Coding Techniques Research Articles

Feedback Overhead in the FDD Downlink Massive MIMO System

One of the fundamental problems in massive multiple input multiple output (MMIMO) systems based on the frequency division duplexing (FDD) mode is the acquisition of the channel state information at the base station (BS). Indeed, in these systems, the reciprocity of the uplink (UL) and downlink (DL) channels is not applicable. Thus, the users are supposed, after having estimated their subchannels, to feedback them to the BS. Since the size of the information to be returned is proportional to the number of base station antennas that is very large, then we witness an excess of the number to be returned for each user. It is therefore essential to find a way to reduce this quantity, which affects the overall performance of the system. In this article, we propose two new MMIMO-OFDM-FDD-DL schemas based on the Givens rotation quantization used in the 802.11ac standard to reduce the amount of information to be returned for each user. The first is a fusion between the Givens rotation (GR) and the arithmetic coding (AC) technique, and the second is based on the combination of the GR method and the universal compression technique, Lempel Ziv Welch (LZW). The proposed schemes named GR & AC and GR & LZW (with an improvement of this proposal based on a modified version of LZW (LZWM) named GR & LZWM) for the first and second, respectively, are compared and both can achieve a compression ratio of up to 95% (or even 98%) and 83%, considerably exceeding recent advanced techniques.

DISCOVERING THE DEFIANCE: EXPLORING THE ISSUES AND CHALLENGES IN CREATING GENDER DIVERSITY AT THE TEAM LEVEL

The study was conducted to explore the perceptual issues and challenges of a gender diversified team to work effectively. With the qualitative phenomenology approach, the lived experiences of the participants were recorded with interviews, and the responses were analyzed with the technique of initial and axial coding. Thematic analysis was performed to explore the issues and challenges. It was investigated that males and females have different issues in the diversified team. Males think that since women are emotional, lack professionalism so it’s difficult to create effectiveness, however, females believe, that males have too much negative ego, and they are hot-tempered, therefore they create troubles. The challenges are lack of trust, frequent conflict, and adherence to fellowship. The study helps managers to train the diversified teams on these findings to make them more effective.

Program Barriers and Facilitators in Virtual Cancer Exercise Implementation: A Qualitative Analysis.

Due to the coronavirus disease 2019 (COVID-19) pandemic, many in-person cancer exercise and rehabilitation programs necessarily transitioned to virtual formats to meet the needs of individuals living with and beyond cancer. The purpose of this study was to qualitatively assess program-level facilitators and barriers to virtual exercise program implementation and to identify preferred strategies to overcome implementation barriers. US-based virtual cancer exercise and rehabilitation programs were recruited from professional networks via an emailed screening questionnaire. Eligible programs identified a point of contact for a 1:1 semi-structured interview to discuss program-level barriers and facilitators to implementing virtual exercise programs. Interview transcript analysis was conducted via inductive coding techniques using NVivo software. Barriers were categorized according to the Consolidated Framework for Implementation Research and a prioritized list of strategies to support implementation was created by mapping barriers to a list of Expert Recommendations for Implementing Change. Of the 41 unique responses received, 24 program representatives completed semi-structured interviews. Interviewees represented individual programs, community-based programs, and hospital-based cancer exercise/rehabilitation programs. Analysis showed high correlation between facilitators and barriers by program type, with both program- and individual-level strategies used to implement exercise programs virtually. Strategies that ranked highest to support implementation include promoting program adaptability, building a coalition of stakeholders and identifying program champions, developing an implementation blueprint, altering organizational incentives and allowances, providing education across stakeholder groups, and accessing funding. Learning from the transition of cancer exercise and rehabilitation programs to virtual formats due to the COVID-19 pandemic, we identify program-level barriers and facilitators encountered in the implementation of virtual programs and highlight implementation strategies that are most relevant to overcome common barriers. We present a roadmap for programs to use these strategies for future work in virtual exercise and rehabilitation program implementation.

Transmission performance in compressed medical images using turbo code

Medical imaging is now an essential support for screening, diagnosis, treatment protocols implementation, patient monitoring, operative preparation and post-operative control. In addition, scientific and technological advances make it possible to set up new imaging methods, often complementary to the existing ones, but also to gradually improve their accuracy. The result is an increase, in the acquisitions number made for the same patient and for information produced for each examination. Since these images must be kept for a certain period, the storage space required for archiving all this data is constantly evolving and images are often viewed locally, and it can be viewed remotely through networks with limited bandwidth such as the long term evolution (LTE) mobile network. The use of compression quickly proves to be essential, whether to facilitate storage or for these data mass browsing remotely. The results of the work carried out in this article are mainly focused on the medical images compression by the set partitioning hierarchical trees (SPIHT) method, which, in fact, allow a significant reduction for data. We are also interested in the transmission of these images on an LTE mobile radio channel in a way that can provide a high bitrate with good transmission quality, by exploiting the channel coding technique, which is effective in combating the noise introduced during the transmission of these images.

Diagnostika a intervence u sociálně podmíněného mentálního postižení: kvalitativní studie v praxi českých pedagogicko-psychologických poraden

The aim of this pilot qualitative research was to take an insight into how counseling psychologists perceive the concept of socially conditioned intellectual disability (further only SCID). Specifically, to find out if they are aware of this concept, their attitude towards it, diagnostic practice, and potential supportive interventions. Data were obtained through a semi-structured interview of 10 respondents (Median experience = 6 years), from 9 Children´s e­ducational care centres (5 regions of the Czech Republic). The obtained data were analyzed by thematic analysis with the elements of the grounded theory using the technique of open coding. The analysis showed that none of the respondents explicitly use the term SCID, however, the term is implicitly present in their practice. According to the respondents, the concept could serve as an inspiration for a broader reflection on the etiology of „suspiciously low“ scores in intellectual tests and the intellectual potential of the child. In practice, the respondents most often determine the conclusion about the level of intellectual and adaptive abilities based only on standardized tests of intellect. The respondents do not use tests and scales for adaptive behaviour, social and family environment – mainly due to a lack of staff and time capacity. The respondents perceive the differential diagnosis of SCID from intellectual disability as complicated. Half of the respondents see dynamic assessment as a promising approach to differentiate between SCID and intellectual disability. The respondents provide follow-up support and interventions to children who may fit the SCID concept very marginally due to limited capacity. The interviews also provided several potential suggestions for practice – the need to create a clear and generally accepted definition of SCID, conduct an assessment in greater depth and complexity, and intensify cooperation with the family of a child from a socially disadvantaged background and other related institutions.

Deep chroma prediction of Wyner–Ziv frames in distributed video coding of wireless capsule endoscopy video

Compression of captured video frames is crucial for saving the power in wireless capsule endoscopy (WCE). A low complexity encoder is desired to limit the power consumption required for compressing the WCE video. Distributed video coding (DVC) technique is best suitable for designing a low complexity encoder. In this technique, frames captured in RGB colour space are converted into YCbCr colour space. Both Y and CbCr representing luma and chroma components of the Wyner–Ziv (WZ) frames are processed and encoded in existing DVC techniques proposed for WCE video compression. In the WCE video, consecutive frames exhibit more similarity in texture and colour properties. The proposed work uses these properties to present a method for processing and encoding only the luma component of a WZ frame. The chroma components of the WZ frame are predicted by an encoder–decoder based deep chroma prediction model at the decoder by matching luma and texture information of the keyframe and WZ frame. The proposed method reduces the computations required for encoding and transmitting of WZ chroma component. The results show that the proposed DVC with a deep chroma prediction model performs better when compared to motion JPEG and existing DVC systems for WCE at the reduced encoder complexity.

Proximal PanNet: A Model-Based Deep Network for Pansharpening

Recently, deep learning techniques have been extensively studied for pansharpening, which aims to generate a high resolution multispectral (HRMS) image by fusing a low resolution multispectral (LRMS) image with a high resolution panchromatic (PAN) image. However, existing deep learning-based pansharpening methods directly learn the mapping from LRMS and PAN to HRMS. These network architectures always lack sufficient interpretability, which limits further performance improvements. To alleviate this issue, we propose a novel deep network for pansharpening by combining the model-based methodology with the deep learning method. Firstly, we build an observation model for pansharpening using the convolutional sparse coding (CSC) technique and design a proximal gradient algorithm to solve this model. Secondly, we unfold the iterative algorithm into a deep network, dubbed as Proximal PanNet, by learning the proximal operators using convolutional neural networks. Finally, all the learnable modules can be automatically learned in an end-to-end manner. Experimental results on some benchmark datasets show that our network performs better than other advanced methods both quantitatively and qualitatively.

Thai High Achieving Lecturers and Their Language Learning Strategies and Motivation: A Case Study of Kalasin University

The purposes of this study were to (1) investigate the language learning strategies used for improving English language skill of the high achieving non-English major lecturers at Kalasin University and (2) investigate the motivation used for improving their English language skills. The sample comprised eight non-English major lecturers. They were purposively selected according to their high TOEIC test scores. Semi structured interview was used as a research tool. The interview was on using the language learning strategies and motivation to develop the English-speaking ability as the main instrument. The modified interview adapted from the Semi-Structured Interview Guide of Paredes (2010). The qualitative data were analyzed through using axial coding technique. The majors finding were as follows: (1) metacognitive, social and cognitive strategies were frequently used respectively by the sample, while compensation and memory strategies were least used equally and (2) the most motivations employed were attitude and competence motivation, the motivations moderately used were achievement, affiliation, and fear motivation equally, and the least motivation used were power and incentive motivation respectively.

Reminisce: Blockchain Private Key Generation and Recovery Using Distinctive Pictures-Based Personal Memory

As a future game-changer in various industries, cryptocurrency is attracting people’s attention. Cryptocurrency is issued on blockchain and managed through a blockchain wallet application. The blockchain wallet manages user’s digital assets and authenticates a blockchain user by checking the possession of a user’s private key. The mnemonic code technique represents the most widely used method of generating and recovering a private key in blockchain wallet applications. However, the mnemonic code technique does not consider usability to generate and recover a user’s private key. In this study, we propose a novel approach for private key generation and recovery. Our approach is based on the idea that a user can hold long-term memory from distinctive pictures. The user can generate a private key by providing pictures and the location of the pictures. For recovering a private key, the user identifies the locations of the pictures that are used in the private key generation process. In this paper, we experiment with the security and usability of our approach and confirm that our proposed approach is sufficiently secure compared to the mnemonic code technique and accounts for usability.

Comparative Study on Various Modulation and Coding Techniques Relevant to Bangabandhu Satellite-2

Comparative Study on Various Modulation and Coding Techniques Relevant to Bangabandhu Satellite-2

Instagram as a research tool for examining tobacco-related content: A methodological review

Social media is rife with modifiable risky health behaviors and substance use topics, pre-cursors to peer-influence and social acceptability, which are drivers of behavioral change. With over a billion active users, Instagram is one of the leading social media platforms across the globe, especially among adolescents and young adults for obtaining, sharing, and promoting tobacco-related content. With an aim to assess the current landscape and inform future research, our review summarizes and analyzes the methodological techniques and approaches used for categorically coding Instagram-based data about tobacco. By using relevant keywords, a literature search was performed in June 2021 within three databases – Web of Science, Scopus, and PubMed – identifying 304 articles. PRISMA (Preferred Reporting Items for Systematics Reviews and Meta-Analyses) guidelines were adopted to direct further analysis and reporting. Inclusion and exclusion criteria were used by two reviewers to systematically assess the eligibility of studies resulting in 27 studies. Key characteristics (product studied, focus of the study, details about data collection, and coding and coded categories) from each study were extracted and analyzed in detail. E-cigarettes were the most frequently investigated tobacco product followed by the hookah/water pipe, cigars/cigarillos, betel nut, and Heated Tobacco Products (HTP). As the data source, Netlytic and Instagram's API/website were commonly used. The coding methods broadly encompass human coding and machine-learning techniques. As a rich and organic source, Instagram-based data is valuable for the surveillance of various forms of tobacco as well as substance use. Open and simpler data collection tools are needed as collecting Instagram data has become challenging. Blending hand-coding with machine-learning techniques may advance future research to classify broader representation and understand nuanced behaviors around tobacco portrayals on Instagram.

The Use of the Consolidated Framework for Implementation Research to Understand Facilitators and Barriers to Sexually Transmitted Infection Screening in Primary Care.

Adolescents bear a disproportionate burden of sexually transmitted infections (STIs) and the sequelae of delayed treatment, yet STI screening is infrequently performed in pediatric primary care clinics with many of those at-risk not administered testing. This study aims to understand contextual factors influencing STI screening and testing among adolescents in pediatric primary care. We used the Consolidated Framework for Implementation Research (CFIR) as part of a stepwise approach to facilitate a deep understanding the pediatric primary care environment. We conducted semistructured interviews of physicians, nurses, and patient-parent dyads from 4 pediatric primary care practices in the St. Louis metropolitan area about STI screening practices and common concerns regarding STI screening. Qualitative analysis was conducted using a categorical coding technique informed by the CFIR followed by a thematic coding technique. We interviewed 23 physicians/nurses and 12 patient-parent dyads. Individual-level barriers to STI screening and testing included wide variability in clinicians' practice patterns and their perception of STI risk in the patient population. Structural barriers included a lack of capacity to perform testing in clinic and time constraints during patient visits. Confidentiality issues also created significant barriers to screening and testing on both individual and structural levels. Adopting confidential methods for testing and educating providers on patients' recommendations for STI testing were discussed as ways to potentially improve STI care in pediatric patients. Our use of the CFIR facilitated a systematic approach to identify gaps in STI care for adolescents and identified opportunities to close those gaps. An integrated, systematic approach that enhances patient confidentiality and improves clinicians' knowledge could address gaps in STI care in pediatric primary care settings.

Early Termination Based Training Acceleration for an Energy-Efficient SNN Processor Design.

In this paper, we present a novel early termination based training acceleration technique for temporal coding based spiking neural network (SNN) processor design. The proposed early termination scheme can efficiently identify the non-contributing training images during the training's feedforward process, and it skips the rest of the processes to save training energy and time. A metric to evaluate each input image's contribution to training has been developed, and it is compared with pre-determined threshold to decide whether to skip the rest of the training process. For the threshold selection, an adaptive threshold calculation method is presented to increase the computation skip ratio without sacrificing accuracy. Timestep splitting approach is also employed to allow more frequent early termination in split timesteps, thus leading to more computation savings. The proposed early termination and timestep splitting techniques achieve 51.21/42.31/93.53/30.36% reduction of synaptic operations and 86.06/64.63/90.82/49.14% reduction of feedforward timestep for the training process on MNIST/Fashion-MNIST/ETH-80/EMNIST-Letters dataset, respectively. The hardware implementation of the proposed SNN processor using 28 nm CMOS process shows that the SNN processor achieves the training energy saving of 61.76/31.88% and computation cycle reduction of 69.10/36.26% on MNIST/Fashion-MNIST dataset, respectively.

Successive Approximation Coding for Distributed Matrix Multiplication

Coded distributed computing was recently introduced to mitigate the effect of stragglers on distributed computing systems. This paper combines ideas of approximate and coded computing to further accelerate computation. We propose successive approximation coding (SAC) techniques that realize a tradeoff between accuracy and speed, allowing the distributed computing system to produce approximations that increase in accuracy over time. If a sufficient number of compute nodes finish their tasks, SAC exactly recovers the desired computation. We theoretically provide design guidelines for our SAC techniques, and numerically show that SAC achieves a better accuracy-speed tradeoff in comparison with previous methods.

FPGA-based experimental board for error control codes

This paper proposed an experimental device that emulates and facilitates teaching the theoretical concepts of error-control coding (ECC) techniques. Two prototypes laboratory boards were designed and implemented. The first board simulates the transmitter side of a typical digital communication system. It mainly contains a data source, (7,4) Hamming encoder, cyclic redundancy check encoder (CRC), and Gaussian noise generator. The second board has two types of Hamming decoders, a syndrome decoder, and a maximum likelihood (ML) decoder that accepts the received soft signals at the channel output. Each board has several control switches so that the trainee can change the code variables, noise power, user ID, in addition to display tools such as light-emitting diodes (LEDs), and test points for the oscilloscope that help the user to observe the results. Moreover, results and setting variables can also be displayed on a PC’s screen connected through a USB port and organic light emitting diodes (OLED) display that is attached to the receiver board. The pipelining architecture of the field programmable gate array (FPGA) device was exploited in this proposed system to reduce the processing delay and hence increase the data throughput. Furthermore, we proved that both theoretical and experimental tests are identical.

Alterations in facial expressions of emotion: Determining the promise of ultrathin slicing approaches and comparing human and automated coding methods in psychosis risk.

Alterations in facial expressions of emotion are a hallmark of psychopathology and may be present before the onset of mental illness. Technological advances have spurred interest in examining alterations based on "thin slices" of behavior using automated approaches. However, questions remain. First, can alterations be detected in ultrathin slices of behavior? Second, how do automated approaches converge with human coding techniques? The present study examined ultrathin (i.e., 1-min) slices of video-recorded clinical interviews of 42 individuals at clinical high risk (CHR) for psychosis and 42 matched controls. Facial expressions of emotion (e.g., joy, anger) were examined using two automated facial analysis programs and coded by trained human raters (using the Expressive Emotional Behavior Coding System). Results showed that ultrathin (i.e., 1-min) slices of behavior were sufficient to reveal alterations in facial expressions of emotion, specifically blunted joy expressions in individuals at CHR (with supplementary analyses probing links with attenuated positive symptoms and functioning). Furthermore, both automated analysis programs converged in the ability to detect blunted joy expressions and were consistent with human coding at the level of both second-by-second and aggregate data. Finally, there were areas of divergence across approaches for other emotional expressions beyond joy. These data suggest that ultrathin slices of behavior can yield clues about emotional dysfunction. Further, automated approaches (which do not require lengthy training and coder time but do lend well to mobile assessment and computational modeling) show promise, but careful evaluation of convergence with human coding is needed. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

“The Stress of the Situation”: How Do Compounding Experiences of Oppression Impact Emotional Distress Among a Diverse Sample of Internally Displaced People in Colombia?

AimsAddressing the mental health needs of Internally Displaced People (IDPs) in Colombia has been identified as a public health priority. Women and disabled IDPs are recognised as under-researched populations, with differences in vulnerabilities to displacement and resettlement prospects. This thematic network analysis employs an intersectional approach to consider how compounding experiences of oppression impact emotional distress among IDPs to enable informed and appropriate service provision.MethodsThis is a qualitative analysis of a subset of data collected by the second author and her research team in 2017–18, as part of a larger action research project. Participants were randomly selected from the Victim's register in an industrialised municipality of Colombia. A subsample (n = 20) were invited to participate in life and family histories. Units of analysis were individual (n = 11) and family interviews (n = 9), with a mixture of self-identifying disabled and non-disabled men and women. River and tree of life tools were used to elicit culturally sensitive discussion of significant life events and ongoing distress. NVivo software and hand coding techniques were used to operationalise thematic webs. The analysis employed a grounded approach to thematic network analysis.ResultsThree global themes, each underpinned by several organisational themes, were developed. The first, Environments and contexts of displacement, considers the loss of land and community alongside the myriad of social institutions, legal entitlements, family circumstances, cultural expectations and stigma influencing participants' access to resources. The second, Making sense of it all, represents the emotional and cognitive responses to perceived injustices and eroded trust. The third, Mechanisms for managing distress, represents strategies employed by IDPs at individual and family levels. Relationships between employment status and gendered divisions of labour were noted, suggesting that non-disabled women were able to meet increased domestic and paid work demands following displacement, though this was a considerable source of stress. Concepts around racial, indigenous and class identities were alluded to by several participants but could not be fully developed due to relative scarcity of accounts within the dataset.ConclusionThe thematic networks presented illustrate several compounding and interrelated oppressions faced by IDPs, offering explanation as to how this produces and sustains emotional distress. Participants’ well-founded worries about economic security and childcare alongside concerns for safety and acceptance in host communities require co-ordinated, locally informed responses. Prevention and recovery programmes should consider interventions at a family level, whilst strengthening participants’ self-developed strategies for managing distress.

A Survey Paper Based On Image Encryption and Decryption Technique

Abstract: Nowadays multimedia system knowledge protection is changing into important. The coding technique is employed to safeguard multimedia system knowledge. There are completely different techniques wont to defend confidential image knowledge from unauthorized access. during this paper, we've got an inclination to survey existing work that's used entirely completely different techniques for image coding and that we to boot give a general introduction relating to cryptography. Keywords: Encryption, Decryption, Data Encryption Standard (DES), Advanced Encryption Standard(AES), Blowfish, Modified Advanced Encryption Standard(MAES),Rubik’s Cube Algorithm

Medical Image Compression and Encryption Using Adaptive Arithmetic Coding, Quantization Technique and RSA in DWT Domain

Nowadays, the advances in information and communication technologies open the wide door to realize the digital world’s dream. Besides, within the clear scientific scope in all fields, especially the medical field, it has become necessary to harness all the scientific capabilities to serve people, especially in medical-related services. The medical images represent the basis of clinical diagnosis and the source of telehealth and teleconsultation processes. The exchange of these images can be subject to several challenges, such as transmission bandwidth, time delivery, fraud, tampering, modifying, privacy, and more. This paper will introduce an algorithm consisting a combination of compression and encryption techniques to meet such challenges in the medical image field. First, compression is done by applying the Adaptive Arithmetic Coding (AAC) technique and controllable frequency quantization process in the Discrete Wavelet Transform. After that, the encryption process is applied using RSA and SHA-256 algorithms to encrypt the compressed file and to create the digital signature. The performance analysis has shown that the algorithm can produce high compression ratio with good image quality, whereas range of PSNR near 45 dB and SIM is 0.88 as average values. For the security analysis, we have adopted data encryption and digital signature to guarantee the main data security services including integrity, authentication, and confidentiality, making the algorithm secure against passive or active attacks.

PAPR and BER analysis of coded FBMC-OQAM system with non-linear companding techniques

Filter Bank Multicarrier with offset QAM (OQAM) is a suitable multicarrier waveform technique for 5 G and future wireless networks and standards. Being a multicarrier technique, it suffers from high peak-to-average-power ratio (PAPR) problem. In this paper we have used some non-linear companding transforms such as μ-law, A-law, rooting, tangent rooting, and logarithmic rooting to overcome the PAPR problem. Since non-linear companding techniques degrade bit-error-rate (BER) performance of a system. Hence, some channel coding techniques such as convolutional codes and turbo codes are employed to enhance the BER performance of non-linearly companded FBMC-OQAM system. It was observed that the combination of convolutional coding with logarithmic rooting companding provides PAPR reduction gain of about 4.896 dB and coding gain of about 2 dB as compared to conventional FBMC-OQAM system and is an appropriate choice for applications requiring reduced system complexity. It was observed that the combination of turbo coding with logarithmic rooting companding provides PAPR reduction gain of about 4.922 dB and a sufficiently high coding gain of about 7 dB as compared to conventional FBMC-OQAM system and is suitable for applications requiring improved data reliability at the expense of increased system complexity and latency.