Miracle-dz/UniFAR-FTUs huggingface dataset:
TPA: Prediction of Spoofing Attack Using Thermal Pattern Analysis in Ultra Dense Network for High Speed Handover Scenario

With the rising demand for a high-data rate by the subscribers, security becomes a prominent and critical issue for the emerging ultra-dense networks (UDNs). Although, more access points (APs) are involved with the purpose to strengthen the signal quality and aid in user equipment (UE) throughput enhancement. Thus, the UDN serves as a promising approach to accommodate a large number of APs (and UE’s) and ensure them with seamless connectivity and ubiquitous coverage. However, this intensification of base station (BS) densities will upsurge the handover (HO) rates for high-speed users. In this context, this paper investigates the security issues for the roaming users in UDN, pertaining to the increased HO percentage. Toward this goal, a novel approach called as thermal pattern analysis (TPA) is proposed to determine the probable region of attack, for high-speed users through tracking their footprints of thermal energy patterns (i.e., energy and spectral efficiency). We also perform the secrecy capacity check on wandering users, considering the fact that eavesdropper (or Eve) location is erratic. Comprehensive simulations are performed for real-time deployment and results validate the effectiveness of the proposed approach. Consequently, thermal analysis can be performed for all variety of mobile communication scenarios to uncover the adversary tremor. A real-time implementation using hardware components related to the prototype study has been carried out to access the performance in the actual world. From the analysis of the results, it is quite evident that TPA is more accurate in finding the probable region of low security for the UDN environment.
Blind Spectrum Sensing Based on Recurrence Quantification Analysis in the Context of Cognitive Radio

In Cognitive Radio, spectrum sensing methods can be classified in three categories: temporal, frequential and hybrid (temporal and frequential) methods. Temporal methods require a long observation period; frequential and hybrid methods have a high calculation cost and they are very sensitive to frequency resolution. In very low signal-to-noise ratio (SNR) and non-cooperative conditions, spectrum sensing methods present some limitations. To overcome these shortcomings, we propose a new blind strategy to detect the unoccupied spectral bands during a very short observation period. This new strategy is a temporal method based on Recurrence Quantification Analysis (RQA) of the received signal. Since the recurrence level in a communication signal is different from that of White Gaussian Noise, the detector can evaluate the recurrence level of the observed signal to detect the presence of a communication signal over a given spectral bandwidth. First, we estimate the three fundamental parameters of the recurrence matrix: the time delay parameter, the embedding dimension and the recurrence threshold. With these parameters, during a detection stage, the detector evaluates the recurrence level through the recurrence rate and compare it to a predetermined threshold estimated in absence of the signal of interest. The spectrum sensing based on RQA is very fast, free of frequency resolution issue and able to distinguish communication signal from a White Gaussian Noise. The results of our simulations prove the robustness of proposed RQA detector acting over limited number of samples and under very low SNR conditions.
Frequency Agnostic RF-Photonic Limiter with GeAsSe Tapered Fiber Brillouin Laser

A RF-photonic limiter capable of frequency agnostic suppression of strong RF interferers was demonstrated. By using stimulated Brillouin scattering in a Brillouin laser comprising polarization maintaining GeAsSe micro-structured optical fiber tapers, limiting of multiple simultaneous in-band interferers can be achieved with minimal degradation of wideband signals.
P-Score: Performance Aligned Normalization and an Evaluation in Score-Level Multi-Biometric Fusion

Normalization is an important step for different fusion, classification, and decision making applications. Previous normalization approaches considered bringing values from different sources into a common range or distribution characteristics. In this work we propose a new normalization approach that transfers values into a normalized space where their relative performance in binary decision making is aligned across their whole range. Multi-biometric verification is a typical problem where information from different sources are normalized and fused to make a binary decision and therefore a good platform to evaluate the proposed normalization. We conducted an evaluation on two publicly available databases and showed that the normalization solution we are proposing consistently outperformed state-of-the-art and best practice approaches, e.g. by reducing the false rejection rate at 0.01% false acceptance rate by 60-75% compared to the widely used z-score normalization under the sum-rule fusion.
Multimodal Gesture Recognition Using Densely Connected Convolution and BLSTM

In this paper, we present a multimodal method based on densely connected convolution and bidirectional long-short-term-memory (BLSTM) for gesture recognition. The proposed method learns spatial features of gestures through the densely connected convolutional network, and then learns long-term temporal features by BLSTM network. In addition, fusion methods are evaluated on our model, and we find that fusion of features with different information can significantly improve the recognition accuracy. This purely data driven approach achieves state-of-the-art recognition accuracy on the ChaLearn LAP 2014 dataset and the Sheffield Kinect gesture (SKIG) dataset. (98.80% on the ChaLearn LAP and 99.07% on SKIG)
Malware static analysis and DDoS capabilities detection

The present thesis addresses the topic of denial of service capabilities detection at malware binary level, with the aim of designing a framework that integrate results from different binary analysis methods and decide on the DDoS capabilities of the analysed malware. We have implemented a process to extract meaningful data from malware samples, the extracted data was used to find characteristics and features that can lead to the detection of DDoS capabilities in binaries. Based on the discoveries, a set of rules was elaborated to detect those features in binaries. The method is tested on a dataset of 815 samples. Another dataset of 525 benign binaries is also used to test false positives rate of the implemented method. The results of our method are compared with Virus Total analysis results to assess our detection approach.
Detection of People Trapped under the Ruins Using Dual-frequency IR-UWB Radar

Detection of people hidden by obstacles using impulse radio ultra wideband radar has become a keen research nowadays. According to theories of the UWB radar, the lower center frequency is, the better penetrability radar has. And the larger bandwidth is, the higher radar range resolution becomes. In order to detect survivors trapped under the ruins of collapsed buildings, researches commonly focus on the radar with center frequency below 500 MHz. However, the radar with low center frequency always has small bandwidth, so it is relatively insensitive to the weak signal of the survivor. And this may lead to miss detection in search and rescue missions. Therefore, a novel dual-frequency IR-UWB radar system is developed in this paper. It can works alternately at each waveform with two center frequencies, which are 270 MHz and 400MHz. And the equivalent bandwidth of the system is relatively extended to enhance the sensitivity to weak vital signals. Moreover, cross-correlation technique is applied to the processed data of the two channels to improve the detected signal to noise ratio (SNR). The experimental results show that this system can both meet the need of low center frequency and large bandwidth in life detection and the detected SNR of the system is much higher than that of the single-frequency IR-UWB radar. So this system can work more reliably and effectively in the detection of people trapped under the ruins.
Sparse Dual of the Density Peaks Algorithm for Cluster Analysis of High-dimensional Data

The density peaks (DP) algorithm for cluster analysis, introduced by Rodriguez and Laio in 2014, has proven empirically competitive or superior in multiple aspects to other contemporary clustering algorithms. Yet, it suffers from certain drawbacks and limitations when used for clustering high-dimensional data. We introduce SD-DP, the sparse dual version of DP. While following the DP principle and maintaining its appealing properties, we find and use a sparse descriptor of local density as a robust representation. By analyzing and exploiting the consequential properties, we are able to use sparse graph-matrix expressions and operations throughout the clustering process. As a result, SD-DP has provably linear-scaling computation complexity under practical conditions. We show with experimental results on several real-world high-dimensional datasets, that SD-DP outperforms DP in robustness, accuracy, self-governess, and efficiency.
Temperature Effects on the Efficiency of Dickson Charge Pumps for Radio Frequency Energy Harvesting

An experimental study is carried out to assess the effect of temperature on the conversion efficiency of ultrahigh-frequency energy harvesters based on diode-capacitor Dickson charge pumps, frequently used in self-energizing circuits, such as in radio frequency identification tags or in wireless sensor nodes. Using off-the-shelf Schottky diodes often adopted for this application, it is shown that the harvester conversion efficiency at 868 MHz is temperature dependent due to the changing rectification ratio, namely the ratio between the forward and the reverse current flowing through the low barrier height Schottky diodes, which both show a positive derivative with T. The experimental study, supported by SPICE simulations, has shown that a temperature variation might be particularly harmful at the lowest incident power regimes, when even a minimal drop in the conversion efficiency might determine the out-of-servicing of a wirelessly energized circuit.
Nonvolatile Portable Power Supply. Performance Capabilities and Organization Principles in Continental Areas

The analysis solar and wind electrical generation units efficiency in the continental areas with determination of the most potential constructive decisions is carried out. The framed wind rotor developed by the authors providing the continuity of electric power supply during the year, portability and transportability of the wind-electric unit. The necessity of using the positioning control systems of solar electrical units’ photosensitive plates with the increased generation energy efficiency up to 42% is scientifically grounded. The organization of nonvolatile electric power supply complexes by heterogeneous energy sources provides for the increase in the energy supply stability by more than 2 times, at the same time, it helps to reduce the complex size and mass 2 times if compared with the homogeneous ones. The analyzed portable systems’ organization principles enable the authors to make autonomous electric power generating complexes that do not require connection to stand-by energy sources.
Experimental Investigation of 4-kb RRAM Arrays Programming Conditions Suitable for TCAM

Resistive random access memories (RRAMs) feature high-speed operations, low-power consumption, and nonvolatile retention, thus serving as a promising candidate for future memory applications. To explore the applications of the RRAM, switching variability and cycling endurance need to be addressed. This paper presents extensive characterizations of multi-kb RRAM arrays during forming, set, reset, and cycling operations. The relationships among programming conditions, memory window, and endurance features are presented. The experimental results are then used to perform variability-aware simulations of a 128-bit RRAM-based ternary content-addressable-memory (TCAM) macro. The tradeoff among endurance, search latency, and reliability in terms of match/mismatch detection is explored, identifying the programming conditions that allow to obtain a searching speed comparable to static random access memory-based TCAMs (2 ns on average and 3 ns at <inline-formula> <tex-math notation="LaTeX">$3\sigma $ </tex-math></inline-formula>) while guaranteeing good reliability metrics (with a time ratio of 3000 on average and 150 at <inline-formula> <tex-math notation="LaTeX">$3\sigma $ </tex-math></inline-formula>).
Controllability Degree of Directed Line Networks: Nodal Energy and Asymptotic Bounds

This paper studies the controllability degree of dynamical networks with continuous-time dynamics. To quantify the controllability degree of a network, we introduce a new notion termed nodal energy, which is the control energy required to change the state of a single node while keeping the final states of the other nodes unchanged. Since it is extremely challenging to analyze the nodal energy of general networks, this paper focuses on a special class of directed line networks with single control nodes. This choice allows us to derive the explicit expression of the nodal energy of different network nodes, and hence, reveal novel controllability properties of complex networks. Our analysis shows that (i) differently from their discrete-time counterpart, directed line networks with continuous-time dynamics are always difficult to control, as the control energy grows linearly or even exponentially with the network cardinality, (ii) the numerical investigation of the controllability degree of line networks is unreliable, because the condition number of the controllability Gramian grows exponentially fast as the network cardinality increases, and (iii) nodal energies may be inversely related to the graphical distance from the control node because, depending on the network weights, distant nodes may require far less nodal energy than immediate neighbors.
Device for Energy Extraction from the Water Flow for the Pipeline Network Monitoring System

The device of extracting energy from the water flow for pipeline network remote monitoring system based on a piezoelectric cantilever immersed in a water stream is considered in the paper. The water flow being in contact with the piezoelectric cantilever causes its oscillations and appearance of a potential on the plates of the piezoelectric element.
Effect of Resistance Training and Fish Protein Intake on Motor Unit Firing Pattern and Motor Function of Elderly

We investigated the effect of resistance training and fish protein intake on the motor unit firing pattern and motor function in elderly. Fifty healthy elderly males and females (69.2 ± 4.7 years) underwent 6 weeks of intervention. We applied the leg-press exercise as resistance training and fish protein including Alaska pollack protein (APP) as nutritional supplementation. Subjects were divided into four groups: fish protein intake without resistance training (APP-CN, n = 13), placebo intake without resistance training (PLA-CN, n = 12), fish protein intake with resistance training (APP-RT, n = 12), and placebo intake with resistance training (PLA-RT, n = 13). Motor unit firing rates were calculated from multi-channel surface electromyography by the Convolution Kernel. For the chair-stand test, while significant increases were observed at 6 weeks compared with 0 week in all groups (p < 0.05), significant increases from 0 to 3 weeks and 6 weeks were observed in APP-RT (18.2 ± 1.9 at 0 week to 19.8 ± 2.2 at 3 weeks and 21.2 ± 1.9 at 6 weeks) (p < 0.05). Increase and/or decrease in the motor unit firing rate were mainly noted within motor units with a low-recruitment threshold in APP-RT and PLA-RT at 3 and 6 weeks (12.3 pps at 0 week to 13.6 pps at 3 weeks and 12.1 pps at 6 weeks for APP-RT and 12.9 pps at 0 week to 13.9 pps at 3 weeks and 14.1 pps at 6 weeks for PLA-RT at 50% of MVC) (p < 0.05), but not in APP-CN or PLA-CN (p > 0.05). Time courses of changes in the results of the chair-stand test and motor unit firing rate were different between APP-RT and PLA-RT. These findings suggest that, in the elderly, the effect of resistance training on the motor unit firing rate is observed in motor units with a low-recruitment threshold, and additional fish protein intake modifies these adaptations in motor unit firing patterns and the motor function following resistance training.
How Useful and Understandable is the APPLIES Framework? a Preliminary Evaluation With Software Practitioners

Although engineering software product lines have proven to be an efficient way to develop similar products with high quality and increased time-to-market, companies must determine if they are willing to do the business and technical changes that this strategy requires. To support decision making, we are proposing APPLIES, a framework for evaluating the motivation and preparation for adopting product lines in an organization. We reviewed drivers, success factors and barriers described in the literature and proposed a comprehensive framework where these aspects can be prioritized and inter-related by diverse stakeholders in a company. Decision makers can use the resulting information to advise or not the implementation of product lines. This paper reports an initial evaluation of our proposal: a quasi-experiment where a group of 14 practitioners discussed motivators and barriers for adopting product lines, applied the framework and answered a set of questions about it. Our results show that reviewing the proposed factors contribute to evaluate if product line engineering could be adopted. However, we noted some problems with the used descriptions and visualizations that we must tackle to improve the framework and prepare further evaluations. This paper presents lessons learned in the process and gives some advice to researchers proposing similar techniques.
Analysis of Queueing Networks with Geo/Geo/1 Clients and Batch Service Queue

Currently, the knowledge of queueing network is an important tool for modeling many complex communication systems. In this work, we consider a tandem open queueing network consisting of multiple Geo/Geo/1 clients, and a server with batch service queue. All of queues are finite-buffer discrete-time. The main purpose of this work is to analyze the average delay and the overflow probability of messages from each client through the system. To solve the problem, we firstly transform the model with plenty of clients and one server into one client and one server according to the coupling clients with independent arrivals. Next, we calculate the stationary distribution of queue lengths of all nodes by the knowledge of Bivariate Discrete Time Markov Chains. Then, we analyze the performance metrics of messages from each client through the queueing network. Finally, simulation results verify the effectiveness of the analytical method and draw some constructive conclusions.
Plenoptic Sensor: Application to Extend Field-of-View

In this paper we study the light field sampling produced by ideal plenoptic sensors, an emerging technology providing new optical capabilities. In particular, we leverage its potential with a new optical design that couples a pyramid lens with an ideal plenoptic sensor. The main advantage is that it extends the field-of-view (FOV) of a main-lens without changing its focal length. To evince the utility of the proposed design we have performed different experiments. First, we demonstrate on simulated synthetic images that our optical design effectively doubles the FOV. Then, we show its feasibility with two different prototypes using plenoptic cameras on the market with very different plenoptic samplings, namely a Raytrix R5 and a Canon 5D MarkIV. Arguably, future cameras with ideal plenoptic sensors will be able to be coupled with pyramid lenses to extend its inherent FOV in a single snapshot.
The Effect of Surface Optimization on Post-grinding Yield of 200 mm Wafer Level Packaging Applications

In this paper, a yield increase on post-grinding of 200 mm wafer level packaging applications is presented. 200 mm wafer level plasma enhanced oxide-oxide direct bonding and wafer grinding are used in the packaging of the wafers. Since the surface conditions of the wafers that are used in the packaging is the most critical point of the wafer bonding and grinding processes, it is focussed to optimizing wafer surfaces to increase the yield. After the optimizations on the surface conditions of the BiCMOS wafer used in the packaging, the 200 mm plasma enhanced wafer to wafer direct bonding yield increases from 0% to 99% and at the same time the post-grinding yield of these wafers increases from 0% to over 90%.
Detection of Sensor Data Injection Attacks with Multiplicative Watermarking

In this paper, the problem of detecting stealthy false-data injection attacks on the measurements is considered. We propose a multiplicative watermarking scheme, where each sensor’s output is individually fed to a SISO watermark generator whose parameters are supposed to be unknown to the adversary. Under such a scenario, the detectability properties of the attack are analyzed and guidelines for designing the watermarking filters are derived. Fundamental limitations to the case of single-output systems are also uncovered, for which an alternative approach is proposed. The results are illustrated through numerical examples.
Microstructure instabilities in solution-processed organic bulk-heterojunction solar cells

The performance of organic solar cells (OSCs) is determined by the delicate, meticulously optimized bulk-heterojunction microstructure, which consists of finely mixed and relatively separated donor/acceptor regions. In this work we examine the reliability and stability of bulk-heterojunction (BHJ) microstructures of a highly-efficiency OSC based on PCE11 as the donor and PCBM as the acceptor. The so called burn-in degradation is identified as a spinodal de-mixing due to the low miscibility of donor and acceptor, which is turned out to be a major challenge for the development of stable and efficient OSCs. Even though the microstructure can be kinetically tuned for achieving high-performance, the inherently low miscibility of donor and acceptor leads to spontaneous phase separation in the solid state, even at room temperature and in the dark. The construction of the spinodal phase diagrams highlight molecular incompatibilities between the donor and acceptor as a dominant mechanism for burn-in degradation, which is to date the major short-time loss reducing the performance and stability of organic solar cells [1].
Completed Grayscale-Inversion and Rotation Invariant Local Binary Pattern for Texture Classification

Local binary pattern (LBP) and its variants (e.g., LTP and CLBP) are powerful descriptors for texture analysis. However, most of these LBP-based methods are sensitive to inverse grayscale changes. To overcome this problem, we present a novel texture descriptor named Completed Grayscale-Inversion and Rotation Invariant Local Binary Pattern (CGRI-LBP). CGRI-LBP is based on the framework of CLBP which jointly encodes three components (i.e., the signs and magnitudes of local differences as well as central pixels) but with two significant improvements: 1) the sign information of local differences is encoded by a rotation-invariant complementary coding scheme, and 2) the intensity information of central pixels is encoded via a dominant intensity order measure. Extensive experiments on three texture databases (Outex, CUReT and KTH-TIPS) demonstrate that the proposed descriptor achieves the state-of-the-art classification performance in the presence of linear and even nonlinear grayscale-inversion changes.