Non-uniform Quantization Research Articles

An Energy-and-Area-Efficient CNN Accelerator for Universal Powers-of-Two Quantization

CNN model computation on edge devices is tightly restricted to the limited resource and power budgets, which motivates the low-bit quantization technology to compress CNN models into 4-bit or lower format to reduce the model size and increase hardware efficiency. Most current low-bit quantization methods use uniform quantization that maps weight and activation values onto evenly-distributed levels, which usually results in accuracy loss due to distribution mismatch. Meanwhile, some non-uniform quantization methods propose specialized representation that can better match various distribution shapes but are usually difficult to be efficiently accelerated on hardware. In order to achieve low-bit quantization with high accuracy and hardware efficiency, this paper proposes Universal Power-of-Two (UPoT), a novel low-bit quantization method that represents values as the addition of multiple power-of-two values selected from a series of subsets. By updating the subset contents, UPoT can provide adaptive quantization levels for various distributions. For each CNN model layer, UPoT automatically searches for the optimized distribution that minimizes the quantization error. Moreover, we design an efficient accelerator system with specifically optimized power-of-two multipliers and requantization units. Evaluations show that the proposed architecture can provide high-performance CNN inference with reduced circuit area and energy, and outperforms several mainstream CNN accelerators with higher ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$8\times $ </tex-math></inline-formula> – <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$65\times $ </tex-math></inline-formula> ) area efficiency and ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2\times $ </tex-math></inline-formula> – <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$19\times $ </tex-math></inline-formula> ) energy efficiency. Further experiments of 4/3/2-bit quantization on ResNet18/50, MobileNet_V2 and EfficientNet models show that our UPoT can achieve high model accuracy which greatly outperform other state-of-the-art low-bit quantization methods by 0.3%–6%. The results indicate that our approach provides a highly-efficient accelerator for low-bit CNN model quantization with low hardware overheads and good model accuracy.

Design and Analysis of Hardware-Limited Non-Uniform Task-Based Quantizers

Hardware-limited task-based quantization is a new design paradigm for data acquisition systems equipped with serial scalar analog-to-digital converters using a small number of bits. By taking into account the underlying system task, task-based quantizers can efficiently recover the desired parameters from the low-bit quantized observation. Current design and analysis frameworks for hardware-limited task-based quantization are only applicable to inputs with bounded support and uniform quantizers with non-subtractive dithering. Here, we propose a new framework based on generalized Bussgang decomposition that enables the design and analysis of hardware-limited task-based quantizers that are equipped with non-uniform scalar quantizers or that have inputs with unbounded support. We first consider the scenario in which the task is linear. Under this scenario, we derive new pre-quantization and post-quantization linear mappings for task-based quantizers with mean squared error (MSE) that closely matches the theoretical MSE. Next, we extend the proposed analysis framework to quadratic tasks. We demonstrate that our derived analytical expression for the MSE accurately predicts the performance of task-based quantizers with quadratic tasks.

Point Cloud Soft Multicast for Untethered XR Users

3D point cloud data formats are used to express three-dimensional (3D) information using numerous points in a 3D space. A key challenge is the delivery of high-quality 3D point cloud for the users under a diverse channel quality and available bandwidth to share the same 3D space across multiple untethered extended reality (XR) users. The existing digital-based schemes suffer from two issues owing to the diversity: cliff and leveling-off effects. This paper proposes a novel soft multicasting scheme of point cloud data for untethered XR users. The key ideas of the proposed scheme are three-fold: 1) integration of graph signal processing and analog modulation to adaptively improve the 3D reconstruction quality according to the channel quality for all individual XR users, 2) integration of Givens rotation and non-uniform adaptive quantization to reduce metadata overhead for the graph Fourier transform, and 3) prioritized transmission of the metadata to realize adaptive quality improvement based on the bandwidth available for each XR user. This paper reveals that the proposed scheme prevents cliff and leveling-off effects even when the XR users experience different channel qualities. Furthermore, the proposed transmission exhibits better 3D reconstruction quality compared with the state-of-the-art graph-based delivery scheme in band-limited environments.

Uplink Signal Detection via Look-Up Table-Based AMP for Massive MIMO Systems

We propose a novel look-up table (LUT)-based multi-user detection (MUD) algorithm to imitate approximate message passing (AMP) for uplink signal detection in massive multi-user multi-input multi-output (MU-MIMO) systems. When AMP is implemented with double-precision arithmetic, the dramatic increase in power consumption and process latency becomes a major barrier to practical application, as the system scale expands. To circumvent this issue, we design a novel referential AMP detector composed of many small LUTs cascaded hierarchically, where only informative integer-valued messages are exchanged on a factor graph (FG). Our method tracks the discrete distribution of the LUT outputs according to the multi-layer structure, and successively determines non-uniform quantization thresholds of the LUTs, using an unsupervised learning approach. Specifically, the thresholds at each level of the hierarchy, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e</i> ., layer, are optimized by clustering with the Lloyd-Max algorithm with initial values given by the k-means++ method, in order to minimize the performance degradation caused by quantization errors. The efficacy of the proposed referential AMP detector is confirmed by numerical simulations, which show that the proposed method significantly outperforms the state-of-the-art (SotA) in terms of the bit error rate (BER) performance in various channel models. The results also indicate the referential AMP is robust to changes in wireless channels, and then clarify the reason in terms of the algorithmic structure.

Digital Event-Based Stabilization of Nonlinear Time-Delay Systems

In this paper, the stabilization problem of nonlinear time-delay systems via quantized sampled-data event-based controllers is investigated. Fully nonlinear (i.e., possibly non-affine in the control) systems affected by state delays are studied. Sufficient conditions are provided for the existence of a suitably fast sampling and of an accurate quantization of the input/output channels such that the digital implementation of the continuous-time controller at hand, updated through a proposed event-triggered mechanism, ensures the semi-global practical stability property, with arbitrarily small final target ball of the origin, of the related closed-loop system. A spline approximation methodology is used in order to cope with the problem of the possible non-availability in the buffer of suitable past values of the system variables needed for the digital implementation of the controller. The stabilization in the sample-and-hold sense theory is used as a tool to prove the results. In the theory here developed, the case of non-uniform quantization of the input/output channels and the case of aperiodic sampling are both included. The proposed theoretical results are validated through an application concerning the plasma glucose regulation problem in type-2 diabetic patients.

A Non-Idealities Aware Software–Hardware Co-Design Framework for Edge-AI Deep Neural Network Implemented on Memristive Crossbar

In this work, a non-idealities aware software-hardware co-design framework for deep neural network (DNN) implemented on memristive crossbar is presented. The device level non-ideal factors such as device conductance variation, nonuniform quantization levels, device-to-device variation and programming failure probability are included in the model. At array level, the impact of line resistance and sneak path are considered using a new fast and accurate line resistance estimation model. The non-linearity and offset of the peripheral circuits are also considered. By incorporating these factors into a unified DNN training process, the neural network performance can be evaluated holistically. Furthermore, the proposed training process can effectively mitigate the impact of these non-idealities and reduce the inference accuracy degradations. Implemented in a hybrid fashion of Python and PyTorch, the proposed framework is evaluated with a simplified 5-layer VGG network implemented on measured <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$128\times 128$ </tex-math></inline-formula> RRAM array with 3-level weight resolution. For CIFAR-10 tasks, 83% inference accuracy is achieved with less than 3% accuracy drop compared to the ideal model.

Cooperative Constrained Control of Autonomous Vehicles With Nonuniform Input Quantization

Responsive and highly controlled autonomous vehicles (AVs) on the road easily form platoons with small inter-vehicle gaps to increase traffic throughput and reduce energy consumption. Unlike human drivers, AVs typically employ discrete quantization signals and only generate finite discrete acceleration. Finite discrete acceleration easily causes unstable inter-vehicle gaps and inter-vehicle collisions, especially when there are inaccurate quantization parameters due to quantizer aging. Based on the backstepping technique, this paper proposes the cooperative constrained control algorithm under unknown quantization parameters for AVs with nonuniform input quantization and motion uncertainties. The control laws of each AV in the platoon only depend on the state information of its consecutive AVs. The position trajectories of consecutive AVs are constructed as inter-vehicle collision-free constraints to avoid emergency braking and inter-vehicle collisions. In addition, the consistent characteristic of two types of nonuniform quantizers (i.e., logarithmic quantizers and hysteresis quantizers) is proposed, and unknown disturbances in vehicle motion are compensated. The Lyapunov based local stability and string stability are proved. Numerical simulation shows that the control algorithm is applicable to AVs with either logarithmic quantizers or hysteresis quantizers. Compared with the previous control algorithms without considering input quantization and inter-vehicle collision avoidance, the control algorithm proposed reduces inter-vehicle spacing errors, avoids high acceleration, and avoids inter-vehicle collisions.

Finite-Bit Quantization for Distributed Algorithms With Linear Convergence

This paper studies distributed algorithms for (strongly convex) composite optimization problems over mesh networks, subject to quantized communications. Instead of focusing on a specific algorithmic design, a black-box model is proposed, casting linearly convergent distributed algorithms in the form of fixed-point iterates. The algorithmic model is equipped with a novel random or deterministic Biased Compression (BC) rule on the quantizer design, and a new Adaptive encoding Non-uniform Quantizer (ANQ) coupled with a communication-efficient encoding scheme, which implements the BC-rule using a finite number of bits (below machine precision). This fills a gap existing in most state-of-the-art quantization schemes, such as those based on the popular compression rule, which rely on communication of some scalar signals with negligible quantization error (in practice quantized at the machine precision). A unified communication complexity analysis is developed for the black-box model, determining the average number of bits required to reach a solution of the optimization problem within a target accuracy. It is shown that the proposed BC-rule preserves linear convergence of the unquantized algorithms, and a trade-off between convergence rate and communication cost under ANQ-based quantization is characterized. Numerical results validate our theoretical findings and show that distributed algorithms equipped with the proposed ANQ have more favorable communication cost than algorithms using state-of-the-art quantization rules.

Photonics-Assisted Millimeter-Wave Communication System Based on Low-Bit Gaussian Mixture Model Adaptive Vector Quantization

The quantization technique with a low-bit resolution can significantly reduce the cost and power consumption of analog-to-digital converter (ADC). It will play an important role in energy conservation and cost reduction for the incoming B5G millimeter-wave (MMW) communication systems. In this paper, we propose and demonstrated experimentally a low-bit Gaussian mixture model (GMM) based non-uniform adaptive vector quantization (AVQ) scheme for the low-cost intensity modulated envelope detection photonics-assisted 28 GHz MMW communication system for the first time. The principles of GMM-based one-dimensional adaptive scalar quantization (ASQ) and multi-dimensional AVQ are first introduced, and then are used to realize the low-bit non-uniform adaptive quantization for reducing the ADC bit resolution of MMW receiver. Furthermore, the performance of traditional uniform quantization, the present <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i> -means and proposed GMM-based non-uniform ASQ/AVQ schemes are evaluated and compared in detail. Utilizing the proposed GMM-based AVQ scheme, the ADC quantization resolution in our MMW receiver can be reduced from 5 bits of the traditional uniform quantization to as low as 2 bits, without noticeable performance penalty. Moreover, as compared with the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i> -means-based quantization scheme, the MMW receiver enabled by GMM-based ASQ/AVQ scheme can save about half of the quantization time under similar performance. This is mainly because the clustering based on probability converges faster than the Euclidean distance, which significantly reduces the number of iterations required. Therefore, the GMM-based AVQ scheme is a promising solution to realize high performance ADCs with low-bit resolution for future MMW-enabled optical fiber wireless access networks.

Two Novel Non-Uniform Quantizers with Application in Post-Training Quantization

With increased network downsizing and cost minimization in deployment of neural network (NN) models, the utilization of edge computing takes a significant place in modern artificial intelligence today. To bridge the memory constraints of less-capable edge systems, a plethora of quantizer models and quantization techniques are proposed for NN compression with the goal of enabling the fitting of the quantized NN (QNN) on the edge device and guaranteeing a high extent of accuracy preservation. NN compression by means of post-training quantization has attracted a lot of research attention, where the efficiency of uniform quantizers (UQs) has been promoted and heavily exploited. In this paper, we propose two novel non-uniform quantizers (NUQs) that prudently utilize one of the two properties of the simplest UQ. Although having the same quantization rule for specifying the support region, both NUQs have a different starting setting in terms of cell width, compared to a standard UQ. The first quantizer, named the simplest power-of-two quantizer (SPTQ), defines the width of cells that are multiplied by the power of two. As it is the case in the simplest UQ design, the representation levels of SPTQ are midpoints of the quantization cells. The second quantizer, named the modified SPTQ (MSPTQ), is a more competitive quantizer model, representing an enhanced version of SPTQ in which the quantizer decision thresholds are centered between the nearest representation levels, similar to the UQ design. These properties make the novel NUQs relatively simple. Unlike UQ, the quantization cells of MSPTQ are not of equal widths and the representation levels are not midpoints of the quantization cells. In this paper, we describe the design procedure of SPTQ and MSPTQ and we perform their optimization for the assumed Laplacian source. Afterwards, we perform post-training quantization by implementing SPTQ and MSPTQ, study the viability of QNN accuracy and show the implementation benefits over the case where UQ of an equal number of quantization cells is utilized in QNN for the same classification task. We believe that both NUQs are particularly substantial for memory-constrained environments, where simple and acceptably accurate solutions are of crucial importance.

Data reduction through optimized scalar quantization for more compact neural networks

Raw data generation for several existing and planned large physics experiments now exceeds TB/s rates, generating untenable data sets in very little time. Those data often demonstrate high dimensionality while containing limited information. Meanwhile, Machine Learning algorithms are now becoming an essential part of data processing and data analysis. Those algorithms can be used offline for post processing and post data analysis, or they can be used online for real time processing providing ultra low latency experiment monitoring. Both use cases would benefit from data throughput reduction while preserving relevant information: one by reducing the offline storage requirements by several orders of magnitude and the other by allowing ultra fast online inferencing with low complexity Machine Learning models. Moreover, reducing the data source throughput also reduces material cost, power and data management requirements. In this work we demonstrate optimized nonuniform scalar quantization for data source reduction. This data reduction allows lower dimensional representations while preserving the relevant information of the data, thus enabling high accuracy Tiny Machine Learning classifier models for online fast inferences. We demonstrate this approach with an initial proof of concept targeting the CookieBox, an array of electron spectrometers used for angular streaking, that was developed for LCLS-II as an online beam diagnostic tool. We used the Lloyd-Max algorithm with the CookieBox dataset to design an optimized nonuniform scalar quantizer. Optimized quantization lets us reduce input data volume by 69% with no significant impact on inference accuracy. When we tolerate a 2% loss on inference accuracy, we achieved 81% of input data reduction. Finally, the change from a 7-bit to a 3-bit input data quantization reduces our neural network size by 38%.

Analysis of Compressing PAPR-Reduced OFDM IQ Samples for Cloud Radio Access Network

A common problem of the virtualized cloud radio access network architecture (C-RAN) is the compression of the time-domain IQ samples before transmission over the fronthaul link. Considering a multicarrier waveform such as OFDM, whose IQ samples follow a quasi-Gaussian distribution, the conventional Gaussian quantizer may be used as the optimal solution to the compression problem. However, since the high peak-to-average power ratio (PAPR) of OFDM signals remains a serious problem, various techniques may be employed to reduce the time-domain fluctuations of the IQ samples in the OFDM, resulting in a change in its distribution. The latter fact makes the Gaussian quantizer suboptimal. The literature lacks a performance analysis of the conventional OFDM-based compression techniques when the PAPR of the OFDM signal is reduced. Therefore, in this paper, we study for the first time the impact of reducing the PAPR of the OFDM signal before compression in the C-RAN architecture through rate-distortion analysis. We consider clipping and tone reservation PAPR reduction algorithms. The former is the simplest PAPR reduction approach, while the latter is one of the most effective algorithms used in broadcasting standards such as DVB-T2 and ATSC 3.0. We first derive the distribution of the PAPR-reduced OFDM IQ samples. This is used to optimize the thresholds and codebook levels of a non-uniform scalar quantizer and the number of quantization bits allocated for each quantized level in the entropy coding stage, along with the MER performance analysis. The simulation results show that the conventional Gaussian-based compression techniques applied to a PAPR-reduced signal is not very robust to the statistical changes in the signal unless the signal distribution at the input of the Gaussian quantizer is not significantly affected. However, a significant gain is obtained when the quantizer is optimized with respect to the true distribution of the PAPR-reduced IQ samples.

Application of Intelligent Analysis Technology of Football Video Based on Online Target Tracking Algorithm of Motion Characteristics in Football Training.

Because the scene of football video is fixed and simple, the events in football video, such as shooting and offside , also have clear semantics. At the same time, they have sufficient domain knowledge and have broad application prospects. The research framework of sports video intelligence analysis is usually regarded as a three-level framework, namely, the low-level feature layer, the middle-level key primitive generation layer, and the high-level event analysis layer. A MF_O2T (Moving Feature Online Target Tracking) algorithm is proposed. First, based on the marked first frame image, this algorithm extracts a set of standardized local images from the target areas of visible and infrared images as target convolution filters, removes the main color of the stadium by using HSV color space nonuniform quantization algorithm, and extracts the histogram of the main color of players in upper and lower blocks. Experimental results show that the algorithm designed in this paper has strong robustness, can better adapt to player tracking in different scenes of football video, and meets the real-time requirements of football training.

Adaptive gradients and weight projection based on quantized neural networks for efficient image classification

Quantization of weights and activations has been introduced into optimization methodologies of deep neural networks (DNNs) to address issues like memory consumption and massive computational demands that impede edge applications of neural networks. There are multiple approaches to quantization which can probably all be classified into two categories: uniform quantization and non-uniform quantization. The key difference between these methods is whether unequal quantization intervals are used to match the non-uniform distribution of weights. However, conventional techniques train weights layer by layer to suit previously determined quantization points, which poses difficulty for reaching optimal points. We proposed a framework called Deep Projection (DP) to train quantized weights with adaptive gradients. Gradients and weights are projected to high dimensional training space through projection layers, resulting in more complicated update paths and randomness, which benefit the generalization of models. Renewal of weights in the network is conducted by values composited by self-updating high dimensional tensors, which means that the resulting gradients are softer and more adaptive to network training. We applied our method via a uniform quantization approach, and the results showed improvements even when we limited the first and last layers with lower bit-width. The results could be comparable with the non-uniform quantization method with 4-bit precision. Distributions of weights in different quantized networks are analyzed to display the advantages of our method. Besides, all of these steps can be realized in a conventional training process. There are no limitations to quantization bit-width and function. Namely, our framework is readily implemented with a concise architecture.

Extracting More Quantum Randomness With Non-Uniform Quantization

Random numbers generated via quantum process is indeterministic, which are of essential to the cryptographic communication and the large-scale computer modeling. However, in realistic scenarios, classical noises can inevitably contaminate the raw sequences of a quantum randomness number generator (QRNG), and then compromise the security of the QRNG. Min-entropy is a useful approach that can quantify the randomness independent of side-information. To enhance the extractable randomness of the raw sequences arising from the QRNG, we propose a new method which exploits non-uniform quantization methods instead of uniform sampling methods and effectively enhances the extractable randomness from the QRNG at a high quantum-to-classical-noise ratio (QCNR). Given a QCNR as 50 dB and a 16-bit analog-to-digital converter (ADC), the worst-case conditional min-entropy of the non-uniform quantization scheme is improved by nearly 11% compared with that of the uniform sampling scheme.

Feature compensation network based on non-uniform quantization of channels for digital image global manipulation forensics

With the popularity of image editing software and technology, it has become very easy to manipulate an image, which has challenged the authenticity and security of digital images. Digital Image Manipulation Forensics (DIMF) has become an important research direction to confirm the authenticity of images, detect the manipulations that images have undergone, and avoid the misuse of image editing. Currently, most DIMF targets the detection of multiple image manipulations with fixed parameters. However, we consider detecting image manipulation in a more complex scenario where the parameters are chosen to be arbitrary. In this paper, we propose a Feature Compensation Network (FCNet) based on non-uniform quantization of channels. Briefly, it contains three important parts: (1) feature enhancement block, which extracts and enhances valid information from low-level features and eliminates semantic gaps between them and the high-level features. (2) sensitivity estimation block, which obtains the importance coefficients of each channel and guides the non-uniform quantization of low-level features. (3) adaptive average pooling, which keeps the resolution of low-level features and high-level features consistent and ensures that subsequent feature fusion is appropriate. Through extensive experiments, we have demonstrated the effectiveness of the proposed method in detecting multiple image manipulations.

Granger causality from quantized measurements

An approach is proposed for inferring Granger causality between jointly stationary, Gaussian signals from quantized data. First, a necessary and sufficient rank criterion for the equality of two conditional Gaussian distributions is proved. Assuming a partial finite-order Markov property, a characterization of Granger causality in terms of the rank of a matrix involving the covariances is presented. We call this the causality matrix. The smallest singular value of the causality matrix gives a lower bound on the distance between the two conditional Gaussian distributions appearing in the definition of Granger causality and yields a new measure of causality. Then, conditions are derived under which Granger causality between jointly Gaussian processes can be reliably inferred from the second order moments of quantized measurements. A necessary and sufficient condition is proposed for Granger causality inference under binary quantization. Furthermore, sufficient conditions are introduced to infer Granger causality between jointly Gaussian signals through measurements quantized via non-uniform or high resolution quantizers. Apart from the assumed partial Markov order and joint Gaussianity, this approach does not require the parameters of a system model to be identified. No assumptions are made on the identifiability of the jointly Gaussian random processes through the quantized observations. The effectiveness of the proposed method is illustrated by simulation results.

A GMM-based non-uniform quantization scheme for improving low-resolution IMDD-UFMC system performance

In this paper, a Gaussian mixture model (GMM) based nonuniform quantization scheme with low-bit resolution is firstly presented in optical intensity-modulated direct detection universal filter multicarrier (IMDD-UFMC) system for increasing system performance. By utilizing the Expectation-Maximum (EM) iterative algorithm of GMM model, the optimal quantized level can be directly obtained, and then is used to realize the nonuniform quantization for reducing the digital-to-analog converter (DAC) bit resolution of UFMC signals. In this processing, the extra estimation operation of signals’ probability density distribution (PDF) is not involved, which can reduce the complexity of the common non-uniform quantization scheme compared to the method based on the PDF estimation. And, the proposed scheme is further validated in a 10 Gb/s IMDD-UFMC transmission system through a 20 km fiber based the commercially OptiSystem15.0 software. And, all the results are obtained by this numerical simulation. Results verified that, in the cases of 3 ∼ 5 bit, 2.7%∼7.8% the error vector magnitude (EVM) and at least 1-dB received sensitivity improvements can be achieved compared to the uniform quantization schemes.

NQRELoc: AP Selection via Nonuniform Quantization RSSI Entropy for Indoor Localization

Received signal strength indicator (RSSI) of WiFi access point (AP) is a primary sensor data used for indoor fingerprint localization. User sends its online RSSI to server to estimate its position by matching with RSSI fingerprints database which built in the offline phase. An important goal of indoor fingerprint localization is to increase the accuracy while reduce the storage cost. Meanwhile, APs perform different effects on target estimation and mapping metric in RSSI fingerprint localization. In this paper, a novel localization model based on the nonuniform quantization RSSI entropy (NQRELoc) is proposed to address these problems. First, to select the APs that contribute more to the localization, the nonuniform quantization RSSI entropy (NQRE) is introduced to quantify AP’s discernibility and select APs whose signals show sufficient differentiation to construct an offline fingerprints database. Then, the entropy-weighted euclidean distance (EWED) is used as a metric to measure the similarity of online RSSI vectors and offline RPs fingerprints. Finally, NQRELoc locates the target by the improved entropy-weighted K nearest neighbor (IEWKNN) algorithm, which takes the APs effect into target estimation. To validate the proposed algorithm, a large scale of experiments and simulations are implemented. The results demonstrate that NQRELoc can not only reduce the storage overhead but also improve the positioning accuracy compared to the other existing techniques.

A Personalized Compression Method for Steady-State Visual Evoked Potential EEG Signals

As an informative electroencephalogram (EEG) signal, steady-state visual evoked potential (SSVEP) stands out from many paradigms for application in wireless wearable devices. However, its data are usually enormous, occupy too many bandwidth sources and require immense power when transmitted in the raw data form, so it is necessary to compress the signal. This paper proposes a personalized EEG compression and reconstruction algorithm for the SSVEP application. In the algorithm, to realize personalization, a primary artificial neural network (ANN) model is first pre-trained with the open benchmark database towards BCI application (BETA). Then, an adaptive ANN model is generated with incremental learning for each subject to compress their individual data. Additionally, a personalized, non-uniform quantization method is proposed to reduce the errors caused by compression. The recognition accuracy only decreases by 3.79% when the compression rate is 12.7 times, and is tested on BETA. The proposed algorithm can reduce signal loss by from 50.43% to 81.08% in the accuracy test compared to the case without ANN and uniform quantization.