Number Of Different Targets Research Articles

Feature Extraction Methods for Underwater Acoustic Target Recognition of Divers.

The extraction of typical features of underwater target signals and excellent recognition algorithms are the keys to achieving underwater acoustic target recognition of divers. This paper proposes a feature extraction method for diver signals: frequency-domain multi-sub-band energy (FMSE), aiming to achieve accurate recognition of diver underwater acoustic targets by passive sonar. The impact of the presence or absence of targets, different numbers of targets, different signal-to-noise ratios, and different detection distances on this method was studied based on experimental data under different conditions, such as water pools and lakes. It was found that the FMSE method has the best robustness and performance compared with two other signal feature extraction methods: mel frequency cepstral coefficient filtering and gammatone frequency cepstral coefficient filtering. Combined with the commonly used recognition algorithm of support vector machines, the FMSE method can achieve a comprehensive recognition accuracy of over 94% for frogman underwater acoustic targets. This indicates that the FMSE method is suitable for underwater acoustic recognition of diver targets.

Theoretical limits on the speed of learning inverse models explain the rate of adaptation in arm reaching tasks

An essential aspect of human motor learning is the formation of inverse models, which map desired actions to motor commands. Inverse models can be learned by adjusting parameters in neural circuits to minimize errors in the performance of motor tasks through gradient descent. However, the theory of gradient descent establishes limits on the learning speed. Specifically, the eigenvalues of the Hessian of the error surface around a minimum determine the maximum speed of learning in a task. Here, we use this theoretical framework to analyze the speed of learning in different inverse model learning architectures in a set of isometric arm-reaching tasks. We show theoretically that, in these tasks, the error surface and, thus the speed of learning, are determined by the shapes of the force manipulability ellipsoid of the arm and the distribution of targets in the task. In particular, rounder manipulability ellipsoids generate a rounder error surface, allowing for faster learning of the inverse model. Rounder target distributions have a similar effect. We tested these predictions experimentally in a quasi-isometric reaching task with a visuomotor transformation. The experimental results were consistent with our theoretical predictions. Furthermore, our analysis accounts for the speed of learning in previous experiments with incompatible and compatible virtual surgery tasks, and with visuomotor rotation tasks with different numbers of targets. By identifying aspects of a task that influence the speed of learning, our results provide theoretical principles for the design of motor tasks that allow for faster learning.

Charging Station Distribution Optimization Using Drone Fleet in a Disaster

A disaster is an unforeseen calamity that can cause damage to properties and can bring about a loss of human lives. Usually, many relief supplies, such as clean water, food, and medical supplies, are required by disaster victims. Quick response and rapid distribution of essential relief items into the affected region can save countless lives and prevent or slow down the effects of disasters. In this regard, disaster management comes into play, which is highly dependent on the topography and access of the disaster-hit area. If the disaster-hit site has little or no road connectivity, the use of UAVs/drones becomes essential in delivering health packages to the affected areas to assist with humanitarian aid. Since the battery capacity of the drone is limited, UAVs/drones require charging stations located at various places to carry out the necessary relief work. These charging stations should be transported using road infrastructure and preinstalled in disaster-prone areas, as access to these areas may be denied once the disaster hits. This article presents a novel optimization model to distribute relief items to disaster-hit areas. The objective of this model is to optimize the location and the number of the charging stations. We consider the relative priority of locations where a preference is given to locations with higher priority levels. The optimal number of charging stations and optimal routes has also been determined by using our optimization model. To illustrate the use of our model, numerical examples have been simulated for a different number of targets. Through our numerical simulation, it was observed that the drone’s maximum distance capacity is the key factor in determining the optimal grid size, which directly correlates to the number of charging stations.

High resolution DOA estimation of acoustic plane waves: An innovative comparison among Cuckoo search heuristics and subspace based algorithms

SONAR signal processing plays an indispensable role when it comes to parameter estimation of Direction of Arrival (DOA) of acoustic plane waves for closely spaced target exclusively under severe noisy environments. Resolution performance of classical MUSIC and ESPRIT algorithms and other subspace-based algorithms decreases under scenarios like low SNR, smaller number of snapshots and closely spaced targets. In this study, optimization strength of swarm intelligence of Cuckoo Search Algorithm (CSA) is accomplished for viable DOA estimation in different scenarios of underwater environment using a Uniform Linear Array (ULA). Higher resolution for closely spaced targets is achieved using smaller number of snapshots viably with CSA by investigating global minima of the highly nonlinear cost function of ULA. Performance analysis of CSA for different number of targets employing estimation accuracy, higher resolution, variance analysis, frequency distribution of RMSE over the monte Carlo runs and robustness against noise in the presence of additive-white Gaussian measurement noise is achieved. Comparative studies of CSA with Root MUSIC and ESPRIT along with Crammer Rao Bound analysis witnesses better results for estimating DOA parameters which are further endorsed from the results of Monte Carlo simulations.

Adaptive Target Extraction Method in Sea Clutter Based on Fractional Fourier Filtering

Target detection in sea clutter is of great significance in military radar research. Because the characteristics of sea clutter are complex and easily affected by wind direction, the method of suppressing sea clutter by using sea clutter characteristics to reproduce is not advantageous. To address this problem, this paper focuses on direct extraction of target echo. According to the difference of fractional characteristics between target echo and sea clutter, fractional Fourier transform is introduced to extract target echo. In this paper, a fractional domain change coefficient clustering method is established to identify the range units of target, and a fractional domain filtering method based on the minimax is proposed to extract the target spectrum of target dominated range units, so as to separate the target echo from sea clutter. Simulated and measured experiments prove that this method can accurately identify targets and extract target information under various sea state and different number of targets.

How to build a fast and accurate code-modulated brain-computer interface

Objective. In the last decade, the advent of code-modulated brain-computer interfaces (BCIs) has allowed the implementation of systems with high information transfer rates (ITRs) and increased the possible practicality of such interfaces. In this paper, we evaluate the effect of different numbers of targets in the stimulus display, modulation sequences generators, and signal processing algorithms on the accuracy and ITR of code-modulated BCIs. Approach. We use both real and simulated electroencephalographic (EEG) data, to evaluate these parameters and methods. Then, we compared numerous different setups to assess their performance and identify the best configurations. We also evaluated the dependability of our simulated evaluation approach. Main results. Our results show that Golay, almost perfect, and deBruijn sequence-based visual stimulus modulations provide the best results, significantly outperforming the commonly used m-sequences in all cases. We conclude that artificial neural network processing algorithms offer the best processing pipeline for this type of BCI, achieving a maximum classification accuracy of 94.7% on real EEG data while obtaining a maximum ITR of 127.2 bits min−1 in a simulated 64-target system. Significance. We used a simulated framework that demonstrated previously unattainable flexibility and convenience while staying reasonably realistic. Furthermore, our findings suggest several new considerations which can be used to guide further code-based BCI development.

An energy aware grouping memetic algorithm to schedule the sensing activity in WSNs-based IoT for smart cities

Wireless Sensor Networks (WSNs) are the main component in the Internet of Things (IoT) and smart cities to sense our environment, gather essential and meaningful data, and forward it to the base station (BS). Nowadays, IoT utilizes WSN as a necessary platform for sensing and communication of the data. One of the main strategies to minimize consumption of energy in a WSN with highly dense sensors is to maximize sleeping sensors at each time. This way implies to schedule the activity of sensors, i.e. determining when a sensor node is kept idle (sleep mode) and when a sensor node is activated to sense environment (active mode). Due to heterogeneity of the sensor nodes in WSNs-based IoT for smart cities, one approach for scheduling the sensing activity is clustering the sensors into K mutually different subsets, so that every subset of sensors alone can cover all targets of the network. In this case, we can solve finding the maximum number of sensor subsets, or equivalently sensor covers problem, by conversion it to SET K-COVER problem. In this paper, an energy aware Grouping Memetic Algorithm (GMA) is proposed for solving the SET K-COVER problem. The proposed GMA varies in four general ways from any of the other evolutionary algorithms for solving the SET K-COVER problem. First, to transform solution structures linked to the SET K-COVER problem into chromosome genes, a new encoding scheme was being used. Second, specific genetic operators appropriate for the chromosomes are used, based on the encoding used. Third, to direct the search process in the solution space of the SET K-COVER problem, a novel proper fitness function is proposed. Fourth, a local improvement algorithm which uses a sensor dominance rule is proposed. This study, carried out detailed experiments of different numbers of targets and different numbers of sensors in WSNs to assess the proposed algorithm. In several instances of the problem, the experiments demonstrate that the algorithm works considerably better in terms of solution quality than many other heuristics and evolutionary algorithms and significantly outperformed the evolutionary algorithms in terms of runtime, suggesting the usefulness of the proposed algorithm to increase the lifetime of WSN in IoT and smart cities.

Performance Analysis of Efficient Computing Techniques for Direction of Arrival Estimation of Underwater Multi Targets

Parameter estimation of Direction of Arrival (DOA) using deterministic and stochastic computing paradigms is an enabling development for underwater acoustic signal processing beside its applications in the field of seismology, astronomy, earthquake and bio-medicine. In this work, the comparative study between state of the art deterministic and heuristics algorithms is presented for viable DOA estimation for different underwater dynamic objects. A Uniform Linear Array (ULA) of eight hydrophones is used for impinging acoustic waves from far-field targets. In order to evaluate the performance, the viability of innovative statistical indices is utilized to explain. Performance analysis of Genetic Algorithm(GA) and Particle Swarm Optimization(PSO) is conducted with standard counterparts including MVDR, MUSIC, ESPRIT and UESPRIT for different number of targets in terms of estimation accuracy, robustness against the number of elements and noise, cumulative distribution function of Root Mean Sqaure Error(RMSE), frequency distribution of the RMSE over the monte carlo trials, the resolution ability and computational complexity in the presence of white Gaussian measurement noise. Crammer Rao Bound (CRB) based analysis is also performed for the validation assessments and results on Monte Carlo simulations depict that the Genetic Algorithm(GA) showed the outperform counterparts on precision, convergence and complexity indices.

SMTS: a swarm intelligence-inspired sensor wake-up control method for multi-target sensing in wireless sensor networks

The different number of targets in wireless sensor networks (WSNs) leads to different detection effects. Because of the existence of multiple targets in the detection area, excessive nodes wake up to detect each target in most cases. This process will consume too much energy and lead to the early failure of the network. We propose an algorithm named sensor wake-up control method for multi-target sensing (SMTS) inspired by swarm intelligence. In SMTS, we first construct the stochastic switching model based on a sliding window mechanism to distinguish single-target or multi-target scenarios. We further research wake-up strategy optimization for dynamic targets to solve the problem associated with excessively awakened nodes. The simulations demonstrate that SMTS significantly reduces the number of wake-up nodes around the intersections of multiple targets, effectively saves energy, and prolongs the service lifetime of WSNs while ensuring detection efficiency and accuracy.

A Distributed Strategy for Cooperative Autonomous Robots Using Pedestrian Behavior for Multi-Target Search in the Unknown Environment.

Searching multiple targets with swarm robots is a realistic and significant problem. The goal is to search the targets in the minimum time while avoiding collisions with other robots. In this paper, inspired by pedestrian behavior, swarm robotic pedestrian behavior (SRPB) was proposed. It considered many realistic constraints in the multi-target search problem, including limited communication range, limited working time, unknown sources, unknown extrema, the arbitrary initial location of robots, non-oriented search, and no central coordination. The performance of different cooperative strategies was evaluated in terms of average time to find the first, the half, and the last source, the number of located sources and the collision rate. Several experiments with different target signals, fixed initial location, arbitrary initial location, different population sizes, and the different number of targets were implemented. It was demonstrated by numerous experiments that SRPB had excellent stability, quick source seeking, a high number of located sources, and a low collision rate in various search strategies.

Motor cost affects the decision of when to shift gaze for guiding movement.

Frequent gait modifications are often required to navigate our world. These can involve long or wide steps or changes in direction. People generally prefer to minimize the motor cost (or effort) of a movement, although with changes in gait this is not always possible. The decision of when and where to shift gaze is critical for controlling motor actions, since vision informs the brain about the available choices for movement-in this case, where to step. Here we asked how motor cost influences the allocation of gaze. To address this, we had participants walk and step to the center of sequential targets on the ground. We manipulated the motor cost associated with controlling foot placement by varying the location of one target in the lateral direction on a trial-to-trial basis within environments with different numbers of targets. Costlier steps caused a switch from a gaze strategy of planning future steps to one favoring visual feedback of the current foot placement when participants had to negotiate another target immediately after. Specifically, costlier steps delayed gaze shifts away from the manipulated target. We show that this relates to the cost of moving the leg and redirecting the body's center of mass from target to target. Overall, our results suggest that temporal gaze decisions are affected by motor costs associated with step-to-step demands of the environment. Moreover, they provide insight into what affects the coordination between the eyes and feet for the control of stable and accurate foot placement while walking. NEW & NOTEWORTHY Changes in gait allow us to navigate our world. For instance, one may step long or wide to avoid a spilled drink. The brain can direct gaze to gather relevant information for making these types of motor decisions; however, the factors affecting gaze allocation in natural behaviors are poorly understood. We show how the motor cost associated with a step influences the decision of when to redirect gaze to ensure accurate foot placement while walking.

Color-coded molecular beacons for multiplex PCR screening assays.

The number of different fluorescent colors that can be distinguished in a PCR screening assay restricts the number of different targets that can be detected. If only six colors can be distinguished, and the probe for each target is labeled with a unique color, then only six different targets can be identified. Yet, it is often desirable to identify more targets. For instance, the rapid identification of which bacterial species (if any) is present in a patient’s normally sterile blood sample, out of a long list of species, would enable appropriate actions to be taken to prevent sepsis. We realized that the number of different targets that can be identified in a screening assay can be increased significantly by utilizing a unique combination of two colors for the identification of each target species. We prepared a demonstration assay in which 15 different molecular beacon probe pairs were present, each pair specific for the same identifying sequence in the 16S ribosomal RNA gene of a different bacterial species, and each pair labeled with a unique combination of two fluorophores out of the six differently colored fluorophores that our PCR instrument could distinguish. In a set of PCR assays, each containing all 30 color-coded molecular beacons, and each containing DNA from a different bacterial species, the only two colors that arose in each real-time assay identified the species-specific target sequence that was present. Due to the intrinsic low background of molecular beacon probes, these reactions were specific and extremely sensitive, and the threshold cycle reflected the abundance of the target sequence present in each sample.

Algorithmic complexity of shape grammar implementation

AbstractComputer-based shape grammar implementations aim to support creative design exploration by automating rule-application. This paper reviews existing shape grammar implementations in terms of their algorithmic complexity, extends the definition of shape grammars with sets of transformations for rule application, categorizes (parametric and non-parametric) sets of transformations, and analyses these categories in terms of the resulting algorithmic complexity. Specifically, it describes how different sets of transformations admit different numbers of targets (i.e., potential inputs) for rule application. In the non-parametric case, this number is quadratic or cubic, while in the parametric case, it can be non-polynomial, depending on the size of the target shape. The analysis thus yields lower bounds for the algorithmic complexity of shape grammar implementations that hold independently of the employed algorithm or data structure. Based on these bounds, we propose novel matching algorithms for non-parametric and parametric shape grammar implementation and analyze their complexity. The results provide guidance for future, general-purpose shape grammar implementations for design exploration.

Using more different and more familiar targets improves the detection of concealed information

When embedded among a number of plausible irrelevant options, the presentation of critical (e.g., crime-related or autobiographical) information is associated with a marked increase in response time (RT). This RT effect crucially depends on the inclusion of a target/non-target discrimination task with targets being a dedicated set of items that require a unique response (press YES; for all other items press NO). Targets may be essential because they share a feature - familiarity - with the critical items. Whereas irrelevant items have not been encountered before, critical items are known from the event or the facts of the investigation. Target items are usually learned before the test, and thereby made familiar to the participants. Hence, familiarity-based responding needs to be inhibited on the critical items and may therefore explain the RT increase on the critical items. This leads to the hypothesis that the more participants rely on familiarity, the more pronounced the RT increase on critical items may be. We explored two ways to increase familiarity-based responding: (1) Increasing the number of different target items, and (2) using familiar targets. In two web-based studies (n = 357 and n = 499), both the number of different targets and the use of familiar targets facilitated concealed information detection. The effect of the number of different targets was small yet consistent across both studies, the effect of target familiarity was large in both studies. Our results support the role of familiarity-based responding in the Concealed Information Test and point to ways on how to improve validity of the Concealed Information Test.

Rationalizing Promiscuity Cliffs.

Compound promiscuity can be viewed in different ways. We distinguish "bad" promiscuity resulting from chemical liabilities, nonspecific binding, or assay artifacts, from "good" promiscuity representing true multitarget activities. Investigating multitarget activities of small molecules is scientifically stimulating and therapeutically relevant. To better understand the molecular basis of multitarget activities, structure-promiscuity relationships (SPRs) are explored. For this purpose, "promiscuity cliffs" (PCs) have been introduced, which can be rationalized as an extension of the activity cliff (AC) concept. A PC is defined as a pair of structural analogues that are active against different numbers of targets (given a difference threshold). As discussed herein PCs frequently capture surprising SPRs and encode many experimentally testable hypotheses.

Cellular mechanisms of peroxynitrite-induced neuronal death.

Peroxynitrite (ONOO-) is a strong biological oxidant formed by the diffusion-limited reaction of nitric oxide (NO-) and superoxide anion (O2-). It has long been theorized that peroxynitrite generation could be the cause in a number of pathological conditions ranging from atherosclerosis to inflammatory, autoimmune, heart and neurodegenerative diseases. Its relatively long biological half-life and high reactivity allows peroxynitrite to oxidize a number of different targets in the cell. In physiologically relevant conditions peroxynitrite can directly react with thiols, or the radical products of peroxynitrite decomposition may indirectly oxidize other cellular components such as lipids, proteins and DNA. Downstream, oxidative modifications caused by peroxynitrite trigger cell death by a variety of mechanisms depending on the concentration of the oxidant. Peroxynitrite stimulates necrosis, apoptosis, autophagy, parthanatos and necroptosis. Here we review the mechanisms activated by peroxynitrite to cause neuronal death.

Simultaneous tracking and counting of targets in a sensor network

Unattended ground sensors (UGS) are widely used to monitor human activities, such as pedestrian motion and detection of intruders in a secure region. This paper presents an algorithm for counting and tracking humans moving through a UGS network. Each node of this sensor network is equipped with a geophone (i.e., seismic sensor) and a microphone (i.e., acoustic sensor). The proposed method analyzes the relational dependence among the responses of sensors at various nodes as the targets walk through the network. The energy distribution across the network for different number of targets walking at different distances from the nodes has been analyzed to predict the number and location of targets in the sensor network field. The proposed concept has the advantages of having fast execution time and low memory requirements and is potentially well-suited for real-time implementation on in-situ computational platforms. Keywords—Personnel detection, seismic sensing, acoustic sensing, sensor-network-based fusion.

Differential expression of sirtuin family members in the developing, adult, and aged rat brain.

The sirtuins are NAD+-dependent protein deacetylases and/or ADP-ribosyltransferases that play roles in metabolic homeostasis, stress response and potentially aging. This enzyme family resides in different subcellular compartments, and acts on a number of different targets in the nucleus, cytoplasm and in the mitochondria. Despite their recognized ability to regulate metabolic processes, the roles played by specific sirtuins in the brain—the most energy demanding tissue in the body—remains less well investigated and understood. In the present study, we examined the regional mRNA and protein expression patterns of individual sirtuin family members in the developing, adult, and aged rat brain. Our results show that while each sirtuin is expressed in the brain at each of these different stages, they display unique spatial and temporal expression patterns within the brain. Further, for specific members of the family, the protein expression profile did not coincide with their respective mRNA expression profile. Moreover, using primary cultures enriched for neurons and astrocytes respectively, we found that specific sirtuin members display preferential neural lineage expression. Collectively, these results provide the first composite illustration that sirtuin family members display differential expression patterns in the brain, and provide evidence that specific sirtuins could potentially be targeted to achieve cell-type selective effects within the brain.

Pharmacological Prospects of Oxygenated Abietane-Type Diterpenoids from <i>Taxodium distichum</i> Cones

Eight naturally occurring diterpenoids, including 6,7-dehydroroyleanone, taxodal, taxodione, salvinolone, 14-deoxycoleon U, 5,6-dehydrosugiol, sandaracopimaric acid, and xanthoperol were isolated from Taxodium distichum cones and their biological properties evaluated in vitro against six different biological screening targets. Taxodione showed potent activity against a number of different targets, and salvinolone and 14-deoxycoleon U showed remarkable inhibitory activities against prolyl oligopeptidase (POP) and 17α-hydroxylase/C17,20-lyase (CYP17), respectively. These three compounds also showed strong cytotoxic activities against HL60 and K562 human leukemia cells. The structure-activity relationships of these compounds have also been considered. The findings in this study could lead to enhanced pharmacological prospects for the natural abietane-type diterpenoids consisting in conifer cones.

Abstract 3370: Use of fine needle aspirate biopsy (FNAB) in evaluating drug responses in tumor models: Better data with less animal use? .

Abstract It is now accepted that the coordinated application of pharmacokinetics (PK) and pharmacodynamics (PD) provides a rational, efficient and informative approach to preclinical and clinical drug development. In clinical studies, core or aspirate biopsies are used normally for PK/PD evaluation. In contrast in the majority of preclinical studies whole tumor samples are collected for PK/PD studies requiring killing of animals at each time point and hence the use of large numbers of animals for such studies. For example to perform a single dose PK/PD experiment in this manner typically uses 20 animals (4 animals at each of 5 time points). In the present study we evaluated the use of FNAB for performing PK/PD studies in tumor bearing mice and rats. The technique involved collecting biopsies from tumors using a fine needle (21 or 25 G) and applying a negative pressure through use of a syringe attached to a Camego gun. The use of a fine needle allowed sampling of serial biopsies from mid-size (400 mm3) tumors. The biopsies contained enough tissue for analysis of biomarkers using one of several techniques (IHC, in situ hybridization, immunoassays or by RTPCR). Depending on the technique used for biomarker analysis compound levels could also be determined in the same biopsies using LC-MS/MS. PK evaluation in small blood samples (20 μL) collected from the tail vein at the same time as the biopsies also allowed determination of PK/PD relationship. Sampling of serial biopsies from mid-sized tumors using FNA allowed determination PK/PD using just 4 animals instead of the 20 typically used when whole tumors were collected for such studies. The data obtained using the two techniques were comparable for a number of different targets (e.g. PI3K and MEK inhibitors) in different tumor types. Potentially the use of FNAB instead of whole tumor sampling could increase the quality of the data since tissue from the same tumor is analyzed at different time points. Studies using a number of different tumors also showed that several biopsies can be collected through the course of an efficacy study (starting at about 200 mm3) without influencing the growth of tumors thus enabling longitudinal analysis of biomarkers/changes in tumors during such studies and potentially providing better PK/PD and efficacy relationship. In conclusion, use of FNAB technique to determine PK/PD responses can reduce animal use by up to 80% compared to a previous method. In addition the technique opens up the possibilities of more readily determining changes which are transient and performing longitudinal studies to evaluate changes leading to development of drug resistance or tumor heterogeneity without using large numbers of animals. Citation Format: Christian R. Schnell, Prakash Mistry, Julien Perdoux, Robert Cozens, Matthew Meyer, Tinya Abrams. Use of fine needle aspirate biopsy (FNAB) in evaluating drug responses in tumor models: Better data with less animal use? . [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 3370. doi:10.1158/1538-7445.AM2013-3370