This paper elaborates on a new class of orthonormal power-based algorithms for fast estimation and tracking of the principal or minor subspace of a vector sequence. The proposed algorithms are closely related to the natural power method that has the fastest convergence rate among many power-based methods such as the Oja method, the projection approximation subspace tracking (PAST) method, and the novel

String sorting is a fundamental kernel of string matching and database index construction; yet, it has not been studied as extensively as fixed-length keys sorting. Because processing variable-length keys in hardware is challenging, it is no surprise that no hardware-accelerated string sorters have been proposed yet. In this paper, we present Parallel Hybrid Super Scalar String Sample Sort (pHS5) on

The scintillator detectors are recalibrated against the datasheet given by the manufacturer. Optimal and mutual dependent values of (a) high voltage at PMT (Photomultiplier Tube), (b) amplifier gain, (c) average time to count the radiation particles (set by operator), and (d) number of instances/sample number are estimated. Total 5: two versions of Central Limit Theorem (CLT), (3) industry preferred

In X-ray spectrum analysis, the pulse pile-up is a long-standing issue which deteriorates the energy resolution and count rates of the radiation detection systems. In this study, a novel pulse pile-up identification method based on particle swarm optimization and double-layer parameter identification model (PSO-DLPIM) is proposed. Different Gaussian pile-up waveforms are realized by exponential pulse

In recent years, there has been an increasing interest in Environmental Sound Classification (ESC), and it is a challenging non-speech audio event classification problem because of the complexity of the environment. However, the classification accuracy of the conventional methods is significantly dependent on the robustness of representative features and the effectiveness of the constructed model,

In this paper we deal with classification of anomalous data detected by the data reduction system of the Gaia space mission, in operation since 2013. Given the size and complexity of intermediate data and plots for diagnostics, beyond practical possibility of full human evaluation, the need for automated signal processing tools is becoming more and more relevant. Our classification task consists in

A real time neutron imaging system is characterized and implemented to demonstrate the non-destructive examination of dynamic events such as bubble dynamics. Computational fluid dynamics (CFD) simulations were performed to estimate the bubble diameter and a phantom was manufactured accordingly to generate such bubbles. 1300 µm diameter bubbles are generated by injecting air through 50 µm diameter orifices

A 2D Compact ultrasound computerized tomography (UCT) system is developed. Fully automatic post-processing tools involving signal and image processing are developed as well. Square of the amplitude values are used in transmission mode with natural 1.5 MHz frequency and rise time 10.4 ns and fall time 8.4 ns and duty cycle of 4.32%. The highest peak to corresponding trough values are considered as transmitting

Single-pixel imaging is an important alternative to conventional camera. Only a single-pixel detector is needed to capture image data by measuring the correlation of the target scene and a series of sensing patterns. Conventionally, Nyquist-Shannon theorem requires measurements not less than the image pixels for an error-free reconstruction. Compressed sensing (CS) enables image reconstructions with

The use of mobile devices has been growing rapidly and at the same time mobile applications has attained increasing popularity. Moreover, mobile services have introduced unexpected demands on the infrastructure of mobile and wireless networking. Recently, Mobile Sensor Networks (MSNs) has developed a class of popular sensor network where mobility has the major responsibility in executing the application

Long Range Wide Area Network (LoRaWAN) emerges to connect devices that require long-range and low-cost (bandwidth and power) communication services. In this context, the adoption of this technology brings new challenges due to the densification of IoT devices, which causes signal interference and affects the QoS directly. On the other hand, the LoRaWAN transmission configurations’ flexibility allows

The next generation of video codecs incorporate multiple transforms as a part of their transform core. However, some of the new transform types introduced in state-of-the-art codecs are still lacking efficient optimization methods. This paper focuses on the optimization of Adaptive Multiple Transforms (AMTs) in the next generation of video decoders. Our solution consists in three main contributions

Low-latency and energy-efficient multi-Gbps LDPC decoding requires fast-converging iterative schedules. Hardware decoder architectures based on such schedules can achieve high throughput at low clock speeds, resulting in reduced power consumption and relaxed timing closure requirements for physical VLSI design. In this work, a fast column message-passing (FCMP) schedule for decoding LDPC codes is presented

With the latest development of automation technology, object detection technology has received more and more research attention. Automated object detection technology can reduce labor costs, avoid the problem visual fatigue, and is more consistent than human observers. Therefore, as deep learning has become commonplace, it has been used for object detection with great results. However, most studies

Recent progress in audio source separation led by deep learning has enabled many neural network models to provide robust solutions to this fundamental estimation problem. In this study, we provide a family of efficient neural network architectures for general purpose audio source separation while focusing on multiple computational aspects that hinder the application of neural networks in real-world

Electrocardiogram (ECG) is widely used by physicians to detect heart ailments. The multi-resolution analysis offered by the Discrete Wavelet Transform (DWT) coupled with its noise-removal capability makes it a suitable tool for ECG signal analysis. In this paper, a DWT-based approach is presented for ECG signal decomposition and noise removal. The algorithm is tested on two ECG signals acquired using

This paper explores the use of domain-specific architectures for energy-efficient and flexible computing of a variety of workloads, including signal processing applications, in wearable devices. As wearable devices become more popular, and with growing consumer demands, these devices are expected to run a wide range of increasingly complex workloads. A general-purpose solution for wearable computing

The joint estimation of the location vector and the shape matrix of a set of independent and identically Complex Elliptically Symmetric (CES) distributed observations is investigated from both the theoretical and computational viewpoints. This joint estimation problem is framed in the original context of semiparametric models allowing us to handle the (generally unknown) density generator as an infinite-dimensional

The world is witnessing a rising number of preterm infants who are at significant risk of medical conditions. These infants require continuous care in Neonatal Intensive Care Units (NICU). Medical parameters are continuously monitored in premature infants in the NICU using a set of wired, sticky electrodes attached to the body. Medical adhesives used on the electrodes can be harmful to the baby, causing

A convolutional neural network (CNN)-based approach for fetal ECG detection from the abdominal ECG recording is proposed. The flow contains a pre-processing phase and a classification phase. In the pre-processing phase, short-time Fourier transform is applied to obtain the spectrogram, which is sent to 2D CNN for classification. The classified results from multiple channels are then fused and high

For the scenarios with high throughput requirements, the belief propagation (BP) decoding is one of the most promising decoding strategies for polar codes. By pruning the redundant variable nodes (VNs) and check nodes (CNs) in the original factor graph, the graph is condensed to a sparse bipartite graph that is similar to the graph for low-density parity-check (LDPC) codes. In this paper, we introduce

The field of pipelined FFT hardware architectures has been studied during the last 50 years. This paper is a survey that includes the main advances in the field related to architectures for complex input data and power-of-two FFT sizes. Furthermore, the paper is intended to be educational, so that the reader can learn how the architectures work. Finally, the paper divides the architectures into serial

The Battery Management System of an Electric Vehicle is a system designed to ensure safe operation of the battery pack, and report its state to other systems. It is a distributed system, and the communication between its sub-modules is performed through wired buses. In this article, we study the opportunity to use a wireless technology named IEEE Std 802.15.4 Time Slotted Channel Hopping, a standardized

Stereo matching aims to estimating disparity by finding the correspondence of each pixel between two images which is crucial to 3D scene reconstruction. Nowadays 3D convolution neural networks achieve impressive performances on stereo matching. However, it is memory consuming and computation complex. And it is challenging to finding the corresponding pixels in textureless and near boundary regions

We describe a control loop over a sequential online algorithm. The control loop either computes or uses the sequential algorithm to estimate the temporal percentiles of any univariate input data sequence. It transforms an input sequence of numbers into output sequence of histograms. This is performed without storing or sorting all of the observations. The algorithm continuously tests whether the input

Minimal cognitive impairment (MCI), a potential precursor to Alzheimer’s disease (AD), may be a heterogeneous entity consisting of distinct subtypes. To evaluate this hypothesis, we applied unsupervised machine-learning to a subset of the Alzheimer’s disease Neuroimaging Initiative (ADNI) data set, and detected MCI subtypes with distinct clinical correlates. Our subtype-detection system consists of

This paper proposes a new boundary uncertainty-based estimation method that has significantly higher accuracy, scalability, and applicability than our previously proposed boundary uncertainty estimation method. In our previous work, we introduced a new classifier evaluation metric that we termed “boundary uncertainty.” The name “boundary uncertainty” comes from evaluating the classifier based solely

IEEE Std 802.15.4-2015 Time Slotted Channel Hopping (TSCH) is the de facto Medium Access Control (MAC) mechanism for industrial applications. It renders communications more resilient to interference by spreading them over the time (time-slotted) and the frequency (channel-hopping) domains. The 6TiSCH architecture bases itself on this new MAC layer to enable high reliability communication in Wireless

Due to melanoma is one of the skin cancers with the highest mortality rate and have a large amount of data during the collection and diagnosis, there is an urgent need to improve the diagnostic efficiency and accuracy. However, there remain problems in analyzing medical big data for skin lesion application, such as the intra-class variation and inter-class similarity in skin lesion images and the lacks

In this research, for an assembly-work guide, an ultrasonic positioning system is proposed to track the positions of an indoor moving object. Composed of an ultrasonic sender (moving object) and a receiver (anchor), the system employs three ultrasonic time-off-flights (TOFs) and trilateration to estimate the positions of the moving object in real time. In addition, to remove measuring noises, a pulse

In this paper, we propose and evaluate two mechanisms aimed at improving the communication reliability of IEEE 802.15.4g SUN (Smart Utility Networks) in industrial scenarios: RTS (Re-Transmission Shaping), which uses acknowledgements to track channel conditions and dynamically adapt the number of re-transmissions per packet, and AMS (Adaptive Modulation Selection), which makes use of reinforcement

Because mobile devices only have the limited resource in socially aware networking, some network nodes are unwilling to sacrifice their resource to forward messages to others for free, thus forming selfish nodes. However selfish nodes will cause dropping in the message delivery rate and affect the network performance, so this paper has proposed a diversity-based selfish node detection algorithm (DSNDA)

In this paper, we develop methods for efficient and accurate information extraction from calcium-imaging-based neural signals. The particular form of information extraction we investigate involves predicting behavior variables linked to animals from which the calcium imaging signals are acquired. More specifically, we develop algorithms to systematically generate compact deep neural network (DNN) models

Inertial sensors based on micro-electro-mechanical systems (MEMS) technology, such as accelerometers and angular rate sensors, are cost-effective solutions used in inertial navigation systems in a broad spectrum of applications that estimate position, velocity and orientation of a system with respect to an inertial reference frame. Although they present several advantages in terms of cost and form

The integration of FPGA-based accelerators into a complete heterogeneous system is a challenging task faced by many researchers and engineers, especially now that FPGAs enjoy increasing popularity as implementation platforms for efficient, application-specific accelerators for domains such as signal processing, machine learning and intelligent storage. To lighten the burden of system integration from

Starting from medical big data, this article uses data mining technology to analyze and study the pathogenic factors of lung cancer based on the lung cancer electronic medical record data from the oncology department of the authoritative third grade A hospital for many years. With respect to the processing of huge data from electronic medical records for lung cancer, traditional serial Apriori algorithm

Nowadays, processing systems are constrained by the low efficiency of their memory subsystems. Although memories evolved into faster and more efficient devices through the years, they were still unable to keep up with the computational power offered by processors, i.e., feed the processors with the data they require at the rhythm it is consumed. Consequently, with the advent of Big Data, the need for

Network monitoring has been traditionally conducted using Simple Network Management Protocol (SNMP): a network monitoring protocol that allows network administrators to keep track of every node in the network and ensure that it behaves correctly. This paper presents the Constrained Monitored Protocol (CoMP): a lightweight resource-efficient alternative to SNMP that targets the low-end devices of the

From a security perspective, identifying Industrial Internet of Things (IIoT) devices connected to a network has multiple applications such as penetration testing, vulnerability assessment, etc. In this work, we propose a feature-based methodology to perform device-type fingerprinting. A device fingerprint consists of the TCP/IP header features and port-based features extracted from the network traffic

Electric power protocol is a typical kind of industrial protocols, and is widely-used in electric power systems. Since most electric power protocols are private and have no public protocol specification, it poses a great challenge for security analysis and vulnerability discovery. Protocol reverse engineering makes it possible to analyze unknown or private protocols. However, previous reverse engineering

The paper presents a risk-driven behavioral biometric-based user authentication scheme for smartphones. Our scheme delivers one-shot-cum-continuous authentication, thus not only authenticates users at the start of the application sign-in process but also, throughout the active user session. The scheme leverages the widely used PIN/password-based authentication technology by giving flexibility to users

Synchronous DataFlow Graph (SDFG in short) is a formalism frequently considered in electronic design and software compilers to model communications between components with different rates. The development of efficient algorithms to evaluate the maximum throughput of SDFGs is a challenging question. This paper presents a mathematical framework to perform schedulability analysis and to compute the maximum

Internet applications remain exposed to pervasive Domain Name System (DNS)–based threats. Blockchain technologies provide a new way for tackling DNS vulnerability issues, and have been highlighted recently. However, traditional blockchain is still not well suited for big data applications such as DNS, because the performance of blockchain consensus greatly limits its practical adoption. In this paper

Automatic classification of musical instruments from audio relies heavily on datasets of acoustic recordings of the instruments to train models of those instruments. To do this, precise labels of the instrument’s events are mandatory. Also, it is very difficult to obtain such labels, especially in polyphonic performances. OpenMic-2018 is a polyphonic dataset created specifically with the aim to train

The hidden periodicity detection using a cyclic spectral function (CSF) is one of the well-known methods for cyclostationary signals analysis. Many two-dimensional signals (2D-signals), such as textures, can be considered cyclostationary or semi-cyclostationary. Applying such an assumption provides a new effective field for the analysis of 2D-signals. This paper presents two new algorithms for two-dimensional

In this paper, a hardware implementation in reconfigurable logic of a single-pass connected component labelling (CCL) and connected component analysis (CCA) module is presented. The main novelty of the design is the support of a video stream in 2 and 4 pixel per clock format (2 and 4 ppc) and real-time processing of 4K/UHD video stream (3840 x 2160 pixels) at 60 frames per second. We discuss several

The rapid contemporary development of wearable devices offers non-invasive and effective approaches for monitoring the human brain. Recent studies have investigated the prediction of epileptic seizures (ESs) using wearable measurements, such as scalp electroencephalography and functional near-infrared spectroscopy. The signal processing tasks are the core component of emerging closed-loop ES prediction

Recently, NB-IoT, LTE Cat-M1, Sigfox, and LoRa have been proposed as promising Low-Power Wide Area Networks (LPWANs) technologies for Internet-of-Things (IoT) applications. These technologies are aimed for IoT applications such as smart meters that can tolerate long transmission delays and only need a narrow band to periodically transmit a small amount of data. Although the performances of these technologies

At present, autonomous management and operation of spacecraft are the main direction and objective of the development of space technology to lighten the burden of ground measurement and control, reduce the cost of operation and management, and expand the application scope of spacecraft. To select the suitable periodic variable stars with a certain quantity at the given conditions of spacecraft, we

On the one hand, processors for hearing aids are highly specialized for audio processing, on the other hand they have to meet challenging hardware restrictions. This paper aims to provide an overview of the requirements, architectures, and implementations of these processors. Special attention is given to the increasingly common application-specific instruction-set processors (ASIPs). The main focus

In recent years, Convolutional Neural Network CNN have been incorporated in a large number of applications, including multimedia retrieval and image classification. However, CNN based algorithms are computationally and resource intensive and therefore difficult to be used in embedded systems. FPGA based accelerators are becoming more and more popular in research and industry due to their flexibility

In recent years, the neural architecture search has continuously made significant progress in the field of image recognition. Among them, the differentiable method has obvious advantages compared with other search methods in terms of computational cost and accuracy to deal with image classification. However, the differentiable method is usually composed of single cell, which cannot efficiently extract

As big data analytics systems are squeezing out the last bits of performance of CPUs and GPUs, the next near-term and widely available alternative industry is considering for higher performance in the data center and cloud is the FPGA accelerator. We discuss several challenges a developer has to face when designing and integrating FPGA accelerators for big data analytics pipelines. On the software

Coarse Grained Reconfigurable Arrays (CGRAs) or Architectures are a concept for hardware accelerators based on the idea of distributing workload over Processing Elements. These processors exploit instruction level parallelism, while being energy efficient due to their simplistic internal structure. However, the incorporation into a complete computing system raises severe challenges at the hardware

Over the last decade, Convolutional Neural Networks (CNNs) have become the go to technique to perform tasks in deep learning applications such as computer vision, speech recognition, etc. LeCun et al (Nature 521(7553), 436–44) 2015. Even though CNNs are very efficient at these tasks they are not suitable for embedded applications due to the limited power budget. In this work we present an improved

Periodic variable navigation can realize the integration of positioning, attitude determination, timing and other functions. As a novel autonomous navigation method, autonomous management and autonomous operation of spacecraft are the main direction of great significance to reduce the burden of ground measurement and control, to improve the viability of spacecraft, While, the precision of the navigation

As a part of a growing information society, nowadays the issue of security is more crucial than ever. In order to achieve high level of security, the potential of accurately recognize subjects based on their unique measurable physiological or behavioral characteristics has been receiving an increased concern by the research and development community. As biometrics has advanced, iris has been considered

Convolutional neural networks (CNNs) exhibit state-of-the-art performance while performing computer-vision tasks. CNNs require high-speed, low-power, and high-accuracy hardware for various scenarios, such as edge environments. However, the number of weights is so large that embedded systems cannot store them owing to their limited on-chip memory. A different method is used to minimize the input image

Whilst FPGAs have been used in cloud ecosystems, it is still extremely challenging to achieve high compute density when mapping heterogeneous multi-tasks on shared resources at runtime. This work addresses this by treating the FPGA resource as a service and employing multi-task processing at the high level, design space exploration and static off-line partitioning in order to allow more efficient mapping

Voice user interface (VUI) has become increasingly popular in recent years. Speaker recognition system, as one of the most common VUIs, has emerged as an important technique to facilitate security-required applications and services. In this paper, we propose to design, for the first time, a real-time, robust, and adaptive universal adversarial attack against the state-of-the-art deep neural network