A recent trend in the electronics industry is the concept of intelligent wearables and the Internet of Things (IoT) such as smart watches, fitness trackers, smart sensors, and smart glasses. Low power consumption is a requirement of the processors and the increasingly large embedded SRAMs in such devices. Lowering the operating voltage of these devices makes them low power. However, process variation at lower voltages causes read and write reliability issues in SRAM bitcells. To overcome issues in SRAM reliability, we present a novel, integrated read and write assist scheme with area and power savings as compared to existing state of the art schemes. For write assist, we inject negative bitline bias to raise the gate to source voltage of the bitcell access transistor, improving write margin. For read assist, we lower bitline precharge levels by adding one transistor per bitline column. Area and power savings come from capacitance sharing between the read and write assist circuitry and by precharging bitlines using recycled charged. In our SRAM implementation of the proposed circuit, we found as high as 10% dynamic power savings during bitline precharging. Using negative bitline techniques further reduced power consumption through an approximately 150mV reduction in operating voltage. Depending on the read and write margin requirements, capacitors in the design can be programmable and isolated for flexibility. The SRAM array presented in this paper also utilizes a variety of leakage reduction modes. In terms of area, for a 16384×72m8fb8 memory instance, the area overhead was only 1.7%. SRAMs using the low power, low area overhead assist scheme and the leakage reduction modes can have significant applications in wearable electronics and IoT.

The purpose of the Power-on Reset (POR) circuit is to reset the latches and flip-flops in an SOC to a known state when the supply is ramping up. During power-up, supply is not stable, and the ramp-up time can vary depending on the applications. A common approach is to generate a POR signal by comparing the supply voltage with a reference voltage. Conventional POR circuits also use a resistor divider circuit along with band gap reference. Low-power applications, like Internet of Things (IoT) devices, comprise of SRAM arrays, sensors, and logic operating at sub-threshold or extremely low voltages. At these low operating voltages, generating a stable reference voltage is difficult because of band gap reference limitations and process variations. In this paper, we present a POR circuit that operates without using a reference voltage, making it robust against different sources of variation. The proposed circuit is self-timed, meaning the reset signal pulse-width varies according to the time needed to reset the latch. The designed circuit has been fabricated in 16nm FINFET technology. Silicon validation shows that the proposed POR circuit works at a minimum supply voltage of 400mv. Simulation verifies that the POR circuit is operational in sub-threshold region, but is limited to 400mV on silicon due to the operational voltage of additional testchip logic. Also, the POR circuit does not consume any dynamic power during normal operation of the SOC and has minimal area overhead of 21.3pm2.

This study aimed to improve the accuracy and robustness of a real-time assistive human machine interface system by classifying between the controlled movements related tongue-movement ear pressure (TMEP) signals and the interfering signals. The controlled movement TMEP signals were collected during left, right, up, down, flicking and pushing tongue motions. The TMEP signals were processed and classified using detection, segmentation, feature extraction and classification. The segmented signals were decomposed into the time-scale domain using a wavelet packet transform. The variance of the wavelet packet coefficients and its ratio between low-to-high scales were defined as features and the intended tongue movement commands and interfering signals were classified using both a Bayesian and support vector machine (SVM) classifiers for comparison. The average classification accuracy for discriminating between the controlled movements and the interfering signals achieved 97.8% (Bayesian) and 98.5% (SVM). The classifiers were robust remaining at a similar performance level when generalised interferences from all subjects were used. It was shown that the Bayesian classifier performed better than the SVM in a real-time environment. The approach of combining the Bayesian classifier and the wavelet packet transform provides a robust and efficient method for a real-time assistive human machine interface based on tongue-movement ear pressure signals.

Real-time implementation of an assistive human-machine interface system based around tongue-movement ear pressure (TMEP) signals is presented, alongside results from a series of simulated control tasks. The implementation of this system into an online setting involves short-term energy calculation, detection, segmentation and subsequent signal classification, all of which had to be reformulated based on previous off-line testing. This has included the formulation of a new classification and feature extraction method. This scheme utilises the discrete cosine transform to extract the frequency features from the time domain information, a univariate Gaussian maximum likelihood classifier and a two phase cross-validation procedure for feature selection and extraction. The performance of this classifier is presented alongside a real-time implementation of the decision fusion classification algorithm, with each achieving 96.28% and 93.12% respectively. The system testing takes into consideration potential segmentation of false positive signals. A simulation mapping commands to a planar wheelchair demonstrates the capacity of the system for assistive robotic control. These are the first real-time results published for a tongue-based human-machine interface that does not require a transducer to be placed within the vicinity of the oral cavity.

A strategy is introduced to rank and select principal component transform (PCT) and discrete cosine transform (DCT) transform coefficient features to overcomethe curse of dimensionalityfrequently encountered in implementing multivariate signal classifiers due to small sample sizes. The criteria considered for ranking include the magnitude, variance, interclass separation, and classification accuracies of the individual features. The feature ranking and selection strategy is applied to overcome the dimensionality problem, which often plagues the implementation and evaluation of practical Gaussian signal classifiers. The applications of the resulting PCT- and DCT-Gaussian signal classification strategies are demonstrated by classifying single channel tongue movement ear pressure signals and multichannel event related potentials. Through these experiments, it is shown that the dimension of the feature space can be decreased quite significantly by means of the feature ranking and selection strategy. The ranking strategy not only facilitates overcoming the dimensionality curse for multivariate classifier implementation but also provides a means to further select, out of a rank ordered set, a smaller set of features that give the best classification accuracies. Results show that the PCT- and DCT-Gaussian classifiers yield higher classification accuracies than those reported in previous classification studies on the same signal sets. Among the combinations of the two transforms and four feature selection criteria, the PCT-Gaussian classifiers using the maximum magnitude and maximum variance selection criteria gave the best classification accuracies across the two sets of classification experiments. Most noteworthy is the fact that the multivariate Gaussian signal classifiers developed in this paper can be implemented without having to collect a prohibitively large number of training signals simply to satisfy the dimensionality conditions. Consequently, the classification strategies can be beneficial for designing personalized human-machine interface signal classifiers for individuals from whom only a limited number of training signals can reliably be collected due to severe disabilities.

The ultimate success of a human-robot-interface system depends on how accurately user control signals are classified. This paper is aimed at developing and testing a strategy to accurately classify human-robot control signals. The primary focus is on overcoming the dimensionality problem frequently encountered in the design of Gaussian multivariate signal classifiers. The dimensionality problem is overcome by selecting, using two different ranking criteria, a small set of linear combinations of the input signal space generated by the discrete cosine transform (DCT). The application of the resulting DCT-Gaussian signal classification strategy is demonstrated by classifying tongue-movement ear-pressure (TMEP) bioacoustic signals that have been proposed for control of an assistive robotic arm. Classification results show that the DCT-Gaussian classifiers outperform classifiers described in a previous study. Most noteworthy is the fact that the Gaussian multivariate control signal classifiers developed in this paper can be designed without having to collect a prohibitively large number of training signals in order to satisfy the dimensionality conditions. Consequently, the classification strategies will be especially beneficial for designing personalized assistive interfaces for individuals from whom only a limited number of training signals can reliably be collected due to severe disabilities.

Probe design is the most important step for any microarray based assay. Accurate and efficient probe design and selection for the target sequence is critical in generating reliable and useful results. Several different approaches for probe design are reported in literature and an increasing number of bioinformatics tools are available for the same. However, based on the reported low accuracy, determining the hybridization efficiency of the probes is still a big computational challenge. Present study deals with the extraction of various novel features related to sequence composition, thermodynamics and secondary structure that may be essential for designing good probes. A feature selection method has been used to assess the relative importance of all these features. In this paper, we validate the importance of various features currently used for designing an oligonucleotide probe. Finally, a classification methodology is presented that can be used to predict the hybridization quality of a probe.

In this paper, a system for oral cancer screening using Laser Induced Fluorescence(LIF) has been developed. A hybrid approach of classification using Support Vector Machine (SVM) and Random Forest (RF) classifier's is proposed. Performance of the classifier is evaluated using several features types such as Wavelet, DFT, LDFT, ILDFT, DCT, LDCT and Slopes features. The most discriminating features are selected using Recursive Feature Elimination(RFE). Analysis of the problem of subset selection from SVM-RFE ranked list is also performed. The hybrid approach has been compared with stand-alone SVM, SVM-RFE and RF classifiers. The proposed technique improves the performance of the classification system significantly. The novelty of the approach lies in the way the most significant features are exstracted in separate modules to arrive at a decision and how the decision are then fused in an intelligent fashion to arrive at a final classification.

The goal in this paper is to overcome the dimensionality problem related to designing human-machine-interface (HMI) signal classifiers. The dimension is decreased by selecting a small set of linear combination of the input space features using the principal components transform (PCT) and the discrete cosine transform (DCT). Issues dealing with the selection of the basis vectors of the PCT and DCT for multi-class classification problems are addressed and four different class-dependant ranking criteria are introduced to select basis vectors from the transformed training vectors in the PCT and DCT domains. The application and evaluation of the resulting PCT and DCT based multivariate classification strategies are demonstrated by classifying ear-pressure signals and event related potentials. The signals in these experiments are typical of control signals used in HMI applications and are also typical of those in which the dimensionality problem occurs. Based on the evaluations and comparisons, it is concluded that the PCT and the DCT based strategies developed in this paper offer viable solutions to overcome the dimensionality problem that frequently plagues the design of practical HMI signal classifiers.

A new communication and control concept using tongue movements is introduced to generate, detect, and classify signals that can be used in novel hands-free human-machine interface applications such as communicating with a computer and controlling devices. The signals that are caused by tongue movements are the changes in the airflow pressure that occur in the ear canal. The goal is to demonstrate that the ear pressure signals that are acquired using a microphone that is inserted into the ear canal, due to specific tongue movements, are distinct and that the signals can be detected and classified very accurately. The strategy that is developed for demonstrating the concept includes energy-based signal detection and segmentation to extract ear pressure signals due to tongue movements, signal normalization to decrease the trial-to-trial variations in the signals, and pairwise cross-correlation signal averaging to obtain accurate estimates from ensembles of pressure signals. A new decision fusion classification algorithm is formulated to assign the pressure signals to their respective tongue-movement classes. The complete strategy of signal detection and segmentation, estimation, and classification is tested on four tongue movements of eight subjects. Through extensive experiments, it is demonstrated that the ear pressure signals due to the tongue movements are distinct and that the four pressure signals can be classified with an accuracy of more than 97% averaged across the eight subjects using the decision fusion classification algorithm. Thus, it is concluded that, through the unique concept that is introduced in this paper, human-computer interfaces that use tongue movements can be designed for hands-free communication and control applications.

A complete signal processing strategy is presented to detect and precisely recognize tongue movement by monitoring changes in airflow that occur in the ear canal. Tongue movements within the human oral cavity create unique, subtle pressure signals in the ear that can be processed to produce command signals in response to that movement. The strategy developed for the human machine interface architecture includes energy-based signal detection and segmentation to extract ear pressure signals due to tongue movements, signal normalization to decrease the trial-to-trial variations in the signals, and pairwise cross-correlation signal averaging to obtain accurate estimates from ensembles of pressure signals. A new decision fusion classification algorithm is formulated to assign the pressure signals to their respective tongue-movement classes. The complete strategy of signal detection and segmentation, estimation, and classification is tested on 4 tongue movements of 4 subjects. Through extensive experiments, it is demonstrated that the ear pressure signals due to the tongue movements are distinct and that the 4 pressure signals can be classified with over 96% classification accuracies across the 4 subjects using the decision fusion classification algorithm

A generalized strategy is developed to predict the occurrence of a multicategory-multifactorial disease from a set of medical risk factors that are most often used to screen patients for the disease. The prediction problem is formulated as an M-class classification problem. The strategy employs fusion to combine risk factors into a single feature vector, normalization to fuse risk factors which have different formats and ranges, rank-sum ordering for feature selection, discrete Karhunen-Loeve transform-based transformation to facilitate parametric classifier development, and the design of parametric classifiers. Two methods, which differ on how the features are selected, are developed. In the first method, features are selected from a set consisting of linear combinations of all risk factors. In the second method, the features are linear combinations of a preselected subset of the risk factors. The methods are applied to predict the occurrence of Alzheimer's disease (AD) into three classes: Probable-AD, Possible-AD, and Uncertain. It is shown that a classification accuracy of over 71% can be obtained. This result is quite encouraging given that AD is very difficult to clinically diagnose. Higher classification accuracies can be expected for diseases that are not as complex to diagnose as AD. Most importantly, it is concluded that the generalized strategy can not only be applied to the multicategory-multifactorial disease prediction problem but also to other multiclass pattern recognition problems involving diverse information collected from different sources.

A complete signal processing strategy is presented to detect and recognize tongue movement precisely by monitoring changes in air flow that occur in the ear canal. Tongue movements within the human oral cavity create unique, subtle pressure signals in the ear that can be processed to produce command signals in response to that movement. Once recognized, the movements can be used in human-machine interface applications, such as communicating with a computer and controlling mechanical devices. The processing strategy includes pressure signal acquisition using a microphone inserted into the ear-canal, PSD analysis to design bandpass filters to reject pressure changes due to sources other than tongue movements, start- and end-point detection in the waveforms through cross-correlation, signal estimation, and the design and evaluation of parametric and non-parametric signal classifiers. The non-parametric signal classifiers include non-linear alignment classifiers and matched filters, while the parametric classification involves a multivariate Gaussian classifier using AR model parameters. The complete strategy was tested on 4 tongue actions touching areas of the mouth: left corner; right corner; top center; bottom center. Experiments show that the pressure signals due to tongue movements are distinct and can be detected with over 97% accuracy. The unique strategy makes hands-free control of devices using tongue movements a practical reality.

A new human-machine interface is introduced for "hands-free" tele-operation of mobile robots. This interface consists of tracking tongue movement by monitoring changes in airflow that occur in the ear canal. Tongue movements within the human oral cavity create unique, subtle pressure signals in the ear that can be processed to produce commands signals in response to that movement. Once recognized, said movements can in turn be used in for robotic tele-operation. The complete strategy is tested on 4 tongue actions: touching the tongue to the left and right corners of the mouth, and to the top and bottom center of the mouth. Through extensive experiments, it is shown that the pressure signals due to tongue movements are distinct and can be detected with over 97% accuracy. A case study to control the Whegs II robotic platform has specifically been investigated. Based on simulation results, it is concluded that this unique strategy will make hands-free robotic tele-operation a practical reality.

The accurate classification of hand gestures is crucial in the development of novel hand gesture-based systems designed for human-computer interaction (HCI) and for human alternative and augmentative communication (HAAC). A complete vision-based system, consisting of hand gesture acquisition, segmentation, filtering, representation and classification, is developed to robustly classify hand gestures. The algorithms in the subsystems are formulated or selected to optimality classify hand gestures. The gray-scale image of a hand gesture is segmented using a histogram thresholding algorithm. A morphological filtering approach is designed to effectively remove background and object noise in the segmented image. The contour of a gesture is represented by a localized contour sequence whose samples are the perpendicular distances between the contour pixels and the chord connecting the end-points of a window centered on the contour pixels. Gesture similarity is determined by measuring the similarity between the localized contour sequences of the gestures. Linear alignment and nonlinear alignment are developed to measure the similarity between the localized contour sequences. Experiments and evaluations on a subset of American Sign Language (ASL) hand gestures show that, by using nonlinear alignment, no gestures are misclassified by the system. Additionally, it is also estimated that real-time gesture classification is possible through the use of a high-speed PC, high-speed digital signal processing chips and code optimization.

A neural network approach is presented for the classification of closed planar shapes. The neural net classifier developed is robust and invariant to translation, rotation, and scaling. The primary foci are the development of an effective representation for planar shapes and the selection of a suitable neural network structure. In particular, planar shapes are represented by an ordered sequence that represents the Euclidean distance between the centroid and all contour pixels of the shape. It is also shown that for this classification problem and the representation derived, the three-layer perceptron with backpropagation training is an appropriate neural network configuration.>

A technique for classifying closed planar shapes is described in which a shape is characterized by an ordered sequence that represents the Euclidean distance between the centroid and all contour pixels of the shape. Shapes belonging to the same class have similar sequences, hence a procedure for classifying shapes is based on the degree of similarity between these sequences. In order to determine the similarity between sequences, a non-linear alignment process is developed to find the best correspondence between the sequences. Optimum alignment is obtained by expanding segments of the sequences to minimize a dissimilarity function between the sequences. Normalization with respect to scaling and rotation is described and an example illustrating the use of dynamic alignment for the classification of noisy shapes is presented.