Miracle-dz/UniFAR-FTUs huggingface dataset:
Joint Downlink and Uplink Design for Wireless Powered Cloud Radio Access Networks

This work deals with a joint downlink and uplink design for wireless powered cloud radio access network, where a baseband processing unit (BBU) communicates with downlink and uplink users through multiple remote radio heads (RRHs) connected to the BBU via finite-capacity fronthaul links. In the downlink, the RRHs send information to the downlink users and transfer energy to the uplink users (ULUs). By using the harvested energy, each ULU transmits information to the BBU through the uplink channels. In this work, we maximize the uplink sum-rate of the ULUs subject to the minimum downlink rate constraint as well as the per-node transmit power and the fronthaul capacity constraints. Numerical results confirm the advantages of the proposed algorithm compared to baseline schemes.
Anomaly Based Novel Intrusion Detection System For Network Traffic Reduction

With the coming of anomaly based intrusion detection systems, numerous methodologies and strategies have been produced to track novel assaults on the systems. High detection rate of 98% at a low caution rate of 1% can be accomplished by utilizing these procedures. In spite of the fact that anomaly-based methodologies are productive, signature-based detection is favored for standard usage of intrusion detection systems. As an assortment of anomaly detection procedures were recommended, it is hard to look at the qualities, shortcomings of these strategies. The motivation behind why ventures don’t support the anomaly-based intrusion detection techniques can be surely knew by approving the efficiencies of the every one of the strategies. To explore this issue, the present condition of the examination hone in the field of anomaly-based intrusion detection is surveyed moreover. In this paper, we utilize Deep learning strategies to actualize an anomaly based Novel-IDS. These procedures demonstrate the touchy intensity of generative models with great arrangement, capacities to reason some portion of its knowledge from inadequate data and the versatility.
Airborne Particulate Matter Monitoring Using UAVs for Smart Cities and Urban Areas

In smart cities and urban areas, localized weather monitoring will be a readily available feature. Checking the air quality in small area urban motifs, specifically schools and universities situated near a national primary road and mid to high-rise condominiums with increasing sources of pollutants, e.g. particulate matter (PM), becomes a need for risk assessment. An aerial sensor system, consisting of an unmanned aerial vehicle (UAV) and a sensor payload, is designed and implemented. Measurements for PM concentration are taken at various elevations, and during heavy and light traffic conditions. Results show trends relating PM concentration with elevation and traffic conditions.
CellinDeep: Robust and Accurate Cellular-Based Indoor Localization via Deep Learning

The demand for a ubiquitous and accurate indoor localization service is continuously growing. Current solutions for indoor localization usually depend on using the embedded sensors on high-end phones or provide coarse-grained accuracy. We present CellinDeep: a deep learning-based localization system that achieves fine-grained accuracy using the ubiquitous cellular technology. Specifically, CellinDeep captures the non-linear relation between the cellular signal heard by a mobile phone and its location. To do that, it leverages a deep network to model the inherent dependency between the signals of the different cell towers in the area of interest, allowing it achieve high localization accuracy. As part of the design of CellinDeep, we introduce modules to address a number of practical challenges such as handling the noise in the input wireless signal, reducing the amount of data required for the deep learning model, as avoiding over-training. Implementation of CellinDeep on different Android phones shows that it can achieve a median localization accuracy of 0.78m. This accuracy is better than the state-of-the-art indoor cellular-based systems by at least 350%. In addition, CellinDeep provides at least 93.45% savings in power compared to the WiFi-based techniques.
Device-Based Network Selection for Edge Computing

In this demonstration, we show that on a device with multiple network interfaces, our proposed network selection method can select a proper interface based on measured delays and information flags with an ICMP reply packet in an edge computing (EC) environment. The methods mainly provide the following two features: the resolution of FQDN through multiinterfaces and the insertion of EC and congestion flags into an ICMP reply packet. These features enable the practical usage of multi-interfaces on a device to select a proper network interface for advanced services and applications based on EC. This paper first explains the features and the procedures of our method. Then our demonstration shows that the device can select a proper interface based on the flag information.
Treatment and Spillover Effects Under Network Interference

Abstract We study nonparametric and regression estimators of treatment and spillover effects when interference is mediated by a network. Inference is nonstandard due to dependence induced by treatment spillovers and network-correlated effects. We derive restrictions on the network degree distribution under which the estimators are consistent and asymptotically normal and show they can be verified under a strategic model of network formation. We also construct consistent variance estimators robust to heteroskedasticity and network dependence. Our results allow for the estimation of spillover effects using data from only a single, possibly sampled, network.
Image Enhancement Prior to Color Filter Array Demosaicking

For color images captured by single-image sensors, enhancement is usually performed after Color Filter Array (CFA) demosaicking due to the fact that most image enhancement algorithms are developed for full color images. As enhancement prior to demosaicking will produce a different result from enhancement after demosaicking, it will be more desirable to enhance an image prior to demosaicking particularly for images with a narrow intensity range. One reason is that demosaicking is the interpolation of missing colors, and interpolation within a narrow intensity range with only a few discrete intensity levels may lead to large interpolation errors. On the other hand, if enhancement is performed prior to demosaicking to widen the range of intensity, the above-mentioned interpolation errors could be reduced. In this paper, we proposed an image enhancement algorithm particularly for CFA images prior to demosaicking. It has been shown that enhancement prior to demosaicking could produce more visually pleasing images than those performed after demosaicking.
Pitch Determination of Noisy Speech Using Cumulant Based Modified Weighted Function

In this paper, we propose a pitch extraction method effective in highly noisy environments. The proposed method utilizes the cumulant based modified autocorrelation function (CMACF) and cumulant based circular average magnitude difference function (CCAMDF) in weighted autocorrelation function (WAF), both of which present the periodicity at the pitch period of voiced speech. The CMACF exhibits a peak while the CCAMDF produces a notch. The modified weighted function is developed by the multiplication of the amplitude spectrum of CMACF and the inverse of the characteristics of CCAMDF, to enhance the accuracy of pitch extraction. Experimental results show that the proposed method provides a superior performance than the conventional methods under lower levels of signal-to-noise ratio in white noise.
A Low-power Low-noise Open-loop Configured Signal Folding Neural Recording Amplifier

This paper proposes a design of low-power and low-noise CMOS neural recording amplifier with an open-loop configuration. The proposed design has been simulated using CMOS 0. 18µm process. The proposed design with the signal folding technique, when compared to the closed-loop configured neural amplifier, has adequately minimized the total power consumption. The performance metrics such as gain, bandwidth, and input referred noise is also finely optimized.
Impact of green buildings on HVAC system located in Algeria

The present work deal to study the impact of green buildings on air conditioning type HVAC system. For this purpose, thermal balance based on Algerian norm has been used to predict the efficiency of installation of air conditioning system. The governing equations of thermal balance are described in detail in order to show the impact of green buildings. The obtain results including comparison between thermal coefficient of construction and thermal balance without and with using of the plants.
Distributed Admission Control-QOS Algorithm to Improve Network Performance in MANET Environments

Mobile Adhoc Network (MANET) environments greatly suffer from fluctuating connectivity conditions and broadcasting contention among the locations that adapt it. Despite several on-going endeavors, no meek and real explanation has been obtainable that can be easily organized and manage with the real-life environments and limitations. It is important to improve the Quality of Service (QoS) and achieve better performance in MANET environments. The QoS Aware Routing protocol (QOSAR) is used to discover backup routes for the active sessions and Link Disjoint Interference Aware (LDIA) QoS routing protocol based on MARIA is used to find multiple paths without any interferences. Admission control procedure not used in QOSAR and LDIA protocol. In this paper, a new distributed admission control procedure is proposed for improving QoS in MANET environment called as Distributed Admission Control Protocol with Quality of service (DACP-QoS) uses the flow based on the per-hop basis. The proposed DACP-QoS greatly improves the QoS of the network. This protocol also supports multimedia applications in MANETs with minimum overhead. The proposed protocol achieved better performance than the existing QOSAR and LDIA protocol.
Analysis and Comparison of Permutation Entropy, Approximate Entropy and Sample Entropy

Permutation entropy, approximate entropy and sample entropy can be used to measure the complexity of time series. The sinusoidal signal changed with length, amplitude, phase and frequency is measured by these three kinds of entropy. Simulation results show that the entropy value is stable when the data length is longer than some value, for example, 2048. It is also shows that the change of amplitude and phase does not affect the entropy value. The permutation entropy can better describe the frequency change of the signal.
Analysis of Mine Blast Induced Vibration Signal for Mine Safety

Computational study of blast induced seismic signal is important for better utilization of blast energy. Generally thirty percent of blast energy is utilized in production process. Remaining seventy percent energy wasted in terms of vibration signal, noise and Sound. Mine safety is also very important aspect in addition to energy utilization and mine production. Vibration can be controlled by optimum blast design for specific site and through computational study of blast induced seismic signal. Blast design parameters like charge per delay may be selected by observing dominant frequency of vibration and peak particle velocity. Time frequency analysis of blast induced seismic signal gives added insight about the time and dominant frequencies present in time limited seismic signal. In our study we have de-noised and analyzed 60 blasts induced seismic signal using discrete wavelet transform and extracted dominant frequency present in the signal. A method has been proposed for safe blasting operation which reduces the risk involved in blasting due to vibration and increases the mine production by tuning the charge per delay and other design parameters in real time.
Lung Nodule Detection via 3D U-Net and Contextual Convolutional Neural Network

Lung cancer is the most common cause of cancer death worldwide. Lung nodule detection based on CT image is the most prevailing method for detecting lung cancer. In this paper, we propose a patch-based 3D U-Net and contextual convolutional neural network (CNN) to automatically segment and classify lung nodule and help the radiologists read CT images. Typically, lung nodule detection task could be divided into three stages, including lung segmentation, nodule detection or segmentation and false positive reduction. In lung segmentation stage, we use morphological methods to segment pulmonary parenchyma from raw CT images. To segment lung nodule, 3D U-Net is employed to extract suspicious nodule from preprocessed CT images. In order improve model accuracy, we use Generative Adversarial Network (GAN) to boost model training. To further enhance model performance, we use online sampling strategy to augment data and use 3D contextual CNN with Inception blocks to determine whether the volume is malignant nodule or not. Experimental results demonstrate that the proposed method could effectively detect the cancerous nodule from the CT scans.
A Wearable Skin-Stretching Tactile Interface for Human–Robot and Human–Human Communication

Currently, the majority of wearable robotic haptic feedback devices rely on vibrations for relaying sensory information to the user. While this can be very effective, vibration as a physical stimulation is limited in modality and is uncommon in the natural world. In many cases, for human–robot and human–human interaction, a more natural, affective tactile interaction is needed to provide comfortable and varied stimuli. In this letter, we present the super-cutaneous wearable electrical empathic stimulator (SCWEES), a tactile device that gently stretches and squeezes the surface of the skin. Our hypothesis is that this device can create a pleasant, unobtrusive sensation that can be used to mediate social interactions or to deliver subtle alerts. We describe the design of the SCWEES, a lightweight 3D-printed semi-flexible structure that attaches to the skin at two points and actuates via two shape-memory alloy coil actuators. We evaluate the SCWEES through a range of human interaction experiments: stimulation strength and pleasantness, contraction and extension, and the conveyance of non-disruptive notifications. Quantitative and qualitative results show that the SCWEES generates a pleasant sensation, can convey useful information in human–machine interactions, and delivers affective stimulation that is less disruptive than conventional vibratory tactile stimulation when the user is engaged in a task.
Fixed-Point Quantization of 3D Convolutional Neural Networks for Energy-Efficient Action Recognition

In this paper, 3D convolutional neural networks (CNNs) are simplified to reduce the energy consumption of the action recognition process. Instead of using floating-point weights and input values, which results in a huge amount of processing energy, we introduce a systematic way to quantize all the values of 3D CNNs without degrading the recognition accuracy. Simulation results show that, compared to the baseline CNN architecture, the proposed method significantly reduces the computational complexity as well as the memory requirements.
A Timed Automata Model for Systems with Gateway-Connected Controller Area Networks

Networked systems where events happen in parallel and interact with each other are hard to analyze and prone to errors. Formal methods can help in these cases to discover errors, unexpected scenarios and verify that some property is satisfied in all possible sequences of events. In this paper a timed automata model is presented and this model together with timed computational tree logic allows formal verification of system properties. Timed automata model for single controller area network (CAN) was published previously, but this paper for a first time presents a model that can handle CAN buses connected by a gateway. For selected system configurations a model checking was performed to prove some qualitative properties of the model and finally the worst case delays for messages with different priorities are determined and formally verified.
CNN-BASED ACTION RECOGNITION USING ADAPTIVE MULTISCALE DEPTH MOTION MAPS AND STABLE JOINT DISTANCE MAPS

Human action recognition has a wide range of applications including biometrics and surveillance. Existing methods mostly focus on a single modality, insufficient to characterize variations among different motions. To address this problem, we present a CNN-based human action recognition framework by fusing depth and skeleton modalities. The proposed Adaptive Multiscale Depth Motion Maps (AM-DMMs) are calculated from depth maps to capture shape, motion cues. Moreover, adaptive temporal windows ensure that AM-DMMs are robust to motion speed variations. A compact and effective method is also proposed to encode the spatio-temporal information of each skeleton sequence into three maps, referred to as Stable Joint Distance Maps (SJDMs) which describe different spatial relationships between the joints. A multi-channel CNN is adopted to exploit the discriminative features from texture color images encoded from AM-DMMs and SJDMs for effective recognition. The proposed method has been evaluated on UTD-MHAD Dataset and achieves the state-of-the-art result.
Image Steganalysis via Multi-Column Convolutional Neural Network

Deep learning that jointly studies and extracts features is very promising for steganalysis. In this article, we design a simple but effective Multi-column Convolutional Neural Network (MCNN) based on steganalysis architecture for images. The proposed MCNN architecture allows the input image to be of arbitrary size or resolution. In particular, by utilizing filters with receptive fields of different sizes, the features learned by each column CNN are adaptive to variations in payloads. Comprehensive experiments on standard dataset show that MCNN model can detect the state of arts steganographic algorithms with a high accuracy. It also outperforms several recently proposed CNN-based steganalyzers in conditions of the same embedding key stego and cover-source mismatch scenarios.
Information Fusion VIA Optimized KECA with Application to Audio Emotion Recognition

As a recent proposed information fusion tool, Kernel Entropy Component Analysis (KECA) has attracted more attentions from the research communities of multimedia. It utilizes descriptor of entropy estimation and achieves improved performance for information fusion. However, KECA roughly reduces to sorting the importance of kernel eigenvectors by entropy instead of by variance as in Kernel Principal Components Analysis (KPCA), without extracting the optimal features retaining more entropy of the input data. In this paper, a novel approach Optimized Kernel Entropy Components Analysis (OKECA) is introduced to information fusion, which can be considered as an alternative method to KECA for information fusion. Since OKECA explicitly extracts the optimal features that retain most informative content, it leads to improving the final performance or classification accuracy. To demonstrate the effectiveness of the proposed solution, experiments are conducted on Ryerson Multimedia Lab (RML) and eNTERFACE emotion datasets. Experimental results show that the proposed solution outperforms the existing methods based on the similar principles, and the Deep Learning (DL) based method.
Design and construction of an ánkle-foot orthosis with anti-equine lock

Currently in the Ecuadorian national market there are fixed orthoses AFO (Ankle Foot Orthosis) and SWOT (Dinamyc Ankle Foot Orthosis) for people who have evolutionary deformation of equine foot, the mountains are at a disadvantage in the recovery of the ankle mobility. At international level, these problems have been solved with the use of safety measures, which allow the control of movement in the cycle of the march, but present the disadvantage that they require a great physical effort by the weight of the device. Therefore, the present document shows the design and construction of a theory of the equilibrium division that helps to stop and counteract the evolutionary deformation of the equine foot, besides contributing to the progress of an adult person suffering from spastic quadriparesia, say, a deformation that does not allow the person to have total control over the movements of the ankle. The design is based on a passive AFO reason of being with an implementation of a load compensation system in the articulation, thus providing active orthosis qualities, because this device allows to perform a stable march; this mechanism will work as "anti-equine" at rest, limiting the angle of plantar flexión. The orthosis is designed for use with normal footwear.En el mercado nacional ecuatoriano actualmente se encuentran órtesis pasivas como las ortesis de tobillo-pie AFO (Ankle Foot Orthosis) y las ortesis dinámicas tobillo-pie DAFO (Dinamyc Ankle Foot Orthosis) para personas que tienen deformación evolutiva de pie equino, las cuáles presentan desventajas en la recuperación de la movilidad del tobillo. A nivel internacional, estos problemas se han solucionado con utilización de órtesis activas, las cuales permiten al tobillo un movimiento controlado en el ciclo de la marcha, pero presentan la desventaja que exigen un gran esfuerzo físico por el peso del dispositivo. El presente documento muestra el diseno y construcción de una Ortesis dinámica de tobillo-pie que ayuda a detener y contrarrestar la deformación evolutiva de pie equino, además de contribuir a mejorar la marcha de una persona adulta que padece de cuadriparesia espaística, es decir, una deformacioín que no permite un control total sobre los movimientos del tobillo. El diseño propuesto se basa en una Ortesis pasiva AFO con una implementación de un sistema de compensación de cargas en la articulación, proporcionándole de esta manera cualidades de órtesis activa, lo cual aporta a la rehabilitación del paciente; además este mecanismo funciona como "anti-equino" en el reposo, limitando el ángulo de flexión plantar.
Millimeter wave resonator and cavity-back slot antenna in Fan-Out Wafer Level Packaging

In this paper, a 77 GHz reconstitute resonator without TMV is integrated in fan-out wafer level packaging (FOWLP) with mold first process. The resonator is pre-fabricated with a high dielectric constant and low tangent loss dielectric material to achieve small volume and high Q. The side walls of the resonator are plated with copper. The resonator is embedded in the molding compound and RDLs are used to connect the chip and resonator. A Q-factor of 354.4 is achieved. A small form factor and self-shielding cavity-back slot antenna is proposed based on reconstitute resonator. The fractional bandwidth is 1.89% at 79 GHz and the maximal antenna gain is 5 dBi.