The Experts below are selected from a list of 363 Experts worldwide ranked by ideXlab platform
R. Raheli - One of the best experts on this subject based on the ideXlab platform.
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On trellis-based truncated-Memory Detection
IEEE Transactions on Communications, 2005Co-Authors: G. Ferrari, G. Colavolpe, R. RaheliAbstract:We propose a general framework for trellis-based Detection over channels with infinite Memory. A general truncation assumption enables the definition of a trellis diagram, which takes into account a considered portion of the channel Memory and possible coding Memory at the transmitter side. It is shown that trellis-based maximum a posteriori (MAP) symbol Detection algorithms, in the form of forward-backward (FB) algorithms, can be derived on the basis of this Memory-truncation assumption. A general approach to the design of truncated-Memory (TM) FB algorithms is proposed, and two main classes of algorithms, characterized by coupled and decoupled recursions, respectively, are presented. The complexity of the derived TM-FB algorithms is analyzed in detail. Moreover, it is shown that MAP sequence Detection algorithms, based on the Viterbi algorithm, follow easily from one of the proposed classes. Looking backward at this duality between MAP symbol Detection algorithms and MAP sequence Detection algorithms, it is shown that previous solutions for one case can be systematically extended to the other case. The generality of the proposed framework is shown by considering various examples of stochastic channels. New Detection algorithms, as well as generalizations of solutions previously published in the literature, are embedded in the proposed framework. The obtained results do suggest that the performance of the proposed Detection algorithms ultimately depends on the truncation depth, almost regardless of the specific Detection strategy.
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Adaptive iterative Detection for the phase-uncertain channel: limited-tree-search versus truncated-Memory Detection
IEEE Transactions on Vehicular Technology, 2004Co-Authors: G. Ferrari, G. Colavolpe, A. Anastasopoulos, R. RaheliAbstract:In this paper, we consider iterative Detection over bandpass channels that introduce an unknown phase rotation in the transmitted signal. This work focuses on the comparison between two adaptive Detection strategies for trellis-based coded modulation: limited-tree-search (LTS) Detection, obtained by reducing a tree search to a limited trellis search, and truncated-Memory (TM) Detection, based on channel-Memory truncation, which automatically leads to a trellis search. Both strategies are used to derive trellis-based forward-backward (FB) algorithms. A quantitative analysis based on simulations, with various coding and modulation schemes, is carried out to evaluate and compare the two approaches. The results show that the channel-phase dynamics should significantly influence the choice of the Detection strategy: For low-phase variations, LTS algorithms are a simple and reasonable choice, while for moderate to fast phase variations, TM algorithms show a considerable robustness.
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On trellis-based truncated-Memory Detection
GLOBECOM '03. IEEE Global Telecommunications Conference (IEEE Cat. No.03CH37489), 2003Co-Authors: G. Ferrari, G. Colavolpe, R. RaheliAbstract:We propose a general framework for Detection over channels with infinite Memory. A general truncation assumption leads automatically to the definition of a trellis diagram. A general approach to the design of forward-backward (FB) algorithms is proposed and two main classes of FB algorithms (with coupled and decoupled recursions, respectively) are presented. Moreover, it is shown that sequence Detection algorithms, in the form of a Viterbi algorithm (VA), follow easily from one of the proposed classes. The generality of the proposed framework is shown by applying it to a few stochastic channels. The performance of the proposed algorithms seems to depend ultimately on the truncation length, almost irrespective of the specific Detection strategy.
Bruno Verschuere - One of the best experts on this subject based on the ideXlab platform.
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Distinguishing true from false confessions using physiological patterns of concealed information recognition - A proof of concept study.
Biological Psychology, 2020Co-Authors: Linda Marjoleine Geven, Gershon Ben-shakhar, Saul M. Kassin, Bruno VerschuereAbstract:Abstract Wrongful conviction cases indicate that not all confessors are guilty. However, there is currently no validated method to assess the veracity of confessions. In this preregistered study, we evaluate whether a new application of the Concealed Information Test (CIT) is a potentially valid method to make a distinction between true and false admissions of guilt. Eighty-three participants completed problem-solving tasks, individually and in pairs. Unbeknownst to the participants, their team-member was a confederate, tempting the participant to break the experimental rules by assisting during an individual assignment. Irrespective of actual rule-breaking behavior, all participants were accused of cheating and interrogated. True confessors but not false confessors showed recognition of answers obtained by cheating in the individual task, as evidenced by larger physiological responses to the correct than to plausible but incorrect answers. These findings encourage further investigation on the use of Memory Detection to discriminate true from false confessions.
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Distinguishing True From False Confessions Using Physiological Patterns of Concealed Information Recognition – A Proof of Concept Study
2020Co-Authors: Linda Marjoleine Geven, Gershon Ben-shakhar, Saul M. Kassin, Bruno VerschuereAbstract:Wrongful conviction cases indicate that not all confessors are guilty. However, there is currently no validated method to assess the veracity of confessions. In this preregistered study, we evaluate whether a new application of the Concealed Information Test (CIT) is a potentially valid method to make a distinction between true and false admissions of guilt. Eighty-three participants completed problem-solving tasks, individually and in pairs. Unbeknownst to the participants, their team-member was a confederate, tempting the participant to break the experimental rules by assisting during an individual assignment. Irrespective of actual rule-breaking behavior, all participants were accused of cheating and interrogated. True confessors but not false confessors showed recognition of answers obtained by cheating in the individual task, as evidenced by larger physiological responses to the correct than to plausible but incorrect answers. These findings encourage further investigation on the use of Memory Detection to discriminate true from false confessions.
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Memory Detection: Past, Present, and Future
The Palgrave Handbook of Deceptive Communication, 2019Co-Authors: Linda Marjoleine Geven, Gershon Ben-shakhar, Merel Kindt, Bruno VerschuereAbstract:The Concealed Information Test (CIT) aims to detect the recognition of concealed knowledge in an interviewee by presenting a series of multiple-choice questions while measuring several psychophysiological (e.g., skin conductance) or behavioral (i.e., reaction times [RTs]) responses. When a suspect consistently shows distinct responses to the critical (e.g., crime-related) items compared to the neutral control items, knowledge is inferred. This chapter provides an overview of Memory Detection using various response measures, including research findings and the underlying mechanisms. Although available data confirm the validity of the CIT, there is quite a gap between these laboratory studies and realistic criminal investigations. Possible ways to tackle challenges that lie ahead, including field validity, leakage of critical information to innocent suspects, and discovering intentions, are discussed.
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It's a match!? Appropriate item selection in the Concealed Information Test.
Cognitive Research: Principles and Implications, 2019Co-Authors: Linda Marjoleine Geven, Gershon Ben-shakhar, Merel Kindt, Bruno VerschuereAbstract:While the Concealed Information Test (CIT) can determine whether examinees recognize critical details, it does not clarify the origin of the Memory. Hence, when unknowledgeable suspects are contaminated with crime information through media channels or investigative interviews, the validity of the CIT can be compromised (i.e. false-positive outcomes). Yet, when the information was disclosed solely at the category level (e.g. the perpetrator escaped in a car), presenting specific items at the exemplar level (e.g. Citroen, Opel, or Volkswagen) might preclude this problem. However, diminished recollection for exemplar-level details could attenuate the CIT effect for knowledgeable suspects, thereby leading to false negatives. The appropriate item level for Memory Detection to reach an optimal balance between sensitivity and specificity remains elusive. As encoding, retention, and retrieval of information may influence Memory performance and thereby Memory Detection, the current study investigated the validity of the CIT on both categorical and exemplar levels. Participants planned a mock robbery (n = 165), with information encoded at the category (e.g. car) or exemplar (e.g. Citroen) level. They were tested immediately or after a one-week-delay, with a response time-based CIT consisting of questions at the categorical or exemplar level. An interaction was found between encoding and testing, such that CIT validity based on reaction time was higher for “matching” (e.g. exemplar-exemplar) than for “mismatching” (e.g. exemplar-categorical) items, while immediate versus one week delayed testing did not affect the outcome. Critically, this indicates that what constitutes a good CIT item depends on the way the information was encoded. This provides a challenge for CIT examiners when selecting appropriate items.
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It’s a match!? Appropriate item selection in the Concealed Information Test
Cognitive Research: Principles and Implications, 2019Co-Authors: Linda Marjoleine Geven, Gershon Ben-shakhar, Merel Kindt, Bruno VerschuereAbstract:Background While the Concealed Information Test (CIT) can determine whether examinees recognize critical details, it does not clarify the origin of the Memory. Hence, when unknowledgeable suspects are contaminated with crime information through media channels or investigative interviews, the validity of the CIT can be compromised (i.e. false-positive outcomes). Yet, when the information was disclosed solely at the category level (e.g. the perpetrator escaped in a car), presenting specific items at the exemplar level (e.g. Citroën, Opel, or Volkswagen) might preclude this problem. However, diminished recollection for exemplar-level details could attenuate the CIT effect for knowledgeable suspects, thereby leading to false negatives. The appropriate item level for Memory Detection to reach an optimal balance between sensitivity and specificity remains elusive. As encoding, retention, and retrieval of information may influence Memory performance and thereby Memory Detection, the current study investigated the validity of the CIT on both categorical and exemplar levels. Results Participants planned a mock robbery ( n = 165), with information encoded at the category (e.g. car) or exemplar (e.g. Citroën) level. They were tested immediately or after a one-week-delay, with a response time-based CIT consisting of questions at the categorical or exemplar level. An interaction was found between encoding and testing, such that CIT validity based on reaction time was higher for “matching” (e.g. exemplar-exemplar) than for “mismatching” (e.g. exemplar-categorical) items, while immediate versus one week delayed testing did not affect the outcome. Conclusion Critically, this indicates that what constitutes a good CIT item depends on the way the information was encoded. This provides a challenge for CIT examiners when selecting appropriate items.
G. Ferrari - One of the best experts on this subject based on the ideXlab platform.
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On trellis-based truncated-Memory Detection
IEEE Transactions on Communications, 2005Co-Authors: G. Ferrari, G. Colavolpe, R. RaheliAbstract:We propose a general framework for trellis-based Detection over channels with infinite Memory. A general truncation assumption enables the definition of a trellis diagram, which takes into account a considered portion of the channel Memory and possible coding Memory at the transmitter side. It is shown that trellis-based maximum a posteriori (MAP) symbol Detection algorithms, in the form of forward-backward (FB) algorithms, can be derived on the basis of this Memory-truncation assumption. A general approach to the design of truncated-Memory (TM) FB algorithms is proposed, and two main classes of algorithms, characterized by coupled and decoupled recursions, respectively, are presented. The complexity of the derived TM-FB algorithms is analyzed in detail. Moreover, it is shown that MAP sequence Detection algorithms, based on the Viterbi algorithm, follow easily from one of the proposed classes. Looking backward at this duality between MAP symbol Detection algorithms and MAP sequence Detection algorithms, it is shown that previous solutions for one case can be systematically extended to the other case. The generality of the proposed framework is shown by considering various examples of stochastic channels. New Detection algorithms, as well as generalizations of solutions previously published in the literature, are embedded in the proposed framework. The obtained results do suggest that the performance of the proposed Detection algorithms ultimately depends on the truncation depth, almost regardless of the specific Detection strategy.
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Adaptive iterative Detection for the phase-uncertain channel: limited-tree-search versus truncated-Memory Detection
IEEE Transactions on Vehicular Technology, 2004Co-Authors: G. Ferrari, G. Colavolpe, A. Anastasopoulos, R. RaheliAbstract:In this paper, we consider iterative Detection over bandpass channels that introduce an unknown phase rotation in the transmitted signal. This work focuses on the comparison between two adaptive Detection strategies for trellis-based coded modulation: limited-tree-search (LTS) Detection, obtained by reducing a tree search to a limited trellis search, and truncated-Memory (TM) Detection, based on channel-Memory truncation, which automatically leads to a trellis search. Both strategies are used to derive trellis-based forward-backward (FB) algorithms. A quantitative analysis based on simulations, with various coding and modulation schemes, is carried out to evaluate and compare the two approaches. The results show that the channel-phase dynamics should significantly influence the choice of the Detection strategy: For low-phase variations, LTS algorithms are a simple and reasonable choice, while for moderate to fast phase variations, TM algorithms show a considerable robustness.
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On trellis-based truncated-Memory Detection
GLOBECOM '03. IEEE Global Telecommunications Conference (IEEE Cat. No.03CH37489), 2003Co-Authors: G. Ferrari, G. Colavolpe, R. RaheliAbstract:We propose a general framework for Detection over channels with infinite Memory. A general truncation assumption leads automatically to the definition of a trellis diagram. A general approach to the design of forward-backward (FB) algorithms is proposed and two main classes of FB algorithms (with coupled and decoupled recursions, respectively) are presented. Moreover, it is shown that sequence Detection algorithms, in the form of a Viterbi algorithm (VA), follow easily from one of the proposed classes. The generality of the proposed framework is shown by applying it to a few stochastic channels. The performance of the proposed algorithms seems to depend ultimately on the truncation length, almost irrespective of the specific Detection strategy.
R. Raheli - One of the best experts on this subject based on the ideXlab platform.
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On Linear Predictive Detection for Communications With Phase Noise and Frequency Offset
IEEE Transactions on Vehicular Technology, 2007Co-Authors: G. Ferrari, G. Colavolpe, R. RaheliAbstract:In this paper, by applying the concept of linear prediction, which is widely used for fading channels, to phase- uncertain communications, we generalize existing linear predictive Detection algorithms for transmission over channels with phase noise and frequency offset. This approach leads to the derivation of Detection algorithms, which are referred to as phasor linear predictive (pLP), for trellis-based maximum a posteriori (MAP) sequence Detection (based on the Viterbi algorithm) and MAP symbol Detection: trellis-based (using the forward- backward algorithm) and graph-based (using the sum-product algorithm). The effectiveness of the proposed pLP Detection algorithms is evaluated for several communication schemes. The derived algorithms outperform previously appeared finite- Memory Detection solutions in terms of robustness against fast channel dynamics. Moreover, the proposed Detection strategy lends itself to attractive extensions to adaptive schemes.
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A unified framework for finite-Memory Detection
IEEE Journal on Selected Areas in Communications, 2005Co-Authors: G. Ferrari, G. Colavolpe, R. RaheliAbstract:In this paper, we present a general approach to finite-Memory Detection. From a semi-tutorial perspective, a number of previous results are rederived and new insights are gained within a unified framework. A probabilistic derivation of the well-known Viterbi algorithm, forward-backward, and sum-product algorithms, shows that a basic metric emerges naturally under very general causality and finite-Memory conditions. This result implies that Detection solutions based on one algorithm can be systematically extended to other algorithms. For stochastic channels described by a suitable parametric model, a conditional Markov property is shown to imply this finite-Memory condition. This conditional Markov property, although seldom met exactly in practice, is shown to represent a reasonable and useful approximation in all considered cases. We consider, as examples, linear predictive and noncoherent Detection schemes. While good performance for increasing complexity can often be achieved with a finite-Memory Detection strategy, key issues in the design of Detection algorithms are the computational efficiency and the performance for limited complexity.
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ISIT - Asymptotic optimality of finite-Memory Detection
International Symposium onInformation Theory 2004. ISIT 2004. Proceedings., 2004Co-Authors: G. Ferrari, G. Colavolpe, R. RaheliAbstract:The subject of this paper is the asymptotic optimality of finite-Memory Detection for transmission over a channel characterized by a single multiplicative time-invariant stochastic parameter (e.g., block frequency nonselective fading). It is known that any finite-Memory Detection algorithm, either trellis-based or graph-based, is characterized by a single basic metric. We present a theorem which proves that this metric tends, asymptotically, to that of a receiver with perfect channel state information
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Asymptotic optimality of finite-Memory Detection
International Symposium onInformation Theory 2004. ISIT 2004. Proceedings., 2004Co-Authors: G. Ferrari, G. Colavolpe, R. RaheliAbstract:The subject of this paper is the asymptotic optimality of finite-Memory Detection for transmission over a channel characterized by a single multiplicative time-invariant stochastic parameter (e.g., block frequency nonselective fading). It is known that any finite-Memory Detection algorithm, either trellis-based or graph-based, is characterized by a single basic metric. We present a theorem which proves that this metric tends, asymptotically, to that of a receiver with perfect channel state information
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GLOBECOM - On trellis-based truncated-Memory Detection
GLOBECOM '03. IEEE Global Telecommunications Conference (IEEE Cat. No.03CH37489), 2003Co-Authors: G. Ferrari, G. Colavolpe, R. RaheliAbstract:We propose a general framework for Detection over channels with infinite Memory. A general truncation assumption leads automatically to the definition of a trellis diagram. A general approach to the design of forward-backward (FB) algorithms is proposed and two main classes of FB algorithms (with coupled and decoupled recursions, respectively) are presented. Moreover, it is shown that sequence Detection algorithms, in the form of a Viterbi algorithm (VA), follow easily from one of the proposed classes. The generality of the proposed framework is shown by applying it to a few stochastic channels. The performance of the proposed algorithms seems to depend ultimately on the truncation length, almost irrespective of the specific Detection strategy.
G. Colavolpe - One of the best experts on this subject based on the ideXlab platform.
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On trellis-based truncated-Memory Detection
IEEE Transactions on Communications, 2005Co-Authors: G. Ferrari, G. Colavolpe, R. RaheliAbstract:We propose a general framework for trellis-based Detection over channels with infinite Memory. A general truncation assumption enables the definition of a trellis diagram, which takes into account a considered portion of the channel Memory and possible coding Memory at the transmitter side. It is shown that trellis-based maximum a posteriori (MAP) symbol Detection algorithms, in the form of forward-backward (FB) algorithms, can be derived on the basis of this Memory-truncation assumption. A general approach to the design of truncated-Memory (TM) FB algorithms is proposed, and two main classes of algorithms, characterized by coupled and decoupled recursions, respectively, are presented. The complexity of the derived TM-FB algorithms is analyzed in detail. Moreover, it is shown that MAP sequence Detection algorithms, based on the Viterbi algorithm, follow easily from one of the proposed classes. Looking backward at this duality between MAP symbol Detection algorithms and MAP sequence Detection algorithms, it is shown that previous solutions for one case can be systematically extended to the other case. The generality of the proposed framework is shown by considering various examples of stochastic channels. New Detection algorithms, as well as generalizations of solutions previously published in the literature, are embedded in the proposed framework. The obtained results do suggest that the performance of the proposed Detection algorithms ultimately depends on the truncation depth, almost regardless of the specific Detection strategy.
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Adaptive iterative Detection for the phase-uncertain channel: limited-tree-search versus truncated-Memory Detection
IEEE Transactions on Vehicular Technology, 2004Co-Authors: G. Ferrari, G. Colavolpe, A. Anastasopoulos, R. RaheliAbstract:In this paper, we consider iterative Detection over bandpass channels that introduce an unknown phase rotation in the transmitted signal. This work focuses on the comparison between two adaptive Detection strategies for trellis-based coded modulation: limited-tree-search (LTS) Detection, obtained by reducing a tree search to a limited trellis search, and truncated-Memory (TM) Detection, based on channel-Memory truncation, which automatically leads to a trellis search. Both strategies are used to derive trellis-based forward-backward (FB) algorithms. A quantitative analysis based on simulations, with various coding and modulation schemes, is carried out to evaluate and compare the two approaches. The results show that the channel-phase dynamics should significantly influence the choice of the Detection strategy: For low-phase variations, LTS algorithms are a simple and reasonable choice, while for moderate to fast phase variations, TM algorithms show a considerable robustness.
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On trellis-based truncated-Memory Detection
GLOBECOM '03. IEEE Global Telecommunications Conference (IEEE Cat. No.03CH37489), 2003Co-Authors: G. Ferrari, G. Colavolpe, R. RaheliAbstract:We propose a general framework for Detection over channels with infinite Memory. A general truncation assumption leads automatically to the definition of a trellis diagram. A general approach to the design of forward-backward (FB) algorithms is proposed and two main classes of FB algorithms (with coupled and decoupled recursions, respectively) are presented. Moreover, it is shown that sequence Detection algorithms, in the form of a Viterbi algorithm (VA), follow easily from one of the proposed classes. The generality of the proposed framework is shown by applying it to a few stochastic channels. The performance of the proposed algorithms seems to depend ultimately on the truncation length, almost irrespective of the specific Detection strategy.