Blind Adaptation Research Articles

Lizzie’s Insistent Rejection to Her Unconscious Desires in Christina Rossetti’s Goblin Market through the Lens of Lacanian Desires

The growing concern in the latest critical practices and processes of a great many scholars as indicated in the continuous development and improvement of literary criticism with respect to the veritable and valuable characterization of this poem Goblin Market (1862) has been shown for the essential and evident exploration of the feasible solutions to the problems as most women character have to be faced with in an unavoidable fashion in Victorian age due to their blind adaptation to the ideological and epistemological values emphasized in the continuation of Christian beliefs regarding the essential and crucial morality to be followed in the increasing enrichment and improvement of their rationalist mentality. However, it has seemed to be quite pitiful that they have failed to probe into the unconscious minds of the vivid characters depicted in this poem to lay an ideological and epistemological foundation for the apparent clarification of the strong will of this character Lizzie has developed to enable her to get free from the imprisonment or enslavement of the hegemonic desires carried in the harmful fruits of the goblin men she and her sister Laura has encountered in their lives. For the sake of this critical blind spot, this paper will explore Lizzie’s strong will used to protect the purity and piety of her unconscious desires and to reduce to the least the possibility for her to fall victim to the vicious desires those goblin men aim to impress and impose upon her mind indebted to the rich theoretical enlightenment and encouragement entailed in Lacanian desires related to the conceptual elaboration of the longing for wholeness, sanity, and, meaning in the rich lives of his analysands. In the long run, this exploration will remind more and more readers of the objective truth that the strong willingness of women as exemplified in what has happened to Lizzie is bound to give rise to their great successes in rejecting their unconscious desires triggered in the popularization of the vicious desires of polluted men like those of the goblin men in this poem as epitomized in the developmental inclined with which the marketization and capitalization of the communicative activities related to the commercialization and commodification of the harmful and sinful fruits peddled in goblin market.

Parallel Blind Adaptive Equalization of Improved Block Constant Modulus Algorithm With Decision-Directed Mode

Parallel Blind Adaptive Equalization of Improved Block Constant Modulus Algorithm With Decision-Directed Mode

The Residual ISI for Which the Convolutional Noise Probability Density Function Associated with the Blind Adaptive Deconvolution Problem Turns Approximately Gaussian.

In a blind adaptive deconvolution problem, the convolutional noise observed at the output of the deconvolution process, in addition to the required source signal, is—according to the literature—assumed to be a Gaussian process when the deconvolution process (the blind adaptive equalizer) is deep in its convergence state. Namely, when the convolutional noise sequence or, equivalently, the residual inter-symbol interference (ISI) is considered small. Up to now, no closed-form approximated expression is given for the residual ISI, where the Gaussian model can be used to describe the convolutional noise probability density function (pdf). In this paper, we use the Maximum Entropy density technique, Lagrange’s Integral method, and quasi-moment truncation technique to obtain an approximated closed-form equation for the residual ISI where the Gaussian model can be used to approximately describe the convolutional noise pdf. We will show, based on this approximated closed-form equation for the residual ISI, that the Gaussian model can be used to approximately describe the convolutional noise pdf just before the equalizer has converged, even at a residual ISI level where the “eye diagram” is still very closed, namely, where the residual ISI can not be considered as small.

Adaptive IQ Mismatch Compensation in Time-Domain Using Frequency-Domain Observations

The imbalance between in-phase (I) and quadrature (Q) branches of quadrature down-conversion receivers creates interference between the mirror frequencies in baseband. We introduce two new adaptive algorithms for compensating frequency-dependent IQ imbalance in orthogonal frequency-division multiplexing (OFDM) systems. The proposed algorithms utilize frequency-domain observations to update filter weights of a time-domain IQ mismatch compensator (IQMC) based on a mini-batch gradient descent search approach. The update rule in the first algorithm is based on the kurtosis criterion, which is a blind adaptation method. The second algorithm is based on minimizing the Euclidean distance (ED) between the frequency-domain impaired signal and the known received reference signal, which is a pilot-aided method. A low-complexity method is introduced to obtain the gradient of cost functions that are only based on the IQMC coefficients and the frequency-domain observations. Numerical results demonstrate that the proposed algorithms reduce IQ imbalance considerably. The proposed adaptation schemes outperform conventional time-domain IQMC schemes in high signal-to-noise ratio region and also use a much smaller number of adaptation parameters than the previous frequency-domain IQMC methods.

All-Analog Adaptive Equalizer for Coherent Data Center Interconnects

In a high-speed coherent optical transmission system, typically the signals obtained at the receiver front-end are digitized using very high-speed ADCs and then processed in the digital domain to remove optical channel impairments. In this work, we show that these signals can instead be processed in the analog domain itself, which can significantly reduce the power consumption as well as the complexity of the receiver. The first all-analog adaptive equalizer for receivers of coherent dual-polarization optical links has been presented with its detailed architecture and measurement results. The proof-of-concept equalizer uses the constant modulus algorithm for blind adaptation of its weight coefficients to implement a 4x4 2-tap FIR filter in 130 nm SiGe BiCMOS technology. Its functionality is evaluated experimentally for 40 Gb /s data rate and 10 km standard single-mode fiber channel. This demonstration shows that the use of all-analog processing for short-reach data-center interconnects is feasible and is a much simpler solution than the use of the high-speed ADC+DSP based approach. Moreover, when implemented in advanced CMOS or FinFET technologies, the power consumption of the equalizer is expected to be significantly lower than the DSP based implementations in similar process technologies.

Investigation and analysis of hyper and hypo neuron pruning to selectively update neurons during unsupervised adaptation

Unseen or out-of-domain data can seriously degrade the performance of a neural network model, indicating the model's failure to generalize to unseen data. Neural net pruning can not only help to reduce a model's size but can improve the model's generalization capacity as well. Pruning approaches look for low-salient neurons that are less contributive to a model's decision and hence can be removed from the model. This work investigates if pruning approaches are successful in detecting neurons that are either high-salient (mostly active or hyper) or low-salient (barely active or hypo), and whether removal of such neurons can help to improve the model's generalization capacity. Traditional blind adaptation techniques update either the whole or a subset of layers, but have never explored selectively updating individual neurons across one or more layers. Focusing on the fully connected layers of a convolutional neural network (CNN), this work shows that it may be possible to selectively adapt certain neurons (consisting of the hyper and the hypo neurons) first, followed by a full-network fine tuning. Using the task of automatic speech recognition, this work demonstrates how the removal of hyper and hypo neurons from a model can improve the model's performance on out-of-domain speech data and how selective neuron adaptation can ensure improved performance when compared to traditional blind model adaptation.

Optimization of Linearized Belief Propagation for Distributed Detection

In this paper, we investigate distributed inference schemes, over binary-valued Markov random fields, which are realized by the belief propagation (BP) algorithm. We first show that a decision variable obtained by the BP algorithm in a network of distributed agents can be approximated by a linear fusion of all the local log-likelihood ratios. The proposed approach clarifies how the BP algorithm works, simplifies the statistical analysis of its behavior, and enables us to develop a performance optimization framework for the BP-based distributed inference systems. Next, we propose a blind learning-adaptation scheme to optimize the system performance when there is no information available a priori describing the statistical behavior of the wireless environment concerned. In addition, we propose a blind threshold adaptation method to guarantee a certain performance level in a BP-based distributed detection system. To clarify the points discussed, we design a novel linear-BP-based distributed spectrum sensing scheme for cognitive radio networks and illustrate the performance improvement obtained, over an existing BP-based detection method, via computer simulations.

Waste Management Strategies for Mini Metro Cities

Urban Solid Waste Management has become an important component of urban development. Various approaches have been developed all over the world for proper waste management and disposal. These include government schemes, NGO activities, private initiatives, community-based organizations, household initiatives and technological advancements. Similar solutions are seen in India, yet there are large tracts of land filled with waste in many parts of the country. This paper attempts to understand what goes wrong in these initiatives and schemes, and what could be done to improve them. Several reviews and reports were consulted to understand the situation in India. The city of Pune was surveyed to comprehend the ground reality. Stakeholders, such as the municipal authorities, private companies providing their services, and nonprofit organizations were interviewed. On the other hand, worldwide initiatives were studied from reports and reviews. The situation in India was compared to the worldwide scenario, and problems and loopholes were identified in the Indian context. From this assessment, possible, implementable solutions were conceived to improve the Indian scenario. It was found that the SWM in India fails due to in organization and non-cooperation among stakeholders, problems in implementation, and blind adaptation of foreign technology. The waste composition being different in India, it should be taken into consideration before adapting the technologies. Recycling should improve, and all stakeholders should be involved. The solutions suggested are inter-related; improving one could improve the rest, thus leading to a better managed waste management system in India.

Fully-Parallel Stochastic Decoder for Rate Compatible Modulation

Rate compatible modulations (RCMs) are attractive for achieving seamless and blind rate adaptation under time varying channel. Although the decoding of RCM is inherently parallel, the highly complex processing nodes, and routing congestion have prohibited the implementation of fully-parallel decoders for high throughput. In this paper, we propose a new stochastic decoding algorithm for RCM to achieve desired parallel decoding. This algorithm provides even much better decoding performance than the floating-point belief propagation decoding algorithm with 20 iterations. To evaluate the effectiveness of the proposed algorithm, we apply it for the implementation of a field-programmable gate-array fully-parallel stochastic decoder that decodes a $ {400 \times 400}$ mapping matrix. Several novel structure techniques, including the RAM-based channel stream generator and the configurable variable node, are exploited to reduce the logical resources consumption. The implemented decoder achieves a clock frequency of 220 MHz and provides a maximum throughput about 136Mb/s at SNR = 10 dB and the number of symbols is 400. To the best of our knowledge, this decoder is the first reported fully-parallel RCM decoder which achieves the highest decoding throughput. This research validates the potential of stochastic RCM decoding as a practical approach for area-efficient and high throughput decoders.

Convolutional Noise PDF at the Convergence State of a Blind Adaptive Equalizer

In the literature, the convolutional noise obtained at the output of a blind adaptive equalizer, is often modeled as a Gaussian process during the latter stages of the deconvolution process where the process is close to optimality. However, up to now, no strong mathematical basis was given supporting this phenomenon. Furthermore, no closed-form or closed-form approximated expression is given that shows what are the constraints on the system’s parameters (equalizer’s tap-length, input signal statistics, channel power, chosen equalization method and step-size parameter) for which the assumption of a Gaussian model for the convolutional noise holds. In this paper, we consider the two independent quadrature carrier input case and type of blind adaptive equalizers where the error that is fed into the adaptive mechanism which updates the equalizer’s taps can be expressed as a polynomial function of the equalized output up to order three. We show based on strong mathematical basis that the convolutional noise pdf at the latter stages of the deconvolution process where the process is close to optimality, is approximately Gaussian if complying on some constraints depending on the step-size parameter, input constellation statistics, channel power, chosen equalization method and equalizer’s tap-length. Simulation results confirm our findings.

Blind adaptive multi-user detection for under-ice acoustic communications with mobile interfering users

The Least Mean Squares Blind Adaptive Multi-user Detection algorithm, the Recursive Least Squares Blind Adaptive Multi-user Detection algorithm, and the Kalman Filter Blind Adaptive Multi-user Detection algorithm, for under-ice communication networks with rising and diving interfering users, are investigated in this paper. Under-ice random asynchronous multi-user communication experiments with rising and diving interfering users were carried out (7 users, 15.7 bit/s per user, 1.2 km under-ice acoustic channels in the Bohai Sea). Good experimental results verified the feasibility and effectiveness of the proposed algorithms in mitigating inter-symbol interference and multiple access interference.

Far-Field RF Wireless Power Transfer with Blind Adaptive Beamforming for Internet of Things Devices

Wireless power transfer (WPT) has long been one of the main goals of Nikola Tesla, the forefather of electromagnetic applications. In this paper, we investigate radio-frequency beamforming in the radiative far field for WPT. First, an analytical model of the channel fading is presented, and a blind adaptive beamforming algorithm is adapted to the WPT context. The algorithm is computationally light, because we need not explicitly estimate the channel state information. Then, a testbed with a multiple-antenna software-defined radio configuration on the transmitting side and a programmable energy harvester on the receiving side is developed in order to validate the algorithm in this specific power application. From the results, it can be seen that the implementation of this version of beamforming indeed improves the harvested power. Specifically, at various distances from 50 cm to 1.5 m, the algorithm converges with two, three, and four antennas with an increasing gain as we increase the number of antennas. These encouraging results could have far-reaching consequences in providing wireless power to Internet of Things devices, our target application.

Millimetre Level Accuracy GNSS Positioning with the Blind Adaptive Beamforming Method in Interference Environments.

The use of antenna arrays in Global Navigation Satellite System (GNSS) applications is gaining significant attention due to its superior capability to suppress both narrowband and wideband interference. However, the phase distortions resulting from array processing may limit the applicability of these methods for high precision applications using carrier phase based positioning techniques. This paper studies the phase distortions occurring with the adaptive blind beamforming method in which satellite angle of arrival (AoA) information is not employed in the optimization problem. To cater to non-stationary interference scenarios, the array weights of the adaptive beamformer are continuously updated. The effects of these continuous updates on the tracking parameters of a GNSS receiver are analyzed. The second part of this paper focuses on reducing the phase distortions during the blind beamforming process in order to allow the receiver to perform carrier phase based positioning by applying a constraint on the structure of the array configuration and by compensating the array uncertainties. Limitations of the previous methods are studied and a new method is proposed that keeps the simplicity of the blind beamformer structure and, at the same time, reduces tracking degradations while achieving millimetre level positioning accuracy in interference environments. To verify the applicability of the proposed method and analyze the degradations, array signals corresponding to the GPS L1 band are generated using a combination of hardware and software simulators. Furthermore, the amount of degradation and performance of the proposed method under different conditions are evaluated based on Monte Carlo simulations.

Recursive Blind Adaptive Equalization for 16-QAM applications

Abstract: An innovative blind adaptive identification algorithm based on least-squares type arguments is offered in this project. Devoid of any matrix inversion operation, parameter estimates are recursively updated with each output measurement. It is confirmed that the parameter estimates converge almost surely (a.s.) toward a scalar multiple of the true parameters. The viable utilization of this algorithm to the channel equalization problem is explained in this paper [3].

Design and Implementation of a Blind Adaptive Equalizer Using Frequency Domain Improved Square Contour Algorithm

Improved Square Contour Algorithm (ISCA) is a blind adaptive equalization technique which employs estimated outputs from the decision device along with the dispersion constant in its cost function, thereby, forcing the equalizer outputs to lie on multiple zero error square moduli instead of a single contour in Square Contour Algorithm (SCA). This leads to greater ISI suppression and an improved convergence rate. In this paper, frequency domain implementation of block based ISCA is developed. Simulation results show that the FDISCA exhibits similar performance as time domain ISCA but with a significantly lower computational complexity. However, by employing normalization in the frequency bins, a noteworthy improvement in the convergence rate for FD-ISCA is observed. Complete hardware architecture of FD-ISCA along with its simulation results is also discussed.

How to promote relationship-building leadership at work? A comparative exploration of leader humor behavior between North America and China

There is a growing consensus that humor could be one of the most important competencies of a leader. This impression seems to be especially so under the clan controls where relationship-building between a leader and employees become crucial in managing human resources (HR). However, we caution against making this a global assumption by exploring the association between humor and leadership in different cultural contexts, in particular comparing China with North America. We argue that there is weaker association between use of humor and perceived leadership in China than North America. While the main message is not about denying the effectiveness of humor per se, this conceptual paper is wary about the blind adaptation or convergence of leader behaviors (e.g. humor) and HR management oriented toward American practices. At the same time, we highlight the need to consider the informal context of an organization in explaining possible paradoxical attitudes toward humor in China.

A realist philosophical understanding of entrepreneurial risk-taking

Purpose – The purpose of this paper is to utilise Vilfredo Pareto ' s Machiavellian-realist social theory to provide a distinctive realist philosophical understanding of entrepreneurial risk-taking. By doing so, this paper seeks to stimulate debate and encourage future empirical testing that has the potential to present a richer understanding of entrepreneurial risk-taking. Design/methodology/approach – To establish that a realist perspective can help theorise entrepreneurship, the authors look through a modern day risk and uncertainty optic at the hidden mechanisms within the social world where enterprises operate. Looking from this unique standpoint, where the long established social theory is reinvigorated by contemporary risk philosophy within a shared realist paradigm, human nature equips entrepreneurs with certain “animal spirits” to muddle blindly and instinctually through their risk environments. Findings – The paper argues that this combined perspective unlocks a much richer understanding of entrepreneurial risk-taking, in particular, by capturing more of its behavioural reality and despite our strong emphasis on the inaccessibility and hiddenness of the risk environment to the entrepreneur, by exploring the entrepreneur-risk environment fit in ecological terms. Originality/value – The paper’s unique blend of the classical Italian social theory with the contemporary risk theory offers a novel ecological view of the entrepreneur’s blind (mal) adaptation to their risk environment.

LMS Blind Adaptive Multiuser Detection for Underwater Acoustic Communication with MMSE and MOE Criteria

In order to solve detection of multi-user detection in underwater acoustic communication networks, blind adaptive multiuser detection method using LMS algorithm was proposed to solve the MAI problem of multi-user detection in underwater acoustic communication channel when users increase. Two type simulations were executed, including synchronous and asynchronous multi-user simulation analysis, to verify the validity of LMS multi-user detection algorithm and inspection machine. The verification of the effectiveness of the MMSE and MOE algorithm for LMS multi-user detection algorithm was done by simulation.

VIDEO CONTENT FOR THE BLIND PEOPLE: METHOD AUDIODESCRIPTION

The problem of video access of people with total or partial loss of vision is studied. The typhlocomment as a method for blind adaptation of video content is described. When researching the problem of access of weak-sight people to the video content, it is required to understand that more than a most part of the information is provided to the viewer in the form of an image. The blind people hear all words of actors, sounds of the environment, processes at the screen, but it is difficult for them to identify the person to whom the specific words belong, what happens with heroes at the very specific moment, what is depicted in the given scene, it is difficult for them to understand reaction of actors, which the latter often express with the help of movements or mimics. Typhlocomments to video content for blind people are one of the real steps towards solution of the problem of limitation of access to such content.

An Approximated Expression for the Residual ISI Obtained by Blind Adaptive Equalizer and Biased Input Signals

Recently, two expressions (for the noiseless and noisy case) were proposed for the residual inter-symbol interference (ISI) obtained by blind adaptive equalizers, where the error of the equalized output signal may be expressed as a polynomial function of order 3. However, those expressions are not applicable for biased input signals. In this paper, a closed-form approximated expression is proposed for the residual ISI applicable for the noisy and biased input case. This new proposed expression is valid for blind adaptive equalizers, where the error of the equalized output signal may be expressed as a polynomial function of order 3. The new proposed expression depends on the equalizer’s tap length, input signal statistics, channel power, SNR, step-size parameter and on the input signal’s bias. Simulation results indicate a high correlation between the simulated results and those obtained from our new proposed expression.