A Bayesian Network Approach to Evaluating the Effectiveness of Modern Mine Hunting

This paper describes a novel technique for estimating how many mines remain after a full or partial underwater mine hunting operation. The technique applies Bayesian fusion of all evidence from the heterogeneous sensor systems used for detection, classification, and identification of mines. It relies on through-the-sensor (TTS) assessment, by which the sensors’ performances can be measured in situ through processing of their recorded data, yielding the local mine recognition probability, and false alarm rate. The method constructs a risk map of the minefield area composed of small grid cells (~4 m2) that are colour coded according to the remaining mine probability. The new approach can produce this map using the available evidence whenever decision support is needed during the mine hunting operation, e.g., for replanning purposes. What distinguishes the new technique from other recent TTS methods is its use of Bayesian networks that facilitate more complex reasoning within each grid cell. These networks thus allow for the incorporation of two types of evidence not previously considered in evaluation: the explosions that typically result from mine neutralization and verification of mine destruction by visual/sonar inspection. A simulation study illustrates how these additional pieces of evidence lead to the improved estimation of the number of deployed mines (M), compared to results from two recent TTS evaluation approaches that do not use them. Estimation performance was assessed using the mean squared error (MSE) in estimates of M.

MULTIPLE CLASSIFICATION ALGORITHMS UNIMODAL AND MULTIMODAL TARGET RECOGNITION SYSTEMS

Target recognition is of great importance in military and civil applications – object detection, security and surveillance, access and border control, etc. In the article the general structure and main components of a target recognition system are presented. The characteristics such as availability, distinctiveness, robustness, and accessibility are described, which influence the reliability of a TRS. The graph presentations and mathematical descriptions of a unimodal and multimodal TRS are given. The mathematical models for a probability of correct target recognition in these systems are presented. To increase the reliability of TRS, a new approach was proposed – to use a set of classification algorithms in the systems. This approach permits the development of new kinds of systems - Multiple Classification Algorithms Unimodal and Multimodal Systems (MAUMS and MAMMS). The graph presentations, mathematical descriptions of the MAUMS and MAMMS are described. The evaluation of the correct target recognition was made for different systems. The conditions of systems' effectiveness were established. The modality of the algorithm's recognition probabilitymaximal value determination for an established threshold level of the system's recognition probability was proposed, which will describe the requirements for the quality and, respectively, the costs of the recognition algorithms. The proposed theory permits the system's design depending on a predetermined recognition probability.

A Frame Detection Method for Real-Time Hand Gesture Recognition Systems Using CW-Radar

In this paper, a method to detect frames was described that can be used as hand gesture data when configuring a real-time hand gesture recognition system using continuous wave (CW) radar. Detecting valid frames raises accuracy which recognizes gestures. Therefore, it is essential to detect valid frames in the real-time hand gesture recognition system using CW radar. The conventional research on hand gesture recognition systems has not been conducted on detecting valid frames. We took the R-wave on electrocardiogram (ECG) detection as the conventional method. The detection probability of the conventional method was 85.04%. It has a low accuracy to use the hand gesture recognition system. The proposal consists of 2-stages to improve accuracy. We measured the performance of the detection method of hand gestures provided by the detection probability and the recognition probability. By comparing the performance of each detection method, we proposed an optimal detection method. The proposal detects valid frames with an accuracy of 96.88%, 11.84% higher than the accuracy of the conventional method. Also, the recognition probability of the proposal method was 94.21%, which was 3.71% lower than the ideal method.

Legislative bargaining and partisan delegation

We use an extension of the Baron–Ferejohn model of legislative bargaining in which there are three legislators, two of whom have partisan ties, to analyze the division of a fixed political resource in a majoritarian legislature. A legislator’s preferences depend on the shares that he and any copartisan receive. We ask whether there are circumstances under which a partisan legislator is willing to delegate proposal-making authority to a party leader so as to take advantage of the special proposal rights accorded by the legislature to this office rather than retaining equal-recognition proposal rights for himself. We show that this is the case only if (i) the leader’s proposal recognition probability is larger than the probability that one of the partisans is recognized when the legislators act independently, (ii) the value of partisan affiliation is sufficiently high, and (iii) the legislators are sufficiently impatient. We explore the relevance of these results to ongoing debates regarding the role and effect of parties and party leaders in Congress.

Sniff Olfactometry: Temporal effects on odorant mixture perception in humans

There is evidence in mice and honeybees that signals initiated by odorants at the olfactory epithelium arrive downstream in the olfactory bulb between 10 and 200ms later and that these latencies are ligand dependent. It has recently been proposed that these latencies could be used by mice to identify or classify. Here we demonstrate that humans are sensitive to the timing of individual of odorant presentation. Using a two-alternate forced choice (2AFC) paradigm—subjects chose which odorant they recognized first after they experienced two 70ms puffs separated in time by some interval in the range of -450ms to +450ms. All subject recognition probabilities yielded the same linear function of latency (p<0.05) even though they differed in their recognition thresholds for the components and their recognition probability to detect them in binary mixtures. These results indicate that temporal structure of odor delivery affects human odor perception and sniff olfactometry (SO) has the temporal resolution necessary to measure these effects. <div><br></div>

Sniff Olfactometry: Temporal effects on odorant mixture perception in humans

There is evidence in mice and honeybees that signals initiated by odorants at the olfactory epithelium arrive downstream in the olfactory bulb between 10 and 200ms later and that these latencies are ligand dependent. It has recently been proposed that these latencies could be used by mice to identify or classify. Here we demonstrate that humans are sensitive to the timing of individual of odorant presentation. Using a two-alternate forced choice (2AFC) paradigm—subjects chose which odorant they recognized first after they experienced two 70ms puffs separated in time by some interval in the range of -450ms to +450ms. All subject recognition probabilities yielded the same linear function of latency (p<0.05) even though they differed in their recognition thresholds for the components and their recognition probability to detect them in binary mixtures. These results indicate that temporal structure of odor delivery affects human odor perception and sniff olfactometry (SO) has the temporal resolution necessary to measure these effects. <div><br></div>

Novel Unconventional-Active-Jamming Recognition Method for Wideband Radars Based on Visibility Graphs

Radar unconventional active jamming, including unconventional deceptive jamming and barrage jamming, poses a serious threat to wideband radars. This paper proposes an unconventional-active-jamming recognition method for wideband radar. In this method, the visibility algorithm of converting the radar time series into graphs, called visibility graphs, is first given. Then, the visibility graph of the linear-frequency-modulation (LFM) signal is proved to be a regular graph, and the rationality of extracting features on visibility graphs is theoretically explained. Therefore, four features on visibility graphs, average degree, average clustering coefficient, Newman assortativity coefficient, and normalized network-structure entropy, are extracted from visibility graphs. Finally, a random-forests (RF) classifier is chosen for unconventional-active-jamming recognition. Experiment results show that recognition probability was over 90% when the jamming-to-noise ratio (JNR) was above 0 dB.