Changes In Signal Strength Research Articles

Analysis of changes in individual retinal layer thickness after cataract surgery using spectral‐domain optical coherence tomography

PurposeTo evaluate whether there were significant changes in macular thickness and each retinal layer thickness after cataract surgery using spectral‐domain optical coherence tomography (OCT)MethodsFrom February to August 2015, a prospective study of 24 eyes of 14 patients, who underwent cataract surgery was conducted. The thickness of each retinal layer was measured with OCT before and one month after surgery. The changes in OCT signal strength and retinal thickness before and after surgery were compared, and the layers more affected were confirmed. Also, correlation analysis was performed to determine the relationship between changes in signal strength, severity of cataract, and changes in refractive index and retinal thickness.ResultsThe mean macular thickness was significantly increased from 263.33 μm to 269.75 μm (p = 0.019). When examining the changes in each retinal layer thickness, there was a significant increase in the thickness of the inner plexiform layer (p = 0.034), outer nuclear layer (p = 0.015) and retinal pigment epithelium (RPE) (p = 0.012). The signal strength of OCT was significantly increased from 28.79 dB to 31.67 dB (p = 0.005), which was significantly correlated with postoperative macular thickness changes (p = 0.046). However, the severity of cataract (p = 0.219) or the change of refractive index (p = 0.098) was not significantly correlated with macular thickness changes.ConclusionsAfter cataract surgery, both improvement of signal strength and structural changes of the retina may increase macular thickness. In particular, the thickness of the inner plexiform layer, outer nuclear layer, and retinal pigment epithelial layer increased. Therefore, the thickness of ganglion cell‐inner plexiform layer after cataract surgery may increase, so it should be carefully considered in the assessment of glaucoma progression.

Differences in tissue oxygenation and changes in total hemoglobin signal strength in the brain, liver, and lower-limb muscle during hemodialysis.

Near-infrared spectroscopy has been used to measure regional saturation of oxygen (rSO2) based on the total hemoglobin (t-Hb) signal strength. To date, few studies have investigated the changes of systemic oxygenation and t-Hb signal strength during hemodialysis (HD). This study aimed to (1) monitor rSO2 and t-Hb signal strength in the brain, liver, and lower-limb muscle during HD and (2) clarify the differences in rSO2 and t-Hb signal strength in each compartment. Fifty-three patients receiving 4-h HD were included and divided into three groups according to the compartments in which tissue oxygenation was measured as follows: brain (n=44), liver (n=42), and lower-limb muscle (n=40). The rSO2 and t-Hb signal strength was monitored using an INVOS 5100c (Covidien Japan, Tokyo, Japan). The rSO2 levels were significantly lower in the brain than in the liver from HD initiation to the end (HD initiation: rSO2 in the brain and liver, 46.5±1.3 and 52.4±1.7%, respectively, p=0.031). Furthermore, compared to the t-Hb signal strength ratio [value at t (min) during HD/initial value before HD] in the brain during HD, there were significant increases in the liver and lower-limb muscle, respectively. In conclusion, deterioration of cerebral oxygenation was remarkable compared to the hepatic oxygenation in HD patients. Our results, which revealed significant differences among the t-Hb signal strength ratios in the brain, liver, and lower-limb muscle during HD, might reflect the non-uniform body-fluid reduction within systemic tissues induced by ultrafiltration.

CONCEPT OF DETECTION SYSTEM TO LOCALIZE INSIDE CLOSED AREA BY RADIO TOMOGRAPHIC IMAGING

. System for navigation and localization inside buildings will determine man's location in confined spaces with level of accuracy never yet achieved. System's operation will be mainly based on Bluetooth signals in accordance with the Beacon standard. A worldwide innovation will be the use of Radio Tomographic Imaging (RTI). This technology will enable tracking of moving objects by image reconstruction based on changes of electromagnetic field signal strength (RSS).

Unobtrusive detection of body movements during sleep using Wi-Fi received signal strength with model adaptation technique

Although sleep is essential to healthy living, many people have issues related to insufficient or poor quality sleep. In this study, we propose a method for unobtrusively detecting body movements during sleep by measuring changes in Wi-Fi signal strength between two Wi-Fi-enabled devices because prior research has found a correlation between sleep state and body movements such as rolling over. In our method, users place two Wi-Fi-enabled devices, on the left and right sides of their bed when sleeping. Our method then detects body movement by measuring changes in Wi-Fi signal strength between the two devices. By doing so, our method can detect motion without any sensors connected to the user, using devices that are equipped with commercially available Wi-Fi modules. The main feature of our method is its ability to train a user’s body movement detection model on other users’ training data. We employ a model adaptation technique called the maximum likelihood linear regression (MLLR) to adapt a user-independent movement detection model to the user of interest. In this study, we evaluated our method using 60 sessions of data collected from six participants, and achieved approximately 82% accuracy on average with user-independent movement detection models.

E-HIPA: An Energy-Efficient Framework for High-Precision Multi-Target-Adaptive Device-Free Localization

Device-free localization (DFL), which does not require any devices to be attached to target(s), has become an appealing technology for many applications, such as intrusion detection and elderly monitoring. To achieve high localization accuracy, most recent DFL methods rely on collecting a large number of received signal strength (RSS) changes distorted by target(s). Consequently, the incurred high energy consumption renders them infeasible for resource-constraint networks, such as wireless sensor networks. This paper introduces an e nergy-efficient framework for high-precision multi-target-a daptive device-free localization (E-HIPA). Compared with the existing methods, E-HIPA demands fewer transceivers, applies the compressive sensing (CS) theory to guarantee high localization accuracy with less RSS change measurements. The motivation behind the proposed E-HIPA is the sparse nature of multi-target locations in the spatial domain. Before taking advantage of this intrinsic sparseness, we theoretically prove the validity of the proposed CS-based framework problem formulation. Based on the formulation, the proposed E-HIPA primarily includes an adaptive orthogonal matching pursuit (AOMP) algorithm, by which it is capable of recovering the precise location vector with high probability, even for a more practical scenario with unknown target number. Experimental results via real testbed demonstrate that, compared with the previous state-of-the-art solutions, i.e., RTI, SCPL, and RASS approaches, E-HIPA reduces the energy consumption by up to 69 percent with meter-level localization accuracy.

Visually Induced Inhibition of Return Affects the Integration of Auditory and Visual Information.

Multisensory integration (MSI) and exogenous spatial attention can both speedup responses to perceptual events. Recently, it has been shown that audiovisual integration at exogenously attended locations is reduced relative to unattended locations. This effect was observed at short cue-target intervals (200-250 ms). At longer intervals, however, the initial benefits of exogenous shifts of spatial attention at the cued location are often replaced by response time (RT) costs (also known as Inhibition of Return, IOR). Given these opposing cueing effects at shorter versus longer intervals, we decided to investigate whether MSI would also be affected by IOR. Uninformative exogenous visual spatial cues were presented between 350 and 450 ms prior to the onset of auditory, visual, and audiovisual targets. As expected, IOR was observed for visual targets (invalid cue RT < valid cue RT). For auditory and audiovisual targets, neither IOR nor any spatial cueing effects were observed. The amount of relative multisensory response enhancement and race model inequality violation was larger for uncued as compared with cued locations indicating that IOR reduces MSI. The results are discussed in the context of changes in unisensory signal strength at cued as compared with uncued locations.

Time-effect observation of stimulating effect on acupoints after micro-invasion embedding in healthy subjects

To in vivo dynamically observe the time-effect characteristic of local stimulating effect on acupoints after micro-invasion embedding, which could provide references for the interval period of micro-invasion embedding. With magnetic resonance imaging (MRI) technique, the local T2WI fat-suppression images and T2 Mapping 8-echo sequence images were collected at multiple time points from 8 healthy subjects who received embedding at left Sanyinjiao (SP 6).After the 8-echo sequence images were transformed into T2 Mapping images by using software FuncTool, the T2 average value of embedding area was measured, and the changes of local signal strength of T2WI fat-suppression images and T2 average value along with time after embedding at left Sanyinjiao (SP 6)were analyzed. Compared before embedding, the signal strength of local T2WI fat-suppression images and the T2 average value began to increase 8 h after being embedded(P<0.01); the signal strength of T2WI fat-suppression images and the T2 average value were significantly increased 3 d and 7 d after being embedded(all P<0.01);the signal strength of local T2WI fat-suppression images and the T2 average value 14 d after being embedded were lower than those at previous 2 time points, but higher than those before embedding(both P<0.01); 21 d, 28 d and 35 d after embedding, the signal strength of local T2WI fat-suppression images and the T2 average value were similar to those before treatment (all P>0.05). After micro-invasion embedding at Sanyinjiao (SP 6), the stimulation effect period on acupoint is approximately 21 days.When applying micro-invasion embedding under similar condition at acupoints which has similar structure as Sanyinjiao (SP 6), the interval period of embedding could consider 21 days as a reference.

Optical coherence tomography angiography offers comprehensive evaluation of skin optical clearing in vivo by quantifying optical properties and blood flow imaging simultaneously.

Tissue optical clearing (TOC) is helpful for reducing scattering and enhancing the penetration depth of light, and shows promising potential in optimizing optical imaging performances. A mixture of fructose with PEG-400 and thiazone (FPT) is used as an optical clearing agent in mouse dorsal skin and evaluated with OCT angiography (Angio-OCT) by quantifying optical properties and blood flow imaging simultaneously. It is observed that FPT leads to an improved imaging performance for the deeper tissues. The imaging performance improvement is most likely caused by the FPT-induced dehydration of skin, and the reduction of scattering coefficient (more than ∼ 40.5%) and refractive-index mismatching (more than ∼ 25.3%) in the superficial (epidermal, dermal, and hypodermal) layers. A high correlation (up to ∼ 90%) between the relative changes in refractive-index mismatching and Angio-OCT signal strength is measured. The optical clearing rate is ∼ 5.83 × 10(-5) cm/s. In addition, Angio-OCT demonstrates enhanced performance in imaging cutaneous hemodynamics with satisfactory spatiotemporal resolution and contrast when combined with TOC, which exhibits a powerful practical application in studying microcirculation.

A Markovian Model for Coarse-Timescale Channel Variation in Wireless Networks

A wide range of wireless channel models has been developed to model variations in received signal strength. In contrast to prior work, which has focused primarily on channel modeling on a short per-packet timescale (millisecond), we develop and validate a finite-state Markovian model that captures variations due to shadowing, which occur at coarser timescales. The Markov chain is constructed by partitioning the entire range of shadowing into a finite number of intervals. We determine the Markov chain transition matrix in two ways: 1) via an abstract modeling approach, in which shadowing effects are modeled as a lognormally distributed random process affecting the received power, and the transition probabilities are derived as functions of the variance and autocorrelation function of shadowing; and 2) via an empirical approach, in which the transition matrix is calculated by directly measuring the changes in signal strengths. We test the assumptions of our Markovian model using signal strength measurements collected over an 802.16e (WiMAX) network and a wireless multihop network deployed by Rice University, Houston, TX, USA. We compare the steady-state and transient performance of the model with those computed using the empirically derived transition matrix and those observed in the actual traces themselves.

Perceiving the Slightest Tag Motion beyond Localization

Existing methods in RFID systems often employ presence or absence fashion to detect the tags’ motions, so they cannot meet motion detection requirement in many applications. Our recent observations suggest that the signal strength backscattered from the tag is hypersensitive to its position, inspiring us to perceive the tag motion through its radio signal strength changes. Motion perception is not trivial and challenged by weak stability of strength in that any other interference or noise may incur significant changes as well, resulting in high false positives. To tackle this issue, we propose to model the strength via the Mixture of Gaussian Model (MoG). The problem is thus converted to foreground segment in computer vision with the help of Strength Image, where the technique of MoG based background subtraction is employed. We then implement a prototype using commercial off-the-shelf products. The evaluation results show that the slightest tag motion ( ${\sim}10$ cm) can be precisely perceived, and the accuracy is up to 92.34 percent while the false positive is suppressed under 0.5 percent.

An Adaptive Framework Coping with Dynamic Target Speed for Device-Free Passive Localization

The problem of device-free passive localization aims on locating moving objects which do not carry any localization devices. The rationale of this problem is based on the fact that a moving object can result in the changes of received signal strength (RSS) of the wireless links. Existing studies on this problem usually do not consider the impact of dynamic target speed on device-free passive localization. However, the experiments show that the localization performance degrades substantially when an object is moving at dynamic speed. To meet this challenge, in this paper, we propose an adaptive device-free passive localization framework which has three components to detect target speed change and perform adaptive localization. This framework can be easily adapted for existing device-free localization methods which are based on the detection of signal strength changes. As demonstrated in the experiments, the proposed framework can lead to 50 and 30 percent improvement on median and maximum error respectively over the localization algorithms without considering dynamic moving speeds of the target.

Spectrum Monitoring Using Energy Ratio Algorithm for OFDM-Based Cognitive Radio Networks

This paper presents a spectrum monitoring algorithm for Orthogonal Frequency Division Multiplexing (OFDM) based cognitive radios by which the primary user reappearance can be detected during the secondary user transmission. The proposed technique reduces the frequency with which spectrum sensing must be performed and greatly decreases the elapsed time between the start of a primary transmission and its detection by the secondary network. This is done by sensing the change in signal strength over a number of reserved OFDM sub-carriers so that the reappearance of the primary user is quickly detected. Moreover, the OFDM impairments such as power leakage, Narrow Band Interference (NBI), and Inter-Carrier Interference (ICI) are investigated and their impact on the proposed technique is studied. Both analysis and simulation show that the energy ratio algorithm can effectively and accurately detect the appearance of the primary user. Furthermore, our method achieves high immunity to frequency-selective fading channels for both single and multiple receive antenna systems, with a complexity that is approximately twice that of a conventional energy detector.

A precise RFID indoor localization system with sensor network assistance

Indoor localization is very critical for medical care applications, e.g., the patient localization or tracking inside the building of the hospital. Traditional Radio Frequency Identification (RFID) technologies are very popular in this area since their cost is very low. In such technologies, each tag acts as the transmitter and the Radio Signal Strength Indicator (RSSI) information is measured from the readers. However, RSSI information suffers severely from the mult i-path phenomenon. As a result, if in a very large area, the localization accuracy will be affected seriously. In order to solve this problem, we introduce Wireless Sensor Networks (WSNs) with only a few nodes, each of which acts as both transmitter and receiver. In such networks, the change of signal strength (referred as dynamic of RSSI) is leveraged to select a cluster of reference tags as candidates. Then the final target location is estimated by using the RSSI relationships between the target tag and candidate reference tags. Thus, the localization accuracy and scalability are able to be improved. We proposed two algorithms, SA-LANDMARC, and COCKTAIL. Experiments show that the localization accuracy of the two algorithms can reach 0.7m and 0.45m, respectively. Compared to most traditional Radio Frequency (RF)-based approaches, the localization accuracy is improved at least 50%.

Monitoring viscosity in asphalt binders using an ultrasonic guided wave approach

A pulse-echo ultrasonic guided wave approach capable of monitoring the viscosity of asphalt binders as function of temperature is presented. The method consists of sending a torsional wave from one end of a cylindrical steel rod embedded in asphalt binder and receiving the reflected signals. Experiments were performed on several binders of different performance grades, at temperatures ranging from 25 to 180 0 C. First, the viscosity of the binders was measured using a rotational viscometer in accordance with ASTM standards. The change in signal strength of the end-of-waveguide reflection of the guided wave was also monitored for the same binders over the same range of temperatures. It was observed that the values obtained using the guided wave approach correlates well with the viscosity values obtained using the rotational viscometer. The method also appears capable of monitoring changes in viscosity due to aging of the binders. The method has the advantage of having no moving parts, which makes it attractive for the development of a system that is capable of monitoring viscosity in asphalt binders in the asphalt industry. Industrial applications examples are briefly summarized.

Do as I Say, Not as I Do: The Role of Advice versus Actions in the Decision to Strategically Default

In this study, I examine relative private signal strength and find that offered advice is significantly more influential in changing strategic mortgage default proclivity than is observed actions. Moreover, these private signals are more reflective of financial herding than they are of an information cascade. From a policy perspective, herds are easier to reverse than are cascades making more effective policies aimed at curbing the incidence of strategic mortgage default. Interestingly, an informationally equivalent change in private signal strength across actions and advice alters strategic default willingness, but not the moral stance of borrowers, which demonstrates the complexity of this life-altering financially and emotionally impactful decision.

Transferring Compressive-Sensing-Based Device-Free Localization Across Target Diversity

Device-free localization (DFL) plays an important role in many applications, such as wildlife population and migration tracking. Most of current DFL systems leverage the distorted received signal strength (RSS) changes to localize the target(s). However, they assume a fixed distribution of the RSS change measurements, although they are distorted by different types of targets. It inevitably causes the localization to fail if the targets for modeling and testing belong to different categories. This paper presents TLCS-a transferring compressive sensing based DFL approach-which employs a rigorously designed transferring function to transfer the distorted RSS changes across different categories of targets into a latent feature space, where the distributions of the distorted RSS change measurements from different categories of targets are unified. A benefit of this approach is that the same transferred sensing matrix can be shared by different categories of targets, leading to a substantial reduction in the human efforts. The results of experiments illustrate the efficacy of the TLCS.

Comparison of Ganglion Cell-Inner Plexiform Layer and Retinal Nerve Fiber Layer after Cataract Surgery

Purpose: To observe the change in the measured thickness of ganglion cell-inner plexiform layer (GCIPL) and retinal nerve fiber layer (RNFL) thickness before and after cataract surgery. Methods: Forty-six eyes of 32 patients, scheduled to have cataract surgery, were imaged with spectral-domain optical coherence tomography (OCT) (Cirrus HD OCT) before and 5 weeks after the surgery to measure GCIPL and RNFL thickness repeatly. Results: All GCIPL and RNFL thickness parameters were significantly higher postoperatively compared with preoperative measurements (p < 0.001). RNFL thickness changed more than GCIPL; the increased amount of mean RNFL thickness (14.16%) was higher than GCIPL thickness (7.36%; p < 0.001). GCIPL and RNFL thicknesses and, GCIPL signal strength changes were significantly increased in all types of cataracts (p < 0.05). RNFL signal strength change was significantly increased in posterior subcapsular cataracts (p < 0.05). Conclusions: Cataracts may affect GCIPL and RNFL thicknesses. After cataract surgery, GCIPL thickness, similar to the RNFL thickness, was increased significantly. As GCIPL thickness may be less affected by cataracts than RNFL thickness, GCIPL thickness may be a more meaningful indicator for the diagnosis of glaucoma with cataract. J Korean Ophthalmol Soc 2015;56(4):485-493

A Route Selection Mechanism for Mobility of Node in Mobile Ad Hoc Network

A mobile ad-hoc network is a method of communication between different nodes (mobile devices) without the use of base stations, which are used in wired networks. In a Mobile ad-hoc network, nodes can play the role of a receiver, sender, or a relay. As movement is flexible in Mobile ad-hoc networks and nodes have limited resources, nodes may join the communication or exit it at any time. Therefore, for Mobile ad-hoc networks, routing techniques - selecting communication routes and maintaining them – is considered important in an environment of constantly changing network topology. To overcome this problem, this paper proposes a reliable routing protocol based on MP-AOMDV, which monitors changes in signal strength not only for GPS signals but reception signals as well. Although MP-AOMDV was researched under the assumption of fixed movement direction and speed, this paper proposes a routing technique that works with changing movement direction or speed of nodes.

Can principal component analysis reliably identify the temporal response of cortical area V1?

The early CI component and its polarity inversion as a stimulus moves from inferior to superior visual field is the basis for many evoked response potential (ERP) based claims of attention and other perceptual effects involving V1 activation. However, recent results have undermined the validity those criteria. Ales et al. (2010) have shown "polarity inversion is an unreliable diagnostic for activity originating in striate cortex". The fact that the CI is the earliest response peak is also not indicative of a V1 signal since recent ERP source modeling demonstrate that the signals from V1 and V2 begin at about the same time (Ales et al, 2009). With historical ERP measures of V1 activation discredited, does any method offer reliable isolation of the striate cortex response besides inverse source modeling along with fMRI to constrain the solution space? Zhang and Hood (2004) suggested principal component analysis provides a means of deriving the V1 response when combined with multi-focal stimulation. The change in the first component signal strength with stimulus position was consistent with the cruciform model of V1. However, we now know the cruciform model is flawed, so we revisited the utility of using PCA to isolate V1. In two subjects, using high density multi-focal stimuli with a 96 electrode recording array we compared the V1 temporal response based on PCA verses inverse source modeling methods. The concordance between the V1 response derived from PCA and fMRI constrained inverse source modeling was high. However, in 21 subjects, simulated ERPs using boundary element forward models and fMRI based source positions corresponding to a 96 patch multi-focal stimulus demonstrated that the first PCA component of simulated ERP's do not always match the V1 source time functions. In conclusion, PCA of the multi-focal ERP does not provide a reliable estimate of an individual's V1 response. Meeting abstract presented at VSS 2012

Dynamic Propagation Modeling for Mobile Users' Position and Heading Estimation in Wireless Local Area Networks

A system for mobile users' position and heading estimation in IEEE 802.11 WLAN (WiFi) using received signal strength (RSS) approach is introduced. The basic contribution the system introduces is that it doesn't need offline training or extra special hardware. It makes use of the fact that only few online RSS measurements from visible access points (AP) around the user is needed to build local propagation model at run-time. Gaussian Process Regression (GPR) is used as a non-parametric modeling that handles non-equally spaced sparse data. Due to the few learning data points, Gaussian kernels calibration and prediction happen in a single step. This enables the system to autonomously adapt to environment changes. The estimated ranges from multiple access points (AP) are used to determine position using weighted least squares. Then, the rate of change of signal strength from multiple APs is used by a novel algorithm to estimate heading. Experiments show reliable meter-level positioning accuracy and heading estimation accuracy of 16.5 degrees.