social force model based on collision prediction. Our work is tested in a simulation environment, and the results show that the proposed model can effectively consider the comfort of human and pedestrian while following human.
A Methodology for Resilient Control and Monitoring in Smart Grids

The increasing importance of decentralized and volatile energy sources causes huge challenges for future energy grids. Besides intelligent grid planning, real-time monitoring and control is necessary to guarantee reliable and sustainable grid operation. The basis for such applications is dependable monitoring of the grid. This paper introduces a methodology for resilient control and monitoring. A self-healing algorithm ensures that the system can operate even when monitoring devices or connections fail. Based on that, a model-free context-aware monitoring algorithm allows for detection of anomalies, drift and, in general, undesired conditions. These core components are embedded in a scaleable system architecture that allow for easy integration even in existing systems.
A Three Phases Procedure for Optic Disc Segmentation in Retinal Images

We introduce a new property based method for optic disc segmentation in retinal images. The suggested method involves parameters whose values can be easily modified to apply the method to retinal images with significantly different size. The method consists of three phases respectively dealing with image cropping, detection of light regions and optic disc segmentation. Cropping is done to reduce the computation burden. Two criteria dealing with the concentration of light pixels and with their distribution within the image are introduced during the second phase to detect a subset of the optic disc. The Circle Hough transform is used to perform segmentation, starting from the set identified during the previous phase. The performance of the method has been evaluated on public datasets. Comparisons with other methods in the literature in terms of average success, sensitivity, specificity, predictive value and overlap show that our method has comparable performance.
A High Efficiency and Fast Response PLL Based Buck Converter: Implementation and Simulation

This paper presents a phase locked loop (PLL) based buck converter implemented in 180nm CMOS technology that is intended for low-voltage applications, where efficiency and fast convergence are key parameters. The system feedback exploits a PLL-based architecture, which is modified to control the feedback voltage instead of the system phase. To drive the power transistors over a wide range of duty cycles, a pulse width modulator is used. This modulator exploits novel delay elements and flip flops. The system offers a convergence time of 26μs from the startup, and a settling time of 4μs when subjected to a transient step load change of 400mA. The system is able to achieve a peak efficiency of 93%.
Multi-modal Wireless Sensor Platform for Railway Monitoring

In the entire transportation system, Railway transportation plays a very important role. However, the current test method is mainly manual inspection, labor intensity, high risk. In recent years, with the rapid development of MEMS technology, wireless network sensors have been widely used. Sensors play an important role in fusing with big data. Sensors are becoming smaller and smarter. The application of wireless network sensor provides a new idea for the safety monitoring of railway transportation. In view of the current backwardness of railway transportation detection methods and high cost of manual maintenance, this study builds a multi-mode wireless sensor platform for railway monitoring based on MEMS sensors. The platform is mainly composed of a power supply system, a monitoring system and wireless sensor network. As the core of the system energy, the power supply system makes full use of solar energy resources, reduces system cost. And the platform achieves the goal of long-term railway monitoring without external DC power supply. The railway generates acceleration information when a train passes by. Wireless network sensors monitor the status of the railway by collecting acceleration information. The monitoring platform realizes real-time transmission of monitoring information without external power supply. And The intelligent monitoring and maintenance of railway transportation system are realized. The experimental results show that the platform can reduce the cost of manual monitoring and maintenance, and has important practical significance for railway maintenance.
Verifying Rule Enforcement in Software Defined Networks With REV

Software defined networking (SDN) reshapes the ossified network architectures, by decoupling the control plane and data plane. Due to such a decoupling, SDN assumes that rules issued by the control plane are always correctly enforced by the data plane. However, this assumption breaks as an adversary can prevent the data plane from enforcing the rules, by exploiting the vulnerabilities of switch OS and control channel. The serious consequence is that packets may deviate from their original paths, thereby violating critical security policies like access control. To this end, this paper introduces rule enforcement verification (REV), which enables the controller to check whether switches have correctly enforced the rules that it issues. Since using message authentication code (MAC) can incur heavy switch-to-controller traffic, we propose the compressive MAC, which lets switches compress MACs before reporting to the controller, thereby significantly reducing the bandwidth cost. Finally, we propose a heuristic flow selection algorithm, which allows the controller to verify much less flows for rule coverage. We implement REV based on Open vSwitch with DPDK, and use experiments to show: (1) by using compressive MAC, REV achieves a 97% reduction in switch-to-controller traffic, and an $8\times $ increase in verification throughput; (2) by using the heuristic flow selection algorithm, REV can reduce the number of flows to verify by 40%–60%.
Research on the Dynamic Trajectory of Cable-Suspended Parallel Robot Considering the Uniformity of Cable Tension

The cable-driven parallel robot (CDPR) uses flexible cables instead of rigid links, and maintains several desirable characteristics, including large workspace, high speed motion, and large payload to weight ratio. Dynamic trajectory planning enables the CDPR to achieve a wide range of motion under small support conditions. In this paper, the stability of dynamic trajectories of cable-suspended parallel robot (CSPR) is investigated. According to the Weak Principle of Equivalence, a stability evaluation index is proposed with the geometric analysis, and is used to carry out the planning of the dynamic trajectories. Taking the planar two degrees of freedom (DoFs) CSPR as object, the simulation is implemented. Results reveal that the cable tension of the dynamic trajectory established with proposed planning method is more uniform, reduces the fluctuation of the cable tension, and improves the stability of the movement. Planning methods of the dynamic trajectory established in this paper provides a new solution for studying the stability of trajectory.
A verification method for array-based vision chip using a fixed-point neural network simulation tool

In recent years, customized chips for accelerating deep learning algorithms have been continuously developed with some emerging challenges in the field of deep-learning-based vision chips. Considering the lack of algorithm-level verification tools, poor reusability of simulation code and low development efficiency in deep learning vision chip verification, a novel verification method, which is based on the fixed-point simulation tool, is proposed and utilized in the development of an array-based deep learning vision chip. This simulation tool, implemented by combination of Matlab and CUDA C, enables efficient and accurate verification of the vision chip by taking the classical MobileNet V1 as the benchmark. This method, integrated with the RTL and FPGA co-verification seamlessly, provides the golden reference and reliable verification during the entire development period of the vision chip.
Research of Modified Level of Detail - Physical Processing Mesh Generation Optimization Method in 3d

For more accurate physical simulation on usual computing platform, the original rendering mesh data need compressing and converting. Generally, the level of detail algorithm just included visual properties of model data in the compressing method. If the result of compressing use in simulation, physical properties will have serious error accumulation. In this paper, we first propose the additional physical evaluation parameter in simplification strategy function whose result have accurate mesh and concave structure. The new reduction method make object have more accurate physical properties and achieve efficient simulation.
System Identification of Industrial Debutanizer Column

Development of a suitable model is the most challenging problem for the distillation columns due to their nonlinear and complex behavior. First Order Plus Time Delay (FOPTD) models using transfer functions and Nonlinear Autoregressive with Exogenous Input (NLARX) models described by sigmoid function were used in this study to identify the dynamics of the industrial debutanizer column. The results demonstrated that linear models were not able to approximate the behavior of system whereas the NLARX models performed well in capturing the nonlinear dynamics of debutanizer column. The prediction capability of the developed NLARX model was found to be 91.26% and 86.56% for top and bottom composition respectively.
Implementation of Spatial/Polarization Diversity for Improved-Performance Circularly Polarized Multiple-Input-Multiple-Output Ultra-Wideband Antenna

In this paper, spatial and polarization diversities are simultaneously implemented in an ultra-wideband (UWB) multiple-input-multiple-output (MIMO) antenna to reduce the correlation between the parallel-placed radiators. The keystone of the antenna is systematically modified coplanar ground planes that enable excitation of circular polarization (CP). To realize one sense of circular polarization as well as ultra-wideband operation, an extended rectangular slot is etched on the left-hand-side of the coplanar waveguide (CPW) feed. This is combined with the asymmetrical ground plane geometry on the right-hand-side of the feeding line. The current flowing on the slotted ground plane forms a quasi-loop and generates CP, whereas the combination of the vertical current on the feedline and the horizontal current on the asymmetric ground plane adds to the axial ratio (AR) bandwidth. To implement the MIMO design with polarization and spatial diversity, the position of the coplanar ground planes is switched with respect to the feedline, and placed in a parallel formation with the edge-to-edge distance of $0.29\lambda _{0}$ . All geometrical parameters are optimized at the full-wave level of description before prototyping and experimental characterization. Simulation and measured results indicate that the proposed MIMO antenna features approximately 82% impedance bandwidth from 2.9 GHz to 7.1 GHz and 68.5% (3.1 GHz- 6.35 GHz) AR bandwidth. Moreover, the peak envelop correlation coefficient (ECC) is below 0.003, which corresponds to almost no correlation between the radiators. The antenna can be operated with either bidirectional or unidirectional characteristics, covering multiple commercial application bands including WLAN and WiMax.
Management of Peptic Ulcer Perforations; Comparison of Open and Laparoscopic Procedures.

Introduction: Peptic ulcer perforation requires urgent surgical intervention, but there is no consensus on a preference for open or laparoscopic surgery. The aim of this study was to compare the results of laparoscopic and open methods of surgery for patients with a peptic ulcer perforation. Materials and Methods: The data of 52 patients who underwent surgery for peptic ulcer perforation at 4 hospitals were investigated retrospectively. The patients were divided into 2 groups according to the surgical technique applied: open surgery or laparoscopic. Both techniques used an omental patch to repair the perforation. Patient age, gender, preoperative blood white blood cell count, blood amylase level, duration of symptoms before admission to the hospital, length of operation, number of drains, quantity of intra-abdominal free fluid observed during the operation, quantity of fluid used for irrigation, patient abdominal operation history, and complications were noted and analyzed. Results: Thirty-seven of the patients underwent open surgery and 15 underwent laparoscopic surgery (mean age: 37.73±16.85 years; female/male 3/49). There was no significant difference in the parameters between the groups. No mortality was observed in any of the patients. Wound infection developed in 2 patients who were operated on using the open technique. Only 1 case of laparoscopic surgery (6.66%) was converted to open surgery. Conclusion: Laparoscopic repair of a peptic ulcer perforation demonstrated results that were similar to open surgery and helps to reduce complications such as wound infection. It may be especially valuable in earlyonset cases and younger patients.
Classification of Arrhythmia using Time-domain Features and Support Vector Machine

Cardiac arrhythmia is a heart condition where the heart does not beat in a regular way. This is one of those diseases which are easy to diagnose. A doctor can detect arrhythmia by just looking at the Electrocardiogram (ECG) of the patient because it has many visual clues, which a doctor is trained to identify. All these visual clues are the time-domain feature. Hence, in this paper, an algorithm is presented which uses only time-domain features to classify between normal sinus rhythm and arrhythmia using Support Vector Machine (SVM). The paper also compares the classification results when the frequency domain features are used along with the time-domain features. The frequency-domain features increase the computational complexity of the algorithm and make it harder to create a portable and reliable hardware device for the realtime detection and classification of arrhythmia. The proposed algorithm can be incorporated in a portable, lightweight and robust device which can detect arrhythmia in real-time. The accuracy of the algorithm is 99.36% on MIT-BIH arrhythmia database, which in comparison to other algorithm is an improvement.
Efficient Power Consumption using Hybrid Emerging Memory Technology for 3D CMPs

The development of computing systems, data analytics, and storages for big data computing has resulted in an increasing need for low-power computational platforms and high-performance efficiency, capable of adjusting the processing capability and storage domains. In this context, high performance acts as a critical issue in future CMPs with restricted of battery lifetime and power consumption. For future CMPs architecting, 3D stacking of Last Level Cache (LLC) has been recently introduced as a new methodology to combat the performance challenges of 2D integration. We propose an uncore hybrid LLC which takes advantage of emerging memory technologies. In the former phase, a reconfiguration unit premised on a simple convex formulation is used to forecast the running application’s bandwidth and selects a high-performance configuration aimed at the LLC. The experimental results showed a reduction in (average memory access time) AMAT 12% as compared to SRAM- cache for PARSEC benchmarks which lead to 88% minimization in power consumption on average.
A Modified Inception-v4 for Imbalanced Skin Cancer Classification Dataset

Deep learning architectures, especially deep convolutional neural networks (CNN) achieve high accuracy on object classification and localization tasks. Achieving such high accuracy requires powerful devices. In this paper, rather than an ensemble of multiple complex models, a single Inception-v4 model is adapted to classify extracted from the HAM10000 dataset. The proposed model is enhanced by employing feature reuse using long residual connection in which the features extracted from earlier layers are concatenated with the high-level layers to increase the model classification performance. The dataset used in this study is imbalanced; therefore, a data sampling approach is used to mitigate the data imbalance effect. The proposed architecture achieves an accuracy of 94.7% using the provided test set at the official benchmark for the International Skin Imaging Collaboration (ISIC) 2018.
Land Use Prediction on Satillite images using Deep Neural Nets

Land is an important natural resource for any species as life and developmental activities depend upon it. Land use and land cover are important aspect of information for land resource management. Using land use and land cover information helps in better management of land resource and it also makes us to understand the relationships and interactions between the human and natural phenomena. So, using satellite images of earth surface and apply concepts of deep learning to identify land use, which class the land belongs to.
Experimental measurement of eddy current loss in permanent magnets of electrical machines with a PWM signal generated by a frequency converter

In today's highly automated world, permanent magnet synchronous machines (PMSMs), commonly used in the industrial automation of traction, robotics or aerospace, require higher power density and efficiency. When developing such PMSMs, the eddy current loss (i.e. permanent magnet loss) in the permanent magnets (PMs), especially with respect to temperature and frequency influence, must be considered. The increase in the temperature of PMs above the allowed maximum temperature leads to a tendency towards irreversible demagnetization and changes in the electrical conductivity of the material. Similarly, higher harmonics with different frequencies influence the eddy current loss of the PMs. The accurate analytical analysis and measurement methods of PM loss can help PMSM designers to create robust PMSMs with high tolerances and higher energy efficiency at low manufacturing costs. In this paper, measurement methods and a finite element method (FEM) simulation for the PM loss of PMSMs are presented and compared. In addition, we consider the eddy current loss (i.e. PM loss) in PMs, which are under external magnetic fields generated with a sinusoidal or non-sinusoidal pulse width modulation (PWM) signal under higher frequency effects.
A Deep Neural Network-Based LOS Classification for Triangulation Positioning

We propose a deep neural network based a line-of-sight classification for triangulation positioning. The accuracy of triangulation decreases in the non-line-of-sight environments but finding the channel which has a line-of-sight path is difficult in the mathematical method. A proposed scheme can solve this problem by taking advantage of deep neural network. Performance evaluation is carried out by the system level simulator of Long Term Evolution-Advanced with Spatial Channel Model. Performance results indicate that the proposed scheme can effectively increase the positioning accuracy and robustness of the inaccurate received signal strength measurements.
Review on the Evolution of Low-power and Highly-linear Time-to-Digital Converters - TDC

Time-to-digital converters (TDC) have been widely used in all-digital phase-locked loops (ADPLL). However, the TDC non-linearity and resolution have negatively impacted the ADPLL performance. A better integration between the TDC and ADPLL would improve the performance of the ADPLL, with a minimum increase in power consumption. This paper addresses the main issues in TDC design providing an overview of published architectures. In particular, the evolution of resolution and non-linearity indicators will be reviewed in detail, since they are the most important figures for proper integration of TDCs in ADPLLs.
A Mininet-Based Emulated Testbed for the I/Ocloud

Considering the proliferation of smart devices connected to the Internet, typically going under the aegis of Internet of Things (IoT), a trend has arisen to promote the Cloud paradigm as a suitable management system for such a complex environment. In this context, an effort to extend the OpenStack ecosystem to make it able to support the management of the IoT infrastructure has been made by virtue of the I/Ocloud approach, leading up to its reference implementation, the Stack4Things (S4T) middleware. S4T provides a set of suitable capabilities and features to make the (remote) IoT devices able to join an edge-based IaaS/PaaS Cloud. In the interest of enhancing the S4T middleware scalability and explore new capabilities in particular, ones related to Fog and Edge paradigms, it is becoming a must to test new features in practice at a low financial cost and particular constraints for instance, number/type of devices, network conditions, etc. For this purpose, the use of network emulation tools is a practical and suitable approach. In this paper, we present an integration between the S4T middleware and an emulation tool namely Containernet. Through the integration approach, we model network conditions (e.g., latency, bandwidth, packet loss) and devices (in forms of containers) using Containernet, and we manage the devices (i.e., containers) by means of S4T.
Patent Analysis and Suggestion of Network Function Virtualization Technology

Network virtualization technology (NFV) is one of the key network technologies in the development of 5G technology. The article analyzes NFV technology from application trend, technology trend, patent competition, PCT application and patent citation. It provides corresponding conclusions and suggestions for China's communication industry and promotes the prosperity and development of China's communication industry by analyzing the development status and future trends of NFV technology.