Narrow Results

The majority of proposed NIRS-BCIs has considered binary classification. Studies considering high-order classification problems have yielded average accuracies that are less than favorable for practical communication. Consequently, there is a paucity of evidence supporting online classification of more than two mental states using NIRS. We developed an online ternary NIRS-BCI that supports the verbal fluency task (VFT), Stroop task and rest. The system utilized two sessions dedicated solely to classifier training. Additionally, samples were collected prior to each period of online classification to update the classifier. Using a continuous-wave spectrometer, measurements were collected from the prefrontal and parietal cortices while 11 able-bodied adult participants were cued to perform one of the two cognitive tasks or rests. Each task was used to indicate the desire to select a particular letter on a scanning interface, while rest avoided selection. Classification was performed using 25 iteration of bagging with a linear discriminant base classifier. Classifiers were trained on 10-dimensional feature sets. The BCI’s classification decision was provided as feedback. An average online classification accuracy of $74.2\pm 14.8$% was achieved, representing an ITR of $1.31\pm 0.86$ bits/min. The results demonstrate that online communication can be achieved with a ternary NIRS-BCI that supports VFT, Stroop task and rest. Our findings encourage continued efforts to enhance the ITR of NIRS-BCIs.

The Blackman's impedance relation formula holds several unique and important features. First and most important is generality. The formula is truly universal and could be applied regardless of feedback topology. The impedances of canonical cases could be presented as special cases of Blackman's impedance relation. This paper revisits the concepts of Blackman's impedance relation. Several alternative derivation approaches are suggested to stand in accord with the gain evaluation procedure. The derivation of the Blackman's impedance relation formula by Superposition, Thevenin, Trans-Admittance and Trans-Impedance methods are offered. The paper extends the ideas and completes the methodology of unified approach to analysis of feedback amplifiers presented earlier. The paper also discusses some additional features of the feedback amplifier model. The paper advocates that the described method of obtaining the loop-gain makes a real difference in application of the formula. Overcoming the loop-gain computational difficulties helps reestablish the Blackman's impedance relation as a viable tool in analysis of feedback circuits.

The majority of work in reversible logic circuits has been limited to combinational logic. Researchers are now beginning to suggest designs for sequential circuits. In this paper we propose a new method to design and optimize feedback reversible logic circuits and a specific group of quantum logic circuits based on the reversible state transition table and genetic algorithms (GA). To show the efficiency of the proposed method, some reversible sequential elements such as D and T flip-flops (FFs), with and without clock and reset, and edge triggered FFs are designed. We have also extended our method to multiple loop feedback circuits. The proposed circuits are highly optimized using a GA synthesis tool that allows don't care values. Some of the designs in this paper are presented in other papers; however, the comparisons show that the quantum cost and number of garbage inputs/outputs are reduced efficiently by our method.

Content addressable memory (CAM) can perform high-speed table look-up with bit level masking capability. This feature makes CAMs extremely attractive for high-speed packet forwarding and classification in network routers. High-speed look-up implies all the CAM word entries to be accessed and compared with a search word to find a suitable match in a single clock cycle. This parallel search activity requires large energy consumption which needs to be reduced. In this paper, a review of the energy reduction techniques of CAM is presented. A comparative study of some popular techniques has been made with the help of simulations carried out in this work and published results.

Conventional combinational circuits are generally acyclic (feed-forward) but these circuits can have feedbacks (cycles) which will give more minimized expressions as compared to conventional combinational circuits. Deliberate incorporation of such cycles or feedbacks in conventional combinational circuits eventually results in reduction in number of literals in the expression of the combinational circuits. The reduction in literal counts decreases the number of gates required to implement the expressions of the combinational circuits. Hence, the decrease in number of gates leads to reduction in transistor counts or layout area for the circuits. A cyclic combinational circuit (CCC) is defined as the circuit whose output depends on present inputs only, but at the same time contains one or more feedbacks (cycles). This paper presents a simplified methodology for introduction of cycles (feedbacks) and finding expressions for the CCC. The methodology is applied on LGSynth93 benchmark circuits and a reduction up to 28% in literal counts for expressions of CCC was found which is higher than the reduction achieved by other methodologies. Further the methodology is applied to implement binary comparator which has got three multiple outputs using cyclic combinational technique. The circuits are verified through simulation in cadence virtuoso tools using 45nm technology. Based on simulation results, performance parameters like power consumption, propagation delay and layout area of CCC are compared with the conventional circuits.

Feedback is an integral part of many analog circuits. This paper presents a method for the design of feedback networks for analog amplifiers based on Fixator–Norator Pair (FNP). The design process for required transfer function includes inserting proper FNPs to the equivalent small signal model of the target circuit, with norators lying along the feedback path, and helps to design the feedback network components. Care must be taken to ensure that the added feedback should not alter the original DC biasing of the circuit. A number of examples are worked out in this paper using the proposed method and the results are verified. The FNP approach gives a one-step solution for the design problems which otherwise require tedious analysis and calculations. Although the scope of this paper is limited to design of feedback for amplifiers, a skillful designer can extend the proposed method to other areas of analog circuits.

Linearity of ramp signals is one of the most important aspects for many applications such as single-slope analog to digital converters (ADCs); another important aspect is the total power dissipation. Applications like high-resolution single-slope ADCs that can be used in portable devices demanded accurate ramp generator with low power dissipation. This paper presents a low power ramp generator with linearity improvement that achieved by a positive feedback circuit and negative feedback for compensation of the variations in process, voltage and temperature. Derived equations of the proposed ramp generator circuit show that linearity of the output ramp, with proper choosing of device sizes, can be enhanced significantly. Also, for proving of linearity enhancement, the circuit design and post-layout simulations were done in TSMC 0.18$\mu$m and 90nm CMOS technologies. Simulation results show that linearity of the circuit improved by a factor of 8 and total ramp resolution improved about 3 bit, whereas power dissipation of the circuit is about 8$\mu$W and entire layout core area is near 800$\mu$m².

A subthreshold MOS-based pseudo-resistor featuring a very high value and ultra-low distortion is proposed. A bandpass neural amplifier with a very low high-pass cutoff frequency is designed, to demonstrate the linearity of the proposed resistor. A BJT less CTAT current generator has been introduced to minimize the temperature drift of the resistor and make tuning easier. The standalone resistor has achieved 0.5% better linearity and a 12% improved temperature coefficient over the existing architectures. A neural amplifier has been designed with the proposed resistor as a feedback element. It demonstrated 31dB mid-band gain and a low-pass cutoff frequency of 0.85Hz. The circuit operates from a 1V supply and draws 950nA current at room temperature.

Network routers use ternary content addressable memory (TCAM) for high-speed table look-up. A match-line (ML) sensing scheme for TCAM combining charge-sharing and positive feedback is presented. The objective is to simplify the ML sense amplifier (MLSA) of existing charge-sharing scheme while reducing ML energy consumption during look-up. The look-up has been performed in two steps. In the first step, a segment of each TCAM word is compared with the search key to detect large percentage of the mismatched words. The detected mismatched words are deactivated in the second step to reduce energy consumption. In the second step, the charge stored in a matched ML first segment is shared with second ML segment. Use of positive feedback in this step makes the MLSA circuit simple. Post-layout simulations implemented using 180nm 1.8V CMOS logic have been performed. In addition to lower scheme complexity and 16.5% reduction in circuit area, the proposed scheme provides dynamic energy saving up to 5.5% and peak power reduction of 52% compared to existing state-of-the-art charge-sharing technique.

A power-efficient, voltage gain enhancement technique for op-amps has been described. The proposed technique is robust against Process, Voltage and Temperature (PVT) variations. It exploits a positive feedback-based gain enhancement technique without any latch-up issue, as opposed to the previously proposed conductance cancellation techniques. In the proposed technique, four additional transconductance-stages (gm stages) are used to boost the gain of the main gm stage. The additional gm stages do not significantly increase the power dissipation. A prototype was designed in 65nm CMOS technology. It results in 81dB voltage gain, which is 21dB higher than the existing gain-boosting technique. The proposed op-amp works with as low a power supply as 0.8V, without compromising the performance, whereas the traditional gain-enhancement techniques start losing gain below a 1.1V supply. The circuit draws a total static current of 295$\mu$A and occupies 5000$\mu$m² of silicon area.

Accurate ramp signal, with low power dissipation, is highly demanded, for applications like counter ADC. This paper presents a novel low power ramp generator circuit with a negative feedback loop for compensation of the variations in process, voltage, and temperature (PVT). While using an opamp for PVT compensation has been essential in the previous ramp generator structures, the proposed ramp generator is opamp-less. Derived equations of the proposed ramp generator circuit show that PVT compensation structure works effectively. In addition, the circuit design and simulations were done in TSMC 0.18$\mu$m CMOS technology. Corner analysis shows that integral non-linearity (INL) of the ramp signal is about 3.7mV, for a wide temperature range, while the power dissipation of the circuit is about 1.16$\mu$W.

In this paper, the method for the design automation of a narrow band-pass amplifier, and hence the amplifier tuned oscillator is discussed. A fixator approach is utilized in this method to design the narrow band-pass amplifiers and a reference circuit is required for this process. The fixator–norator pair helps to generate an extra sub-circuit, generally the feedback network; the addition of this sub-circuit in the actual amplifier circuit will modify the frequency response of the amplifier. The amplifier now behaves like an active narrow band-pass filter, which exactly follows the frequency response of the model circuit. This can be turned into an oscillator by providing positive feedback. Such a circuit possesses independent frequency and amplitude control. Hence, the re-designed circuit can be employed as an active filter or an oscillator at the selected center frequency. In addition to the technical merits, the proposed method has pedagogical importance. Few case studies are worked out in this paper to demonstrate the method.

The evaluation of feedback collected from students at the end of the year is very essential for every educational institution. It is important to improve the teaching–learning process and the annual appraisal process. The existing approach utilizes a Likert scale questionnaire, which allows students to express their level of agreement or disagreement with given statements or provide a neutral response. Additionally, the feedback form includes open-ended questions where students can provide textual feedback. This study introduces a Lexicon-based approach to automatically analyze the textual feedback concerning different aspects of teaching. Aspect-based Sentiment Analysis (ABSA) of student feedback aims to identify sentiments expressed toward various aspects of teachers, such as their ability to address student doubts and their overall knowledge. This study explores linguistic characteristics found in sentences, including negation, modifiers and contact shifters. To assess the sentiment of a sentence, the SentiWordNet lexicon is utilized to assign scores to individual words. Based on these scores, the sentence is categorized as either positive, negative or neutral. According to the experimental findings, the Aspect-Oriented Lexicon-Based (AOLB) approach demonstrates superior performance compared to other baseline methods when it comes to accurately scoring sentiment. The approach achieved a high accuracy rate of 94% for the student feedback dataset-I, 74% for the student feedback dataset-II, 55% for laptop reviews and 59% for restaurant reviews in the SemEval 2014 dataset-III.

This paper aims to explore two crucial aspects of collaborative work and learning: on the one hand, the importance of enabling collaborative learning applications to capture and structure the information generated by group activity and, on the other hand, to extract the relevant knowledge in order to provide learners and tutors with efficient awareness, feedback and support with regards to group performance and collaboration. To this end, in this paper we first propose a conceptual model for data analysis and management that identifies and classifies the many kinds of indicators that describe collaboration and learning into high-level aspects of collaboration. Then, we provide a computational platform that, at a first step, collects and classifies both the event information generated asynchronously from the users' actions and the labeled dialogues from the synchronous collaboration according to these indicators. This information is then analyzed in next steps to eventually extract and present to participants the relevant knowledge about the collaboration. The ultimate aim of this platform is to efficiently embed information and knowledge into collaborative learning applications. We eventually suggest a generalization of our approach to be used in diverse collaborative learning situations and domains.

Structural and parametric identification of nonlinear continuous dynamic systems with a closed cycle on a set of continuous block-oriented models with feedback is considered. The method of structural identification in the steady state based on the observation of the system's input and output variables at the input periodic influences is proposed. The solution of the parameter identification problems, which can be immediately connected with the structural identification problem, is carried out in the steady and transient states by the method of least squares. The structural and parametric identification algorithms are investigated by means of both theoretical analysis and computer modeling.

The paper analyzes some of Tesla's works and his most remarkable views concerning the problem of formulating theoretical bases of automatic control. As a tribute to Tesla's work on remote control of automated systems, as well to his (at the time) far-seeing visions, special attention is paid to solving the complex problem of control and feedback application. A more detailed discussion of the way and origin of formulating theoretical bases of automatic control is given. Besides, in more detail are presented the related pioneering works of Professor Nicholas Bernstein, great Russian physiologist who formulated the basic rules of the self-regulating movements of man. Bernstein has achievements of highest scientific significance that has been in a direct function of identifying and proving the priority of his pioneering contributions in the domain of feedback, i.e. control and principles of cybernetics.

The article presents the facts about the pioneering research results of Professor Nikolai Bernstein in the area of man's voluntary movements. Relevant data are given concerning the priority of introducing the notion of feedback in the process of active voluntary human movements, twelve years before the known Wiener's publication. Bernstein demonstrated how the problems of general physiology can be explored in terms of the structural analysis of movements. He dealt with the most important aspects of the vital activity of higher organisms, and how this has been accorded the place in physiology and, when it developed, promised to be of the greatest value in cybernetics and in the exact mathematical formulation of a physiological theory of motor behavior. In his research, Bernstein modeled the function of the central nervous system and offered the cyberneticists a system for the development of analogs for experimental model-making that was not only incomparably richer than examples of internal stabilizing processes (blood-pressure, temperature and sugar-level regulating systems, for example), and also more complex than the systems of dynamic regulation that have already been studied in some depth, such as the mechanisms of ocular accommodation, or of the pupillary reaction.

In this paper, a user-centered framework is proposed for video database modeling and retrieval to provide appealing multimedia experiences on the content-based video queries. By incorporating the Hierarchical Markov Model Mediator (HMMM) mechanism, the source videos, segmented video shots, visual/audio features, semantic events, and high-level user perceptions are seamlessly integrated in a video database. With the hierarchical and stochastic design for video databases and semantic concept modeling, the proposed framework supports the retrieval for not only single events but also temporal sequences with multiple events. Additionally, an innovative method is proposed to capture the individual user's preferences by considering both the low-level features and the semantic concepts. The retrieval and ranking of video events and the temporal patterns can be updated dynamically online to satisfy individual user's interest and information requirements. Moreover, the users' feedbacks are efficiently accumulated for the offline system training process such that the overall retrieval performance can be enhanced periodically and continuously. For the evaluation of the proposed approach, a soccer video retrieval system is developed, presented, and tested to demonstrate the overall retrieval performance improvement achieved by modeling and capturing the user preferences.

The world has become a global village, and mastering multiple languages has become necessary; therefore, the need for computer-assisted language learning (CALL) applications is growing. Speech being the most spontaneous and widespread mode of communication and learning pronunciation occupies a considerable place, and computer-assisted pronunciation teaching (CAPT) is increasingly integrated into CALL systems. Herein, pronunciation assessment is a critical component that aims to detect and diagnose mispronunciation to provide informative feedback for the learners. On the other hand, Arabic is among the top five languages to learn, but it is sorely lacking in resources for CAPT. This paper aims to bridge the gap between Arabic and high-resource languages in CAPT. As neither a review nor a survey on pronunciation assessment for Arabic exists, this paper provides an overview of the existing research on automatic pronunciation assessment and feedback for Arabic. Many studies are addressed based on three main tasks: pronunciation rating, mispronunciation detection and diagnosis (MDD), and feedback. The paper summarizes existing findings of Arabic pronunciation assessment, underlines challenges, and sheds some light on the opportunities offered to researchers on the topic.

This chapter undertakes an in-depth analysis of the interaction between OCR and information retrieval. In particular, after providing an introduction to information retrieval, we report on retrieval effectiveness from OCR generated document collections. It will be shown that, in general, average precision and recall is not affected while document term assignment, weighting and document ranking may be affected. We also point out that even though OCR errors do not affect average retrieval effectiveness, there are other consequences that should be considered when OCR text is applied.