Small Error Rate Research Articles

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Measurement o f human height is usually done with the help o f others. But with the Sobel method, measurement o f human height can be done alone. It can simplify the process o f measuring human height. The method is a development method robert sobel using filter HPF (high pass filter) that given a zero buffer. This method takes the principle o f the laplacian and gaussian function is known as a function to generate HPF. Applications designed using sobel method can be used for high meassure an object with a small error rate. Best distance o f 20 cm from the background to the object seen from the results o f the error rate.

On the use of frequency-domain cross-layer diversity techniques to cope with lost packets

The Quality of Service (QoS) requirements for modern broadband wireless systems can be very high, with small error rates and delays. To preclude significant degradation on the spectral efficiency, these systems also require high throughputs. Lost packets, either due to errors or collisions, are usually discarded and need to be retransmitted, leading to performance degradation. An efficient alternative to simple retransmissions is to combine the signals associated to different transmission attempts.This paper analyses two time diversity approaches to cope with lost packets that are relatively similar at physical layer but treat different packet loss causes: a low-complexity diversity combining ARQ scheme (Automatic Repeat reQuest) employed in a TDMA (Time Division Multiple Access) architecture; and a Network Diversity Multiple Access (NDMA), which is a multi-packet reception approach able to separate multiple mobile terminals transmitting simultaneously in one slot using temporal diversity. This paper combines the use of these techniques in Single Carrier Frequency Domain Equalization (SC-FDE) systems, which are widely recognized as the best candidates for the uplink of future broadband wireless systems. It aims to present a comparison among the approaches focusing on error rate, throughput and delay performances, not discarding the implementation complexity issues.

Online Coregularization for Multiview Semisupervised Learning

We propose a novel online coregularization framework for multiview semisupervised learning based on the notion of duality in constrained optimization. Using the weak duality theorem, we reduce the online coregularization to the task of increasing the dual function. We demonstrate that the existing online coregularization algorithms in previous work can be viewed as an approximation of our dual ascending process using gradient ascent. New algorithms are derived based on the idea of ascending the dual function more aggressively. For practical purpose, we also propose two sparse approximation approaches for kernel representation to reduce the computational complexity. Experiments show that our derived online coregularization algorithms achieve risk and accuracy comparable to offline algorithms while consuming less time and memory. Specially, our online coregularization algorithms are able to deal with concept drift and maintain a much smaller error rate. This paper paves a way to the design and analysis of online coregularization algorithms.

A Space Lower Bound for Dynamic Approximate Membership Data Structures

An approximate membership data structure is a randomized data structure representing a set which supports membership queries. It allows for a small false positive error rate but has no false negative errors. Such data structures were first introduced by Bloom in the 1970s and have since had numerous applications, mainly in distributed systems, database systems, and networks. The algorithm of Bloom (known as a Bloom filter) is quite effective: it can store an approximation of a set $S$ of size $n$ by using only $\approx 1.44 n \log_2(1/\varepsilon)$ bits while having false positive error $\varepsilon$. This is within a constant factor of the information-theoretic lower bound of $n \log_2(1/\varepsilon)$ for storing such sets. Closing this gap is an important open problem, as Bloom filters are widely used in situations where storage is at a premium. Bloom filters have another property: they are dynamic. That is, they support the iterative insertions of up to $n$ elements. In fact, if one removes this requirement, there exist static data structures that receive the entire set at once and can almost achieve the information-theoretic lower bound; they require only $(1+o(1)) n \log_2(1/\varepsilon)$ bits. Our main result is a new lower bound for the space requirements of any dynamic approximate membership data structure. We show that for any constant $\varepsilon>0$, any such data structure that achieves false positive error rate of $\varepsilon$ must use at least $C(\varepsilon) \cdot n \log_2(1/\varepsilon)$ memory bits, where $C(\varepsilon)>1$ depends only on $\varepsilon$. This shows that the information-theoretic lower bound cannot be achieved by dynamic data structures for any constant error rate.

Robust Watermarking Using Compressed Sensing Framework with Application to MP3 Audio

In this paper a watermark embedding and recovery technique is proposed based on the compressed sensing framework. Both the watermark and the host signal are sparse, each in its own domain. In recovery, the L1-minimization is used to recover the watermark and the host signal almost perfectly in clean conditions. The proposed technique is tested on MP3 audio compression-decompression attack and additive noise attack. Bit error rates are compared with standard spread spectrum embedding. The proposed technique is implemented for both time domain and frequency domain embedding with a unified approach. The WalshHadamard transform (WHT), the discrete cosine transform (DCT) and the Karhunen-Loeve transform (KLT) are compared in the host signal sparsifying process. Significant performance improvements in all tested conditions are achieved against the spread spectrum embedding. A payload as high as 172bps in additive noise attacks, 86bps in 128kbps MP3 attacks and 11bps in 64kbps MP3 attacks are achieved at small bit error rates and acceptable MP3 audio signal quality.

On the Superiority of the Negative Binomial Test Over the Binomial Test for Estimating the Bit Error Rate

This paper examines the binomial and negative binomial tests for estimating the bit error rate using Monte Carlo simulations. Whereas the estimator error variances and simulation times do not recommend one test over the other, confidence interval analysis and test design criteria recommend the negative binomial test for estimating a small bit error rate based on a limited number of error events. The Clopper-Pearson interval length is much more consistent for the negative binomial test in the small bit error rate case. The logarithmically centered confidence interval is introduced and analyzed. For a given confidence interval length and confidence probability, the negative binomial test requires fewer error events than the binomial test. The asymptotic analyses of the Clopper-Pearson and logarithmically-centered confidence intervals yield useful design criteria for the negative binomial test in a way that is not possible for the binomial test.

Maximum Sum Rate of Repeat-Accumulate Interleave-Division System by Fixed-Point Analysis

A multi-user repeat-accumulate interleave-division (RAID) system is considered for a multiple-access channel (MAC) with binary inputs, equal-power, and symbol synchronization. In the system, a regular repeat-accumulate (RA) code serially concatenated with block spreading is employed for each user. At the receiver, multi-user message-passing decoding is performed on a single factor graph. Over the MAC with additive white Gaussian noise (AWGN), a fixed point analysis is developed to obtain the optimal code rate and the spreading length that give the maximum sum rate for an arbitrary small decoding error rate.

AWG-based optical switches performance using crosstalk limiting schedulers

According to a generalized opinion of the scientific community, Arrayed Waveguide Gratings (AWG) are one of the most promising passive optical devices for terabit optical switching systems. These devices permit to build high-performance all-optical WDM switches thanks to their wavelength routing capabilities, in addition to their high information density and lower power consumption. In previous works, we showed that wavelength reuse across different ports introduces in-band crosstalk which strongly limits scalability of AWG-based backplanes. We also proved that this limitation can be overcome by modified scheduling algorithms that reduce the probability of reusing the same wavelength in different ports of the AWG device, significantly reducing or even avoiding the effect of in-band crosstalk. In this paper, we extend several previously proposed scheduling algorithms to enhance their performance. The new algorithms permits to build AWG-based switches of larger sizes while maintaining small bit error rates (BER).

Performance of Diversity Combining ARQ Error Control in a TDMA SC-FDE System

The Quality of Service (QoS) requirements for modern broadband wireless systems can be very high, with small error rates and delays. Packets with errors are usually discarded and need to be retransmitted, leading to performance degradation. An alternative to simple retransmissions like Automatic Repeat reQuest (ARQ) schemes, that can minimize the degradation is to combine the signals associated to different transmission attempts, usually called diversity combining techniques. This paper considers the use of Single-Carrier (SC) modulations with Frequency-Domain Equalization (FDE) and low-complexity soft combining ARQ schemes. The time diversity combining technique presented in this paper, allows packets associated to different transmission attempts to be combined in a soft way so as to improve the performance. This technique is employed in a Time Division Multiple Access (TDMA) scheme. An accurate analytical model is proposed for the evaluation of the uplink packet delay in non-saturated traffic condition, and for the system's throughput (goodput) considering a generic packet arrival process. Physical (PHY) layer (packet error rates) and system-level (goodput and packet delay considering both PHY and Medium Access Control (MAC) layers) results are presented and validated by simulations for two distinctive arrival processes: Poisson and Geometric.

Probabilistically Programmed STT-MRAM

Novel memory programming methods and corresponding memory structures are presented in this paper. Unlike conventional memory programming, this programming technique does not require deterministic switching of memory elements. This technique explicitly exploits the probabilistic switching characteristics of memory elements such as spin-transfer torque magnetic tunnel junction (STT-MTJ) to reduce programming power and delay. This technique also allows multilevel cell (MLC) spin-transfer torque magnetoresistive random access memory (STT-MRAM) to be fabricated with existing STT-MTJ fabrication processes, thus making high capacity STT-MRAM chips readily achievable. The optimal STT-MTJ switching probabilities are given in this paper for reaching minimum programming delay, power, and iteration. Moreover, this paper proves, by applying probabilistic programming to existing STT-MTJs, both programming delay and power can be reduced to levels beyond the reach of conventional deterministic programming. Furthermore arbitrarily small programming bit error rate (BER) can be accomplished in theory using probabilistic programming without much penalty on average programming delay and power. On the contrary, deterministic programming always presents finite programming BER, which is expensive to reduce in terms of programming power and delay. The MLC capability of STT-MTJ clusters has also been confirmed using fabricated STT-MTJ devices. The major circuitries for implementing probabilistically programmed MLC STT-MRAM are also presented in this paper.

Performance of Five Models to Predict the Naturalization of Non-Native Woody Plants in Iowa

Use of risk-assessment models that can predict the naturalization and invasion of non-native woody plants is a potentially beneficial approach for protecting human and natural environments. This study validates the power and accuracy of four risk-assessment models previously tested in Iowa, and examines the performance of a new random forest modeling approach. The random forest model was fitted with the same data used to develop the four earlier risk-assessment models. The validation of all five models was based on a new set of 11 naturalizing and 18 non-naturalizing species in Iowa. The fitted random forest model had a high classification rate (92.0%), no biologically significant errors (accepting a plant that has a high risk of naturalizing), and few horticulturally limiting errors (rejecting a plant that has a low risk of naturalizing) (8.7%). Classification rates for validation of all five models ranged from 62.1 to 93.1%. Horticulturally limiting errors for the four models previously developed for Iowa ranged from 11.1 to 38.5%, and biologically significant errors from 4.2 to 18.5%. Because of the small sample size, few classification and error rate results were significantly different from the original tests of the models. Overall, the random forest model shows promise for powerful and accurate risk-assessment, but mixed results for the other models suggest a need for further refinement.

Recursive Bit Metric Generation for PSK Signals with Gray Labeling

This letter presents a novel bit metric generation approach for phase shift keying (PSK) with Gray labeling. This approach generates bit metrics recursively based on a simplified function which can reduce the computational complexity greatly at a very small bit error rate (BER) performance loss. At last, the generalized mutual information (GMI) is used to demonstrate the good performance of this method in bit-interleaved coded modulation (BICM) systems.

RazorProtector: Maintaining Razor DVS Efficiency in Large IR-Drop Zones by an Adaptive Redundant Data-Path

Recently, the DVS (Dynamic Voltage Scaling) method has been aggressively applied to processors with Razor Flip-Flops. With Razor FF detecting setup errors, the supply voltage in these processors is down-scaled to a near critical setup timing level for a maximum power consumption reduction. However, the conventional Razor and DVS combinations cannot tolerate well error rate variations caused by IR-drops and environment changes. At the near critical setup timing point, even a small error rate change will result in sharp performance degradation. In this paper, we propose RazorProtector, a DVS application method based on a redundant data-path which uses a multi-cycle redundant calculation to shorten the recovery penalty after a setup error occurrence. A dynamic redundancy-adapting scheme is also given to use effectively the designed redundant data-path based on a study of the program, device and error rate characteristics. Our results show that RazorProtector with the adaptive redundancy architecture can, compared to the traditional DVS method with Razor FF, under a large setup rate caused by a 10% unwanted voltage drop, reduce EDP up to 78% at 100µs/V, 88% at 200µs/V voltage scaling slope.

Random Survival Forests

Random Survival Forests

Prominent reflexive eye-movement orienting associated with deafness

Profound deafness affects orienting of visual attention. Until now, research focused exclusively on covert attentional orienting, neglecting whether overt oculomotor behavior may also change in deaf people. Here we used the pro- and anti-saccade task to examine the relative contribution of reflexive and voluntary eye-movement control in profoundly deaf and hearing individuals. We observed a behavioral facilitation in reflexive compared to voluntary eye movements, indexed by faster saccade latencies and smaller error rates in pro- than anti-saccade trials, which was substantially larger in deaf than hearing participants. This provides the first evidence of plastic changes related to deafness in overt oculomotor behavior, and constitutes an ecologically relevant parallel to the modulations attributed to deafness in covert attention orienting. Our findings also have implications for designers of real and virtual environments for deaf people and reveal that experiments on deaf visual abilities must not ignore the prominent reflexive eye-movement orienting in this sensory-deprived population.

Technology-specific error signatures in the 1000 Genomes Project data

Next-generation sequencing (NGS) will likely facilitate a better understanding of the causes and consequences of human genetic variability. In this context, the validity of NGS-inferred single-nucleotide variants (SNVs) is of paramount importance. We therefore developed a statistical framework to assess the fidelity of three common NGS platforms. Using aligned DNA sequence data from two completely sequenced HapMap samples as included in the 1000 Genomes Project, we unraveled remarkably different error profiles for the three platforms. Compared to confirmed HapMap variants, newly identified SNVs included a substantial proportion of false positives (3-17%). Consensus calling by more than one platform yielded significantly lower error rates (1-4%). This implies that the use of multiple NGS platforms may be more cost-efficient than relying upon a single technology alone, particularly in physically localized sequencing experiments that rely upon small error rates. Our study thus highlights that different NGS platforms suit different practical applications differently well, and that NGS-based studies require stringent data quality control for their results to be valid.

Point-and-Click Cursor Control With an Intracortical Neural Interface System by Humans With Tetraplegia

We present a point-and-click intracortical neural interface system (NIS) that enables humans with tetraplegia to volitionally move a 2-D computer cursor in any desired direction on a computer screen, hold it still, and click on the area of interest. This direct brain-computer interface extracts both discrete (click) and continuous (cursor velocity) signals from a single small population of neurons in human motor cortex. A key component of this system is a multi-state probabilistic decoding algorithm that simultaneously decodes neural spiking activity of a small population of neurons and outputs either a click signal or the velocity of the cursor. The algorithm combines a linear classifier, which determines whether the user is intending to click or move the cursor, with a Kalman filter that translates the neural population activity into cursor velocity. We present a paradigm for training the multi-state decoding algorithm using neural activity observed during imagined actions. Two human participants with tetraplegia (paralysis of the four limbs) performed a closed-loop radial target acquisition task using the point-and-click NIS over multiple sessions. We quantified point-and-click performance using various human-computer interaction measurements for pointing devices. We found that participants could control the cursor motion and click on specified targets with a small error rate (< 3% in one participant). This study suggests that signals from a small ensemble of motor cortical neurons (∼40) can be used for natural point-and-click 2-D cursor control of a personal computer.

Application of Kinase Activity Profiles to Predict Upcoming TKI Resistance In CML-Patients.

AbstractAbstract 3425 Background:Major breakthroughs in CML research have revolutionized therapy by the introduction of a specific inhibitor of BCR-ABL activity; first Imatinib and now even more potent TKIs like Nilotinib and Dasatinib are available. Imatinib can induce complete hematologic responses in virtually all patients and complete cytogenetic responses in almost 90% of patients and progression free survival in 84% of patients. Patients that reach a more than 1000-fold reduction of the BCR-ABL transcript level (major molecular response) have durable responses with not a single event of progression. Unfortunately, not all patients attain such a favourable response. Primary and secondary resistance develops in around 20% of cases.Predicting the development this resistance would be of great clinical value. Aim:To establish a classifier based on peptide phosphorylation patterns as fingerprints using samples of peripheral blood cells or bone marrow of CML patients undergoing Imatinib treatment in order to enable prediction of emerging resistance against Imatinib. Methods:Stored (“snapfrozen”) mononuclear cell fractions, isolated by Ficoll-Paque centrifugation of bone marrow or peripheral blood of CML patients under continuous Imatinib treatment and in various stages of their disease were lysed in M-PER buffer supplemented with phosphatase and protease inhibitors. Kinase activity profiles of these lysates were generated with standard Tyrosine Kinase PamChip® Arrays that contain 144 peptides as kinase substrates on their porous surface. Peptide phosphorylation was followed through binding of a fluorescently labelled anti-phosphotyrosine antibody during flow-through cycling of the lysates. Activity profiles were generated with PamGene's BioNavigator software. The resulting data were analysed using the R-based package CMA (“Classification for MicroArrays”) that enables the survey and evaluation of most usual classification methods with double cross-validation procedures. Results:A distinct classifier for Imatinib response prediction could be derived for the bone marrow samples, collected at various intervals after diagnosis (18 Imatinib sensitive versus 19 Imatinib resistant patients, of which the samples were collected 3 months to 1.5 year before resistance emergence). Of the twenty-one classification methods that were studied the SupportVectorMachine (SVM) algorithm resulted in the smallest error rate: this was 16% with a standard error of 0.062 and a sensitivity of 84% and specificity of 82% respectively, meaning 3 misclassified sensitive patients and 3 misclassified resistant patients. This classifier has to be validated in a blinded, independent test set. Conclusions:We demonstrated that with this method it is possible to predict Imatinib resistance. Differences in phosphorylation patterns as detected using PamGene's peptide microarray technology with the aid of multivariate statistical analysis suggest the presence of an ongoing process in CML-patients destined in due time to a relapse in spite of continuous Imatinib treatment. We regard these initial class prediction results to be a basis for further development of this kinase activity based test for Imatinib response prediction in CML patients already at the time of diagnosis.Perspectives: Our approach of using kinase activity profiling for the prediction of Imatinib resistance, could equally well be applied to predict response to various other kinase inhibitors. These response predictions could be combined in the same test by adding these inhibitors in vitro and might direct the optimal drug selection at the time point of diagnosis. Disclosures:Boender:PamGene International BV: Employment. Ruijtenbeek:PamGene International BV: Employment. van den Berg:PamGene Inetrnational BV: Employment. de Wijn:PamGene International BV: Employment.

Ferret: An RFID-enabled pervasive multimedia application

This paper presents the design and implementation of Ferret, a system for locating nomadic augmented with RFID tags and visually displaying them to a user in real-time. We present a novel algorithm to infer location of tagged objects using the location of a camera and reader that observes them. We also present techniques to refine location estimates using multiple observations and a method to display and update object locations on a video camera screen. An experimental evaluation of the Ferret prototype shows that (i) Ferret can refine object locations to only 1% of the reader’s coverage region in less than 2 min with small error rate (2.22%); (ii) Ferret can detect nomadic objects with 100% accuracy when the moving distances exceed 20 cm; and (iii) Ferret is robust against different movement patterns of user’s mobility.

King Tutankhamun’s Family and Demise

To the Editor: In their study, Dr Hawass and colleagues reported ancient DNA data from 11 royal Egyptian mummies and used microsatellites to ascertain kinship among specimens. We question the reliability of the genetic data presented in this study and therefore the validity of the authors’ conclusions. Furthermore, we urge a more critical assessment of the ancient DNA data in the context of DNA degradation and contamination. The long-term survival of DNA is determined by the environmental history of the samples, and Gilbert et al argued in reference to mitochondrial DNA that “in most, if not all, ancient Egyptian remains, [ancient DNA] does not survive to a level that is currently retrievable.” The age of the mummies (more than 3300 years before the present) coupled with their preservation history suggests that DNA survival is highly unlikely. Longterm survival of nuclear DNA sequences, as accessed by Hawass et al, is even less likely than mitochondrial DNA, given lower copy numbers per cell. Success in the retrieval of putative nuclear DNA sequences is also surprising given the use of traditional polymerase chain reaction techniques rather than newly developed capture approaches coupled with second-generation sequencing that allow for successful capture of degraded (shorter) DNA sequences. Contamination is a major obstacle in human ancient DNA research. Although laboratory members involved in the study were genotyped, no persons handling the specimens prior to the study were included, raising a question of the reliability of the microsatellite profiles. Precautions such as genotyping of associated nonhuman remains and including information on the microsatellite allele frequencies in presentday Egypt would have clarified the issue of modern contamination. Another cause for concern is the lack of reported quality control measures in the genotyping of microsatellites. Potential genotyping errors include allelic stutters, allelic dropout, short allele dominance, and null alleles, all of which can result in the incorrect identification of alleles. Even small error rates (0.01 per allele) can lead to high error rates in downstream applications, such as false paternity exclusion in kinship testing.