Decoding Of Data Research Articles

PolarScout: Wi-Fi Interference-Resilient ZigBee Communication via Shell-Shaping

The prosperity of IEEE 802.11-based Wi-Fi networks aggravates cross-technology interference to IEEE 802.15.4-enabled ZigBee networks widely deployed to enable various Internet-of-Things applications. To make ZigBee communication reliable and robust even in a dense Wi-Fi environment, taming Wi-Fi interference in ZigBee networks especially from the perspective of physical layer is of paramount importance. In this context, this work takes aim to design a novel Wi-Fi interference-resilient ZigBee decoder called PolarScout, which separates collided ZigBee signal samples out of Wi-Fi interference to bootstrap ZigBee data decoding. Unlike several existing solutions which need clear signal preamble, tremendous signal strength difference between ZigBee and Wi-Fi, and Wi-Fi interference recognition in prior to ZigBee decoding, PolarScout aims at direct ZigBee decoding in a more generic and challenging case where Wi-Fi interference features a wide range of power levels and arises within a ZigBee packet at an arbitrary position. At the heart of PolarScout lies a subtle shell-shaping technique which harnesses a customized sample sequence to smooth the shell of corrupted signal samples. PolarScout then refers to the resulting shell to recover each contaminated ZigBee sample. Experimental results validate the superiority of PolarScout and its resilience to a wide range of Wi-Fi interference types.

Data dissemination with interoperability in IoT network

SummaryInternet of Things (IoT) is connected to heterogeneous devices. Efficient adaptive scheduling with encoding and decoding of data is an unaddressed issue in IoT. This paper processes the data under three major hierarchy: namely, adaptability, scheduling of data, and network coding for that data. The reliable access to the information is ensured by a device which is a primary eminence in IoT. Device must be able to adapt itself according to the changes in the network and to maintain its reliability as well as transparency and seamless access to the resources. To enhance the performance of the data dissemination, the scheduling process is investigated using the spatial grouping in IoT devices; this is achieved by joint spatial and code domain scheduling scheme, and the novel preconfigured access scheme is coined in order to minimize the collision rate of arbitrary access; during the data dissemination, the erasure coding scheme is used for the encoding and decoding of packets which provides optimal redundancy. We carried the simulation using Contiki and it shows the proposed Polymorphic Erasure Coding with Markov decision Adaptability and Neural networks (PECMAN) improves in terms of cost, overhead, and delay when compared with Multi‐user Shared Access (EMUSA), Polynomial‐time Optimal Storage Allocation (OSA) scheme, and Event‐Aware Back pressure Scheduling Scheme (EABS).

Design and Development of Fast Protection and Fast Monitoring Module Compatible With NI Compact RIO System for ITER-India Gyrotron Test Facility

The ITER electron cyclotron (EC) system will have gyrotron sources used for plasma heating and current drive applications. During gyrotron operation, different faults may be encountered and in the case of critical faults, the High Voltage Power Supplies (HVPS) should be switched off in $ time scale, which otherwise could damage the internal components of a gyrotron tube. Also during the operation, the main operating parameters need to be acquired on fast time scales (~5 Msps) to know the gyrotron output characteristics. A solution is required catering to both the requirements, which should be cost-effective, and also comply with recommended hardware and software framework. In this context, a custom prototype module compatible with NI-CompactRIO (NI-cRIO) system is under development for ITER-India Gyrotron Test Facility (IIGTF). The module has protection and monitoring functions both in parallel in a single unit. It has a transmitter (TX) and receiver (RX) card connected optically to complete the link. The TX has fast sampling analog-to-digital converter (ADC), fast comparator, field-programmable gate array (FPGA), and an optical transmitter. The TX sends monitoring and trip information optically to the RX. The RX card has an optical receiver, FPGA, and digital-to-analog converter (DAC), which generates back the original signals. The heart of the module is FPGA logic, which performs encoding and decoding (as per 8b10b standard) of ADC serial data, thereby eliminating the use of any serial converter ICs. The module can be plugged into the cRIO chassis and also can be used as a standalone module. This article discusses the concept, design, development, and test results of fast protection and fast signal monitoring module

Exploiting classification for fountain data estimation in wireless sensor networks

SummaryIn order to correct and avoid channel error, fountain codes were the best solution by limiting feedback channels and reducing energy consumption. Multi‐hops transmission is the principal limitation of the deployment and the use of these codes. Indeed, relayed transmission conducts to the generation of useless data, named overflow leading to a waste of energy, the most critical issue, and the big challenge in WSN. In this paper, based on a clustered architecture and estimation, we consider a distributed estimation scheme composing of sensor members and the cluster head. In order to reduce the number of a useless encoded packet generated as well as the impact of the overflow, we determine the optimal minimal number of encoded packets needed for data decoding. Sensor observations are encoded using fountain codes, and then messages are collected at the cluster head where a final estimation is provided within learning method. Then messages are collected at the cluster head where a final estimation is provided with a classification based on Bayes rule. The main goal of this paper is to determine the number of encoded packets by exploiting the classification model for fountain data estimation to minimize the overflow and extend the network lifetime.

Electrosense+: Crowdsourcing radio spectrum decoding using IoT receivers

Web spectrum monitoring systems based on crowdsourcing have recently gained popularity. These systems are however limited to applications of interest for governamental organizations or telecom providers, and only provide aggregated information about spectrum statistics. The result is that there is a lack of interest for layman users to participate, which limits its widespread deployment. We present Electrosense+ which addresses this challenge and creates a general-purpose and open platform for spectrum monitoring using low-cost, embedded, and software-defined spectrum IoT sensors. Electrosense+ allows users to remotely decode specific parts of the radio spectrum. It builds on the centralized architecture of its predecessor, Electrosense, for controlling and monitoring the spectrum IoT sensors, but implements a real-time and peer-to-peer communication system for scalable spectrum data decoding. We propose different mechanisms to incentivize the participation of users for deploying new sensors and keep them operational in the Electrosense network. As a reward for the user, we propose an incentive accounting system based on virtual tokens to encourage the participants to host IoT sensors. We present the new Electrosense+ system architecture and evaluate its performance at decoding various wireless signals, including FM radio, AM radio, ADS-B, AIS, LTE, and ACARS.

Fundamental limitations of dispersion mitigation filters.

We investigate the fundamental limitations of dispersion mitigation filters. By analyzing the dispersion compensating process from basic principles, we demonstrate how a digital filter can mitigate arbitrarily weak dispersion without oversampling. We calculate the maximum distance the signal can pass with and without dispersion compensation, beyond which no data decoding is possible. Furthermore, we show the exact mathematical relation between this maximum distance and the length of the compensating filter - with and without a Forward Error Correction (FEC).

Nonlinear sparse partial least squares: an investigation of the effect of nonlinearity and sparsity on the decoding of intracranial data

Objective. Partial Least Squares (PLS) regression is a suitable linear decoder model for correlated and high dimensional neural data. This algorithm has been widely used in the application of brain–computer interface (BCI) for the decoding of motor parameters. PLS does not consider nonlinear relations between brain signal features. The nonlinear version of PLS that considers a nonlinear relationship between the latent variables has not been proposed for the decoding of intracranial data. This nonlinear model may cause overfitting in some cases due to a larger number of free parameters. In this paper, we develop a new version of nonlinear PLS, namely nonlinear sparse PLS (NLS PLS) and test it in BCI applications. Approach. In motor related BCI systems, improving the decoding accuracy of both kinetic and kinematic parameters of movement is crucial. To do this, two BCI datasets were chosen to decode the force amplitude and position of hand trajectory using the nonlinear and sparse versions of PLS algorithm. In our new NLS PLS method, we considered a polynomial relationship between the latent variables and used the lasso penalization in the latent space to avoid overfitting and to improve the decoding accuracy. Main results. Some linear and nonlinear based PLS models and our new proposed method, NLS PLS, were applied to the two datasets. According to our results, significant improvement from the NLS PLS method is confirmed over other methods. Our results show that nonlinear PLS outperforms generic PLS in the force decoding but it has lower accuracy in the hand trajectory decoding because of high dimensional feature space. By using lasso penalization, we presented a sparse nonlinear PLS-based model that outperforms generic PLS in both datasets and improves the coefficient of determination, 34% in the force decoding and 10% in the hand trajectory decoding. Significance. We constructed a simple PLS-based model that considers a nonlinear relationship between features and it is also robust to overfitting because of using the lasso penalty in the latent space. This model is suitable for a high dimensional and correlated datasets, like intracranial data and can improve the accuracy of estimation.

Device Activity Detection and Non-Coherent Information Transmission for Massive Machine-Type Communications

In the grant-free massive machine-type communication (mMTC) scenario, a key challenge is the joint device activity detection and data decoding. The sporadic nature of mMTC makes compressed sensing a promising solution to the activity detection problem. However, the typical two-phase coherent transmission scheme, which divides channel training and data decoding into two separate phases, suffers performance losses, especially when only a few bits of data are transmitted by each active device. This paper follows a newly proposed non-coherent transmission scheme in which the data bits are embedded in the pilot sequences and the BS simultaneously detects active devices and decodes the embedded data bits without explicit channel estimation. To exploit statistical channel information and the specific structure of the sparsity pattern introduced by the non-coherent transmission scheme, i.e., only one row in each section can be non-zero, we propose a receiving method based on the approximate message passing (AMP) algorithm with non-separable minimum mean-squared error denoisers specifically designed for the problem. The corresponding state evolution equations, which can be used to predict the section error rate (SER) performance, is obtained and simplified under certain assumptions. We also derive closed-form expressions of the SER performance based on the state evolution results. Finally, numerical simulations are given to validate the accuracy of the performance analysis and to show the superiority of the proposed receiving method over the conventional method based on AMP with separable denoisers in the literature.

Simultaneous Power Harvesting and Cyclostationary Spectrum Sensing in Cognitive Radios

We expect that 5G will support a large volume of data traffic to provide various services with a low latency. This will inevitably require an increased amount of energy. Wireless power transfer needs a higher receiver sensitivity than data decoding. Increased electromagnetic fields may introduce harmful effects on living organisms. Fortunately, massive multiple-input and multiple-output (MIMO) can provide significant gains in radiated energy and spectral efficiencies. Cognitive radio and 5G are emerging technologies. In this paper, we show that cognitive spectrum sensing and power harvesting can be accomplished simultaneously. Energy detection (ED) with energy harvesting has been widely investigated. However, ED may not work at a low signal-to-noise ratio with a harsh signal fluctuation in millimeter-wave in 5G. Therefore, we focus on cyclostationary spectrum sensing in this paper. We show that the maximum likelihood cyclostationary detection that can be used for power harvesting is the power squared. The cyclic power can be conveniently harvested and used for spectrum sensing.

Ethernet Embedded Readout Interface for Timepix2—Katherine readout for Timepix2

This paper introduces a readout system for the Timepix2. Firstly, this chip is described and the readout modes are discussed in detail. The new readout system presented is based on the Gigabit Ethernet interface and implements pre-processing, i.e. decoding of the raw pixel data, directly in the hardware. The device suppresses zero pixels, so that only useful data are sent to the computer/server. In a special independent mode, the readout can send completed data files to a remote server via SSH and does not need to use a control software. In island mode, the device stores measured data to a local storage (SD card). The process of calibration and its results are also discussed. An energy resolution of approximately 1.5 keV was achieved for 60 keV gamma-rays from an 241Am source. We present enhanced features of the readout system facilitating measurements and data evaluation, such as the HW support of clustering and Matrix Occupation Control. The former implements the pixel clustering directly in the hardware and sends energy calibrated results of the cluster finding algorithm to the computer. The latter automatically controls acquisition time of detector in order to reduce cluster overlapping. Measurements with Timepix2 are presented in iron and electron test beams. Results show that pixels of Timepix2 saturate at a per-pixel energy deposition of 1.9 MeV.

MIMO Cross-Layer Secure Communication Algorithm for Cyber Physical Systems Based on Interference Strategies

In this paper, a detailed analysis of multi-input multi-output (MIMO) cross-layer secure communication algorithms in information-physical systems is investigated employing an interference strategy. A three-stage data-assisted channel estimation method is proposed in this paper for the acquisition of channel state information for complex jamming channels in large-scale MIMO two-layer systems. To implement the data-assisted scheme, assuming that there are no errors and no delay in the system, if the data detection and decoding data sequences are completed at a small cell base station, they are sent to the macro base station via a wired backhaul. Due to the sparsity of the channel at the macro base station after user grouping, a channel estimation algorithm based on optimal block orthogonal matching tracking(s) is proposed in the case where the downlink channel at the macro base station utilizes the decoded uplink data and known training sequences. The simulation results show that the data-assisted method proposed in this paper is effective in improving channel estimation accuracy. A machine learning algorithm is directly used to classify the channel difference or channel matrix to obtain the authentication results. In this paper, the scheme is first simulated using channel data from dynamic communication scenarios, its feasibility is analyzed, and the parameters in the scheme are compared, and the optimal scheme is the bagging tree authentication scheme using a 128-dimensional channel matrix as input. To address the interference problem caused by the dense arrangement of SAPs in heterogeneous networks and the unbalanced network load, large-scale MIMO techniques are introduced to reduce the downlink interference caused by the microcell boundary expansion in heterogeneous networks.

Analysis and Solution of Abnormal Fault of Data Storage Based on NAND-flash

NAND-flash memory has the advantages of large capacity and fast rewriting speed. It is suitable for the storage of large amounts of data and is often used as an online storage device for embedded products. However, NAND-flash has the problems of bad blocks and other insufficient reliability.In a certain aerospace model temperature/strain measurement system, the NAND-flash memory is erased and written through DSP software, and the strain/temperature measurement data collected by the sensor is stored in real time. After the system was powered on many times and completed data collection and decoding, it was found that the data stored in the NAND-flash had abnormal faults. By analyzing the test phenomenon and failure mechanism, the failure problem is attributed to the fact that the data in the NAND-flash is not erased, and it is coupled with the newly written data, and it is finally located because the data in the original data address cannot be effectively erased when the data address is stored. Coupling also occurred, resulting in an error in the data address, and the data was coupled after power-on again. Based on the above-mentioned reasons, this paper proposed a troubleshooting method and conducted a test. The verification was successfully passed and the problem was resolved.This method has high reference significance in the large-capacity and high-reliability data storage of NAND-flash in the field of aerospace models.

Near Sensors Computation based on Embedded Machine Learning for Electronic Skin

The electronic skin system is usually made of distributed tactile sensors integrated with an embedded electronic system for tactile data decoding. Meaningful information e.g. texture classification and pattern recognition can be decoded from tactile data by employing machine learning methods. Near sensors computation using embedded machine learning algorithms may enable the electronic skin system to be used in various application domains such as wearable Internet of Things devices, prosthetics, and robotics. However, embedding machine-learning algorithms is constrained by the high computational complexity of Machine Learning methods. This poses relevant challenges on 1) real-time operation and 2) very low (e.g. pJ/op) power/energy consumption due to the limited energy budget available on wearable/portable systems. In this perspective, the paper presents our recent achievements in the implementation of embedded machine learning methods for near sensors tactile data processing. The paper provides an overview about the implementation on dedicated hardware platforms. Finally, efficient techniques for embedded machine learning highlighting the challenges and perspectives are discussed with major emphasis on energy-efficient intelligent electronic skin systems.

Fine Doppler scale estimations for an underwater acoustic CP-OFDM system

In the challenging underwater acoustic (UWA) communication channels, the performance of orthogonal frequency division multiplexing (OFDM) systems is severely affected by the Doppler effect. Hence, the Doppler scale estimation and compensation are critical for UWA OFDM systems. As a commonly adopted solution, the Doppler scale can be estimated by the maximization of auto-correlation between the received cyclic prefix (CP) and OFDM symbol. However, the accuracy of the auto-correlation based Doppler scale estimation is decided by the sampling rate of the signal. To achieve higher estimation accuracy, higher sampling rate is required and which means more computational complexity. To overcome this problem, we propose several fine Doppler scale estimation methods by interpolating the auto-correlation peak values, based on the derived expression of the expected auto-correlation outputs corresponding to a CP-OFDM signal after the UWA channel. Simulation results demonstrate the effectiveness of the proposed methods, and the decoding of experimental data further verifies the superiority of those methods.

SDN-Enabled Network Coding-Based Secure Data Dissemination in VANET Environment

Security and reliability in data transmission are considered as the challenging concerns in VANET. Software-defined networking (SDN) and network coding (NC) are the two key apprehensions in networking that have caught much attention from both industry and academia in recent years. Decoupling of control plane and data plane in SDN enables the centralized control of the network, providing flexibility to a great extent. On the other hand, NC has shown immense potential for improving robustness and security when deployed on VANET. This article advocates for the design of an architecture which exercises the SDN concept by incorporating NC with multigeneration-mixing (MGM) functionalities to increase reliability as well as security of data transmission in vehicular networks. We design an MGM-based NC protocol for encoding and decoding of data. Moreover, a centralized SDN controller takes charge of authentication of vehicles. Finally, we build a simulation model based on realistic traffic and communication characteristics. The simulation results show that the proposed protocol leveraged by the SDN framework outperforms the conventional NC-based protocol in case of security and reliability. Moreover, the simulation model is exhibited and point-by-point execution assessment is given to demonstrate the scalability and prevalence of the proposed strategy.

Optimal Relaying in Energy Harvesting Wireless Networks With Wireless-Powered Relays

In this paper, we consider a wireless cooperative network with a wireless-powered energy harvesting (EH) relay. The relay employs a time switching (TS) policy that switches between the EH and data decoding (DD) modes. Both energy and data buffers are kept at the relay to store the harvested energy and decoded data packets, respectively. In this paper, we derive static and dynamic TS policies that maximize the system throughput or minimize the average transmission delay. In particular, in the static policies, the EH or DD mode is selected with a pre-determined probability. In contrast, in a dynamic policy, the mode is selected dynamically according to the states of data and energy buffers. We prove that the throughput-optimal static and dynamic policies keep the relay data buffer at the boundary of stability. More specifically, we show that the throughput-optimal dynamic policy has a threshold-based structure. Moreover, we prove that the delay-optimal dynamic policy is threshold-based and keeps at most one packet at the relay. We notice that unlike the static case, the delay-optimal and throughput-optimal dynamic policies coincide in most cases. Finally, through extensive numerical results, we demonstrate the efficiency of the optimal dynamic policies compared with the static ones.

A Novel Fractional Harmonic $d\text{--}q$ Domain Based Power Line Signaling Technique for Power Converters in a Microgrid

Traditionally, the communication between power converters connected in a microgrid is established using a wired method or a wireless droop method. The shortcomings of these methods have opened up the possibility of power-line-based communication between converters. However, the conventional power line communication systems have the disadvantages of using high-frequency data carrying signals that are prone to attenuation. These systems also make use of explicit couplers, transmitters, and amplifiers that adds to the overall cost and infrastructure. In this paper, a novel fractional harmonic $d\text{--}q$ domain based power line communication technique is proposed. A switched fractional harmonic current space vector is used to carry data. The primary control loop and the modulation algorithm of the converters are used for encoding and decoding of data. The proposed technique thus overcomes the disadvantages of the conventional power line communication systems and at the same time carries forward the advantages of it. The proposed technique is validated with sufficient experimental test results.

Downlink Training in Cell-Free Massive MIMO: A Blessing in Disguise

Cell-free Massive MIMO (multiple-input multiple-output) refers to a distributed Massive MIMO system where all the access points (APs) cooperate to coherently serve all the user equipments (UEs), suppress inter-cell interference and mitigate the multiuser interference. Recent works demonstrated that, unlike co-located Massive MIMO, the \textit{channel hardening} is, in general, less pronounced in cell-free Massive MIMO, thus there is much to benefit from estimating the downlink channel. In this study, we investigate the gain introduced by the downlink beamforming training, extending the previously proposed analysis to non-orthogonal uplink and downlink pilots. Assuming single-antenna APs, conjugate beamforming and independent Rayleigh fading channel, we derive a closed-form expression for the per-user achievable downlink rate that addresses channel estimation errors and pilot contamination both at the AP and UE side. The performance evaluation includes max-min fairness power control, greedy pilot assignment methods, and a comparison between achievable rates obtained from different capacity-bounding techniques. Numerical results show that downlink beamforming training, although increases pilot overhead and introduces additional pilot contamination, improves significantly the achievable downlink rate. Even for large number of APs, it is not fully efficient for the UE relying on the statistical channel state information for data decoding.

Molecular access to multi-dimensionally encoded information

Polymer scientist have only recently realized that information storage on the molecular level is not only restricted to DNA-based systems. Similar encoding and decoding of data have been demonstrated on synthetic polymers that could overcome some of the drawbacks associated with DNA, such as the ability to make use of a larger monomer alphabet. This feature article describes some of the recent data storage strategies that were investigated, ranging from writing information on linear sequence‑defined macromolecules up to layer-by-layer casted surfaces and QR codes. In addition, some strategies to increase storage density are elaborated and some trends regarding future perspectives on molecular data storage from the literature are critically evaluated. This work ends with highlighting the demand for new strategies setting up reliable solutions for future data management technologies.

Multiuser Variable Rate GO-OFDMA Architecture and Its FPGA Prototype

With the advancements in next-generation communications systems, the demand for integration of services, such as video, audio, text, image of variable rates, etc., is a challenging issue. Internet of Things needs to integrate billions of devices and sensor nodes with different sampling rates. To integrate these services of multiple devices with variable rates, group orthogonal–orthogonal frequency division multiple access (GO-OFDMA) is an efficient multicarrier scheme. But to the authors’ knowledge and literature survey, there is no hardware architecture of GO-OFDMA for real-time testing. Therefore, a new architecture is proposed for GO-OFDMA baseband transmitter and receiver and prototyped on commercially available virtex5 field-programmable gate array (FPGA) device. Further challenges of this proposed architecture are integration of multiple user data with variable rates in the baseband transmitter and decoding of multiple user data within the same class at the baseband receiver. To validate the GO-OFDMA baseband transmitter and receiver, the transmitted and received bits in real time are captured using chipscope analyzer. The bit error rate and peak-to-average power ratio performance of the proposed architecture are presented. The hardware resource utilization of the GO-OFDMA architecture along with the maximum operating frequency is also reported.