Observer Analyzer Free Download
Poppy Yentsch
The Fisheries Monitoring and Analysis Division (FMA) monitors groundfish and halibut fishing activities in the Federal fisheries off Alaska and conducts research associated with sampling commercial fishery catches, estimation of catch and bycatch mortality, and analysis of fishery-dependent data. The Division is responsible for training, briefing, debriefing, and oversight of observers who collect catch data onboard fishing vessels and at onshore processing plants and for quality control/quality assurance of the data provided by these observers. Division staff process data and make it available to the Sustainable Fisheries Division of the Alaska Regional Office for quota monitoring and to scientists in other AFSC divisions for stock assessment, ecosystem investigations, and an array of research investigations.
The North Pacific Observer Program, which is administered by the Fisheries Monitoring and Analysis Division, plays a vital role in the conservation and management of the Bering Sea, Aleutian Islands, and Gulf of Alaska groundfish and halibut fisheries. The program trains, briefs, debriefs, and oversees over 450 observers annually who collect catch data onboard fishing vessels and at onshore processing plants that is used for in-season management and scientific purposes such as stock assessments and ecosystem studies. The program ensures that the data collected by observers are of the highest quality possible by implementing rigorous quality control and quality assurance processes for the data collected by observers.
Ideal observer analysis is a method for investigating how information is processed in a perceptual system.[1][2][3] It is also a basic principle that guides modern research in perception.[4][5]
The ideal observer is a theoretical system that performs a specific task in an optimal way. If there is uncertainty in the task, then perfect performance is impossible and the ideal observer will make errors.
Geisler (2003)[6] (slightly reworded): The central concept in ideal observer analysis is the ideal observer, a theoretical device that performs a given task in an optimal fashion given the available information and some specified constraints. This is not to say that ideal observers perform without error, but rather that they perform at the physical limit of what is possible in the situation. The fundamental role of uncertainty and noise implies that ideal observers must be defined in probabilistic (statistical) terms. Ideal observer analysis involves determining the performance of the ideal observer in a given task and then comparing its performance to that of a real perceptual system, which (depending on the application) might be the system as a whole, a subsystem, or an elementary component of the system (e.g. a neuron).
In sequential ideal observer analysis,[7] the goal is to measure a real system's performance deficit (relative to ideal) at different processing stages. Such an approach is useful when studying systems that process information in discrete (or semi-discrete) stages or modules.
Das and Geisler [8] described and computed the detection and classification performance of ideal observers when the stimuli are normally distributed. These include the error rate and confusion matrix for ideal observers when the stimuli come from two or more univariate or multivariate normal distributions (i.e. yes/no, two-interval, multi-interval tasks and general multi-category classification tasks), the discriminability index of the ideal observer (Bayes discriminability index) and its relation to the receiver operating characteristic.
Red blood cell distribution width (RDW) and visual assessments of anisocytosis assess variability in erythrocyte size. Veterinary studies on the correlation between the two methods and on observer agreement are scarce. The objectives were to assess the correlation of the grading of anisocytosis by means of conventional microscopy of canine blood smears to RDW, and to assess intra- and inter-observer variation in assessing the degree of anisocytosis. The study included 100 canine blood samples on which blood smear examination and RDW measurement were performed. RDW was measured on the Advia 2120i analyzer. The degree of anisocytosis was based on a human grading scheme assessing the ratio between the size of the representative largest red blood cell and that of the representative smallest red blood cell (1+ if 4x). Three observers participated and assessed the blood smears by conventional microscopy twice, 3 weeks apart by each observer. The correlation was assessed for each observer on each occasion using Kendahl-tau-b analysis. Intra-observer agreement was assessed using quadratically weighted kappa. Inter-observer agreement was assessed using free-marginal multi-rater kappa. Anisocytosis graded on blood smears correlated significantly with RDW values as assessed by Kendahl-tau-b ranging between 0.37 and 0.51 (p < 0.0001). Intra-observer agreement ranged from weak to moderate with resulting kappa-coefficients being 0.58, 0.68, and 0.75, respectively. Inter-observer agreement was weak (Kappa-values 0.44). The weak to moderate observer agreement in the visual assessment of anisocytosis indicates that the more precise and more repeatable RDW measurement should be used for clinical decision-making.
Using fluorescent stains and flow cell technology, the XN-30 RUO standardizes detection and enumeration of intracellular malarial parasites. Three levels of quality control material ensure analyzer performance and provide a new level of confidence in the results produced.
We recommend the observer analyser for monitoring your network.The observer analyser monitors the data traffic of your communication services, and supports in troubleshooting problems. It can also be used in virtual environments. -The observer analyzer offers a complete package for managers, architects and engineers.
The versatility of the ObserVR1000 dynamic signal analyzer suits many test setups. Connect it to WiFi and collect data in the field. With the VR Mobile application, you can even monitor the data stream from a distance.
Two different observer modes allow distinct types of monitoring. The envelope mode offers long sweep times suitable for the analysis of the shape of envelopes and signal dynamics. The waveform mode allows you to view individual waveforms and oscillations by activating a specifically developed stabilization algorithm.
Conclusions:: Our studies show that use of the Drusen AnalyzerTM program would be helpful in the quantification of drusen by observers of different experience levels. While the process of drusen analysis now can be somewhat time intensive and prone to subjectivity, the use of such a program may be helpful in creating a baseline level of objectivity. The results show that the data is reproducible and with a brief orientation to its nuances, the program can be accurately employed.
Microscopic examination of peripheral blood smear (PBS) is essential in clinical hematology laboratories. Manual counting is, however, inefficient because the process is technically demanding and labor-intensive resulting in long turnaround time (TAT), and the results may be subjective with inter-observer variation.
Digital morphology (DM) analyzers can provide analysis of cell morphology (pre-classification) with reduced TAT and inter-observer variation. In a recent study, DM analyzers showed advantages over manual counting in laboratory efficiency including shortened TAT. DM analyzers can be used mainly in large-volume laboratories, and they are too large and expensive to be used in small to medium-volume laboratories.
The Sysmex CellaVision DC-1 (DC1) is a newly launched digital morphology analyzer that was developed mainly for small to medium-volume laboratories. The scientists evaluated the precision, qualitative performance, comparison of cell counts between DC-1 and manual counting, and turnaround time (TAT) of DC-1. Pre-classification on DC-1 included total 18 cell classes (12 WBC classes and six non-WBC classes). The 12 WBC classes include blasts, promyelocytes, myelocytes, metamyelocytes, band neutrophils, segmented neutrophils, lymphocytes, monocytes, eosinophils, basophils, variant lymphocytes, and plasma cells. The six non-WBC classes include nucleated RBCs (nRBCs), smudge cells, artifact, giant platelet, platelet aggregation, and unidentified cells.
I am using the TX1 and RX1 pins to communicate with a UART device but I am sending a bad command somewhere. So I am trying to debug using a logic analyzer(haven't used one in 25yrs). The only problem is I can't seem get the right data when connected to the transmit line. Does anyone know a good source for figuring out how to set the # of bits, parity and such...I've read a dozen or so wiki pages and understand that the it can be half or full duplex, 8 bit, no parity....it depends on the hardware. The device I have hooked up seems to work, but fails on one command and i just want to see what is actually being sent.
Generally 8 bits, no parity, 1 stop bit.
Do you have a RS223 to USB adapter cable? You can connect your TX pin & ground to the RX pin and ground on the cable and monitor on a PC also, might be easier than a logic analyzer (since what you really want is a protocol analyzer). I have several of these laying around for some reason. I think mine have a Prolific chip in them.