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

Multicore processors serve as target platforms in a broad variety of applications ranging from high-performance computing to embedded mobile computing and automotive applications. But, the required parallel programming opens up a huge design space of parallelization strategies each with potential bottlenecks. Therefore, an early estimation of an application’s performance is a desirable development

NP-hard problems such as the maximum clique or minimum vertex cover problems, two of Karp’s 21 NP-hard problems, have several applications in computational chemistry, biochemistry and computer network security. Adiabatic quantum annealers can search for the optimum value of such NP-hard optimization problems, given the problem can be embedded on their hardware. However, this is often not possible due

Non-orthogonal multiple access (NOMA) with power domain multiplexing and successive interference cancellation (SIC) is one of the promising technologies for future wireless communication. The performance of NOMA is highly dependent on resource allocation such as power allocation and channel assignment. In this paper, we investigate the power allocation (PA) scheme, to optimize the weighted maximin

Polar codes have received a lot of attention due to their capacity-achieving performance and low complexity. This paper proposes a novel scaling offset min-sum (SOMS) algorithm and adapts the offset min-sum (OMS) algorithm for polar codes, and both algorithms are improved via learning. For all message updates, conventional min-sum decoding algorithms use the same scaling factor or offset, which is

We propose a novel reconfigurable hardware architecture to implement Monte Carlo based simulation of physical dose accumulation for intensity-modulated adaptive radiotherapy. The long term goal of our effort is to provide accurate dose calculation in real-time during patient treatment. This will allow wider adoption of personalised patient therapies which has the potential to significantly reduce dose

Real-time coupling of cell cultures to neuromorphic circuits necessitates a neuromorphic network that replicates biological behaviour both on a per-neuron and on a population basis, with a network size comparable to the culture. We present a large neuromorphic system composed of 9 chips, with overall 2880 neurons and 144M conductance-based synapses. As they are realized in a robust switched-capacitor

This paper studies vehicle attribute recognition by appearance. In the literature, image-based target recognition has been extensively investigated in many use cases, such as facial recognition, but less so in the field of vehicle attribute recognition. We survey a number of algorithms that identify vehicle properties ranging from coarse-grained level (vehicle type) to fine-grained level (vehicle make

Recently, Memory Augmented Neural Networks (MANN)s, a class of Deep Neural Networks (DNN)s have become prominent owing to their ability to capture the long term dependencies effectively for several Natural Language Processing (NLP) tasks. These networks augment conventional DNNs by incorporating memory and attention mechanisms external to the network to capture relevant information. Several MANN architectures

Indoor localization using a Received Signal Strength Indicator (namely, RSSI localization) has been considered a poor measurement for target tracking. The main cause of this inaccurate measurement is that RSSI’s behaviors heavily depend on environmental factors. That is, one significant challenge to localization using RSSI is that the strength of a signal varies with the environment confounding wireless

The modern portable devices exhibiting multimedia applications demand higher energy efficient signal processing due to limited battery size. The approximate adders have shown a remarkable energy-efficiency over the accurate adders for error tolerant applications. In this paper, three novel approximate carry look-ahead adder (ACLA) architectures are proposed. These approximate ACLAs are achieved by

Surrogate-model based optimization algorithms can be applied to solve expensive black-box function optimization problem. With the introduction of ensemble model, surrogate-model based algorithms can be automatically adjusted to adapt to various specific problems with different parameter spaces and no need for manual design of surrogate model. However, introduction of ensemble model significantly increases

Speech emotion recognition is very challenging because the definition of emotion is uncertain and the feature representation is complex. Accurate feature representation is one of the key factors for successful speech emotion recognition. Studies have shown that 3D data composed of static, deltas and delta-deltas of log-Mel spectrum is very effective in filtering irrelevant features. The challenge of

While neural network models keep scaling in depth and computational requirements, biologically accurate models are becoming more interesting for low-cost inference. Coupled with the need to bring more computation to the edge in resource-constrained embedded and IoT devices, specialized ultra-low power accelerators for spiking neural networks are being developed. Having a large variance in the models

Fast Fourier Transform (FFT) processor is a paramount signal processing component in MIMO-OFDM wireless communication systems. Furthermore, novel applications introduced in 5G incur much more rigorous requirements for FFT designs including complexity, latency, and re-configurability. This paper presents the VLSI architecture design and circuit implementation of a FFT processor that is jointly optimized

Vehicles with abundant sensors and sophisticated communication capabilities have contributed to the emergency of vehicular crowdsensing systems. Vehicular crowdsensing is becoming a popular paradigm to collect a variety of traffic event-reports in intelligent transportation research. However, event-reports trustworthiness and drivers’ privacy are under the threats of the openness of sensing paradigms

Bayesian compressive sensing (BCS) helps address ill-posed signal recovery problems using the Bayesian estimation framework. Gibbs sampling is a technique used in Bayesian estimation that iteratively draws samples from conditional posterior distributions, which is inherently sequential. In this work, we propose a two-stage parallel coefficient update scheme for wavelet-based BCS, where the first stage

This paper presents a new system for pattern recognition in digital images, called Fractional Fourier-Radial Transform, invariant to translation, scale and rotation (TSR invariant) taking advantage of the well-known properties of some integral transform as Fourier Transform, Mellin Transform and the Radial Hilbert Transform. The main contribution of this work is the use of the Fractional Fourier Transform

This paper presents a secure reconfigurable hierarchical hardware architecture at the pixel and region level for smart image sensors to accelerate machine vision applications. The design maintains hierarchical processing that begins at the pixel level. It aims to reduce the computational burden on the sequential processor and increases the confidentiality of the sensor. We achieve this goal by preprocessing

Stringent power budgets in battery powered platforms have led to the development of energy saving techniques such as Dynamic Voltage and Frequency scaling (DVFS). For embedded system designers to be able to ripe the benefits of these techniques, support for efficient design space exploration must be available in system level simulators. The advent of the edge computing paradigm, with power constraints

This paper proposes an ultra-low-power multi-mode baseband transceiver for capsule endoscopy. We set a new specification for capsule endoscopy to achieve ultra-low power and short-latency transceiver. The proposed baseband transceiver can operate multi-mode to transmit ten images per seconds depending on the presence of the lesion and the position of the capsule endoscope. The proposed one does not

To best leverage high-bandwidth storage and network technologies requires an improvement in the speed at which we can decompress data. We present a “refine and recycle” method applicable to LZ77-type decompressors that enables efficient high-bandwidth designs and present an implementation in reconfigurable logic. The method refines the write commands (for literal tokens) and read commands (for copy

With the enhanced performance and convergence speed than their binary counterparts, NB-LDPC codes have been considered for emerging wireless communication and storage applications. However, one challenging issue to apply NB-LDPC codes in low-power embedded applications is the high decoding complexities. In this paper, we propose a comprehensive message length control technique that adaptively truncates

Eliminating the negative effect of adverse environmental noise has been an intriguing and challenging task for speech technology. Neural networks (NNs)-based denoising techniques have achieved favorable performance in recent years. In particular, adding skip connections to NNs has been demonstrated to significantly improve the performance of NNs-based speech enhancement systems. However, in most of

In this article, we propose a novel ECG classification framework for atrial fibrillation (AF) detection using spectro-temporal representation (i.e., time varying spectrum) and deep convolutional networks. In the first step we use a Bayesian spectro-temporal representation based on the estimation of time-varying coefficients of Fourier series using Kalman filter and smoother. Next, we derive an alternative