Humans are better at recognising faces from their own vs. another ethnic background. Socio-cognitive theories of this own-race bias (ORB) propose that reduced recognition of other-race faces results from less motivation to attend to individuating information during encoding. Accordingly, individuation instructions that explain the phenomenon and instruct participants to attend to other-race faces during learning attenuate or eliminate the ORB. However, it is still unclear how exactly such instructions affect other-race face processing. We addressed this question by investigating encoding-related event-related brain potentials, contrasting neural activity of subsequently remembered and forgotten items (Dm effects). In line with socio-cognitive accounts, individuation instructions reduced the ORB. Critically, instructions increased Dm effects for other-race faces, suggesting that more processing resources were allocated to these faces during encoding. Thus, compensating for reduced experience with other-race faces is possible to some extent, but additional resources are needed to decrease difficulties resulting from a lack of perceptual expertise.

1. Introduction

Face recognition is crucial to our social interactions, and we are remarkably good at it. However, not all faces are recognised equally well. One of the most widely researched phenomena in the face memory literature is the so-called own-race bias (ORB, or other-race effect)1 , the well-documented finding that people more accurately remember faces of their own ethnic group compared to faces of another ethnicity (for a review, see Meissner & Brigham, 2001). Although these difficulties with other-race face recognition can pose substantial challenges for applied contexts, such as passport control and eyewitness testimony, the exact mechanisms underlying the ORB remain an issue of debate. Particularly relevant for the present study, it has been suggested that the ORB results from a lack of motivation to individuate other-race faces and from a failure to attend to individuating information in these faces (Hugenberg, Young, Bernstein, & Sacco, 2010). Accordingly, an explicit instruction to individuate other-race faces has been reported to reduce or even eliminate the effect (e.g., Hugenberg, Miller, & Claypool, 2007). In the present study, we examined the extent to which individuation instructions modulate neural correlates of the ORB. Importantly, previous purely behavioural work has focused exclusively on the effect of giving individuation instructions during learning on memory performance at test, thus providing only indirect evidence of an encoding-based mechanism underlying the effect. Here, we directly investigated whether individuation instructions modulate encoding-related neural processes, and whether they do so selectively for other-race faces.

2- Novel signal processing methods for few EEG electrode-based neural decoding

3- Remaining challenges and future directions

References

Introduction

The brain is the commander of voluntary movement control. It generates the oscillatory neural activity at specific frequency bands traveling through the corticospinal tract to activate the musculoskeletal system for movement execution [1]. Neural oscillations at mu (8e13 Hz) and beta (15e35 Hz) bands measured around the sensorimotor cortex, known as cortical sensoryemotor rhythms (CSMRs), are thought to be associated with voluntary control of movements [2]. The coupling between CSMR and muscle activities has been previously reported at these frequency bands, confirming the key functional role of CSMR in movement control [3,4]. Damage to the corticospinal tract following a brain or spinal injury can result in a decrease in the coupling between CSMR and muscle activities, and associated motor impairments, such as muscle weakness and loss of independent movement control [5,6]. However, the CSMR may be preserved at the sensorimotor cortex that allows the identification of motor intentions via measuring and decoding the CSMR [7].

Electroencephalography (EEG) is an electrophysiological monitoring technique that records the oscillatory cortical activity including the CSMR. By placing the electrodes on the scalp, the EEG measures cortical activity without surgery. Compared to other brain signal recording methods (e.g., functional MRI, electrocorticography, positron emission tomography), the advantages of EEG are that it is inexpensive, low-risk, and portable [8]. These advantages allow EEG to be an online monitoring method for daily use. However, due to the volume conduction through the scalp, skull, and other layers of the brain, EEGs recorded by a scalp sensor are a “blurred” copy of multisource activities, which increases the difficulty of EEG signal decoding. Advanced signal processing methods are required to address this challenge. Traditional signal processing methods such as independent component analysis [9] and common spatial pattern (CSP) filter [10,11] need a large number of EEG electrodes covering the whole scalp during the measurement for disentangling the mixed multisource signals. This whole-scalp recording reduces the feasibility of EEG in neuro-rehabilitation for daily use. Several novel signal processing methods have been recently proposed to improve EEG data analysis for accurate identification of motor intentions using only a few electrodes. These novel methods can be combined with various new EEG devices with very few electrodes, such as the Emotiv Epoc headset and LooxidVR package for daily use.

1- Introduction

2- Methods

3- Datasets

4- Results

5- Discussion

6- Conclusions

References

Abstract

We introduce two Convolutional Neural Network (CNN) classifiers optimized for inferring brain states from magnetoencephalographic (MEG) measurements. Network design follows a generative model of the electromagnetic (EEG and MEG) brain signals allowing explorative analysis of neural sources informing classification. The proposed networks outperform traditional classifiers as well as more complex neural networks when decoding evoked and induced responses to different stimuli across subjects. Importantly, these models can successfully generalize to new subjects in real-time classification enabling more efficient brain–computer interfaces (BCI).

Introduction

Deep Neural Networks have revolutionized many domains such as image recognition and natural language processing. To date, their application in the analysis of electro- and magnetoencephalographic (EEG and MEG) data has been limited by several domain-specific factors. First of all, electromagnetic brain signals are characterized by very low signal-to-noise ratio (SNR). Here, the term “noise” is understood widely and includes external interference, physiological (e.g. cardiac or oculomotor) artifacts as well as background brain activity unrelated to the studied phenomena. SNR in single-trial EEG and MEG measurements is typically assumed to be < 1 for evoked responses and
1 for oscillatory activity, which puts these data to stark contrast with those in traditional applications of deep learning. Typical EEG/MEG analysis employs a wide range of techniques to increase the SNR, e.g. spatial and temporal filtering, averaging a large number of observations, sourceseparation algorithms, and other complex feature extraction methods (e.g. wavelet transform). Thus, efficient noise suppression is required for high-accuracy classification of EEG/MEG signals. Second, these data have a complex, high-dimensional spatiotemporal structure. Modern EEG and MEG systems comprise several hundreds of sensors capable of sampling brain activity with sub-millisecond temporal resolution. In the case of MEG, these sensors may also measure different components of the neuromagnetic field. On one hand, this multitude of data points enables sophisticated analysis methods to extract finer details of brain function. On the other hand, manual analysis and interpretation of these data become increasingly complex and time-consuming. Machine-learning algorithms can be of great help in such tasks but the mere classification result is often not sufficient; ideally, the experimenter should understand why the algorithm is able to classify the data, i.e., the learned model should be interpretable in neurophysiological terms. A model able to reliably identify those neural sources that contribute to the discrimination between given experimental conditions could enable efficient exploitative analysis of these complex data sets and ultimately allow more complex experimental designs. Finally, deep-learning models require large numbers of training samples to perform optimally. In a typical EEG/MEG or BCI experiment, however, time constraints and data acquisition costs limit the sample sizes severely. Open datasets usually comprise data from a large number of individuals, providing a promising strategy to overcome this limitation, but in this case, the classifier needs to be robust to high interindividual variability stemming from differences in cortical anatomy, mappings of function to structure and physiological state. Taken together, these factors may easily lead to over-fitting (especially in more complex models) and poor interpretability of the findings. To address these challenges, we propose a Convolutional Neural Network (CNN) whose architecture is based on a generative model of non-invasive electromagnetic measurements of the brain activity (Daunizeau and Friston, 2007).

In this paper, we propose a fast encoding method to facilitate an affine motion estimation (AME) process in versatile video coding (VVC) encoders. The recently-launched VVC project for next-generation video coding standardization far outperforms the High Efficiency Video Coding (HEVC) standard in terms of coding efficiency. The first version of the VVC test model (VTM) displays superior coding efficiency yet requires higher encoding complexity due to advanced inter-prediction techniques of the multi-type tree (MTT) structure. In particular, an AME technique in VVC is designed to reduce temporal redundancies (other than translational motion) in dynamic motions, thus achieving more accurate motion prediction. The VTM encoder, however, requires considerable computational complexity because of the AME process in the recursive MTT partitioning. In this paper, we introduce useful features that reflect the statistical characteristics of MTT and AME and propose a method that employs these features to skip redundant AME processes. Experimental results show that—when compared to VTM 3.0—the proposed method reduces the AME time of VTM to 63% on average, while the coding loss is within 0.1% in the random-access configuration.

Introduction

The amount of video data has increased rapidly, especially with the growing use of Internet-based streaming services and devices that receive video broadcasts. The bandwidth and storage capacity of video applications is limited, requiring efficient video compression techniques. This need for video compression will further increase due to the higher resolutions of volumetric content such as 360-degree and high dynamic range (HDR) videos. Considering this diverse and growing demand for more powerful compression, a new video coding standardization project called versatile video coding (VVC) was launched recently by the Joint Video Exploration Team (JVET) of two expert groups: ISO/IEC Moving Picture Experts Group (MPEG) and ITU-T Video Coding Experts Group (VCEG). The JVET published the initial draft of VVC in 2018 [1] and released the VVC test model (VTM). VTM has a similar structure to the High Efficiency Video Coding (HEVC) test model (HM), but it uses advanced tools that provide better compression performance. A key concept among these tools is the multiple-type tree (MTT) segmentation structure [2].

Parity-check (PC) polar codes can yield better error-correcting performance compared with the cyclic redundancy check (CRC) aided polar codes under successive cancellation list decoder. However, PC bits are incapable of detecting error as effective as CRC. To overcome this shortage, this paper proposes a scheme of CRC-aided PC polar codes. The proposed scheme can detect error before decoding is completed and outperform the standard CRC-assisted polar codes with better capability of error detecting.

Introduction

POLAR codes, introduced by [1], have been adopted in the 5 th generation wireless communication standard as the control channel coding scheme for the enhanced Mobile Broadband (eMBB) service. Currently, the most dominant decoding scheme for polar codes is the cyclic redundancy check (CRC) assisted successive cancellation list (CA-SCL) algorithm [2], [3] which enables polar codes to be competitive with state-of-the-art codes. To further improve the performance of CA-SCL at high signal noise ratio (SNR), some research was conducted based on the CRC code design. Zhang et al. searched the optimal CRC polynomials for the standard CA-SCL decoder to eliminate the erroneous polar codewords with minimum Hamming weight (MHW) in decoding [4]. Using the same idea, [5] and [6] respectively designed the protected bits of CRC and the locations of CRC bits. However, all the above schemes have two drawbacks. First, they cannot detect error in the intermediate decoding process.1 Second, they cannot correct the decoding error. To solve the first problem, [7] first proposed a multiCRC polar codes which can also reduce the decoding delay and memory space. Meanwhile, another partitioning method which can reduce the memory requirements associated with SCL decoding was proposed by [8]. Considering these structures suffer from performance degradation, [9] proposed an optimized scheme. However, its designing complexity is high and it fails to give a universal concatenation scheme. The second defect can be addressed by parity-check (PC) polar coding which is first proposed by [10]. After that, [11] designed the PC coding based on the polar kernel to improve the performance. Especially, [12] used a single cycle shift register (CSR) to design a PC polar coding scheme, which has been verified to provide significant performance gain.

I. INTRODUCTION

II. MOTIVATION

III. HEVC INTRA PREDICTION

IV. PROPOSED VLSI ARCHITECTURE

V. IMPLEMENTATION RESULT

VI. CONCLUSION

REFERENCES

In the High Efficiency Video Coding (HEVC), a variety of CU sizes and intra prediction modes significantly improve coding efficiency, but also bring higher computational complexity. This paper proposes a new compact VLSI architecture for HEVC intra prediction, which is geared towards 8K video decoding. It supports all the transform unit (TU) sizes and 35 HEVC intra prediction modes. First, this paper introduces a TU-oriented intra predictor with a throughput of 32 pixels, which can be newly arranged with the TU size. It can be a line of 32 pixels, two lines of 16 pixels, 4 lines of 8 pixels or four lines of four pixels. This TU-oriented architecture allows intra-prediction and inverse discrete cosine transform (IDCT) to be computed in parallel, removing the memory between them. In addition, a horizontal and vertical line buffer for reference sample is proposed, which only cost 0.8K bit and is implemented by register files with SRAM-free. Finally, to further reduce hardware consumption, multipliers can be shared in the prediction. The implementation result shows that the compact architecture supports 8K video application and costs 66.2K logic gates, which is synthesized with the TSMC 65nm process under 400MHz.

INTRODUCTION

The Joint Collaborative Team on Video Coding (JCT-VC) has proposed the latest video coding standard, High Efficiency Video Coding (HEVC) [1]. It can achieve about a 50% bit-rate reduction than the previous video coding standard, H.264/AVC [2], but with higher computational complexity. Like AVC, HEVC also uses block-based coding. Each video frame is divided into multiple CTUs, and each CTU is further divided into smaller CUs. And in HEVC, the CU is further divided into PUs and TUs, where PU is the basic prediction unit and TU is for transform and quantization. The maximum CU size in HEVC is 64×64, not 16×16 in AVC, but the maximum size of TU is only 32 × 32. The PU size of intra prediction is suitable for TU, so the largest PU is 32 × 32. In addition, the number of HEVC intra prediction modes is 35, not 9 in AVC, in order to describe the texture better [4]. Although the complex block partition scheme and the prediction mode can improve the coding quality, the VLSI implementation is more challenging for high-throughput and high-resolution video coding. Due to the higher complexity and data dependencies, it is difficult to propose a compact high-performance intra prediction architecture. Previously, there was some related work on the intra prediction VLSI architecture, and some were optimized for the reference pixel preparation and prediction process.

Abstract

In this paper, we proposed a novel concatenated coded modulation composed of adaptive turbo code with trellis-coded modulation (ATTCM) that is based on generalized frequency division multiplex (GFDM) system. The proposed scheme has low complexity and provides high spectrum efficiency and significant coding gain. An experiment with 4 Gbit/s novel concatenated coded modulation GFDM system is successfully demonstrated with the proposed method. It is shown that the novel concatenated coding modulation signal (ATTCM 32QAM) provides 2.3 dB coding gain over that of TCM32QAM signal at bit error rate of 1e–3. This paper indicates a prospect solution for the future fifth generation optical access system.

Introduction

At present, the dramatic growth of services demands of the optical fiber communication increases rapidly. Optical access network is a strong candidate for connecting the backbone network with the end users, which provides high speed and high reliability for bandwidth hungry applications [1], [2].Currently commercial access systems mainly apply lean single carrier, However, the increasing demands of users are pushing forward to extend bandwidth resources, the multicarrier technology has been proposed in the past few years, fourth generation (4G) cellular systems based on OFDM technology have been optimized to provide high data rates and reliable coverage to mobile users [3]. Nevertheless, OFDM is susceptible to inter-carrier interference and synchronization errors. Additional overhead such as cyclic prefix (CP) is needed to alleviate the interference [3]. While generalized frequency division multiplexing (GFDM) based on multicarrier filter bank is a strong modulation technique for the fifth generation (5G) standard [4]–[7]. The main characteristic of GFDM is low out of band emission, insensitive to frequency offset and phase noise. Cyclic prefix is added to each GFDM block instead of each GFDM symbol. The peak-to-average power ratio (PAPR) can be reduced by designing the flexible pulse shaping of individual subcarriers [6], [7]. In addition, GFDM technology with high spectral efficiency enables the access system to support a huge number of optical network units. They are more susceptible to transmission impairments from channel and components in optical access network, which leads to inter-symbol interference (ISI) and degrade the performance of the system [6]. Therefore, the forward error correction (FEC) codes can be employed to improve the BER performance by providing extra gain to the optical access network; it is a powerful technique to increase the system’s margin [8]–[10]. Turbo code as one of the FEC code, has very good performance for error correction. It not only has superior performance in high noise environment, but also has strong anti-fading and anti-interference ability. Moreover, the ability to provide communication near the shannon limit in various harsh conditions has been demonstrated. Turbo code has been as deep space communication standard by the United States advisory committee of space data system which provides an important background for the research of Turbo code implementation [11]. However, Turbo code has the problem of error floor, this phenomenon will seriously affects the high reliability in the actual communication. While the technology of combing turbo code with trellis coded modulation (TCM) can improve spectrum efficiency and has robust error correction quality with simple construction where the error floor problem can be avoided, which makes it an attractive solution for optical access network in the near future [12]–[16]. In this paper, we propose and experimentally demonstrate a novel concatenated coded modulation (adaptive turbo trellis-coded modulation (ATTCM)) in 4 Gbit/s GFDM optical access system. The code weight decision algorithm (CWD) is used for adaptive code. The decoder of ATTCM used Log-MAP algorithm and reduced the complexity of the receiver. By combing these techniques, the proposed scheme provides significant performance improvement for access system.

چکیده

در این مقاله، ما یک مدولاسیون کدگذاری شده زنجیره‌ای نوین که از ترکیب کد توربو تطبیقی با مدولاسیون کدگذاری مدور (ATTCM) ساخته شده و مبتنی بر سیستم تقسیم فرکانس کلی (GFDM) می‌باشد را ارائه داده‌ایم. طرح پیشنهاد شده دارای پیچیدگی کمتر بوده و کارایی طیفی زیاد و بهره‌ی کدگذاری قابل توجهی را فراهم می‌آورد. یک آزمایش با سرعت 4 گیگابیت بر ثانیه، سیستم مدولاسیون کدگذاری شده زنجیره‌ای نوین مبتنی بر GFDM را توسط طرح پیشنهادی با موفقیت نشان داده است. در این مقاله همچنین نشان داده شده‌است که سیگنال مدولاسیون کدگذاری شده زنجیره‌ای نوین (ATTCM32QAM) در نرخ خطای بیت 3-1 بهره‌ی سیگنال 3/2 دسی‌بل را بر روی سیگنال TCM32QAM ایجاد می‌کند. این مقاله یک راه‌حل امیدوارانه برای سیستم دسترسی نوری در نسل پنجم ارتباطات در آینده را نشان می‌دهد.

مقدمه

در حال حاضر، رشد چشمگیر سرویس‌ها و خدمات باعث افزایش سریع ارتباط فیبر نوری شده‌است. شبکه‌ی دسترسی نوری یک گزینه‌ی مناسب و قوی برای اتصال شبکه‌ی اصلی با کاربران انتهایی است که سرعت بالا و قابلیت اطمینان زیادی را برای برنامه‌های کاربردی نیازمند پهنای‌باند فراهم می‌کنند [1] و [2].

2- مدل اصلی سیستم 

بلوک دیاگرام مدولاسیون کدگذاری شده زنجیره‌ای GFDM در شکل 1 نشان داده شده‌است. این بلوک دیاگرام از مدولاسیون ATTCM و GFDM تشکیل شده‌است. اصول مربوط به هر قسمت در بخش زیر توضیح داده شده‌است.

A new coded splitting imaging technique is proposed to reconstruct the complex amplitude of a light field iteratively using a single-shot measurement. In this technique, a specially designed coded splitting plate is adopted to diffract the illuminating beam into multiple beams of different orders and code their wavefronts independently and differently. From the diffraction pattern array recorded on the detector plane, both the modulus and phase distributions of the illuminating beam can be reconstructed iteratively using known transmission functions of different orders of the coded splitting plate. The feasibility of the proposed technique is verified both numerically and experimentally.

Introduction

Coherent diffraction imaging (CDI) method is a lensless imaging technique that can measure the phase and modulus distributions of a light field from the intensity of recorded diffraction patterns using an iterative approach. Since it does not require high-quality optics and can obtain diffraction limited spatial resolution theoretically, CDI can be adopted to observe a sample with a broad range of radiations including visible light, high-energy electron, and X-ray photons and has become an independent tool in many research fields (Fienup, 1993; Kostli ¨ and Beard, 2003; Zuo et al., 2003; Roy et al., 2011). The first widely accepted CDI technique is the G–S algorithm proposed by Gerchberg and Saxton (Gerchberg, 1972). Moreover, it was further developed using Fienup algorithms and an oversampling method (Fienup, 1982; Miao et al., 2003) and it can retrieve complex amplitude with a single frame of diffraction pattern intensity. Although these CDI algorithms based on the G–S algorithm demonstrated significant achievements in imaging samples with short wavelengths including X-ray and electron beam, they also suffer from problems such as low convergence speed, limited field of view, and low reliability, especially in imaging large objects with complex phase distributions (Fienup and Wackerman, 1986; Fienup, 1987). Many modified CDI algorithms have been proposed to overcome these drawbacks, and they can be generally divided into two categories. The first group, termed as multishot CDI, involves using a multiwavelength source for illumination (Bao et al., 2008), scanning the diffraction field axially (Ivanov et al., 1992; Allen and Oxley, 2001; Almoro et al., 2006), scanning the sample transversely (Faulkner and Rodenburg, 2004; Maiden and Rodenburg, 2009), and scanning the illuminating direction (Ou et al., 2013; Bian et al., 2014; Dong et al., 2015). Significant data redundancy in these algorithms offers several remarkable advantages over conventional CDI methods, including significant improvement in robustness to noise, no requirement for prior knowledge of the sample, faster convergence speed, and more reliable reconstruction. However, these methods require significant data acquisition time, precluding their application for imaging fast dynamics. Furthermore, since these methods rely heavily on the stability of the imaging system, both vibration and degeneration of the sample may result in failure of the experiments. Note that even tiny imprecisions in the scanning steps can degrade the reconstructed resolution. The second group, termed as singleshot CDI, can realize real-time measurement of a light field. The recently developed coherent modulation imaging method uses a random phase plate to modulate the light field, and it adopts a spatial constraint at the entrance plane which is usually the focal plane (Zhang and Rodenburg, 2010; Tao et al., 2016). However, since only one frame of diffraction patterns is applied, the reconstruction is noisy when compared to that of multishot CDI algorithms. In order to obtain high-quality reconstruction with single-shot measurement, a feasible approach is to record a diffraction pattern array with a single exposure of detector to increase data redundancy, and based on this idea, two kinds of single-shot ptychography methods have been demonstrated (Pan et al., 2013; Sidorenko and Cohen, 2016).

Many communication channels are time-varying and unknown to the transmitter. Incremental redundancy hybrid automated repeat request (IR-HARQ) as shown in Fig. 1 is a scheme that transmits additional redundancy bits until the data bits can be reconstructed. For turbo codes (such as those used in LTE), a low rate mother code is punctured with different patterns for several transmissions. The coding scheme for enhanced mobile broadband (eMBB) in 5G uses protographbased, Raptor-like LDPC codes [10] that allow for both flexible block length and code rate adaptation. The standard defines two base matrices that offer optimized performance for different operating regimes. With cyclic redundancy check (CRC) outer codes and successive cancellation list (SCL) decoding [3], polar codes [1], [2] outperform state-of-the-art turbo and LDPC codes in the short to medium length regime. Polar-coded modulation (PCM) is discussed in [5]–[7]. The performance comparison and efficient code design methods of three polar-coded modulation schemes are presented in [8, Fig. 11]. Multilevel polar coding (MLPC) with set partitioning (SP) labeling in [5] performs best and is around 1 dB more power efficient than an AR4JA [9] LDPC code decoded with 200 iterations. A quasi-uniform puncturing (QUP) algorithm was proposed in [4] to efficiently design length-flexible polar codes, i.e., polar codes where the number of coded bits is not limited to be a power of two. In [11], a scheme the authors called “polar codes with incremental freezing” and in [12] “parallel-concatenated polar codes” is proposed. The capacity-achievability of this scheme is proved in [11] by using the nesting property. In [13], a polar code extension method is presented which outperforms the scheme in [11], [12] for finite block length. In this work, we prove that the scheme in [13] can achieve capacity asymptotically in the block length under some design constraints. In addition, a length-flexible IR-HARQ scheme based on dynamically frozen bits and QUP is proposed. This scheme is extended to polar-coded modulation with amplitude shift keying (ASK) and quadrature amplitude modulation (QAM) constellations. Simulation results show that the polar codes designed by our algorithm have similar error correction performance as directly designed polar codes. This work is organized as follows. In Sec. II, we review polar codes, PCM and QUP. We discuss existing and proposed IR-HARQ schemes in Sec. III. Sec. IV provides design examples and numerical results. The performance of the proposed scheme is compared with directly designed polar codes and 4G/5G codes [14], [15] in additive white Gaussian noise (AWGN) channels. We conclude in Sec. V.

Arikan in his paper [1], proposed a new channel coding scheme by “polarizing” channels w.r.t. their capacities. The process of channel polarization helps to classify every bit channel w.r.t. channel capacity which is used to create a scheme of encoding information bits into a codeword. This form of classification provides a new family of linear block codes called Polar Codes. BICM-ID is a state-of-the-art error correcting scheme. The coded-modulation scheme was proposed in [2] for improving error correction capabilities by using channel coding and modulation schemes as a combined unit instead of independent modules. Adding diversity to the codes by interleaving, should improve the error correction performance as proposed in [3], to hence provide the Bit Interleaved Coded Modulation (BICM) design described in [4]. BICM’s demerits can be overcome by exchanging extrinsic information between multiple modules of the Iterative Decoding (ID) chain iteratively, to improve error correcting capability over every iteration [5]. Such a BICM-ID scheme is helpful for error correction of codes received over AWGN and Fading channels. The idea is to encode the input data bits into codewords generated by polar encoding, interleaving and then modulating them at the transmitter end. Similarly, at the receiver end polar decoder, demodulator, interleaver and de-interleaver iteratively perform their tasks and exchange extrinsic information to produce codeword estimates. The aim in this paper is to develop such a novel BIPCM-ID system and analyze its error correcting capability. Although Arikan’s original idea of channel polarization is limited only to Binary Erasure Channels (BEC) the BIPCM-ID needs to be analyzed over AWGN channels. Thus, a modified approach of channel polarization for AWGN channels is used. The polar decoder which is to be integrated in an ID chain should be a Soft-Input Soft-Output (SISO) decoder so that it can produce extrinsic information to be exchanged with, as well as process the inputs from, the other modules in the ID chain. Modifications can also be made to remove the error floor altogether. These issues have been addressed in this paper. The rest of the paper is organized as follows. In Section II, an overview of Polar Codes and BICM-ID is provided for the reader to get a basic understanding of these concepts. Section III describes the novel approach of integrating Polar Codes in a BICM-ID design by generating the extrinsic information from the decoder module to be used for ID and how the channels are polarized for a AWGN channel. Section IV, provides the simulation results of the BIPCM-ID system, with detailed discussions about the parameters used and the corresponding performance results obtained. Section V provides the key points which merit further research and the existing limitations which can be improved upon. The concluding remarks are mentioned in Section VI. Notations: Letters in bold fonts denote vectors. X and Y denote the Random Variables (RV) corresponding to the input and output of a channel respectively. W denotes a channel as well as the Probability Density Functions (PDF) of the RVs across the channel. N denotes the length of a codeword. Z denotes the Bhattacharyya parameter. L denotes the LogLikelihood Ratio (LLR) values.

A. Related Work: BMI

It has been reported clinically that, diseases like stroke and traumatic brain injury do cause long-term, unilateral loss of motor control. Neurological disabilities and related diseases are causing the permanent loss of motoring of limbs, in addition to sensory malfunction. In particular, even in specific and related cases, limbs disability is very severe that it is not likely to feed oneself or even communicate. With the advances on technology, and in specific – BMI – Brain Machine Interface, there is a new research direction that aims to support disabled patients by translating neural signals from the brain into useful control signals for guiding prosthetic limbs. The prime objective in developing a neural prosthesis is to substitute neural circuitry in the brain, that no longer functions correctly or efficiently, Fig.1. While achieving such goal, this requires artificial reconstruction of neuron-to neuron connections in a way that can be recognized by the remaining normal circuitry, and that promotes appropriate interaction. It is estimated that approximately (9 million) people worldwide suffer from stroke every year and that almost (30%) of stroke survivors suffering from irreversible motor impairments, Teo et al. [2]. According to Andrew et al. [3], brain-controlled interfaces are devices that capture brain transmissions involved in a subject’s intention to act. Neural activated prosthetic devices is becoming an important domain, and progressively relevant to a number of clinical and various neurological related diseases treatments. Fig. 1. Electroencephalogram (EEG) brain waves have been used extensively for people with disabilities, [1]. In particular, we refer to Fayad and Elmiyeh, [4], where it was mentioned about such input devices for stimulating zones within the nervous system, in particular, have been especially successful in achieving therapeutic effects. Charles et al. [5] also reviewed the challenges to clinical translation and discusses potential solutions. They made a focuses on hardware reliability, state-of-the-art decoding algorithms, and surgical considerations during implantation.