Miracle-dz/UniFAR-FTUs huggingface dataset:
Adaptive Density Graph-Based Manifold Alignment for Fingerprinting Indoor Localization

The received signal strength (RSS) fingerprint-based indoor localization has been considered as a promising solution, due to its relatively high localization accuracy and its ease of use in widespread Wireless Local Area Network (WLAN) infrastructure. A major bottleneck is that the offline fingerprint calibration is time consuming and labor intensive. In this study, inspired by our analysis that multi-density is inherent to the RSS distribution, we present a new radio map construction scheme, called Adaptive Density Graph-based Manifold Alignment (ADG-MA), which can reduce the number of Reference Points (RPs) in offline phase. In particular, it utilizes the density features to capture the exact neighborhood relations of RSS. Furthermore, the approach labels the RSS from user traces to construct the radio map. The extensive experiments demonstrate that the proposed method can construct an accurate radio map at a low deployment cost, as well as achieve a high localization accuracy.
Three Planar Devices for Extracting Capacitance per Unit Length

Circuit designers need materials with well-known properties to make predictive circuit models, which is particularly challenging in the millimeter-wave regime. Typical material characterization approaches include on-wafer devices. To get the material properties from these measurements, on-wafer calibrations require a known characteristic impedance. Often, this requirement uses low-loss substrates and a series or shunt load to calculate the capacitance per unit length of the transmission line. This paper investigates three devices with independent analysis procedures to estimate the capacitance per unit length. Although the devices have the same cross section, we observed a significant different in the extracted values and discuss possible mechanisms which may contribute to this difference.
Towards Physical Design Management in Storage Systems

In the post-Moore era, systems and devices with new architectures will arrive at a rapid rate with significant impacts on the software stack. Applications will not be able to fully benefit from new architectures unless they can delegate adapting to new devices in lower layers of the stack. In this paper we introduce physical design management which deals with the problem of identifying and executing transformations on physical designs of stored data, i.e. how data is mapped to storage abstractions like files, objects, or blocks, in order to improve performance. Physical design is traditionally placed with applications, access libraries, and databases, using hard-wired assumptions about underlying storage systems. Yet, storage systems increasingly not only contain multiple kinds of storage devices with vastly different performance profiles but also move data among those storage devices, thereby changing the benefit of a particular physical design. We advocate placing physical design management in storage, identify interesting research challenges, provide a brief description of a prototype implementation in Ceph, and discuss the results of initial experiments at scale that are replicable using Cloudlab. These experiments show performance and resource utilization trade-offs associated with choosing different physical designs and choosing to transform between physical designs.
Rethinking Online Action Detection in Untrimmed Videos: A Novel Online Evaluation Protocol

The Online Action Detection (OAD) problem needs to be revisited. Unlike traditional offline action detection approaches, where the evaluation metrics are clear and well established, in the OAD setting we find very few works and no consensus on the evaluation protocols to be used. In this work we propose to rethink the OAD scenario, clearly defining the problem itself and the main characteristics that the models which are considered online must comply with. We also introduce a novel metric: the Instantaneous Accuracy ( $IA$ ). This new metric exhibits an online nature and solves most of the limitations of the previous metrics. We conduct a thorough experimental evaluation on 3 challenging datasets, where the performance of various baseline methods is compared to that of the state-of-the-art. Our results confirm the problems of the previous evaluation protocols, and suggest that an IA-based protocol is more adequate to the online scenario. The baselines models and a development kit with the novel evaluation protocol will be made publicly available.
Machine Learning Methods Based on Convex Hulls Analysis in Medical Diagnostics Tasks

The methods of machine learning using the apparatus of computational geometry are investigated. The use of convex hulls of sets in a multidimensional feature space allows implementing algorithms for visualization of class intersection area, reduction of feature space dimension and classification algorithms. A method of measurement of convex hull proximity to test point, if the test point is within the convex hull, is proposed. It is based on assessment of directional penetration depth of convex hull vertices projected onto the direction vector from this point to class centroid. Nearest convex hull algorithm, based on the proposed simplified method of assessment of proximity to convex hull, is described. Results of classification for breast cancer diagnosis task solution are presented. The results showed sufficient efficiency of the proposed methods.
Complex Permittivity Measurement System for Solid Materials Using Complementary Frequency Selective Surfaces

This paper describes a novel method of characterizing complex permittivity using a complementary frequency selective surface (CFSS). The CFSS provides a passband behavior and the change in the passband when a material under test (MUT) is placed adjacent to the CFSS has been used for retrieving of the complex permittivity of the MUT. The complex permittivity of the MUT are determined based on the measured bandpass resonant frequency and insertion loss of the CFSS with the MUT. This is an amplitude-only method where phase measurements are not required. This technique offers a convenient, fast, low-cost and nondestructive measurement that is not restricted by the sample size or shape.
Proposal and Comparative Study of Evolutionary Algorithms for Optimum Design of a Gear System

This paper proposes a novel metaheuristic framework using a Differential Evolution (DE) algorithm with the Non-dominated Sorting Genetic Algorithm-II (NSGA-II). Both algorithms are combined employing a collaborative strategy with sequential execution, which is called DE-NSGA-II. The DE-NSGA-II takes advantage of the exploration abilities of the multi-objective evolutionary algorithms strengthened with the ability to search global mono-objective optimum of DE, that enhances the capability of finding those extreme solutions of Pareto Optimal Front (POF) difficult to achieve. Numerous experiments and performance comparisons between different evolutionary algorithms were performed on a referent problem for the mono-objective and multi-objective literature, which consists of the design of a double reduction gear train. A preliminary study of the problem, solved in an exhaustive way, discovers the low density of solutions in the vicinity of the optimal solution (mono-objective case) as well as in some areas of the POF of potential interest to a decision maker (multi-objective case). This characteristic of the problem would explain the considerable difficulties for its resolution when exact methods and/or metaheuristics are used, especially in the multi-objective case. However, the DE-NSGA-II framework exceeds these difficulties and obtains the whole POF which significantly improves the few previous multi-objective studies.
COMPASS: Directing Named Data Transmission in VANETs by Dynamic Directional Interfaces

Inefficient data transmission has been a development bottleneck of Vehicular Ad-hoc NETworks (VANETs), especially in urban areas. It has been proved that many complex IP-based solutions are difficult to be applied in the highly dynamic and link-interrupted vehicular environment. In recent years, Named Data Networking (NDN) has become the most popular realization of Information-Centric Networking (ICN) for future networks. Its characteristics of multi-source, multi-path and in-network caching are helpful for improving the data transmission in VANETs. However, the bottom layer of vehicles cannot provide multiple interfaces to different domains like the routers in wired networks. Thus interface-based forwarding degenerates into directionless broadcasting with low performance and high overhead. Against this problem, we propose COMPASS, a novel named data transmission protocol for VANETs. Firstly, a dynamic directional interface model is built as the cornerstone of our COMPASS. Secondly, the forwarding strategies are improved for rapid interest dissemination and named data retrieving. Besides, an interface remapping method and the update strategies of Forwarding Information Base (FIB) and Pending Interest Table (PIT) are designed to enhance the robustness in high-mobility environment. Finally, the performance of COMPASS is verified on ndnSIM. Compared with the other three state-of-the-art protocols, COMPASS obtains the highest interest satisfaction ratio and the shortest transmission delay in urban traffic scenarios with restricted communication and storage overhead.
Static MRI Reconstruction with RPCA Based on Non-local Self-similarity

The main idea of RPCA (Robust Principal Component Analysis) is to decompose a matrix into a low-rank component L and a sparse component S. In dynamic MRI (Magnetic Resonance Imaging) reconstructions, due to the regular movement or pulsation of organs, the change is relatively slight and the similarity between frames is very high. Therefore, the RPCA model is very suitable for the reconstruction of dynamic MR images. However, for static images, their low-rank attribute is not obvious and we cannot achieve better results if we apply the RPCA model to the reconstruction of static images directly. Based on the non-local self-similarity and sparsity of an image, this paper successfully applied the RPCA model to the reconstruction of static MR images and achieved good reconstruction results.
Latency-Optimal Network Intelligence Services in SDN/NFV-Based Energy Internet Cyberinfrastructure

Energy internet (EI) is a very complex system with various applications that not only require a high-level of cyber-security but also need low-latency communication. Thus, cyberinfrastructure with latency-optimal network intelligence services (NIS), in which application data flows are deeply examined in real-time, is inevitable. In the future internet system, a set of NIS can flexibly be implemented in network function virtualization (NFV)-based middleboxes that overlay on software-defined networking (SDN) architecture, becoming an SDN/NFV-based cyberinfrastructure. However, how to deploy these middleboxes is a non-deterministic optimization problem, which is complicated and time-consuming. Hence, by focusing on latency minimization, we develop an artificial intelligence (AI)-powered solution consisted of two phases. First, middleboxes placement based on the graph cluster analysis, and second, NIS resource allocation based on the prediction of service usage-ratio in each corresponding cluster. The simulation-based experimental evaluation shows that our proposed strategy using an optimized K-means algorithm outperforms the recent state-of-the-art middleboxes placement approaches. The average end-to-end flow latencies are around 23.81%, 18.44%, and 11.49% lower compared with the simulated annealing method, the basic sequential algorithmic scheme, and the minimum spanning tree procedure, respectively. Besides, the proposed resource allocation scheme optimizes further the latency minimization around 4.24%. We believe that the work presented in this paper will aid the communication service providers (CSP) in providing a secure and low-latency SDN/NFV-based cyberinfrastructure for the EI ecosystem.
Improved 3D Perception based on Color Monocular Camera for MAV exploiting Image Semantic Segmentation

In this paper, we propose an improved 3D perception for MAV based on color monocular camera. We exploit the semantic segmentation of scene color images to derive semantic and geometric constraints on the 2D keypoints and 3D reconstruction. The high-level components of the system are ego-motion estimation of the drone and sparse 3D reconstruction and segmentation of the explored environment. The ego-motion and 3D structure components are highly coupled and any significant improvement in either one greatly influences the other one. The proposed method aims at reducing the outliers that come from dynamic objects by avoiding extraction of keypoints from entities that could potentially exhibit dynamic behavior (cars, trucks, pedestrians, etc.) exploiting the semantic segmentation of the image. Also, we derive a set of semantic and geometric constraints which improve the 3D reconstruction. The resulting 3D point cloud is semantically segmented by transfering the semantic class of the originating points from which it was triangulated. Ultimately, all these improvements have a great impact on the speed-up and accuracy of the optimization method used, in our case windowed bundle adjustment.
A 0.096-mm $^{2}~1$ –20-GHz Triple-Path Noise- Canceling Common-Gate Common-Source LNA With Dual Complementary pMOS–nMOS Configuration

This article proposes a novel wideband common-gate (CG) common-source (CS) low-noise amplifier (LNA) with a dual complementary pMOS–nMOS configuration to provide a current-reuse output. Triple-path noise-cancellation is effectively revealed to eliminate the thermal noise of the two CG transistors. Simultaneously, partial cancellation of intrinsic third-order distortion of output-stage transistors improves the input third-order intercept point (IIP3). In addition, we embed a resistive feedback in one of the auxiliary CS amplifiers to balance the multiple tradeoffs between noise figure (NF), input matching (S<sub>11</sub>), and forward gain (S<sub>21</sub>). Fabricated in 65-nm CMOS, the proposed wideband LNA exhibits an IIP3 of 2.2–6.8 dBm and an NF of 3.3–5.3 dB across a 19-GHz BW while consuming 20.3 mW at 1.6 V. S<sub>11</sub> is <−10 dB up to 23 GHz by designing a <inline-formula> <tex-math notation="LaTeX">$\pi $ </tex-math></inline-formula>-type input-matching network. The LNA exhibits a peak <inline-formula> <tex-math notation="LaTeX">$\text{S}_{\vphantom {D_{j}}21}$ </tex-math></inline-formula> of 12.8 dB and occupies a very compact die area of 0.096 mm<sup>2</sup>.
Matlab Simulation of DTC-SVM of Doubly Fed Induction Generator for Wind Energy System

This paper presents matlab simulation of direct torque control - space vector modulation (DTC-SVM) based method for doubly-fed induction generator (DFIG). In this rotor side converter is used to control stator side power. Flux proportional integral (PI) controller and Torque proportional integral controller is used to control the electromagnetic torque and flux of the machine. In classical DTC control i.e., Hysteresis control there are some limitations, variable switching frequency, high switching losses, torque ripples, and high sampling frequency. These limitations are reduced by using SVM of DTC to obtain gate pulse. DTC-SVM provides low switching losses, constant switching frequency, and low sampling frequency as compared to classical DTC control. Matlab simulation result for DTC-SVM of a DFIG system is presented at different operating conditions.
Reduction of Classifier Size and Acceleration of Classification Algorithm in Malware Detection Mechanism Using Processor Information

In recent years, the increase in attacks on IoT devices has become a problem, and security measures for IoT devices are urgently needed. We have proposed a malware detection mechanism using processor information and confirmed that mechanism can detect malware and its variants. In some past research, a malware detection mechanism using processor information was proposed and confirmed that the mechanism can detect malware and its variants. By offloading the proposed mechanism to hardware, we aim to realize a lightweight malware detection mechanism for IoT devices in the future. In this paper, we consider reduction of the classifier size and acceleration of the classification algorithm, which are issues in the our proposed mechanism.
Indoor Positioning System With Cellular Network Assistance Based on Received Signal Strength Indication of Beacon

With the development of the mobile Internet, the demand for indoor location based services(ILBS) is increasing. Applications and devices in indoor environments can determine their position based on Received Signal Strength Indication(RSSI) of bluetooth Beacons. However, because of multipath effects, shadow fading and the blockage of obstacles, RSSI values are unstable for positioning. The propagation model are various in different environments, which have caused great difficulty in high-precision indoor positioning. In this paper, a novel positioning algorithm based on cellular network and bluetooth Beacon’s RSSI is proposed for ILBS. The whole algorithm is divided into two parts: Offline Preparation and Online Positioning. During Offline Preparation, Beacon node is cellular networked in regular quadrilateral; a universal ranging model is derived from traditional ranging model for high-precision positioning. During Online Positioning, missing RSSI values are replaced firstly; next, Outliers Removed Median-Kalman Filter is used to process RSSI, which makes the signal smoother to range; then, the cell in which the mobile terminal is located is determined according to the strongest four RSSI values; after that, using filtered RSSI, distances without height influence could be calculated; finally, Weighted Multi-Point Positioning Algorithm With Cellular Network Assistance is used to calculate the real-time location of the mobile terminal. Experiment results show that our algorithm achieves average accuracy in 0.3m−0.5m and limits the maximum error within 0.8 m.
Context-Aware Sliding Window for Sentiment Classification

Sentiment classification is an active area of research with applications in many domains. Many researchers in the past have proposed techniques to identify sentiments with reasonable accuracy. However, the focus is more on the syntactic and semantic features of the documents. These features are effective but they ignore the user’s past sentiments. In this research, we hypothesize that the past sentiments help the classifier to effectively link the user’s history along with the contents of the current tweet. Thus, allowing learning algorithms to correlate past activities in determining the current sentiments. For this sake, we propose three sliding window features to accumulate past sentiments from the time series data. In this paper, we propose seven variations of Context-aware Sliding Window (CSW) features on different machine learning and deep learning algorithms. Furthermore, we propose a temporal dataset of user tweets, which is manually labeled by nine human annotators. The proposed dataset consists of 36 users having 4,557 tweets. Results indicate significant improvements over six state-of-the-art baseline methods.
Cooperative Spectrum Sensing with Imperfect Knowledge of Secondary Users’ Detection Capability

Cooperative spectrum sensing is a key technology for cognitive radio networks (CRNs) where it can be used to identify idle spectrum holes. In a CRN, secondary users periodically monitor the spectrum to determine an idle band that can be used to transmit their information. Nevertheless, the fading and noisy environments limit the accuracy of individual secondary users decisions. Consequently, multiple secondary users may cooperate to improve the decision making process. Nevertheless, fusing the decisions of the individual secondary users reliably requires the knowledge of the detection and false alarm probabilities of individual users, Pd and Pfa, which have to be estimated at the cognitive radio base station (CRBS), and hence, may suffer from certain estimation errors. Therefore, the main aim of this work is to evaluate the impact of imperfect knowledge of Pd and Pfa on the fusion process. Towards this goal, extensive Monte Carlo simulation experiments are conducted where the estimation error is modeled as a truncated Gaussian random variable. In addition, adaptive detection threshold at the CRBS is considered to improve the system detection performance. The obtained results show that the global detection and false alarm probabilities may deteriorate significantly in the absence of accurate knowledge of Pd and Pfa. Moreover, using adaptive thresholds may be beneficial only when Pd and Pfa are known accurately. Otherwise, the performance may excessively deteriorated.
Investigation of Focal Loss in Deep Learning Models For Femur Fractures Classification

This paper develops an approach based on deep learning for the classifications of a common critical type of bone fractures, namely proximal femur. The performance of the state-of-the-art deep learning architecture, DenseNet, is investigated along with a recently introduced loss function, focal loss, to address the problem of imbalanced classes. Quantitative assessment is carried out on a real dataset consisting of 1347 X-ray images. Results demonstrate that the proposed deep learning approach utilizing focal loss show better performance for the fracture detection case and comparable results for the classification scenarios.
Space Breakdown Method A new approach for density-based clustering

Overlapping clusters and different density clusters are recurrent phenomena of neuronal datasets, because of how neurons fire. We propose a clustering method that is able to identify clusters of arbitrary shapes, having different densities, and potentially overlapped. The Space Breakdown Method (SBM) divides the space into chunks of equal sizes. Based on the number of points inside the chunk, cluster centers are found and expanded. Even if we consider the particularities of neuronal data in designing the algorithm – not all data points need to be clustered, and the data space has a relatively low dimensionality – it can be applied successfully to other domains involving overlapping and different density clusters as well. The experiments performed on benchmark synthetic data show that the proposed approach has similar or better results than two well-known clustering algorithms.
Career Trajectory Prediction based on CNN

With the rapid development of online recruitment, it is very important for recruitment enterprises to analyze job seekers' experience and recommend suitable and satisfied job to them. This paper describes the dataset of job seekers' resumes from the internet. According to the basic personal information of job seekers such as gender, age, specialty and education, as well as multiple work experiences including working duration, company industry, company scale, monthly salary and position name, we predict job seekers' future job information. Considering that there are both textual data and numerical data in each resume. In order to extract semantic features accurately, the text data is transformed into vectors with the help of Word2Vec provided by Google. Ultimately, we construct a convolutional neural network (CNN) model to address the career trajectory prediction problem. The validity of the model is verified on a real-world dataset with 70,000 resumes.