In the first stage, the mammogram was filtered so that only suspicious MC candidates remained. To do so, a hybrid filter consisting of a wavelet filter, a top-hat filter, and 15 Laws filters was applied to alleviate the problem of low contrast between MCs and surrounding breast tissue. This filtering not only lessened the low contrast problem but also reduced the tremendous computation time because only high-frequency components remained for further processing. In the second stage, all candidates were examined by a knowledge-based classifier to reduce the number of false positives (FPs). Furthermore, the remaining candidates were classified by support vector machines (SVM) via a set of features after an automatic feature selection, in which the optimal parameter sets for SVM were also determined.

Finally, we clustered individual MCs to MCCs and marked the identified MCCs on the images as a result.The rest of this paper is organized as follows: Section 2 introduces our mammogram database and methods of pre-processing, filtering, feature extraction, automatic feature selection, training, and classification. Our experimental results are shown in Section 3. We then discuss our method and methods from other groups in Section 4. Finally, the conclusions are provided in Section 5.2.?Methods2.1. Datasets and Ground TruthFifty-two patients (cases) with clinical reports were collected, from which a total of 111 digital mammograms were acquired. The image gray-level resolution was 14-bit per pixel. Each patient had at least one craniocaudal (CC) view and one mediolateral oblique (MLO) view.

All the mammograms, which were representative images containing MCCs, were acquired from China Medical University Hospital. The patient mammograms were selected by two radiologists, who selected mammograms that they both agreed contained precisely recognizable MCs. Patients whose mammograms were not able to be identified consistently by these two radiologists were excluded. To establish ground truth, all mammogram readings were performed by these two experienced radiologists independently. One radiologist was a senior clinician who has worked in this area for over ten years. The other radiologist was young and has worked more than two years. In each mammogram, a rectangle (or some rectangles) was drawn to enclose the MCCs, and a point was manually marked in the center of each MC. The rectangles were drawn as small as possible to cover the MCCs. The manually identified Drug_discovery MCs were set as the gold standard used as the ground truth to which the automated results were compared.To make a statistical analysis, we used 2-fold cross-validation [22,23] to test our algorithm. The dataset was randomly separated into two subsets.

�� FDMA. Transmission channel is split up into different carrier frequencies that are simultaneously available. It requires a complex receiver at the reader.�� CDMA. Tag IDs are multiplied with a pseudo-random sequence before transmission. It demands elevated power consumption.�� TDMA. Transmission channel is divided between the participants chronologically.In RFID systems, TDMA procedures are the most used techniques in RFID and they have the largest group of anti-collision methods. These can be categorized in: Aloha-based protocols which are probabilistic, tree-based protocols which are deterministic, and hybrid protocols which are a mixture of the previous ones [6].2.1.1. Aloha-Based ProtocolsThe aloha protocol is the origin of the Aloha-based protocols.

An improvement of that is the slotted-Aloha, which introduces the slot concept. A slot is a period of time during which the reader sends a command and the tags respond to the reader. Slotted-Aloha divides time into slots thus improving its throughput [3]. Later, framed-slotted-Aloha (FSA) is developed. In FSA all nodes must respond choosing a slot into a fixed length frame (a group of slots). As the throughput of the FSA decreases with the increase of the total amount of nodes, a dynamic-framed-slotted-Aloha (DFSA) is developed [7,8]. This protocol changes the length of the frame dynamically using an estimator to adjust the frame size. Some protocols like I-Code [8] change the frame size at the end of the last frame slot, and other algorithms, as the EPC C1G2 Slot Counter [9], adjust the frame size after a slot transmission.

Early cited, the tag starvation problem affects probabilistic algorithms, this is a tag that may not be correctly read during a reading cycle. Besides, estimation involves some disadvantages [13]: an increase in the computational cost of the reader [8] and the tag [20]; an error that degrades the efficiency; and lastly, an initial frame length cannot be set according to the estimated number of tags.2.1.2. Tree-Based ProtocolsThe main feature of this kind of protocols is that they are deterministic. This is that all tags in the reader’s interrogation zone are going to be identified. These protocols usually have simple design tags and work well with uniform set of tags but are slower than Aloha-based protocols. They can be categorized into [6]: Tree Splitting (TS), Query Tree (QT), Binary Search (BS) and Bitwise Arbitration (BTA).

A virtual tree to organize and identify each tag was firstly proposed by the authors Dacomitinib of the TS in [10]. This algorithm splits the set of tags in B subsets (B > 1) after a collision. These subsets become increasingly smaller until they contain one tag. The TS does not need clocking circuitry but they must maintain a counter, so if a tag get discharged, it loses cycle information. Moreover, the QT is proposed in [11].

In the past years, some research about star detection sensitivity has been reported. In [5] a rough estimation method for star detection sensitivity utilizing the SNR model for static conditions was first reported. Reference [6] gives a general expression of star detection sensitivity based on the theory described in [5], but under highly dynamic conditions the star-spots model can’t use the two-dimensional Gaussian distribution like in static conditions, so that the star detection sensitivity model developed for static conditions is not suitable for dynamic conditions. Reference [7] gives a dynamic star-spot imaging model, and obtains the regularity of star detection sensitivity at different angular velocities for a star tracker.

The movement of the star-spots during the exposure time also increases the difficulty of locating the star-spots and lowers the star location accuracy. The star location accuracy is the primary factor determining the attitude accuracy of a star tracker. In the past, many researchers have concentrated on the exploration of star location errors. Reference [8] obtains the star location error of the ideal star-spots in a 5 �� 5 centroiding window by calculation of the effects of various noise components. Reference [9] shows an explicit expression of the S-curve systematic error caused by the different positions of the star-spot center in a certain pixel. Reference [10] gives a typical model of star location error containing a systematic contribution and a random one.

However, all these researches mainly focus on static conditions, and we need to make research in depth the star location errors under highly dynamic conditions.This paper presents a method for optimizing the exposure time from the two aspects: star detection sensitivity and star location accuracy, and obtains the optimal exposure times for different angular velocities of a star tracker. This paper is divided into six sections. Following the Introduction, we first introduce the dynamic star-spot imaging model and star detection sensitivity with regard to the exposure time in Section 2. In Section 3, the star location error is deduced based on the error analysis of the sub-pixel centroiding algorithm, and the effect of the exposure time on the star location error is obtained.

Combining the analyses in Sections 2 and 3, the overall effect Anacetrapib of exposure time on attitude accuracy is obtained in Section 4, and the optimal exposure time is determined with the highest attitude accuracy as the criterion. Night sky experiments with a real star tracker are carried out in Section 5. Conclusions are drawn in the last section.2.?Star Detection Sensitivity2.1. Dynamic Star-Spot Imaging ModelUnder static conditions, the angular velocity of the carrier is very low and stars can be assumed to be point sources.

As shown in Figure 2(a), the use of coverslip resulted in less fluorescence intensity (approximately 50%) compared to a hydrophobic barrier. It should also be noted that the hydrophobic barrier occupied less surface area compared to the cover slip, while both were exposed to the same volume. Thus, with a cover slip, only about 8% as many bacterial cells came into contact with each spot relative to the hydrophobic barrier technique. Also, with a hydrophobic barrier, the bacterial cells can be added directly over the spots, while a cover slip requires that the cells be added at one end and flow across the slide via capillary action.Figure 2.Bacterial capture as affected by the use of a coverslip. (a) Detection of 4.

7 �� 108 cells/mL (with 1 ng capture antibody/nL spots) exhibited lower AFU (arbitrary fluorescence units, background corrected) values with the use of a coverslip versus …With the addition of solutions directly over the spots (v
Autonomous sensors can be defined as devices that autonomously execute their measurement functions in the measurement environment. They are also unwired from the acquisition unit; they are characterized by autonomous power supplies and the ability to measure and transmit data. They can achieve different functionalities ranging from simple detectors, giving an alarm signal when the sensor passes a threshold, up to monitoring systems collecting measurement data of different physical or chemical quantities. Autonomous sensors are increasingly used in many applications, mostly in measuring physical phenomena.

They can be applied Entinostat for measurement of quantities both in mobile devices, or in protected environments, or in spaces where electrical energy is absent. Their use widens also to applications where wires connecting a data acquisition unit and the sensor element cannot be used such as, for examples, in implantable devices inside the human body to avoid risk of infections or skin damage [1-3] in rotating machinery, [4], or in hermetic environments [5]. In the industrial field a cable connection of the machine produces friction, stiffness and damping, limiting movement. The cables can be easily damaged, which affects the reliability of the measurement system. Hermitically sealed bags are essential for dry foods such as potato chips and various types of cereals to retain their freshness and safety. Autonomous sensors can improve the current shelf life labels by letting both consumers and producers know when the packaged food is fresh and safe. In the food logistics field autonomous sensors are related to the product and follow it along all the food chain, acquiring data and registering the crossing of several thresholds in terms of temperature, humidity, light or gas concentrations [6-7].