Query: Extended object tracking using IMM approach for a real-world vehicle sensor fusion system

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Modeling and Tracking the Driving Environment With a Particle-Based Occupancy Grid <=SEP=> Modeling and tracking the driving environment is a complex problem due to the heterogeneous nature of the real world. In many situations, modeling the obstacles and the driving surfaces can be achieved by the use of geometrical objects, and tracking becomes the problem of estimating the parameters of these objects. In the more complex cases, the scene can be modeled and tracked as an occupancy grid. This paper presents a novel occupancy grid tracking solution based on particles for tracking the dynamic driving environment. The particles will have a dual nature-they will denote hypotheses, as in the particle filtering algorithms, but they will also be the building blocks of our modeled world. The particles have position and speed, and they can migrate in the grid from cell to cell, depending on their motion model and motion parameters, but they will be also created and destroyed using a weighting-resampling mechanism that is specific to particle filtering algorithms. The tracking algorithm will be centered on particles, instead of cells. An obstacle grid derived from processing a stereovision-generated elevation map is used as measurement information, and the measurement model takes into account the uncertainties of the stereo reconstruction. The resulting system is a flexible real-time tracking solution for dynamic unstructured driving environments.

A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise <=SEP=> Spatial Databases Require to detect knowledge from great amount of data Need to handle with arbitrary shape Requirements of Clustering in Data Mining • Scalability • Dealing with different types of attributes • Discovery of Clusters with arbitrary shape • Minimal requirements for domain knowledge to determine input parameters • Able to deal with noise and outliers • Insensitive to the order of input data • High dimensionality of data • Interpretability and usability Introduction(cont..)

Bayesian Occupancy Filtering for Multitarget Tracking: An Automotive Application <=SEP=> Reliable and efficient perception and reasoning in dynamic and densely cluttered environments are still major challenges for driver assistance systems. Most of today’s systems use target tracking algorithms based on object models. They work quite well in simple environments such as freeways, where few potential obstacles have to be considered. However, these approaches usually fail in more complex environments featuring a large variety of potential obstacles, as is usually the case in urban driving situations. In this paper, we propose a new approach for robust perception and risk assessment in highly dynamic environments. This approach is called Bayesian occupancy filtering; it basically combines a four-dimensional occupancy grid representation of the obstacle state space with Bayesian filtering techniques. KEY WORDS—multitarget tracking, Bayesian state estimation, occupancy grid

Object tracking with de-autocorrelation scheme for a dynamic occupancy gridmap system <=SEP=> Autonomous driving poses unique challenges for vehicle environment perception in complex driving environments. Due to the uncertain nature of the vehicle environment and imperfection of any perception framework, multiple stages of estimation might be necessary to achieve the desired performance. However, it is highly possible that the estimation of one stage might result in output estimates with significant auto/cross-correlation, which would pass to another stage. In such situations, a decorrelation procedure is required. We present an object tracking approach taking into consideration the auto-correlation (of the velocity components) introduced by an upfront dynamic occupancy gridmap system. More specifically, we use a linear state-space system to approximately “reconstruct” the nonlinear estimation/mapping procedure for the purpose of auto-correlation quantification. We focus on demonstrating an estimation improvement of the proposed decorrelation tracker over a direct Kalman filter (i.e., KF that ignores the auto-correlation). For this we recorded several scenarios of different target motion behaviors. It is shown that the decorrelation tracker indeed introduces noticable estimation improvement in particularly velocity space if the target moves in a relatively smooth manner.

Fusion of laser and radar sensor data with a sequential Monte Carlo Bayesian occupancy filter <=SEP=> Occupancy grid mapping is a well-known environment perception approach. A grid map divides the environment into cells and estimates the occupancy probability of each cell based on sensor measurements. An important extension is the Bayesian occupancy filter (BOF), which additionally estimates the dynamic state of grid cells and allows modeling changing environments. In recent years, the BOF attracted more and more attention, especially sequential Monte Carlo implementations (SMC-BOF), requiring less computational costs. An advantage compared to classical object tracking approaches is the object-free representation of arbitrarily shaped obstacles and free-space areas. Unfortunately, publications about BOF based on laser measurements report that grid cells representing big, contiguous, stationary obstacles are often mistaken as moving with the velocity of the ego vehicle (ghost movements). This paper presents a method to fuse laser and radar measurement data with the SMC-BOF. It shows that the doppler information of radar measurements significantly improves the dynamic estimation of the grid map, reduces ghost movements, and in general leads to a faster convergence of the dynamic estimation.

Characterizing Online Discussion Using Coarse Discourse Sequences <=SEP=> In this work, we present a novel method for classifying comments in online discussions into a set of coarse discourse acts towards the goal of better understanding discussions at scale. To facilitate this study, we devise a categorization of coarse discourse acts designed to encompass general online discussion and allow for easy annotation by crowd workers. We collect and release a corpus of over 9,000 threads comprising over 100,000 comments manually annotated via paid crowdsourcing with discourse acts and randomly sampled from the site Reddit. Using our corpus, we demonstrate how the analysis of discourse acts can characterize different types of discussions, including discourse sequences such as Q&A pairs and chains of disagreement, as well as different communities. Finally, we conduct experiments to predict discourse acts using our corpus, finding that structured prediction models such as conditional random fields can achieve an F1 score of 75%. We also demonstrate how the broadening of discourse acts from simply question and answer to a richer set of categories can improve the recall performance of Q&A extraction.

Audience Involvement in Advertising : Four Levels <=SEP=> The effectiveness of advertising messages is widely believed to be moderated by audience involvement. In this paper, psychological theories of attention and levels of processing are used to establish a framework that can accommodate the major consumer behavior theories of audience involvement. Four levels of involvement are identified (in order from low to high) as preattention, focal attention, comprehension, and elaboration. These levels allocate increasing attentional capacity to a message source, as needed for analysis of the message by increasingly abstract-and qualitatively distinct-representational systems. Lower levels use relatively little capacity and extract information needed to determine whether higher levels will be invoked. The higher levels require greater capacity and result in increasingly durable cognitive and attitudinal effects.

(In-)Secure messaging with the Silent Circle instant messaging protocol <=SEP=> Silent Text, the instant messaging application by the company Silent Circle, provides its users with end-to-end encrypted communication on the Blackphone and other smartphones. The underlying protocol, SCimp, has received many extensions during the update to version 2, but has not been subjected to critical review from the cryptographic community. In this paper, we analyze both the design and implementation of SCimp by inspection of the documentation (to the extent it exists) and code. Many of the security properties of SCimp version 1 are found to be secure, however many of the extensions contain vulnerabilities and the implementation contains bugs that affect the overall security. These problems were fed back to the SCimp maintainers and some bugs were fixed in the code base. In September 2015, Silent Circle replaced SCimp with a new protocol based on the Signal Protocol.

The sil Locus in Streptococcus Anginosus Group: Interspecies Competition and a Hotspot of Genetic Diversity <=SEP=> The Streptococcus Invasion Locus (Sil) was first described in Streptococcus pyogenes and Streptococcus pneumoniae, where it has been implicated in virulence. The two-component peptide signaling system consists of the SilA response regulator and SilB histidine kinase along with the SilCR signaling peptide and SilD/E export/processing proteins. The presence of an associated bacteriocin region suggests this system may play a role in competitive interactions with other microbes. Comparative analysis of 42 Streptococcus Anginosus/Milleri Group (SAG) genomes reveals this to be a hot spot for genomic variability. A cluster of bacteriocin/immunity genes is found adjacent to the sil system in most SAG isolates (typically 6-10 per strain). In addition, there were two distinct SilCR peptides identified in this group, denoted here as SilCRSAG-A and SilCRSAG-B, with corresponding alleles in silB. Our analysis of the 42 sil loci showed that SilCRSAG-A is only found in Streptococcus intermedius while all three species can carry SilCRSAG-B. In S. intermedius B196, a putative SilA operator is located upstream of bacteriocin gene clusters, implicating the sil system in regulation of microbe-microbe interactions at mucosal surfaces where the group resides. We demonstrate that S. intermedius B196 responds to its cognate SilCRSAG-A, and, less effectively, to SilCRSAG-B released by other Anginosus group members, to produce putative bacteriocins and inhibit the growth of a sensitive strain of S. constellatus.

Chimera: Collaborative Preemption for Multitasking on a Shared GPU <=SEP=> The demand for multitasking on graphics processing units (GPUs) is constantly increasing as they have become one of the default components on modern computer systems along with traditional processors (CPUs). Preemptive multitasking on CPUs has been primarily supported through context switching. However, the same preemption strategy incurs substantial overhead due to the large context in GPUs. The overhead comes in two dimensions: a preempting kernel suffers from a long preemption latency, and the system throughput is wasted during the switch. Without precise control over the large preemption overhead, multitasking on GPUs has little use for applications with strict latency requirements.
 In this paper, we propose Chimera, a collaborative preemption approach that can precisely control the overhead for multitasking on GPUs. Chimera first introduces streaming multiprocessor (SM) flushing, which can instantly preempt an SM by detecting and exploiting idempotent execution. Chimera utilizes flushing collaboratively with two previously proposed preemption techniques for GPUs, namely context switching and draining to minimize throughput overhead while achieving a required preemption latency. Evaluations show that Chimera violates the deadline for only 0.2% of preemption requests when a 15us preemption latency constraint is used. For multi-programmed workloads, Chimera can improve the average normalized turnaround time by 5.5x, and system throughput by 12.2%.

Striatal Plasticity and Basal Ganglia Circuit Function <=SEP=> The dorsal striatum, which consists of the caudate and putamen, is the gateway to the basal ganglia. It receives convergent excitatory afferents from cortex and thalamus and forms the origin of the direct and indirect pathways, which are distinct basal ganglia circuits involved in motor control. It is also a major site of activity-dependent synaptic plasticity. Striatal plasticity alters the transfer of information throughout basal ganglia circuits and may represent a key neural substrate for adaptive motor control and procedural memory. Here, we review current understanding of synaptic plasticity in the striatum and its role in the physiology and pathophysiology of basal ganglia function.

All-solid-state dye-sensitized solar cells with high efficiency <=SEP=> Dye-sensitized solar cells based on titanium dioxide (TiO2) are promising low-cost alternatives to conventional solid-state photovoltaic devices based on materials such as Si, CdTe and CuIn1−xGaxSe2 (refs 1, 2). Despite offering relatively high conversion efficiencies for solar energy, typical dye-sensitized solar cells suffer from durability problems that result from their use of organic liquid electrolytes containing the iodide/tri-iodide redox couple, which causes serious problems such as electrode corrosion and electrolyte leakage. Replacements for iodine-based liquid electrolytes have been extensively studied, but the efficiencies of the resulting devices remain low. Here we show that the solution-processable p-type direct bandgap semiconductor CsSnI3 can be used for hole conduction in lieu of a liquid electrolyte. The resulting solid-state dye-sensitized solar cells consist of CsSnI2.95F0.05 doped with SnF2, nanoporous TiO2 and the dye N719, and show conversion efficiencies of up to 10.2 per cent (8.51 per cent with a mask). With a bandgap of 1.3 electronvolts, CsSnI3 enhances visible light absorption on the red side of the spectrum to outperform the typical dye-sensitized solar cells in this spectral region.

Process Mining Based on Regions of Languages <=SEP=> In this paper we give an overview, how to apply region based me thods for the synthesis of Petri nets from languages to process min ing. The research domain of process mining aims at constructing a process model from an event log, such that the process model can reproduce t he log, and does not allow for much more behaviour than shown in the log. We her e consider Petri nets to represent process models. Event logs can be int erpreted as finite languages. Region based synthesis methods can be used to constr u t a Petri net from a language generating the minimal net behaviour including t he given language. Therefore, it seems natural to apply such methods in the proc ess mining domain. There are several different region based methods in literat ure yielding different Petri nets. We adapt these methods to the process mining doma in and compare them concerning efficiency and usefulness of the resulting P etri net.

Enterprise architecture modelling--the issue of integration <=SEP=> The problem of aligning and integrating business and IT is hampering many companies in their strategic and tactical development. Constructing integrated architecture models contributes to tackling this problem. Unfortunately, no enterprise architecture description language currently exists that fully enables integrated enterprise modelling. A variety of architectural domains are commonly distinguished for which architects use their own modelling techniques and concepts, tool support, visualisation techniques, etc. In this paper we outline such an integrated language and describe concepts that relate architectural domains. This language also serves as a bridge between other existing modelling languages. Furthermore, we present the design of a workbench for enterprise architecture that serves as a modelling tool and a tool integration environment at the same time: it supports both the integration of models in existing modelling languages and the integration of existing modelling tools. q 2005 Elsevier Ltd. All rights reserved.

The IHS Transformations Based Image Fusion <=SEP=> The IHS sharpening technique is one of the most commonly used techniques for sharpening. Different transformations have been developed to transfer a color image from the RGB space to the IHS space. Through literature, it appears that, various scientists proposed alternative IHS transformations and many papers have reported good results whereas others show bad ones as will as not those obtained which the formula of IHS transformation were used. In addition to that, many papers show different formulas of transformation matrix such as IHS transformation. This leads to confusion what is the exact formula of the IHS transformation?. Therefore, the main purpose of this work is to explore different IHS transformation techniques and experiment it as IHS based image fusion. The image fusion performance was evaluated, in this study, using various methods to estimate the quality and degree of information improvement of a fused image quantitatively.

MetaLDA: A Topic Model that Efficiently Incorporates Meta Information <=SEP=> Besides the text content, documents and their associated words usually come with rich sets of meta information, such as categories of documents and semantic/syntactic features of words, like those encoded in word embeddings. Incorporating such meta information directly into the generative process of topic models can improve modelling accuracy and topic quality, especially in the case where the word-occurrence information in the training data is insufficient. In this paper, we present a topic model, called MetaLDA, which is able to leverage either document or word meta information, or both of them jointly. With two data argumentation techniques, we can derive an efficient Gibbs sampling algorithm, which benefits from the fully local conjugacy of the model. Moreover, the algorithm is favoured by the sparsity of the meta information. Extensive experiments on several real world datasets demonstrate that our model achieves comparable or improved performance in terms of both perplexity and topic quality, particularly in handling sparse texts. In addition, compared with other models using meta information, our model runs significantly faster.

A Novel Approach of an Absolute Encoder Coding Pattern <=SEP=> This paper presents a novel approach of an absolute rotary and linear optical encoder codding patterns. The concept is based on the analysis of 2-D images to find a unique sequence that allows to unambiguously determine an angular shaft position. The adopted coding method allows to achieve a very high data density for specified number of tracks on the code pattern disc. Encoders based on the proposed solution enable the production from readily available and inexpensive components. Nevertheless, encoders retain high measuring accuracy and high dependability obtained in the classical approach. The optical device and a design of processing system is proposed. Finally, the feasibility of the pattern coding method is further proved with encoder prototype.

Intrinsically Motivated Reinforcement Learning: A Promising Framework for Developmental Robot Learning <=SEP=> One of the primary challenges of developmental robotics is the question of how to learn and represent increasingly complex behavior in a self-motivated, open-ended way. Barto, Singh, and Chentanez (Barto, Singh, & Chentanez 2004; Singh, Barto, & Chentanez 2004) have recently presented an algorithm for intrinsically motivated reinforcement learning that strives to achieve broad competence in an environment in a tasknonspecific manner by incorporating internal reward to build a hierarchical collection of skills. This paper suggests that with its emphasis on task-general, self-motivated, and hierarchical learning, intrinsically motivated reinforcement learning is an obvious choice for organizing behavior in developmental robotics. We present additional preliminary results from a gridworld abstraction of a robot environment and advocate a lay-ion of a robot environment and advocate a layered learning architecture for applying the algorithm on a physically embodied system.

Searching for build debt: Experiences managing technical debt at Google <=SEP=> With a large and rapidly changing codebase, Google software engineers are constantly paying interest on various forms of technical debt. Google engineers also make efforts to pay down that debt, whether through special Fixit days, or via dedicated teams, variously known as janitors, cultivators, or demolition experts. We describe several related efforts to measure and pay down technical debt found in Google's BUILD files and associated dead code. We address debt found in dependency specifications, unbuildable targets, and unnecessary command line flags. These efforts often expose other forms of technical debt that must first be managed.

Soft contact lens biosensor for in situ monitoring of tear glucose as non-invasive blood sugar assessment. <=SEP=> A contact lens (CL) biosensor for in situ monitoring of tear glucose was fabricated and tested. Biocompatible 2-methacryloyloxyethyl phosphorylcholine (MPC) polymer and polydimethyl siloxane (PDMS) were employed as the biosensor material. The biosensor consists of a flexible Pt working electrode and a Ag/AgCl reference/counter electrode, which were formed by micro-electro-mechanical systems (MEMS) technique. The electrode at the sensing region was modified with glucose oxidase (GOD). The CL biosensor showed a good relationship between the output current and glucose concentration in a range of 0.03-5.0mM, with a correlation coefficient of 0.999. The calibration range covered the reported tear glucose concentrations in normal and diabetic patients. Also, the CL biosensor was applied to a rabbit for the purpose of tear glucose monitoring. The basal tear glucose was estimated to 0.11 mM. Also, the change of tear glucose induced by the change of blood sugar level was assessed by the oral glucose tolerance test. As a result, tear glucose level increased with a delay of 10 min from blood sugar level. The result showed that the CL biosensor is expected to provide further detailed information about the relationship between dynamics of blood glucose and tear glucose.

Fourier Transform Decoding of Non-Binary LDPC Codes <=SEP=> Binary low-density parity-check (LDPC) codes have recently emerged as a promising alternative to turbo codes and have been shown to achieve excellent coding gains in AWGN channels. Non-binary LDPC codes have demonstrated improved performance over binary LDPC codes but their decoding is computationally intensive. Fourier transform decoding can reduce the decoding complexity but has a number of problems which can be avoided by implementing this algorithm in the log domain. In this paper Fourier transform decoding of non-binary LDPC codes in the log domain is described. The computational complexity and performance of this algorithm is then discussed. Differential evolution is then used to search for optimal binary and non-binary LDPC codes and it is shown by simulation that non-binary LDPC codes are superior.

Selective separation of virgin and post-consumer polymers (PET and PVC) by flotation method. <=SEP=> More and more polymer wastes are generated by industry and householders today. Recycling is an important process to reduce the amount of waste resulting from human activities. Currently, recycling technologies use relatively homogeneous polymers because hand-sorting waste is costly. Many promising technologies are being investigated for separating mixed thermoplastics, but they are still uneconomical and unreliable. At present, most waste polymers cause serious environmental problems. Burning polymers for recycling is not practiced since poisonous gases are released during the burning process. Particularly, polyvinyl chloride (PVC) materials among waste polymers generate hazardous HCl gas, dioxins containing Cl, etc., which lead to air pollution and shorten the life of the incinerator. In addition, they make other polymers difficult to recycle. Both polyethylene terephthalate (PET) and PVC have densities of 1.30-1.35g /cm(3) and cannot be separated using conventional gravity separation techniques. For this reason, polymer recycling needs new techniques. Among these techniques, froth flotation, which is also used in mineral processing, can be useful because of its low cost and simplicity. The main objective of this research is to recycle PET and PVC selectively from post-consumer polymer wastes and virgin polymers by using froth flotation. According to the results, all PVC particles were floated with 98.8% efficiency in virgin polymer separation while PET particles were obtained with 99.7% purity and 57.0% efficiency in post-consumer polymer separation.

Designing a robotic assistant for healthcare applications <=SEP=> The population of the world is ageing rapidly. By 2050, the population aged 85 and over will be three times more than it is now. This phenomenon has caused several issues in the current health service system, especially workforce shortages in the health sector and a lack of space in aged care facilities (ACFs). In the face of these issues, home-based and community-based healthcare services have been identified as necessary in many developed countries to promote ageing-inplace and independent living in order to: 1. Lower the demands on health services and hence improve the quality of the services delivered, and 2. Maintain the quality of life of the older population by enabling them to be close to their families. For the last decade, a rising interest in personal robots as part of the technical solution in decentralised health services has led to an extensive range of research and implementations of health service and personal assistant robots. This paper describes a new research project to develop an assistant robot capable of interacting with patients, taking vital signs measurements and recording the data in healthcare environments such as aged care facilities, hospitals or personal homes. Current progress includes a comprehensive literature survey on recent health service robots with a list of issues in the area and an initial human-robot interaction study. The robot is currently interfaced with a blood pressure monitor and has a 3D face which is capable of displaying a range of different emotions with lips synchronized to speech.

Independent Factor Reinforcement Learning for Portfolio Management <=SEP=> In this paper we propose to do portfolio management using reinforcement learning (RL) and independent factor model. Factors in independent factor model are mutually independent and exhibit better predictability. RL is applied to each factor to capture temporal dependence and provide investment suggestion on factor. Optimal weights on factors are found by portfolio optimization method subject to the investment suggestions and general portfolio constraints. Experimental results and analysis are given to show that the proposed method has better performance when compare to two alternative portfolio management systems.

A Proposal for Statistical Outlier Detection in Relational Structures <=SEP=> This paper extends unsupervised statistical outlier detection to the case of relational data. For nonrelational data, where each individual is characterized by a feature vector, a common approach starts with learning a generative statistical model for the population. The model assigns a likelihood measure for the feature vector that characterizes the individual; the lower the feature vector likelihood, the more anomalous the individual. A difference between relational and nonrelational data is that an individual is characterized not only by a list of attributes, but also by its links and by attributes of the individuals linked to it. We refer to a relational structure that specifies this information for a specific individual as the individual’s database. Our proposal is to use the likelihood assigned by a generative model to the individual’s database as the anomaly score for the individual; the lower the model likelihood, the more anomalous the individual. As a novel validation method, we compare the model likelihood with metrics of individual success. An empirical evaluation reveals a surprising finding in soccer and movie data: We observe in the data a strong correlation between the likelihood and success metrics.

Detecting unexpected obstacles for self-driving cars: Fusing deep learning and geometric modeling <=SEP=> The detection of small road hazards, such as lost cargo, is a vital capability for self-driving cars. We tackle this challenging and rarely addressed problem with a vision system that leverages appearance, contextual as well as geometric cues. To utilize the appearance and contextual cues, we propose a new deep learning-based obstacle detection framework. Here a variant of a fully convolutional network is proposed to predict a pixel-wise semantic labeling of (i) free-space, (ii) on-road unexpected obstacles, and (iii) background. The geometric cues are exploited using a state-of-the-art detection approach that predicts obstacles from stereo input images via model-based statistical hypothesis tests. We present a principled Bayesian framework to fuse the semantic and stereo-based detection results. The mid-level Stixel representation is used to describe obstacles in a flexible, compact and robust manner. We evaluate our new obstacle detection system on the Lost and Found dataset, which includes very challenging scenes with obstacles of only 5 cm height. Overall, we report a major improvement over the state-of-the-art, with a performance gain of 27.4%. In particular, we achieve a detection rate of over 90% for distances of up to 50 m. Our system operates at 22 Hz on our self-driving platform.

Differentially Private Online Learning for Cloud-Based Video Recommendation With Multimedia Big Data in Social Networks <=SEP=> With the rapid growth in multimedia services and the enormous offers of video content in online social networks, users have difficulty in obtaining their interests. Therefore, various personalized recommendation systems have been proposed. However, they ignore that the accelerated proliferation of social media data has led to the big data era, which has greatly impeded the process of video recommendation. In addition, none of them has considered both the privacy of users' contexts (e.g., social status, ages, and hobbies) and video service vendors' repositories, which are extremely sensitive and of significant commercial value. To handle these problems, we propose a cloud-assisted differentially private video recommendation system based on distributed online learning. In our framework, service vendors are modeled as distributed cooperative learners, recommending videos according to user's context, while simultaneously adapting the video-selection strategy based on user-click feedback to maximize total user clicks (reward). Considering the sparsity and heterogeneity of big social media data, we also propose a novel geometric differentially private model, which can greatly reduce the performance loss. Our simulation shows the proposed algorithms outperform other existing methods and keep a delicate balance between the total reward and privacy preserving level.

Embree: a kernel framework for efficient CPU ray tracing <=SEP=> We describe Embree, an open source ray tracing framework for x86 CPUs. Embree is explicitly designed to achieve high performance in professional rendering environments in which complex geometry and incoherent ray distributions are common. Embree consists of a set of low-level kernels that maximize utilization of modern CPU architectures, and an API which enables these kernels to be used in existing renderers with minimal programmer effort. In this paper, we describe the design goals and software architecture of Embree, and show that for secondary rays in particular, the performance of Embree is competitive with (and often higher than) existing state-of-the-art methods on CPUs and GPUs.

Barycentric Lagrange Interpolation <=SEP=> Barycentric interpolation is a variant of Lagrange polynomial interpolation that is fast and stable. It deserves to be known as the standard method of polynomial interpolation.

Feature warping for robust speaker verification <=SEP=> We propose a novel feature mapping approach that is robust to channel mismatch, additive noise and to some extent, nonlinear effects attributed to handset transducers. These adverse effects can distort the short-term distribution of the speech features. Some methods have addressed this issue by conditioning the variance of the distribution, but not to the extent of conforming the speech statistics to a target distribution. The proposed target mapping method warps the distribution of a cepstral feature stream to a standardised distribution over a specified time interval. We evaluate a number of the enhancement methods for speaker verification, and compare them against a Gaussian target mapping implementation. Results indicate improvements of the warping technique over a number of methods such as Cepstral Mean Subtraction (CMS), modulation spectrum processing, and short-term windowed CMS and variance normalisation. This technique is a suitable feature post-processing method that may be combined with other techniques to enhance speaker recognition robustness under adverse conditions.