Miracle-dz/UniFAR-FTUs huggingface dataset:
Systematic Wavelet Codes: Designed and Decoded by DFT Vectors

A class of complex-valued convolutional codes, wavelet codes, that encode numbers into numbers, is constructed by assigning consecutively indexed discrete Fourier transform (DFT) vectors to the parity-checking requirements. For a rate <inline-formula> <tex-math notation="LaTeX">$k/n$ </tex-math></inline-formula> code with constraint length <inline-formula> <tex-math notation="LaTeX">$m$ </tex-math></inline-formula>, the parameters must satisfy (m + 1)(n-k)<n. The algebraic structures are very similar to the finite field convolutional codes but the decoding procedures operate over the complex numbers. These wavelet codes are effective against the impulse errors even when round off noise due to computational operations is allowed too. When the groups of impulsive errors are separated by the constraint length <inline-formula> <tex-math notation="LaTeX">$m$ </tex-math></inline-formula>, the errors can be corrected by extracting from the convolutional code’s syndromes, new syndromes associated with a DFT block code inherent in the code’s algebraic structure. A modified Berlekamp–Massey algorithm computes the error locations and the correcting values are determined by an inverse DFT operation. Systematic forms of these codes are developed by manipulating the original generating transfer function. Each systematic form is a subcode of the original wavelet code from which it was determined. Moreover, this form may be decoded by DFT code methods too. Extensive simulation results verify the power and viability of both forms of the codes and their decoding techniques.
Tap Based User Authentication on Smartphones for Visually Impaired People

User authentication is essential to provide security, privacy, and accountability. The identity of an individual or a device is verified through the authentication process. Traditional authentication techniques (text-based, token-based) are not suitable for the person with vision impairment, as it needs visual interaction. In this paper, we propose a secure, lightweight and user-friendly tap-based authentication technique specially designed for visually impaired people. In this, we use the touch-sensitive screen of the smart devices for obtaining user's credentials. The proposed system uses the tapping location as password. The challenges of the proposed system include simplicity, usability, memorability and most importantly security. We develop an android based application, to experimentally verify the performance of the proposed authentication technique. The goal is to understand the feasibility of the proposed technique on smart devices and to study the pros and cons of the same.
NVM-Based FPGA Block RAM With Adaptive SLC-MLC Conversion

The capacity of SRAM-based FPGA block RAM (BRAM) is restrained by the low density and high leakage power of the current CMOS technology. In this paper, we propose a nonvolatile memory (NVM)-based BRAM architecture which enables flexible conversions between single-level cell (SLC) and multilevel cell (MLC) states. We show that despite the high per-access latency and power consumption, MLC-based BRAM blocks reduce the routing cost between logic units and on-chip data storages, which potentially leads to a smaller critical path delay and power consumption. Therefore, we propose an NVM BRAM architecture and an EDA framework which adaptively packs data into SLC- or MLC-state BRAMs during FPGA design flow in order to achieve better system performance. This paper illustrates that a simple memory device replacement from SRAM to NVM leads to nonoptimal system performance. On the other hand, compared with operating all NVM BRAM blocks in the SLC state with better per-access latency and power consumption, the proposed hybrid SLC-MLC architecture and design flow improves the critical path delay by 18.51%, with a system power reduction of 25.83% at the same time. Moreover, compared with the traditional “fast” SRAM-based BRAM blocks under the same BRAM area constraint, our hybrid NVM BRAM architecture improves the critical path delay by 8.55% on average, with an average system power reduction of 54.34% at the same time.
Tracking-by-Segmentation Using Superpixel-Wise Neural Network

A tracking-by-segmentation algorithm, which tracks and segments a target object in a video sequence, is proposed in this paper. In the first frame, we segment out the target object in a user-annotated bounding box. Then, we divide subsequent frames into superpixels. We develop a superpixel-wise neural network for tracking-by-segmentation, called TBSNet, which extracts multi-level convolutional features of each superpixel and yields the foreground probability of the superpixel as the output. We train TBSNet in two stages. First, we perform offline training to enable TBSNet to discriminate general objects from the background. Second, during the tracking, we fine-tune TBSNet to distinguish the target object from non-targets and adapt to color change and shape variation of the target object. Finally, we perform conditional random field optimization to improve the segmentation quality further. Experimental results demonstrate that the proposed algorithm outperforms the state-of-the-art trackers on four challenging data sets.
A Heuristic Approach to Assess the Traffic Matrix of an ISP Exploiting Segment Routing Flexibility

The Ingress Egress Traffic Matrix (IE TM) assessment is a fundamental step of the network management for an ISP network, since it represents the key input parameter used by any Traffic Engineering solution to optimize the resource utilization and to improve the Quality of Service. The actual TM assessment procedures are based on estimation algorithms or measurement based approaches. This paper presents a method to measure the intensity of traffic flows, that overcomes the limits of the classical measurement/estimation based approaches. The idea is to exploit the flexibility of the Segment Routing paradigm to implement controlled routing changes so that to measure the intensity of a subset of network flows. The main contribution of the work is to show the feasibility of the proposed approach by means of a low complexity heuristic, referred to as Path Cost Bases (PaCoB), able to identify the list of routing changes that allow to improve the TM assessment procedure. The heuristic is composed of successive steps, referred to as snapshots: in each snapshot the routing of a set of flows is changed so that to assess their intensities. The performance evaluation show thatPaCoB assesses the intensity of more than 90% of flows. Moreover, when used in conjunction with an estimation algorithm, PaCoB allows to reduce the estimation error by more than 50% performing only 10 snapshots.
The Defect Detection Algorithm for Tire X-Ray Images Based on Deep Learning

For current domestic and international tire detection systems, the software operation of them is complex and poor to be put into application. In reality, it is necessary to do the task of defect detection by observing the X-ray image of the tire with the help of human eyes. This practice is affected by some subjective factors and both the accuracy and efficiency vary from person to person without strong robustness. To tackle this issue, one detection algorithm for tire defects based on deep learning is proposed. In this case, the model is trained, learnt and tested using the collected defect samples preprocessed from tire X-ray images. The designed algorithm was verified by the developed automatic tire defect detection software, in which the desired results were obtained.
Evaluation of the Impact of the Size of Storage Devices in Grid-Connected Microgrids

Storage devices play an essential role in increasing the flexibility of microgrids. In addition to improving the security and the resiliency of the microgrids, and in turn facilitating the handling of energy generated from intermittent sources, storage devices also allow microgrids to take tactical decisions to improve their efficiency or to increase their revenues. Demand side management, load following, and switching between an island mode and a grid-connected mode are typical examples of applications that are better enabled by the presence of storage devices. In this context, the objective of this paper is to explore how the size of storage devices affect the efficiency of microgrids, and their ability to gain revenues in a competing market. Results obtained in realistic Monte Carlo simulations on the IEEE 39-bus system are provided and discussed for this purpose.
An Improved LFM Signal Reconstruction Method and its Application

In this paper, a time-domain signal reconstruction method based on the Lv distribution (LVD) is introduced for multi-component linear frequency modulated (LFM) signals. Comparing to the LVD based signal reconstruction (LSR) which had been reported to recover signals based on the auto-terms, our approach can reduce recovery errors by subtracting cross-terms mixed in the auto-terms. Therefore it is an improved method of LSR, referring to as the LSR with suppressed cross-terms (LSRSC). Examples are simulated to show that the LSRSC is able to reconstruct multi-component LFM signals effectively. Finally, the proposed method is employed on re-sampling to arbitrary sampling rate and achieves better performance than the LSR and fractional Fourier transform.
A Dual Loop 8-Channel ECG Recording System with Fast Settling Mode

This paper presents a dual-loop 8-channel ECG recording system with fast settling mode for 12-lead application. It employs frequency division multiplexing (FDM) technique to achieve multi-channel IA (MCIA). This leads to smaller area due to the use of smaller input capacitors and sharing of PGA, buffer and ADC. To suppress the DC offset of bio-potential signal, dual DC servo loops are proposed. A fast settling loop can be enabled to shorten the settling time from 14s down to only 0.6 s. The MCIA achieves 113.2-dB CMRR, 113-dB PSRR, and 1.44-µVrmsnoise performance. The MCIA occupies only 0.3 mm2 and consumes 60 µW for 8 channels. The whole ECG recording system was implemented in 130 nm CMOS technology.
Photovoltaic Array Modelling and Boost-Converter Controller-Design for a 6kW Grid-Connected Photovoltaic System - DC Stage

Grid-connected Photovoltaic (PV) systems have increased dramatically in the last few years due to the increased global interest in renewable energy sources and the growth in energy demand. Consequently, new and modern control strategies should be applied to improve the efficiency, reliability, and stability of grid-connected PV systems. To achieve this, the whole PV system should be tested under various weather and grid conditions. However, the wide variety of the PV system devices and control algorithms, in addition to the destructive nature of many types of scenarios and faults, make the practical testing of the PV system very difficult, very expensive, and impossible in many cases. Therefore, this paper utilizes the modelling and simulation tools of Matlab/Simulink to develop an accurate model for a grid-connected PV system-DC stage. The developed model implements all components of the grid-connected PV system at the DC side; these components are a PV array, a Boost converter, and a Maximum Power Point Tracking (MPPT) controller. Each component of the system is modelled, simulated and validated. Then the PV system is used to simulate many scenarios under various grid and weather conditions. The simulation results show that the model is reliable, stable and suitable for studying the grid-connected PV system.
Reasoning with Justifiable Exceptions in Contextual Hierarchies

The problem of reasoning with context dependent knowledge has recently gained interest in the area of description logic-based knowledge bases (KBs). Among the several proposals, we consider the Contextualized Knowledge Repository (CKR) framework. The CKR model has been recently extended with the capability of reasoning with global (context independent) defeasible axioms that can be overridden by local (context speciﬁc) knowledge. In CKR applications it is often useful to reason over a hierarchical organization of contexts. We highlight here our recent e ﬀ orts on extending the CKR framework to allow for the representation of exception handling in the inheritance of knowledge across local contexts. We ﬁrst concentrated on a limitation to a particular kind of context organization, i.e., ranked hierarchies, which allows us to simplify the deﬁnition of reasoning procedures. We then further generalized the proposal to extend the reasoning on exception handling over general contextual hierarchies. In this paper we summarize the basic deﬁ-nitions for simple CKRs with Justiﬁable Exceptions, the emerging computational properties, and the ASP-based reasoning procedures that we developed. Moreover, we highlight the open challenges in generalizing the approach and our future directions.
Resolving Threats in IoT: ID Spoofing to DDoS

Internet-of-Things (IoT) is a resource-constrained network with machines low on power, processing and memory capabilities. Resource constraints in IoT impact the adoption of protocols for design and validation of unique identity (ID) for every machine. Malicious machines spoof ID to pose as administrative machines and program their neighbour systems in the network with malware. The cycle of ID spoofing and infecting the IP-enabled devices with malware creates an entire network popularly termed as the Botnet. In this paper, we study 6LoWPAN and ZigBee for DDoS and ID spoofing vulnerabilities. We propose a design for generation and validation of ID on such systems called Pseudo Random Identity Generator (PRIG). We compare the performance of PRIG-adapted 6LoWPAN with 6LoWPAN in a simulated personal area network (PAN) model under DDoS stress and demonstrate a 93% reduction in ID validation time as well as an improvement of 67% in overall throughput.
Implicit Dual-Domain Convolutional Network for Robust Color Image Compression Artifact Reduction

Several dual-domain convolutional neural network-based methods show outstanding performance in reducing image compression artifacts. However, they are unable to handle color images as the compression processes for gray scale and color images are different. Moreover, these methods train a specific model for each compression quality, and they require multiple models to achieve different compression qualities. To address these problems, we proposed an implicit dual-domain convolutional network (IDCN) with a pixel position labeling map and quantization tables as inputs. We proposed an extractor-corrector framework-based dual-domain correction unit (DCU) as the basic component to formulate the IDCN; the implicit dual-domain translation allows the IDCN to handle color images with discrete cosine transform (DCT)-domain priors. A flexible version of IDCN (IDCN-f) was also developed to handle a wide range of compression qualities. Experiments for both objective and subjective evaluations on benchmark datasets show that IDCN is superior to the state-of-the-art methods and IDCN-f exhibits excellent abilities to handle a wide range of compression qualities with a little trade-off against performance; further, it demonstrates great potential for practical applications.
Fine-Grained Bird Classification Based on Low-Dimensional Bilinear Model

Bilinear model has been shown to be very effective in a variety of fine-grained classification tasks. For the deficiencies of high-dimensional feature, a low-dimensional bilinear model is proposed. The model saves computing time greatly as well as decreases a large number of parameters by principal component analysis (PCA), and we use singular value decomposition (SVD) to replace covariance calculation. To enhance the generality of the model in the wild, we collected millions of images from bird community and flicker websites, then a new density-distance clustering method is used to eliminate a large number of noise images. Finally, a large-scale fine-grained bird dataset is constructed, which consists of 516981 images with 1329 bird categories in China. The low-dimensional bilinear model is validated on standard small-scale CUB bird dataset and our new bird dataset. The experimental results show that the proposed low-dimensional bilinear model performs better than other competing models in term of bird recognition accuracy.
Factors driving continued use of online health promotion competitions: Evidence from an online weight-loss community

Purpose Gamification elements have been increasingly used in online weight-loss communities to help users lose weight. The purpose of this paper is to systemically examine whether and how social interactions influence users’ continued participation in the context of online weight-loss competitions (OWCs). Design/methodology/approach This study empirically investigated sustained involvement in OWCs using a Cox proportional hazards model. Additionally, the research utilized a text-mining technique to identify various types of social support and explored their roles in sustaining participation behavior in OWCs. Findings Community response both within and outside OWCs positively influence users’ continued participation in OWCs. Moreover, whereas emotional support and companionship received within OWCs have a greater impact on users’ continued participation than informational support received within OWCs, informational support received outside OWCs has a greater impact on users’ continued participation than emotional support and companionship received outside OWCs. Originality/value This paper highlights users’ social needs in OWC engagement and provides empirical evidence on how different types and sources of social support influence continued participation behavior in OWCs. The research additionally provides management implications for online health community service providers.
Customizing Magnetic and Structural Properties of Nanomaterials

To make nanoparticles useful to fit various applications, the fundamental understanding of their magnetic and structural behavior is essential. A systematic in-depth study of the effect of field, temperature, and nanoparticles size on the critical points, and the magnetic properties of chemically synthesized Y2Fe17 nanoparticles have been performed. The blocking temperature varies with the change in the applied magnetic field as well as nanoparticles’ size variations. The Curie temperature was mostly influenced by particles’ size. Saturation magnetization and coercivity were shown to be temperature, size, and shape dependent. The results are significant and especially of interest in material science as they pave the path to a better understanding of different phenomena in nanomaterials in order for them to be used in diverse scientific applications.
Custom Magnet Design for a Multi-Channel Magnetic Microcytometer

Magnetic microflow cytometers integrate magnetic field sensors and microfluidic channels to detect cell suspensions labeled with magnetic beads. As the magnetic beads are superparamagnetic, commercial permanent magnets (PMs) are typiacally positioned bellow the microchip for an out-of-plane magnetization. Though, they require careful alignment as their magnetic field can impact the sensors sensitivity. In this paper, we overcome this drawback by presenting an innovative custom-made PM that notably combines high out-of-plane magnetic field (>100 mT) and low in-plane magnetic field (<1mT) over a large area (30.2mm2). This configuration qualifies the setup for simultaneous detection in multiple microfluidic channels within the same microchip, allowing parallel analysis of different cell samples or faster analysis of larger volumes of cell suspension.
Flexibility of Micromagnetic Flagella in the Presence of an Oscillating Field

Magnetic microbeads have recently been the subject of much study because of their potential applications in microfluidic systems which facilitate mixing, labeling, separation, and transport in lab-on-a-chip devices. To manipulate the microbeads chain swimming in the low-Reynolds-number environment, various magnetic actuation methods have been employed to obtain the higher propulsive efficiency for the locomotion in the viscous fluid. A flexible flagellum is arguably the simplest mechanism to duplicate as it is a 1-D structure. However, a challenge to create the stable planar beating motion for propulsion generation is to fabricate the magnetic flagellum simultaneously flexible and stable structure at a microscale. Driven by this motive, this paper has constructed a series of artificial flexible flagella composed of self-assembled beads whose flexibility was probed in the influence of an oscillating external field. To effectively use the oscillating magnetic flagellum for the application in the microfluidic system, the measurement of the maximum dimensionless curvature (<inline-formula> <tex-math notation="LaTeX">$C_{\mathbf {max}}$ </tex-math></inline-formula>) and bending rigidity for the flexible microchain are experimentally and theoretically investigated in this paper. At a lower frequency of <inline-formula> <tex-math notation="LaTeX">$f=1$ </tex-math></inline-formula> Hz, the value of <inline-formula> <tex-math notation="LaTeX">$C_{\mathbf {max}}$ </tex-math></inline-formula> of the flagellum increases linearly with the applied field intensity and gets higher then declines with the increase of the flagellum’s length. On the other hand, the longer flagellum has the more stable flexible structure at a higher frequency of <inline-formula> <tex-math notation="LaTeX">$f=3$ </tex-math></inline-formula>–7 Hz, which resists the amplitude and enhances the deformation of the longer flagellum.
Performance Analysis of Dense Small Cell Networks With Dynamic TDD

Small cell networks (SCNs) are envisioned to embrace dynamic time division duplexing (TDD) in order to tailor downlink (DL)/uplink (UL) subframe resources to quick variations and burstiness of DL/UL traffic. The study of dynamic TDD is particularly important because it serves as the predecessor of the full duplex transmission technology, which has been identified as one of the candidate technologies for the 5th-generation (5G) networks. In this paper, we study the performance of the synchronous dynamic TDD from a media access control layer viewpoint, which has been widely adopted in the existing 4G systems. Furthermore, we analyze the coverage probability and the area spectral efficiency in the DL and UL of dense SCNs considering the synchronous dynamic TDD transmissions, and the performance impact of dynamic TDD transmissions on the ASE in the DL and UL of dense SCNs is discussed. Moreover, the performance impact of interference cancellation is also explored. Our analytical results shed new light on the performance of dynamic TDD in future synchronous 5G networks.
Smart Inverter for Voltage Regulation: Physical and Market Implementation

Lack of efficient coordination schemes between photovoltaic (PV) panels may result in voltage stability issues. In this paper, we exploit the power control potential enabled by the PV inverters for voltage regulation. There are considerable obstacles to design a viable coordination scheme. Physically, the power flow equations lead to a highly nonconvex constraint set. With respect to the market implementation, voltage regulation is predominately conducted by the system operator, and voltage regulation related products rarely exist. We cast the voltage regulation as a nonconvex optimization problem, and devise an analytical framework to show that based on a linearized model, one can design a gradient descent-based distributed scheme, which, when implemented in the nonconvex branch flow model, will converge to a local minimum exponentially fast. Additionally, we design a compensation scheme which incentivizes the PV panel owners to provide voltage regulation. The compensation naturally leads to a game between all the PV panel owners. By design, the equilibria coincide with the global minimizers to the social planner problem. Simulation results confirm the convergence rate of the control actions in practice.