Miracle-dz/UniFAR-FTUs huggingface dataset:
A DAG-Based Forwarding Paradigm for Large Scale Software Defined Networks

The Software Defined Network (SDN) paradigm represents a major breakthrough in the networking field, due to its unprecedented capabilities in terms of flexibility and programmability. SDNs have been successfully deployed in data centers and small to medium enterprises. However, adopting the SDN paradigm in the context of wide area networks is more challenging, due to a number of factors including the higher probability that node and link failures occur and the unavailability of a dedicated control channel. In this paper, we present a DAG (Directed Acyclic Graph) based forwarding paradigm addressing the challenges that arise when the SDN concept is applied to large scale networks. Specifically, the proposed paradigm aims to limit the number of entries required on the SDN switches, to provide a fast local restoration of single node/link failures without the intervention of the SDN controller and to prevent the possibility of having inconsistent forwarding tables during updates. The proposed paradigm does not require any extension to the OpenFlow protocol and we show how it can be implemented by only using standard features. The DAG-based forwarding paradigm requires to compute a DAG between every pair of ingress-egress switches and to design an index-based hashing scheme to balance the load across the paths in the DAG while avoiding TCP reordering issues. In this paper, we present heuristic algorithms providing a solution to such problems and report the results of a simulation study conducted to assess the performance of the proposed forwarding paradigm.
An Automatic State Detection Algorithm for Non-intrusive Load Monitoring

Knowing the power consumption of individual household appliances is useful for end-user as well as utilities. There are two ways for appliance load monitoring (ALM), namely intrusive load monitoring (ILM) and non-intrusive load monitoring (NILM). This paper focuses on the NILM approach, and discusses a simple yet effective method to improve its accuracy by constructing a better knowledge-base. The proposed methodology is initially verified with the simulation using the Reference Energy Disaggregation Data (REDD) dataset, and later tested on a lab-scale hardware setup as well. Test results reveal that careful construction of knowledge-base can increase the performance of NILM algorithms. MATLAB is used as the programming platform.
Time Domain Energy Detector approach for Detection of Moving Ground Vehicle

This paper introduces a time domain energy detector (ED) approach for vehicle detection using a single microphone. The detection of a deterministic, as well as the stochastic signal in the presence of white Gaussian noise (WGN), is a very challenging task. The ground vehicle generates a similar kind of signal with unknown information. Therefore, there is need of robust vehicle detection algorithm which must detect a vehicle with low false alarm. We proposed an algorithm based on energy detector and estimation of threshold value depending on noise's variance for moving vehicle detection using acoustics data. The energy distribution of the signal follows the chi-square distribution. These decision statistics reveal the vehicle presence information from the signal and detection probability of a true event. The algorithm is tested on the experimental data acquired from the field trials of the moving vehicle. The receiver operating characteristics are computed through Monte Carlo simulations for validation of the proposed algorithm. The results show that the algorithm is capable to detect the moving vehicle with low false alarm even at low signal to noise ratio.
Detecting Abnormal Event in Traffic Scenes using Unsupervised Deep Learning Approach

In today’s digital age, detecting abnormal events in the traffic scene is a more significant problem in video surveillance. The proposed scheme is adopted for detecting abnormalities such as wrong side driving, people crossing the road illegally, vehicle moving in pedestrain pathway and which uses spatio-temporal attributes. Although numerous machine learning approaches are described in existing literature focusing on pixel-wise differnce with less performance in identifying the abnormalities in video traffic scenes. In this paper, a novel frame work for identifying abnormal detection using unsupervised deep learning algorithm. The scheme uses joint based methodology (ConvLSTM with kmeans) with a combination of reconstruction and clustering loss respectively. The convolutional neural network with LSTM is used to detect and recognize anomalies in traffic scenes with refernce of previous frame information. To identify the normal or abnormal events, the proposed work is composed of training and testing phase. The proposed model is implemented in real time and it is found that the accuracy rate for detecting abnormalities 93.02% abnormality than state-of-art technique.
MPPT Control for PV based Wireless Power Transfer System in Lunar Rover by Secondary Side Converter

Lunar rover is an effective method to investigate the Moon information and in recent years it has approached great developments. The application of wireless power transfer system in lunar rover reduce the heat leakage from rover instrument to outside circumstances. Therefore, in this paper, a novel MPPT method conducted with the buck converter in secondary side for Lunar rover wireless power transfer system has been proposed. Compared with the conventional MPPT method with the primary side converter, there is no need to conduct the primary side digital control, which can improve the system reliability and limit the thermal system burden. The proposed method is verified with experiment, and it takes about 0.8s for the proposed system to track the maximum power point and the oscillation arount the maximum power point is limited within the required range.
Data Aggregation Trees with QoS in Sensor Networks

Wireless Sensor Networks (WSNs) are instrumental facilitators in the development of the Internet of Things (IoT). In the smart world of IoT, energy efficient data gathering is an essential requirement to extend the sustainability of the network. In WSNs, Data Aggregation Trees (DATs) are employed for energy efficient data gathering. While DAT gathers data, it is imperative that the network provides enhanced Quality of Service (QoS). Constructing a DAT that caters to the requirements of the application results in improved QoS. This work explores QoS of DATs with Network Lifetime (NL) as a parameter for QoS. QDD, an algorithm to enhance QoS of DATs with Deterministic Network Model(DNM) is proposed. In DNM, any two sensors either communicate or they do not. However, in practical scenarios the communication between a pair of sensors is probabilistic and is represented using the Probabilistic Network Model (PNM). QDP, an algorithm to enhance QoS of DATs with PNM is proposed. Simulation results show the effectiveness of the proposed algorithms and demonstrates improved QoS through DAT path refinements.
Control by social influence: durables vs. non-durables

Individual behavior such as the adoption of new products is influenced by taking account of others’ actions. We study social influence in a heterogeneous population and analyze the behavior of the dynamic processes. We distinguish between two information regimes: (i) agents are influenced by the adoption ratio, (ii) agents are influenced by the usage history. We identify the stable equilibria and long-run frequencies of the dynamics. We then show that the two processes generate qualitatively different dynamics, leaving characteristic ‘footprints’. In particular, (ii) favors more extreme outcomes than (i). This has direct implications for the control of policy interventions.
Unfolding the City: Spatial Preference Based on Individual Demographic Characteristics

A city is shaped not only by its assembled infrastructures but also by the people living inside it. People with a wide range of individual demographic characteristics visit places at different weights and with different preferences, sculpting a city into a space of diverse spatial preferences. Due to the challenges in the collection and analysis of data considering multi-dimensional demographic characteristics, the daily movement of characterized individuals has been less studied. Therefore, we aim to develop a visual analytics system to better explore spatial preference based on individual demographic characteristics. We take a step forward and perform an online survey to collect individual profiles and movements from volunteers on a large-scale. Then, we design a visual analytic system to investigate the spatial preferences of groups by specifying demographic characteristics. To facilitate the identification of characterized groups, individuals are embedded in t-SNE projection for an abstract overview, and vivid graphics are drawn by a data-driven profile method for detailed examination. A 2.5D spatial visualization is proposed to maintain a compact multivariable analysis by relaxing the z-axis to encode information, such as visiting frequency, demands, and traveling distance. Together with a cross-filter and flexible 2.5D interactions, the effectiveness and usability of the system are demonstrated well by a study conducted in Shenzhen.
A Review of Generative Image Modeling Techniques

Generative image modeling is an area of research in computer vision that many works has been conducted to address various issues that has been encountered in the domain. In this paper, we categorize review several works that has been proposed for generative image modeling. The goal of this paper is to encourage the future researchers to pursue the work through the limitations of existing generative image modeling techniques.
Comparison of the Carbon Nanofiber-/Fiber- and Silicone-Based Electrodes for Bioimpedance Measurements

Electrodes for bioimpedance measurements remain a challenge. In practice, commercially available nonpolarizable silver/silver chloride (Ag/AgCl) gel electrodes prove to be the best option for bioimpedance-based testing. The hydrogel layer reduces the problematic electrode–skin interface impedance, which results in a highly reliable signal. Our workgroup is developing a wearable device that estimates aortic blood pressure from the bioimpedance taken from the wrist area. Understandably, wet electrodes do not suit for a wearable, and there is a need for dry, soft, reusable, and stable electrodes. This study proposes stretchable carbon nanofiber/carbon fiber silicone electrodes as an alternative for cardiac signal measurements with bioimpedance. Five different electrode materials are tested and analyzed: Ag/AgCl gel, Ag/AgCl dry, two carbon and silicone composite materials, and carbon textile. To compare the electrodes’ characteristics, the current through the tissue under the changing pressure, frequency, contact duration, and skin preparation is registered, and the electrode–skin impedance is calculated. The soft and stretchable carbon fiber silicone electrodes proved to have similar response as rigid nonpolarizable Ag/AgCl dry electrodes. Selected methods and proposed instrumentation ensure acceptable reproducibility of the base value and the modulation depth of the measured impedance despite the different electrode materials, large variation of the electrode–skin impedance, and the actual measurement current.
The Development of Wind Power Under the Low-Carbon Constraints of Thermal Power in the Beijing-Tianjin-Hebei Region

Thermal power has consumed a lot of coal, which causes serious air pollution in the Beijing-Tianjin-Hebei region. Previous studies on low-carbon development often separated the three regions, ignoring the coordinated development relationships in the regions. Under the low-carbon development constrictions of thermal power, this paper studied the development of wind power. Firstly, from the integration development of the Beijing-Tianjin-Hebei region, the changes in industrial structure and the total electricity consumption were predicted. Secondly, a comprehensive analysis of the low-carbon development policy for these regions, in accordance with the analytical results, controls the development of thermal power. Finally, a system dynamics model is used to analyse the wind power development from 2018 to 2025 under the influence of development on purchased electricity, the development of thermal and wind power policy. The study found that under the existing policies the Beijing-Tianjin-Hebei region can achieve the goal of installed wind power capacity in the “13th Five-Year Plan for Electrical Power Development”. And from 2020 to 2025, the installed capacity of wind power significantly increases. Based on sensitivity analysis for wind power development, installed wind power capacity is the most sensitive to the wind power feed-in tariff, followed by thermal power generation capacity and then purchased electricity. To prevent wind power growing too fast, this paper suggests reducing the feed-in tariff of wind power during the “14th Five-Year Plan”.
Adaptive Sliding Mode Control Design for Nonlinear Unmanned Surface Vessel Using RBFNN and Disturbance-Observer

Unmanned surface vessel(USV) has been applied in the maritime security inspection and resources exploration to execute complex works with its advantages in automation and intelligence. While the nonlinear USV working in the complex ocean environment, the good trajectory tracking performance is an important capacity. However, the nonlinearity, modeling uncertainties (e.g., modeling error and parameter variations) and external disturbance (wind, wave, current, etc) are the main difficulties, which deteriorates the control performance. To solve this issue, most existing algorithms for USV’s tracking have been developed based on the linearization of the USV’s nonlinear dynamic model at specific equilibrium point. However, the integrated effect of nonlinearities, modeling uncertainties and external disturbance has not been well considered, which can degrade the USV’s tracking performance. Therefore, to achieve the good tracking performance for USV, a nonlinear dynamic model is strictly derived in this paper with the integrate consideration of abovementioned issues, and an adaptive sliding mode control design using RBFNN(Radial Basis Function Neural Network) and disturbance-observer is subsequently developed, where a RBFNN approximator is designed to approximate and compensate modeling uncertainties, and a disturbance-observer is designed to estimate and compensate the effect of the external disturbance. Furthermore, the global stability of the overall closed-loop system of USV are strictly guaranteed. The comparative simulation is carried out to validate the fast response, better transient performance and robustness of our proposed control design via comparing with the existing methods.
Road Surface Quality Detection using Smartphone Sensors: Egyptian Roads Case Study

Road anomalies have significant negative effects on both passengers and vehicles such as traffic congestion and accidents. Nowadays, smartphones are ubiquitous and used by so many drivers, at least to know the driving directions to their destination. Several studies utilized this observation and used the smartphone embedded sensors to detect road anomalies. In this paper, we evaluate the effectiveness of this methodology with sensor readings obtained while driving in Egyptian roads. An android application is developed to record sensor readings while driving over the road anomalies. Four datasets are collected for different streets in Cairo of total duration of 80 minutes and about 50K records. To automatically label these datasets, two clustering techniques (K-Means and DBSCAN) are evaluated to give the ground truth for the sensor readings if they represent road anomalies or normal road surface. It is noticed that DBSCAN can accurately cluster sensor readings than K-Means can do. Finally, a classification model is built to classify unseen sensor readings and identify the road surface quality. An accuracy of 96% can be obtained from the built classifier confirming the effectiveness of the adopted methodology in evaluating the road surface quality in Egypt.
Design for Visual Quality Enhancement of Artificial Infrastructure Facilities: An Application to Electricity Pylons

(1) Background: The visual impact of artificial infrastructures on natural landscapes generates a common negative perception in public opinion. However, as in the case of electrical energy, the increasing demand for power supply and its need for capillary distribution require the installation of new lines, commonly overhead lines with tall tower-like pylons. In most countries, this situation is faced with many attempts of solutions, as participatory workshops and design contests. Nevertheless, the solutions are usually not further developed into real structures due to many limitations (e.g., regulatory, safety, lack of feasibility). (2) Methods: This paper presents a systematic method for the design of tower-like pylons (e.g., electric ones) able to improve the visual quality on the landscape areas in which they will be installed. The method identifies a design strategy that advantageously exploits the inevitable visual impact of pylons on the landscape by integrating the symbolic morphology and the topologically optimized pylon structure from the earliest design phases. (3) Results: The resulting structure is designed in three steps. First, a concept is morphologically developed by integrating symbolic references to the landscape, environment, or cultural society. Second, the concept is topologically optimized, by reducing the structural weight and its visual impact, and respecting regulatory requirements. Third, the resulting structure is engineered and embodied into an industrially feasible layout. (4) Conclusions: The method is able to develop an original, brand new tower-like pylon integrating all the types of requirements, such as regulatory, industrial feasibility, and social components’ needs. The resulting electricity pylon presents an enhanced visual quality according to the citizens’ feedback.
Surface Potential based modeling of Sheet Charge Density and Estimation of Critical Barrier Thickness in AlGaN/GaN HEMT

The two dimensional electron gas (2DEG) density in AlGaN/GaN HEMT is modeled by considering surface potential of barrier layer. The critical thickness is calculated to be 3.3nm for Al0.27Ga0. 73N/GaN HEMT which is in agreement with the experimentally observed value of 3nm for the same mole fraction of Al. The model also depicts that, critical barrier thickness decreases with increase in Al mole fraction.
Characterization and Modeling of Mismatch in Cryo-CMOS

This paper presents a device matching study of a commercial 40-nm bulk CMOS technology operated at cryogenic temperatures. Transistor pairs and linear arrays, optimized for device matching, were characterized over the temperature range from 300 K down to 4.2 K. The device parameters relevant for mismatch, i.e., the threshold voltage and the current factor, were extracted, from which the change in both absolute value and variability as a function of temperature and device size were investigated. It is shown that the Pelgrom scaling law is valid also at 4.2 K and that the simplified Croon model is able to accurately predict drain-current mismatch from moderate to strong inversion over the entire temperature range. Additionally, the characterization of linear device arrays shows exacerbated edge-effects at extremely low temperatures, thus requiring the addition of dummy devices at the array boundary. The result of this study is the first model capable of predicting mismatch over a wide range of operating regions and temperatures.
Electromagnetic Interaction of Nickel Oxide polymeric thin film for microwave

Nickel oxide (NiO) thin film was made using solvent casting method by keeping polyvinyl alcohol (PVA) as a base. Transparent film of Nickel oxide (NiO) was formed which was further examined by Vector Network Analyzer (VNA) for understanding its properties in electromagnetic frequency. The measurement was carried out X-band for understanding its application microwave devices. The result indicated the decrease in dielectric constant with 0.5% concentration doping in PVA, the dielectric loss was about 0.15 in 9.5 GHZ. The scattering parameters (S11 and S12) were measured which give good reflection coefficient of -45dB and lower transmission value of -0.1dB. This material is suitable for microwave reflecting material.
Motion Artifact Removal for Bio Signals using Regularized Autoencoder

The advancements in wireless communication technology and micro electro mechanical system (MEMS) based sensor technology led to the rise of body area communication in healthcare field. The bio signals acquired through this on-body communication mainly suffers from motion artifacts caused by shadowing due to the body shape and body movement. These noises may lead to wrong prediction on data. Therefore, it is important to consider denoising the bio signals in order to perform accurate diagnosis and analysis. This paper proposes regularized denoising autoencoder (DAE) to reconstruct the clean signal from its noisy form. Here two regularization terms L1 and L2 which updates the cost function to avoid over fitting problem. This paper proves that L1 is better than L2 in terms of recovering the signal quality and it is measured by Signal to Noise Ratio (SNR). The dataset used here are taken from a neurokit a python tool box for statistics and neurophysiological signal processing.
Commentary (Czito et al): Combined-Modality Treatment for Operable Pancreatic Adenocarcinoma

Drs. Pisters, Wolff, Crane, and Evans have provided an excellent overview of contemporary approaches to staging, surgical management, and treatment of patients with potentially resectable pancreatic cancer. Given the impressive advances in our understanding of the biology and genetics of pancreatic cancer, we would agree that current opportunities for progress against this malignancy are encouraging. The reality, however, is that mortality rates still exceed 95%. While the article addresses the clinical management of patients with operable pancreatic cancer, this subset of patients constitutes only 10% to 15% of all patients with the disease. This group as well as patients with locally advanced and metastatic disease are in need of new and innovative treatment strategies. In this review, we will highlight several of the points made by the authors.
A Quantilized Mean Field Game Approach To Energy Pricing With Application To Fleets Of Plug-In Electric Vehicles

We consider the problem of designing the price of electricity by an energy provider to a pool of homogeneous loads. The energy provider is risk sensitive and considers that its energy production cost at any particular time is related to the instantaneous maximum excursion of the random aggregate demand of the loads. A statistical measure of this excursion is the α- quantile of the distribution of the individual electricity demands of the loads, or equivalently the value dα at risk α, of the electricity demand per vehicle. The price is assumed to be a known and possibly time varying function of dα. The loads are associated with individual price sensitive costs. For a very large number of loads, in particular a large fleet of electric vehicles, this results in a mean field game (MFG). The existence of an MFG equilibrium associated with a price trajectory, and the epsilon- Nash property of the resulting limiting control laws, are established in this work.