Miracle-dz/UniFAR-FTUs huggingface dataset:
Accurate and Automatic Detection of Oblique RFID-Enabled Objects in Mobile Manner

In the scenarios of warehousing or industrial manufacturing, the objects usually should be placed vertically, box containing bottles of wine cannot be inverted, and the books should be vertically placed on the shelves. RFID has many attractive advantages, no requirement on line-of-sight and battery-free for long-term monitoring. Hence, an RFID-based solution was proposed to detect whether tagged objects are oblique. However, the existing solution has two major limitations: (i) it requires that both RFID reader and tagged objects should be static; (ii) it can only deal with the case of upside down but fails when objects are oblique with angle less than 180^∘. To overcome these limitations, this paper proposes an RFID approach called Mobile Detection of Oblique objects (MDO). In MDO, the tagged objects move relatively in a straight line at a uniform velocity with respect to the reader antenna, RFID reader antenna is fixed and the objects move on conveyor belt. MDO compares the collected phase profile and the theoretical phase profile to determine whether the concerned object is oblique or not. We use Commercial-Off-The-Shelf (COTS) RFID devices to implement the MDO approach. Extensive experimental results prove that MDO can precisely detect the oblique objects with an accuracy larger than 90% under various conditions.
Positioning-Aided 3D Beamforming for Enhanced Communications in mmWave Mobile Networks

The extension from centimeter wave frequencies to millimeter wave (mmWave) frequencies has triggered an enormous transformation in terms of radio access architecture for future wireless networks, and it has therefore empowered unlimited opportunities for the user-oriented services and applications. Besides mmWave as a driving element, beamforming (BF) will be incorporated as a key enabling technology for the future wireless networks. In this paper, we propose a positioning-aided beamforming (PA-BF) framework for enhanced downlink communications in a cloud-oriented mmWave mobile networks. We show that the proposed PA-BF achieves a higher effective transmit ratio that is equivalent to a lower initial access latency than the conventional codebook-based BF, which in turn manifests its capability to support high-velocity mobile users. We also analyze the impact of positioning accuracies on the performance of PA-BF and discuss the trade-offs between different BF strategies with varied system parameters. Our simulation results demonstrate that, with a narrow beam phased array and reasonably good positioning accuracy, the PA-BF framework is capable of achieving higher spectral efficiency than the considered codebook-based BF especially at higher velocities.
EA-HRT: An Energy-Aware scheduler for Heterogeneous Real-Time systems

Developing energy-efficient schedulers for real-time heterogeneous platforms executing periodic tasks is an onerous as well as a computationally challenging issue. This research presents a heuristic strategy named, EA-HRT, for DVFS based energy-aware scheduling of a set of periodic tasks executing on a heterogeneous multicore platform. Initially it calculates the execution demands of every task on each of the different type of cores. Then, it simultaneously allocates each task on available cores and selects operating frequencies for the concerned cores such that the summation of execution demands of all tasks are met as well as there is minimum change in energy consumption for the system. Experimental results show that our proposed strategy is not only able to achieve appreciable energy savings with respect to state-of-the-art (2% to 37% on average) but also enables significant improvement in resource utilization (as high as 57%).
Design and Simulation of Metamaterial Sensor and Numerical Formulation for Dielectric Properties

Metamaterial (MTM) sensor based on Twin-Slit Complementary Split Rectangular Resonator (TS-CSRR) is proposed for measurement of dielectric properties of the materials under test (MUT). Array of two TS-CSRR structures on single board is designed and simulated on CST Microwave Studio by using low cost FR4 substrate. Very sharp response with notch depth of −49.9 dB is obtained at resonance frequency of 7.9 GHz. Array of multi structured CSRR on single board with single band and very sharp response is the novelty. DNG (Double Negative) and NIM (Negative Indexed) properties are extracted from scattering parameters. The transcendental equation is derived to calculate relative permittivity and resonance frequency of unknown MUTs. The response of proposed sensor is like a narrow stop band filter. This approach is very modest, simple in fabrication and decreases cost and computational time. Proposed sensor can be used effectively in numerous fields like satellite communications, radar systems, bio-sensing and liquid characterization.
Research on Taxi Travel Time Prediction Based on GBDT Machine Learning Method

Predicting taxi travel times throughout the city can help organize taxi teams and minimize waiting times for passengers and drivers, creating smart cities. In this paper, we propose to use the Gradient Lift Decision Tree (GBDT) learning model to predict the future travel time based on historical taxi trajectory information. Remembering past information is very important here because future taxi requests are related to information about past actions that took place. For example, someone needs to go to a shopping center and predict the time to better organize friends and follow-up schedules. We proposed a multidimensional feature method based on machine learning gradient enhancement decision tree model. This method uses the original data to construct multi-dimensional basic features and inputs it into the GBDT model to obtain high-level features and further improve the prediction accuracy.
TriBHMM: An Energy-Efficient and Latency-Aware Hybrid Main Memory

Hybrid main memory has the advantages both of DRAM and PCM while overcome the disadvantages both of them. However, the existing hybrid main memory systems are not good at the advantages of DRAM and PCM. So we propose triple-based hybrid main memory (TriBHMM), a novel hybrid main memory system which consists of L1 DRAM and L2 DRAM and L2 PCM. How places data and migrates pages to DRAM or PCM with the page properties will significantly affect the main memory system performance. In this paper, we propose a new algorithm TriBHMM-LRU algorithm for sloving the page replacement problem in TriBHMM. TriBHM-LRU uses the locality of programm and the status of different memory types to place data to reduce PCM writes for improving the lifetime while reducing the latency of the TriBHMM. When pages migration happening, the recently updated pages are migrated from L1 DRAM to L2 DRAM. If some of these pages will be used again at soon, then the pages are replaced from L2 DRAM to L2 PCM. We evaluate the performance of TriBHM to others main memory system, and show the performance of TriBHM is more significant than others. The experiment shows that our TriBHMM is able to efficiently improve the whole performance and reduce PCM writes for improving the lifetime while reducing the latency.
Evaluation and Analysis of FBMC/OQAM Systems Based on Pulse Shaping Filters

Cyclic prefix orthogonal frequency division multiplexing (CP-OFDM) is the most widespread multicarrier modulation scheme. However, the use of a rectangular filter in CP-OFDM causes poor out-of-band (OoB) radiation. Moreover, the CP causes spectral efficiency loss. To overcome these limitations, a filter bank multicarrier (FBMC) was proposed which uses prototype pulse shaping filters that can be adapted to fulfil the system requirements. The choice of the filter is crucial for FBMC/offset quadrature amplitude modulation (OQAM) due to its significant impact on the achieved performance. In this paper, new prototype pulse shaping filters in FBMC/OQAM systems are proposed, aiming to improve the system performance. A number of prototype pulse shaping filters, such as raised cosine pulse (RC), root raised cosine pulse (RRC), better than raised cosine pulse (BTRC), modified Bartlett-Hanning (MBH), improved sinc power pulse (ISP), phase modified sinc pulse (PMSP), parametric linear pulse (PLP), linear combination pulse (LCP), PHYDYAS and Hermite, are considered. The performance of each pulse shaping filter in FBMC/OQAM is evaluated and compared other in terms of the power spectral density (PSD), spectral efficiency, signal-to-interference ratio (SIR), time offset (TO), carrier frequency offset (CFO) and bit error rate (BER) performance over various channels. In addition, as multiple input multiple output (MIMO) is one of the main challenges associated with FBMC, we propose the new approach that use the frequency/time block spreading based on a Walsh-Hadamard (WH) code in MIMO FBMC/OQAM and investigate the combination of the our proposed prototype pulse shaping filters with MIMO systems. The proposed filters are shown to be suitable candidates for FBMC/OQAM systems.
Cooperative Guidance Technology for Multi-Agent System within Given Shape Target

In order to improve the communication support capability of the telecommunication satellites in low altitude field, considering multi-agent system with high flexibility and wide reconnaissance as communication base station. A distributed control algorithm which combines artificial potential field with cluster movement based on sky-earth integration background is proposed in this paper. The motion model of air-launching multi-agent system is established based on artificial potential field between agents and targets. The fixed topology mode, distributed communication method together with virtual leader are used to control the movement direction of the cluster. The convergence conditions for the control system are discussed using Lyapunov stability. Simulations and experimental results illustrate the efficacy of the proposed method.
Breathing Dynamics in a Gain-Guided Dissipative Soliton-Similariton Fiber Laser

Dispersion distribution and optical gain dynamics introduce vivid pulse evolution in fiber lasers. Here, we report on a multi-output fiber laser mode-locked by nonlinear polarization rotation (NPR) in net normal-dispersion regime. In particular, the asymmetry settings of the laser parameters can introduce the spectral reshaping and pulse breathing process at different output ports. Both the transformation from gain-guided dissipative solitons (GGDSs) to similaritons and periodic stretching and compression of pulse widths are experimentally observed. Additionally, intriguing pulse fusion and splitting of GGDS molecules are recorded, which further validates the pulse breathing process during propagating in the laser cavity. To the best of our knowledge, we first experimentally investigate the pulse self-evolution based on a multi-output fiber laser in GGDS-similariton regime. All these findings can enrich our understanding towards the breathing dynamics of ultrashort pulses.
Software Defined Network-Based Management for Enhanced 5G Network Services

Due to the fast development in communication technology and the emerging usage of Internet of Things (IoT) devices that produce a huge amount of data, the fifth generation (5G) mobile network is introduced to support this development. This mobile network can provide many advanced communication features in cellular phones. But unfortunately, this technology faces many challenges. One of its defective challenges is the management of a massive number of devices running different services, so Software Defined Network (SDN) is proposed as a key technology to overcome this drawback. SDN architecture provides higher flexibility, scalability, cost-effectiveness, and energy efficiency in 5G mobile networks. There are usually different architectures for the SDN control plane. We study some of these architectures, and we conciliate the usage of Logically Centralized-Physically Distributed (LC-PD) controller management architecture in 5G networks. This architecture enables providing higher throughput, and lower latency compared to other control plane architectures. In this paper, we focus on the demonstration that the LC-PD control plane architecture improves communication efficiency and the Quality of Services (QoS) of running internet services in the 5G mobile network. We use the Mininet-WIFI emulator in our simulation tests. Our conducted simulations show that employing the LC-PD control plane architecture in 5G networks enhances the QoS of Internet services compared to other SDN implementations.
Spam Review Detection Using the Linguistic and Spammer Behavioral Methods

Online reviews regarding different products or services have become the main source to determine public opinions. Consequently, manufacturers and sellers are extremely concerned with customer reviews as these have a direct impact on their businesses. Unfortunately, to gain profits or fame, spam reviews are written to promote or demote targeted products or services. This practice is known as review spamming. In recent years, the spam review detection problem has gained much attention from communities and researchers, but still there is a need to perform experiments on real-world large-scale review datasets. This can help to analyze the impact of widespread opinion spam in online reviews. In this work, two different spam review detection methods have been proposed: (1) Spam Review Detection using Behavioral Method (SRD-BM) utilizes thirteen different spammer’s behavioral features to calculate the review spam score which is then used to identify spammers and spam reviews, and (2) Spam Review Detection using Linguistic Method (SRD-LM) works on the content of the reviews and utilizes transformation, feature selection and classification to identify the spam reviews. Experimental evaluations are conducted on a real-world Amazon review dataset which analyze 26.7 million reviews and 15.4 million reviewers. The evaluations show that both proposed models have significantly improved the detection process of spam reviews. Specifically, SRD-BM achieved 93.1% accuracy whereas SRD-LM achieved 88.5% accuracy in spam review detection. Comparatively, SRD-BM achieved better accuracy because it works on utilizing rich set of spammers behavioral features of review dataset which provides in-depth analysis of spammer behaviour. Moreover, both proposed models outperformed existing approaches when compared in terms of accurate identification of spam reviews. To the best of our knowledge, this is the first study of its kind which uses large-scale review dataset to analyze different spammers’ behavioral features and linguistic method utilizing different available classifiers.
Designing Efficient Shortcut Architecture for Improving the Accuracy of Fully Quantized Neural Networks Accelerator

Network quantization is an effective solution to compress Deep Neural Networks (DNN) that can be accelerated with custom circuit. However, existing quantization methods suffer from significant loss in accuracy. In this paper, we propose an efficient shortcut architecture to enhance the representational capability of DNN between different convolution layers. We further implement the shortcut hardware architecture to effectively improve the accuracy of fully quantized neural networks accelerator. The experimental results show that our shortcut architecture can obviously improve network accuracy while increasing very few hardware resources $( 0.11 \times$ and $0.17 \times$ for LUT and FF respectively) compared with the whole accelerator.
Machine Learning Approach for Improving the Intelligibility of Noisy Speech

Recently machine learning based speech enhancement approaches have shown immense promise to improve the intelligibility of noisy speech for both normal hearing and hearing impaired listeners. In this paper we study speech intelligibility potential of the single-microphone speech enhancement based on Deep Neural Networks (DNNs), a part of machine learning family. We have shown that DNN based speech enhancement approach, once trained purposely to handle many types of noise and signal-to-noise ratios (SNRs), shown immense potential of attaining large speech intelligibility improvements. The deep neural network models are trained to learn mapping from the noisy speech features and the coefficients of ratio time-frequency masks are estimated. The estimated masks are applied to noisy speech magnitude spectra in order to attain an enhanced intelligibility speech by utilizing the phase of noisy speech. The results at many different noisy conditions including exhibition hall, coffee shop, airport, car and babble and five SNRs: −10dB, −5dB, 0dB, 5dB and 10dB reported that deep neural network-based ratio mask outperformed the competing methods including Nonnegative matrix factorization (NMF) and log minimum mean square error (LMMSE) estimation in terms of the short time objective intelligibility (STOI) and Normalized subband envelope correlation (NSEC) objective speech intelligibility metrics.
A Comparison of Traditional Epidural and Low-Dose Epidural Techniques

The purpose of this thesis was to compare traditional epidural analgesia to other low-dose epidural analgesic techniques e.g. combined spinal-epidural analgesia and mobile epidural analgesia. The risk and side-effects of each type of epidural had on both mother and the newborn was researched and discussed. This was a comprehensive analysis of several studies dating from 1989 to 2016. It was found that traditional epidural analgesia increased the risk for cesarean deliveries, vaginal assisted deliveries, longer duration of labor, postpartum hemorrhage, breastfeeding cessation, higher temperature, lower maternal satisfaction and failed catheters. No effect on the newborn was found. Combined spinal was associated a with significantly decreased risk for cesarean deliveries, vaginal assisted deliveries motor leg weakness, and catheter failure; along with mild side-effects; pruritus, nausea, drowsiness, and some motor weakness. No effect on the newborn was found. Mobile epidural was associated with less fecal incontinence, and an increased need for oxytocin. Women can get an epidural without the risks of long-term side-effects on her nor the newborn. I would recommend combined spinal epidural analgesia due to its effectiveness, low associated risk and high maternal satisfaction.
Short vector problems and simultaneous approximation

In 1982, Lagarias showed that solving the approximate Shortest Vector Problem also solves the problem of finding simultaneous Diophantine approximations. Here we provide the reverse reduction with polynomial complexity. It is gap-preserving provided a consistent norm is used to measure approximation quality in both problems. We also give an alternative to the Lagarias algorithm by first reducing his version of simultaneous approximation to one with no explicit range in which a solution is sought.
Bayesian sequential joint detection and estimation under multiple hypotheses

Abstract We consider the problem of jointly testing multiple hypotheses and estimating a random parameter of the underlying distribution. This problem is investigated in a sequential setup under mild assumptions on the underlying random process. The optimal method minimizes the expected number of samples while ensuring that the average detection/estimation errors do not exceed a certain level. After converting the constrained problem to an unconstrained one, we characterize the general solution by a nonlinear Bellman equation, which is parameterized by a set of cost coefficients. A strong connection between the derivatives of the cost function with respect to the coefficients and the detection/estimation errors of the sequential procedure is derived. Based on this fundamental property, we further show that for suitably chosen cost coefficients the solutions of the constrained and the unconstrained problem coincide. We present two approaches to finding the optimal coefficients. For the first approach, the final optimization problem is converted into a linear program, whereas the second approach solves it with a projected gradient ascent. To illustrate the theoretical results, we consider two problems for which the optimal schemes are designed numerically. Using Monte Carlo simulations, it is validated that the numerical results agree with the theory.
Long-term instability of water in oil microdroplets

Water in oil emulsions have a wide range of applications from chemical technology to microfluidics, where the stability of water droplets is of paramount importance. Here we describe and experimentally characterize a yet overlooked phenomenon, the dissolution of water in oil, which renders nanoliter-sized droplets unstable, resulting in their shrinkage and disappearance in a time scale of hours. The rate of dissolution is determined by the product of the diffusion coefficient and the saturation density of water in oil, which also provides a novel method to measure either of these parameters.
FT-CNN: Algorithm-Based Fault Tolerance for Convolutional Neural Networks

Convolutional neural networks (CNNs) are becoming more and more important for solving challenging and critical problems in many fields. CNN inference applications have been deployed in safety-critical systems, which may suffer from soft errors caused by high-energy particles, high temperature, or abnormal voltage. Of critical importance is ensuring the stability of the CNN inference process against soft errors. Traditional fault tolerance methods are not suitable for CNN inference because error-correcting code is unable to protect computational components, instruction duplication techniques incur high overhead, and existing algorithm-based fault tolerance (ABFT) techniques cannot protect all convolution implementations. In this article, we focus on how to protect the CNN inference process against soft errors as efficiently as possible, with the following three contributions. (1) We propose several systematic ABFT schemes based on checksum techniques and analyze their fault protection ability and runtime thoroughly. Unlike traditional ABFT based on matrix-matrix multiplication, our schemes support any convolution implementations. (2) We design a novel workflow integrating all the proposed schemes to obtain a high detection/correction ability with limited total runtime overhead. (3) We perform our evaluation using ImageNet with well-known CNN models including AlexNet, VGG-19, ResNet-18, and YOLOv2. Experimental results demonstrate that our implementation can handle soft errors with very limited runtime overhead (4%<inline-formula><tex-math notation="LaTeX">$\sim$</tex-math><alternatives><mml:math><mml:mo>∼</mml:mo></mml:math><inline-graphic xlink:href="zhao-ieq1-3043449.gif"/></alternatives></inline-formula>8% in both error-free and error-injected situations).
HERS: Homomorphically Encrypted Representation Search

We present a method to search for a probe (or query) image representation against a large gallery in the encrypted domain. We require that the probe and gallery images be represented in terms of a fixed-length representation, which is typical for representations obtained from learned networks. Our encryption scheme is agnostic to how the fixed-length representation is obtained and can therefore be applied to any fixed-length representation in any application domain. Our method, dubbed HERS (Homomorphically Encrypted Representation Search), operates by (i) compressing the representation towards its estimated intrinsic dimensionality with minimal loss of accuracy (ii) encrypting the compressed representation using the proposed fully homomorphic encryption scheme, and (iii) efficiently searching against a gallery of encrypted representations directly in the encrypted domain, without decrypting them. Numerical results on large galleries of face, fingerprint, and object datasets such as ImageNet show that, for the first time, accurate and fast image search within the encrypted domain is feasible at scale (500 seconds; $275\times $ speed up over state-of-the-art for encrypted search against a gallery of 100 million). Code is available at https://github.com/human-analysis/hers-encrypted-image-search.