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

In this paper, we introduce a Back-to-Back Butterfly Network (B2BN) based on multiplexers (MUXs) in which any kind of permutation can be performed. However, for a given permutation, it is not an easy task to select the appropriate paths in B2BN without any conflict in terms of MUXs. In this paper, we propose a formal model to efficiently solve such conflicts. The proposed method relies on collecting

Coarse Grained Reconfigurable Arrays (CGRAs) are emerging as energy efficient accelerators providing a high grade of flexibility in both academia and industry. However, with the recent advancements in algorithms and performance requirements of applications, supporting only integer and logical arithmetic limits the interest of classical/traditional CGRAs. In this paper, we propose a novel CGRA architecture

Deep Learning provided powerful tools for forecasting financial time series data. However, despite the success of these approaches on many challenging financial forecasting tasks, it is not always straightforward to employ DL-based approaches for highly volatile and non-stationary time financial series. To this end, in this paper, an adaptive input normalization layer that can learn to identify the

Establishing conclusive cortical activity dynamics from neuroimages and high dimensional neuronal data post-processing, such as scalp-EEG/ERP or its localized source data, is always challenging. For addressing this, we introduce LEDA, localized-EEG dynamics analyzer, offering our novel techniques, namely (1)the localized source activity to duration (LSAD) ratio that elegantly combines voxel activation

Commonly used remote sensing image de-haze methods include: the image enhancement method and a physical model-based. However, when the above methods are applied to high-resolution remote sensing images, problems with texture information loss and insufficient enhancement often occur. These problems affect further analysis and application of high-resolution remote sensing images. This paper proposes

Quantum computers are imminent threat to secure signal processing because they can break the contemporary public-key cryptography schemes in polynomial time. Ring learning with error (RLWE) lattice-based cryptography (LBC) is considered as the most versatile and efficient family of post-quantum cryptography (PQC). Polynomial multiplication is the most compute-intensive routine in the RLWE schemes.

Noise removal in images denotes an interesting and a relatively challenging problem that has captured the attention of many scholars. Recent denoising methods focus on simultaneously restoring noisy images and recovering their semantic features (edges and contours). But preservation of textures, which facilitate interpretation and analysis of complex images, remains an open-ended research question

This paper proposes a subspace identification method for closed-loop EIV (errors-in-variables) problems based on instrumental variables . First, a unified framework is derived, and then the reason is discussed why some existing subspace methods based on instrumental variables could be biased under closed-loop conditions. Afterwards a remedy is given to eliminate the bias by simply replacing the instrumental

MIMO system identification is a fundamental concern in a variety of applications. Various iterative and recursive MIMO system identification algorithms exist in the literature. The iterative algorithms suffer from high computational cost due to large matrix computations and recursive algorithms suffer from slow convergence speed. This paper proposes a fast recursive exact least squares algorithm for

Computational complexity of state of the art Convolutional Neural Networks (CNNs) makes their integration in embedded systems with low power consumption requirements a challenging task. This requires the joint design and adaptation of hardware and algorithms. In this paper, we propose a new general CNN compression method to reduce both the number of parameters and operations. To solve this, we introduce

The objective of this work is to evaluate the effectiveness of a wearable physiological stress monitoring system in distinguishing between stressed and non-stressed state in older adults using machine learning techniques. This system utilizes EDA and BVP signal to detect occurrence of stress as indicated by salivary cortisol measurement which is a reliable objective measure of physiological stress

This paper presents a reconfigurable frequency response masking (FRM) wideband channelizer architecture which is characterized by low computational and hardware complexity. The proposed hardware efficient architecture is realized by incorporating resource shared non-maximally decimated filter bank in the implementation of the FRM wideband channelizer structure. The coefficients of the proposed architecture

Segmentation of thigh tissues (muscle, fat, inter-muscular adipose tissue (IMAT), bone, and bone marrow) from magnetic resonance imaging (MRI) scans is useful for clinical and research investigations in various conditions such as aging, diabetes mellitus, obesity, metabolic syndrome, and their associated comorbidities. Towards a fully automated, robust, and precise quantification of thigh tissues,

In recent years, target detection framework based on deep learning has made brilliant achievements. However, real-life traffic sign detection remains a great challenge for most of the state-of-the-art object detection methods. The existing deep learning models are inadequate to effectively extract the features of small traffic signs from large images in real-world conditions. In this paper, we address

Convolutional Neural Networks (CNNs) have improved several computer vision applications, like object detection and classification, when compared to other machine learning algorithms. Running these models in edge computing devices close to data sources is attracting the attention of the community since it avoids high-latency data communication of private data for cloud processing and permits real-time

With the rapid development of 5G network and Internet of Things, Clustered Look-Ahead (CLA) technique becomes a promising approach to further accelerate recursive digital filters for real-time applications, such as smart robots and automatic driving. However, it is not easy to quickly obtain a stable CLA pipelined VLSI (Very Large Scale Integration) architecture using the existing methods. This paper

With the rise of graphics processing units (GPUs), the parallel computing community needs better tools to productively extract performance from the GPU. While modern compilers provide flags to activate different optimizations to improve performance, the effectiveness of such automated optimization has been limited at best. As a consequence, extracting the best performance from an algorithm on a GPU

Automatic age estimation from the face images is a growing research interest nowadays. Various literature works have contributed towards the age detection scheme, besides only a few have resulted in providing good performance. This is due to the influence of the external factors, such as environment, lifestyle, and various expressions present in the face image. This paper proposes a deep belief network

As nodes have limited resources in the socially aware networks, they will have strong selfish behaviors, such as not forwarding messages and losing packets, which will lead to poor network performance. Thus, an equivalent-exchange-based data forwarding incentive scheme (EEIS) will be proposed in this paper. It is main that messages forwarding will be abstracted into a transaction in EEIS. The buyer

Technological advances allow the production of increasingly complex electronic systems. Nevertheless, technology and voltage scaling increased dramatically the susceptibility of new devices not only to Single Bit Upsets (SBU), but also to Multiple Bit Upsets (MBU). In safety critical applications, it is mandatory to provide fault-tolerant systems, providing high reliability while meeting applications

To develop an automated pulmonary fibrosis (PF) segmentation methodology using a 3D multi-scale convolutional encoder-decoder approach following the robust atlas-based active volume model in thoracic CT for Rhesus Macaques with radiation-induced lung damage. 152 thoracic computed tomography scans of Rhesus Macaques with radiation-induced lung damage were collected. The 3D input data are randomly augmented

Fully homomorphic encryption (FHE) is a post-quantum secure cryptographic technology that enables privacy-preserving computing on an untrusted platform without divulging any secret or sensitive information. The core of FHE is the bootstrapping algorithm, which is the intermediate refreshing procedure of a processed ciphertext. However, this step has been the computational bottleneck that prevents real-world

Dynamic successive cancellation flip (DSCF) decoding of polar codes is a powerful algorithm that can achieve the error correction performance of successive cancellation list (SCL) decoding, with an average complexity that is close to that of successive cancellation (SC) decoding at practical signal-to-noise ratio (SNR) regimes. However, DSCF decoding requires costly transcendental computations to calculate

It is a fact that Artificial Intelligence is having an ever-growing impact on society. That is not just because of advances in computational power and in machine learning models, such as deep neural networks, but also because of the availability of a large volume of heterogeneous data from diverse sources. The Internet of Things (IoT) paradigm is helping gather massive amounts of data from sensor networks

Over the past, deep neural networks have proved to be an essential element for developing intelligent solutions. They have achieved remarkable performances at a cost of deeper layers and millions of parameters. Therefore utilising these networks on limited resource platforms for smart cameras is a challenging task. In this context, models need to be (i) accelerated and (ii) memory efficient without

This paper describes a low cost computer vision system able to obtain traffic metrics at urban intersections. The proposed system is based on a Bayesian network based reasoning model. It employs the data extracted from background subtraction and contrast analysis techniques applied to predefined regions of interest of the video sequences, to evaluate different traffic metrics. The system has been designed

For vehicles with limited computation resources offloading their computational tasks to a mobile edge computing (MEC) server has been studied in the past as an effective means for improving their computational capabilities. However, most of these studies do not consider, in a meaningful way, the economic aspects related to both the computation offloading of the vehicles and the MEC service providers

In computer-assisted pronunciation training (CAPT) system, feedback for non-native mispronunciation verification is important, for the reason that it is beneficial to the second language (L2) learners in respect of pronunciation improving. In pronunciation evaluation at the phone level, the pairwise distances between embeddings of native phones and non-native phones could be an ideal predictor of L2

This paper presents a novel IEEE 802.16e (WiMAX) based decoder that performs close to the 5G code but without the expensive hardware re-development cost. The design uses an extension of the existing WiMAX parity check code to reduce the initial decoding latency and power consumption while keeping the decoder throughput at maximum. It achieves similar Frame Error Rate (FER) compared to 5G (0.1 dB off)

The traditional information search technology performs full-text indexing on the data in the Internet, searches for information by means of keyword matching index, and returns information to the user. This retrieval method does not accurately understand the user’s needs, and returns relevant links rather than the information the user needs. The user needs to find relevant information from the linked

With the acceleration of economic information, networking, globalization and the new technological revolution, more and more scholars abroad have begun to pay attention to the research of knowledge networks. Some scholars even regard knowledge networks as a new field in the field of knowledge management research theoretical paradigm. This paper takes the research literature of knowledge network in

The power sector continues to accumulate a large amount of data resources, including relevant standard specifications, technical documents, management documents, fault resolution records. How to quickly query and intelligently search these documents is of great value for grid dispatching and fault recovery. The domain search system of traditional power grid is based on keywords, and has the problems

Grasping the future change trend of soil fertility has great significance in improving the soil quality and achieving high-quality crop production and sustainable agricultural development. However, studies predicting the future change trend of farmland soil fertility are scarce. In this paper, with Yanzhou District of Shandong Province as the research area, a study was conducted based on the sampled

As the Internet rapidly develops, the types and quantity of malware continue to diversify and increase, and the technology of evading security software is becoming more and more advanced. This paper proposes a malware detection method based on deep learning, which combines malware visualization technology with convolutional neural network. The structure of neural network is based on VGG16 network.

The following Guest editors were missing in the author group.

Besides the use of information transmission, vehicular communications also perform an essential role in intelligent transportation systems (ITS) for exchanging critical driving information among end users, vehicles, and infrastructures. Moreover, to enhance the understanding of the local environment, increasingly more data are collected by sensors, inducing an extensive use of deep learning (DL)-based

This paper presents a novel adaptive Fast Motion Estimation (FME) Algorithm, which reduces the number of search points, computational complexity and therefore lowering power consumption, while providing improved quality per watt in FME. The algorithm minimizes computation and storage and from there, power consumption, while trying to preserve quality as much as possible. It is very suitable for hardware

This paper presents the VLSI architecture of a highly-efficient and low-complexity Generalized Frequency Division Multiplexing (GFDM) receiver based on the joint Minimum Mean Square Error (joint-MMSE) approach. To be specific, a novel data processing flow is proposed in this paper where the bit reverse permutation after the FFT operation and the shuffle permutation are conducted concurrently. As a

In dataflow representations for signal processing systems, applications are represented as directed graphs in which vertices represent computations and edges correspond to buffers that store data as it passes among computations. The edges in the dataflow graph are single-input, single-output components that manage data transmission in a first-in, first-out (FIFO) fashion. In this paper, we formulate

The article was published online with unupdated Fig. 4. The original article has been corrected.

In this paper, we propose a scheme named “Adaptive Clustering and Scheduling for Dynamic Region-based Resource Allocation” (ACSR) to solve the problems in 3GPP’s fixed zone resource allocation schemes for 3GPP’s infrastructure-aided Vehicle-to-Vehicle (V2V) communication technology:Cellular V2V or C-V2V communications. In 3GPP’s fixed-zone resource allocation schemes, the radio channels are separated

Compute-bound problems like matrix-matrix multiplication can be accelerated using special purpose hardware scheme such as Systolic Arrays (SAs). However, processing elements in SAs have a long critical path delay, thus limiting the performance benefits of SAs. This paper presents a scheme to achieve high-performance matrix multiplication using SAs. Two approximate matrix multiplier designs (Ax1 and

High Level Synthesis (HLS) tools enable application domain experts to implement applications and algorithms on FPGAs. The majority of present FPGA applications is following a stream processing model which is almost entirely implemented statically and not exploiting the benefits enabled by partial reconfiguration. In this paper, we propose a generic approach for implementing and using partial reconfiguration

In recent years, applying machine learning methods to neurological and psychiatric disorder diagnoses has grasped the interest of many researchers; however, currently available machine learning toolboxes usually require somewhat intermediate programming knowledge. In order to use machine learning methods more quickly and conveniently, we developed an intuitive toolbox named BrainSort. BrainSort used

This paper presents a new methodology of the Real-Time monitoring (IRT-OPTICS) for the detection of defect in rolling bearing by combining three domain features (time, frequency and scale), and reducing dimension by two methods: Principal Component Analysis (PCA) and Kernel Principal Component Analysis (KPCA), then classifying data by OPTICS method (Ordering Points To Identify the Clustering Structure)

Neuromorphic architectures implement biological neurons and synapses to execute machine learning algorithms with spiking neurons and bio-inspired learning algorithms. These architectures are energy efficient and therefore, suitable for cognitive information processing on resource and power-constrained environments, ones where sensor and edge nodes of internet-of-things (IoT) operate. To map a spiking

Successive-cancellation list (SCL) decoding for polar codes has the disadvantage of high latency owing to serial operations. To improve the latency, several algorithms with additional circuits have been proposed, but the area becomes larger. This paper proposes a fast multibit decision method having-high area efficiency based on the SCL decoding algorithm. First, multiple bits can be determined to

Sample rate conversion (SRC) is ubiquitous and critical function of software defined radio and other signal processing systems (speech coding and synthesis, computer simulation of continuous-time systems, etc..). In this paper, we present a survey on linear phase finite impulse response (FIR) based sampling rate conversion. Many different FIR-based SRC solutions exist, such as classical FIR, polyphase

Advanced Internet-of-Things applications require control-oriented codes to be executed with low latency for fast responsivity while their advanced signal processing and decision making tasks require computational capabilities. For this context, we propose three multi-issue core designs featuring an exposed datapath architecture with high performance, while retaining energy-efficiency. These features

The growth in the size of deep neural network (DNN) models poses both computational and memory challenges to the efficient and effective implementation of DNNs on platforms with limited hardware resources. Our work on segmented logarithmic (SegLog) quantization, adopting both base-2 and base-\(\sqrt {2}\) logarithmic encoding, is able to reduce inference cost with a little accuracy penalty. However

As the development of quantum computers progresses rapidly, continuous research efforts are ongoing for simulation and emulation of quantum algorithms on classical platforms. Software simulations require use of large-scale, costly, and resource-hungry supercomputers, while hardware emulators make use of fast Field-Programmable-Gate-Array (FPGA) accelerators, but are limited in accuracy and scalability

The US federal communications commission announced new location accuracy requirements for emergency calls. To examine these requirements, we first establish a 3GPP system-level simulator and calibrate its performance for radio access technology-dependent techniques. After considering the 3D channel model, we test and conclude that existing technologies fail to satisfy the indoor vertical accuracy requirement

Large dataflow designs are a result of behavioral specification of modern complex digital systems and/or a result of unfolding and transforming looped and branched programs. Since deep-submicron silicon technology provides large amounts of available resources, pipelining optimization without (or with minimal) resource sharing can give significant advantages in performance. High-level synthesis of CAL-programs

With increasing use of millimeter-wave radars in driving safety applications, interference between vehicles becomes a significant issue. Moreover, oscillator imperfections and relative velocity effects induce inter-carrier interference (ICI) owing to frequency offset, leading to degradation of target detection. In this paper, time-frequency resources are divided into several orthogonal logical channels

This paper presents verification and implementation methods that have been developed for the design of the BrainScaleS-2 65 nm ASICs. The 2nd generation BrainScaleS chips are mixed-signal devices with tight coupling between full-custom analog neuromorphic circuits and two general purpose microprocessors (PPU) with SIMD extension for on-chip learning and plasticity. Simulation methods for automated

In this work, we present an approach to alleviate the potential benefit of adder graph algorithms by solving the transposed form of the problem and then transposing the solution. The key contribution is a systematic way to obtain the transposed realization with a minimum number of cascaded adders subject to the input realization. In this way, wide and low constant matrix multiplication problems, with

Long-Short Term Memory (LSTM) can retain memory and learn from data sequences. It gives state-of-the-art accuracy in many applications such as speech recognition, natural language processing and video classifications. Field-Programmable Gate Arrays (FPGAs) have been used to speed up the inference of LSTMs, but FPGA-based LSTM accelerators are limited by the size of on-chip memory and the bandwidth

The Artificial Neural Networks (ANNs), like CNN/DNN and LSTM, are not biologically plausible. Despite their initial success, they cannot attain the cognitive capabilities enabled by the dynamic hierarchical associative memory systems of biological brains. The biologically plausible spiking brain models, e.g., cortex, basal ganglia, and amygdala, have a greater potential to achieve biological brain

Recurrent Neural Networks, in particular One-dimensional and Multidimensional Long Short-Term Memory (1D-LSTM and MD-LSTM) have achieved state-of-the-art classification accuracy in many applications such as machine translation, image caption generation, handwritten text recognition, medical imaging and many more. However, high classification accuracy comes at high compute, storage, and memory bandwidth