cocoon 1100 manual

Note that email addresses and full names are not considered private information. Please mention this; Therefore, avoid filling in personal details. Please enter your email address. We're committed to dealing with such abuse according to the laws in your country of residence. We'll get back to you only if we require additional details or have more information to share. Note that email addresses and full names are not considered private information. Please mention this; Therefore, avoid filling in personal details. The manual is 0,54 mb in size. If you have not received an email, then probably have entered the wrong email address or your mailbox is too full. In addition, it may be that your ISP may have a maximum size for emails to receive. Check your email Please enter your email address. Lastmanuals provides you a fast and easy access to the user manual UCOM COCOON 1100. We hope that this UCOM COCOON 1100 user guide will be useful to you. You have acquired a Cocoon 1100, which is a product made in accordance with the Digital Enhanced Cordless Telecommunications (DECT). DECT technology is characterized by high-security protection against interceptions as well as high-quality digital transmission. Select the time format (12h or 24h notation) and press 2. Set the date and time and use your handset to give you a reminder alarm. You can have different alarm settings for each handset registered to your base. The alarm rings only at the handset, not at the base or any other handset.Cocoon 1100The phonebook allows you to memorise 200 telephone numbers and names. You can enter names of up to 16 characters length and numbers of up to 24 digits length. If the desired number appears on the display, the number will be automatically dialed by 13.Press 15 to delete each letter of the name and use the alphanumeric keypad to enter the correct name. Cocoon 1100Or if the name was send by the network, you can edit it before saving. The handset returns to the call list.

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You have acquired a Cocoon 1100, which is a product made in accordance with the Digital Enhanced Cordless Telecommunications (DECT). DECT technology is characterized by high-security protection against interceptions as well as high-quality digital transmission. Please read carefully through the following information concerning safety and proper use. Make yourself familiar with all the functions of the equipment. Be careful to keep these advice notes and if necessary pass them on to a third party. If not, the telephone will not work optimally.The handset charging indicator (LED) on the base unit will light up. Each menu leads to a list of options. When the handset is switched On and in standby, press the left menu key under to open the main menu. When you dial or redial this number, the pause (3 seconds) is automatically included. The symbol will appear on the display. To re- activate, press key again for 3 seconds. The standby screen will appear when successfully registered and handset name and number will be shown. All manuals on ManualsCat.com can be viewed completely free of charge. By using the 'Select a language' button, you can choose the language of the manual you want to view. Perhaps the users of ManualsCat.com can help you answer your question. By filling in the form below, your question will appear below the manual of the Topcom Cocoon 1100. Please make sure that you describe your difficulty with the Topcom Cocoon 1100 as precisely as you can. The more precies your question is, the higher the chances of quickly receiving an answer from another user. You will automatically be sent an e-mail to inform you when someone has reacted to your question. Post your question here in this forum. We're committed to dealing with such abuse according to the laws in your country of residence. When you submit a report, we'll investigate it and take the appropriate action. We'll get back to you only if we require additional details or have more information to share.

An image processing procedure is then applied to extract significant shape-related features from each image instance, which, combined with the weight data, are provided as inputs to train a Support Vector Machine-based pattern classifier for gender classification. Subsequently, an air blower mechanism and a conveyor system sort the cocoons into their respective bins. The developed system was trained and tested on two different types of silkworm cocoons breeds, respectively CSR2 and Pure Mysore. The system performances are finally discussed in terms of accuracy, robustness and computation time. Keywords: multi-sensor, image processing, support vector machine, pattern recognition 1. Introduction Silk is the most distinguished textile in the world. Like most insects, the silkworm life cycle has four stages of development, respectively egg, larva, pupa, and adult moth ( Figure 1 a). A pair of male and female fully-grown adult moth mate with each other, and the female subsequently lays eggs and dies. The egg hatches and emerges out as a larva (also called a caterpillar), which feeds on mulberry leaves and grows for a period of 4 weeks. After 3 weeks, the chrysalis emerges from the cocoon as a moth, it mates, and the female lays eggs permitting the life cycle to restart. Among these four stages, cocoons are of commercial importance since a continuous filament of raw silk is directly produced from cocoons by terminating the growth of the caterpillar while inside the cocoon. Open in a separate window Figure 1 ( a ) Silkworm life cycle, ( b ) silkworm process flow for commercial usage. The sericulture industry is labor intensive, mostly rural-based, and multidisciplinary in nature. It involves on-farm activities such as mulberry cultivation, egg production, silkworm rearing, cocoon production, and off-farm activities like raw silk reeling, spinning, throwing, and weaving.

In any way can't Lastmanuals be held responsible if the document you are looking for is not available, incomplete, in a different language than yours, or if the model or language do not match the description. Lastmanuals, for instance, does not offer a translation service. Help! Call the company at 1-800-456-2355 and they can possibly email you a manual. Don How can I obtain a second mounting bar.Bugaboo UK It has been there all winter and is now moving on the inside of cocoon.It has been there all winter and is now moving on the inside of cocoon. Help! Call the company at 1-800-456-2355 and they can possibly email you a manual. Don How can I obtain a second mounting bar.It has been there all winter and is now moving on the inside of cocoon.It has been there all winter and is now moving on the inside of cocoon.Perhaps there's one here (listed as UK) so it's most likely English: SilverCrest SNMD 33 A1 SilverCrest SNMD 33 A1 SEWINGGUIDE. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( ). Abstract Sericulture is traditionally a labor-intensive rural-based industry. In modern contexts, the development of process automation faces new challenges related to quality and efficiency. During the silkworm farming life cycle, a common issue is represented by the gender classification of the cocoons. Improper cocoon separation negatively affects quantity and quality of the yield resulting in disruptive bottlenecks for the productivity. To tackle this issue, this paper proposes a multi sensor system for silkworm cocoons gender classification and separation. Utilizing a load sensor and a digital camera, the system acquires weight and digital images from individual silkworm cocoons.

Figure 2 b shows the effect of selfing due to incorrect classification on the quantity of laid eggs compared to the ideal separation ( Figure 2 c). The literature review provides detailed information about various methods and techniques used for gender separation of silkworm cocoons with a focus on feasibility of usage at grainage centers by untrained professionals. The available techniques can be broadly classified as destructive and non-destructive methods. Destructive methods effectively differentiate the silkworms, but cause permanent damage either to egg, larva, cocoon, or pupae and therefore cannot be further used for seed production or reeling. Non-destructive methods cause less or minimal damage to the silkworms and allow the insect (egg, larva, cocoon, or pupae) to be used in subsequent process. Figure 3 illustrates these methods and the following paragraph reports them in detail. Open in a separate window Figure 3 Methods of silkworm gender separation process. It is a chemical based method which required highly trained professionals and suitable only for constrained working environments. The authors considered 1071 samples from three hybrid breeds and have reported an accuracy of 93.68% with kNN classifier. Further the frequency data reflected from the pupa are analyzed and compared with the predefined threshold, based on which the pupa was classified as male or female. These images were transferred to a computer for processing and image analysis. Under analysis, the gender gland of the silkworm was observed, based on which the sorting was carried out. In both cases, the cocoons are opened to take out the pupa resulting in a high chance of the pupa getting damaged, and further cut cocoons cannot be used for reeling. Moreover, the method is slow and requires trained professionals to examine each pupa accurately. The cocoon being tested presented different fluorescence characteristics based on gender.

These seeds are utilized by the farmers for cocoon production and later based on the quality and requirements, the cocoons are either sent for reeling, to obtain raw silk or made available to the grainage centers for increased seed production. Table 1 summarizes the commercially-available machinery for both pre- and post-cocoon stages, the latter of which can rely on automated machines to reduce the manual labor involved and to improve the production yield. Table 1 Available machinery for pre and post-cocoon stage. Pre-Cocoon Stage Post-Cocoon Stage Machine for crushing shoots Cocoon de-flossing machine Mulberry pruning machine Denier detecting device in silk reeling Litter separation machine Long skein silk book making machine Stages Methods Remarks Chromosome Presence or absence of the “W” chromosome Female—ZW chromosome Male—ZZ chromosome Not practical—high cost Egg Color of the egg Males are usually light yellow Females are dark brown in color Not practical—need for skilled workers Larval Markings are exhibited on the larval body Female—crescent marking Males—plain Sex separation is possible only on the 1st day of 5th instar. Then the best cocoon pairs are kept in circular cubicles ( Figure 2 a) to subsequently allow the moth to emerge, mate and lay the eggs. This manual cocoon-sorting is made possible since females are bulkier and heavier than the male chrysalises. Open in a separate window Figure 2 ( a ) Image of cubicles used for egg production. ( b ) Eggs produced by incorrect separation of cocoons. ( c ) Eggs produced by pairing best male and female cocoons. According to the industrial partner best practices, the weight of Pure Mysore-breed silkworm cocoons ranges from 0.8 to 1.1 g for male and from 1.2 to 1.4 g for female cocoons. The CSR 2 breed cocoons weight ranges from 0.7 to 1.4 g for male and 1.5 to 2.0 g for female cocoons. Moreover, overall circumference of the female cocoon is larger when compared to the male cocoons.

The corresponding 3D model and developed prototype are shown in Figure 5 and Figure 6. Open in a separate window Figure 4 Schematic of silkworm cocoons separating machine. Open in a separate window Figure 5 3D model of silkworm cocoons separating machine. Open in a separate window Figure 6 Developed prototype of silkworm cocoons multi-sensor classification system. The cocoons were initially stored in the hopper, then individually picked by a vertical conveyor module (VCM). The cocoons entered one by one into the feature extraction module (FEM), where each cocoon was analyzed and their features such as shape and weight were extracted. A dedicated software, which executes on a standalone workstation, acquired a digital image and weight of each cocoon, and subsequently extracted significant shape-related features from image instances. Image features and weight data were then combined in an input feature vector, which was inputted to a pre-trained pattern recognition classifier for decision-making on gender classification. Eventually, individual cocoons were transported through a horizontal conveyor module (HCM) which performed the physical sorting of the cocoons and disposed them into dedicated male or female bins. Each module is illustrated in detail in the remainder of this paper. 2.1. Vertical Conveyor Module (VCM) The purpose of VCM is to pick individual cocoons from the hopper and feed them into the feature extraction module at a constant velocity without causing major physical damage to cocoons. It consists of a 60 cm-long conveyor belt, which passes through a 12 cm-diameter pulley mounted on the frame plates with the help of bearing support ( Figure 7 a,c). The VCM is endowed with 16 specially-designed concave-shaped spoons that can accommodate one cocoon at a time. Such spoons are riveted on the conveyor belt as shown in Figure 7 a,c. The distance between two consecutive spoons is 10 cm. Spoon edges are smoothed to avoid sticking to the cocoon fibers.

Two pairs of flappers are mounted on both sides of the metal frame ( Figure 7 c). The position of flappers was determined experimentally to align the cocoons with the concavity of the spoons and to avoid clinging to the fibrous outer shells of the other cocoons. Once the cocoons were picked up by these spoons, they were transferred to FEM for analysis. In this respect, a guide plate ( Figure 8 a,b) is positioned on rear side of VCM to enable smooth transfer of cocoons from VCM to FEM. Open in a separate window Figure 7 ( a ) 3D model of vertical conveyor module (VCM) module; ( b ) 3D model of spoon carrying a cocoon; ( c ) prototype of VCM module. Open in a separate window Figure 8 ( a ) 3D model of FEM; ( b, c ) prototype of FEM shown in two different angles. 2.2. Features Extraction Module (FEM) The FEM shown in Figure 8 consists of a camera support bracket, flip plate, slope box, exit box, a detachable load sensor, and an air blower mechanism. The module extracts both the shape features of cocoon via image processing and the weight of the cocoon (in g) via the load sensor and feeds such information into a binary classifier to determine the cocoon gender. A 5 mega pixel digital camera is mounted on the support bracket and a flip plate made of acrylic is mounted below the camera assembly. The structure allows continuous acquisition of objects on the flip plate. When the system is switched on, the flip plate is positioned horizontally to receive cocoons from the VCM. Silkworm cocoons have a hard shell that is covered by fibrous outer coating as shown in Figure 9 a,b. The accuracy of the classifier depends on how well the shape features are extracted from the cocoon rigid shell images by eliminating the negative effect of fibrous coating. Practically, it is not possible to remove the fibers from each cocoon manually at grainage centers before loading cocoons into the hopper.

The entire process has to be carried out in a dark room and the assessment is based on the human vision. Additionally, this method is labor intensive and requires additional overhead of 200 W power source and 3600 A wavelength UV light source. Later the fibers are subject to a chemical process to assess the methionine content and aspartic acid value of three amino acids, based on which the gender can be determined. This is again a non-automated chemical-based technique, which requires heating of the cocoon which can damage the pupa. The MRI image of the cocoon along with live pupa is acquired and later transformed by fast Fourier transform and T2 weighted images (to accurately reflect the tissue contrast into picture contrast) were obtained that aids in distinguishing the gender of the silkworms. Although the method is non-destructive and causes minimal damage to cocoon and the pupa, the imaging process is expensive and practically unsuitable for grainage centers. The cocoons were sorted based on the size of the cocoons. The female cocoons are bulkier whereas males are thin and slender. The cocoons are transferred into the vibrating grids of the sorting machine which are of varying size to separate the cocoons. Though the system was able to achieve an accuracy of 96% in sorting, the device is not meant for gender separation but mainly used for grading the quality of Tasar variety of cocoons. The graded cocoons are later sent to reeling where raw silk is extracted from the graded cocoons. The methodology integrated the weight, volume, and ZM-based shape features of the cocoons to form an integrated feature vector for training kNN, LDA, NN, and SVM classifiers. To validate the integration of these features, the performance was compared with the one obtained from integration of geometric shape features and integration of weight and volume with geometric shape features. The method used CSR2 and pure Mysore breeds of cocoons to conduct the experiment.

The results indicated a better performance of NN and SVM classifiers. An accuracy of 91.3% was achieved from CSR2 cocoon with NN classifier and 100% from pure Mysore cocoons via SVM-based classifier. This detailed literature review indicates the existing technologies used for silkworm gender separation at different stages of their life cycle. X-ray or MRI images of the cocoon are high-cost alternatives which provide accurate classification, but the radiation can damage the pupa inside the cocoon. Currently, at grainage centers, the sorting process is manual, where the cocoons’ weight (which includes the live pupa) and shape are used as features to distinguish their gender. Taking into account literature and industrial practice gaps, this paper presents the design and development of a novel non-destructive multi-sensor-based system to classify silkworm cocoons according to their gender. The system extracts the features of cocoons (weight and shape) individually and provides them as inputs to a pre-trained pattern classification model which in turn classifies the cocoons as male or female. Subsequently, a pair of air blowers and a conveyor system sort the cocoons into their respective bins. The developed system was trained and tested on two types of silkworm cocoons breeds, namely CSR2 and Pure Mysore, both provided by Central Silk Board Registered Grainage Center, Karnataka, India. The prominent advantages of the developed system are (a) elimination of human intervention in separation process, (b) reduction in mis-classification error, (c) good repeatability when compared to manual separation process, and (d) overall increase in speed of separation process. 2. Design and Development of Silkworm Cocoon Gender Classification Multi-Sensor System The multi-sensor system was designed and prototyped with the aim of performing automatic silkworm cocoons gender classification process. A schematic diagram of the proposed system is shown in Figure 4.

To tackle this issue, the FEM is endowed with an 18 W square LED panel light (shown in Figure 9 a,b) attached to the flip plate. Open in a separate window Figure 9 ( a ) RGB image of the cocoon on flip plate captured by FEM camera without backlight, ( b ) RGB image of the cocoon on flip plate captured by FEM camera with backlight illumination, and ( c ) binarized image of the cocoon shell with fibrous outer surface removed. By comparing the two images, the cocoon sample in normal light conditions without backlight ( Figure 9 a) and the same cocoon sample placed on the flip plate with backlight ( Figure 9 b), the advantage of the adopted illumination system is evident in the results. After image acquisition, the cocoon is transferred to the load sensor by letting it fall through a slope box (see Figure 4 ), where the velocity of falling cocoon is attenuated by travelling through number of inclined slopes. Following the weight data acquisition, an air blower mechanism ( Figure 10 ) is employed to transfer the cocoon from the load sensor unit to the HCM. The blower mechanism consists of an air blower which continuously provides a compressed air supply and a freely rotating swivel arm which is used to stop the air flow instantly. One end of the swivel arm is coupled with a servo motor and the other end is fixed on the side wall by a freely rotating cylindrical pin joint. Figure 10 a shows the mechanism in closed position (the air stream is blocked). Figure 10 b shows the mechanism in open position (the air stream is directed to the cocoon). Normally, the air blower mechanism is in the closed position, hence no air flow is directed on the cocoon. Once the weight data are acquired, the system sends a command to the servo motor and the swivel arm opens for 2 s allowing for the cocoon to be transferred from the load sensor to the HCM. Open in a separate window Figure 11 ( a ) Horizontal Conveyor Module; ( b ) IR proximity sensor close-up.

Their positions were determined empirically based on the computation time required by the workstation to provide the classification index. The HCM classifies the cocoons based on the index obtained from the workstation. Each component performs an individual function to carry out the cocoon sorting process. These components need to be synchronized in order perform automated operation. Figure 12 presents the flow diagram of silkworm cocoons gender sorting machine. Open in a separate window Figure 12 Flow diagram of silkworm cocoons gender classification machine. The acquired cocoon image in FEM module is sent to the workstation, where the shape features (area, perimeter, major axis length, minor axis length, etc.) are computed. At times, there is a chance of entering more than one cocoon into the FEM module. This condition is detected by computing the cocoon area from the binarized image and comparing it to an empirical threshold value. If the computed area exceeds the threshold, then exceeding cocoons are ejected by rotating the flip plate in counterclockwise direction. Ejected cocoons move out of the module through exit box ( Figure 4 ) to be fed back to the hopper. If the binarized image area results within the threshold limit (i.e., only one cocoon present on the flip plate), a signal is provided to the microcontroller to rotate the flip plate in clockwise direction to transfer the cocoon to the load sensor smoothly through slope box. At this point, the sample cocoon weight is acquired and provided to the algorithm in the workstation. Shape and weight features are then combined and fed to a pre-trained SVM to determine the gender of cocoon under examination. Further, the cocoon index and its corresponding predicted label are stored in the workstation and the cocoon present on the load sensor is moved to HCM module by the air blower mechanism.

As the cocoon moves along the HCM, the IR proximity sensors ( Figure 11 ) provide an input signal to microcontroller-2 which in turn retrieves the classification label of the current cocoon obtained from the workstation. The label is used to control the respective blowers. In this respect, if the predicted label is “male”, the first blower is triggered, and the cocoon is pushed on to the “male cocoon tray”. Conversely, if the cocoon label is “female”, the second blower is triggered and pushes the cocoon into the “female cocoon tray”. 3. Experimental Methodology The experimental campaign was carried out on two silkworm cocoons breeds, namely CSR2 and Pure Mysore, both provided by the industrial partner. Prior to the experimental tests’ commencement, the cocoons were manually labelled as male and female by highly trained and skilled professionals using a weight threshold as discriminating parameter. The cocoon weight is an important factor since it is highly correlated to the cocoons gender (i.e., cocoons above the weight threshold are considered as females and ones below are males) and is most commonly used gender separation method employed in the grainage centers. A weight threshold of 1.4 g for CSR2 breed and 1.1 g for Mysore breed was used to separate the cocoons as male and female to build the ground truth used for benchmarking. A total number of 167 cocoons was used to build the dataset, which included 76 Pure Mysore and 91 CSR2 breeds. For Pure Mysore breed, there were 35 male and 41 female specimens; similarly, CSR2 breed contained 47 males and 44 females. The training set was used to pre-train the SVM classifier for decision-making on gender classification. Table 3 Dataset for silkworm cocoons. The training set was used to pre-train the Support Vector Machine (SVM) classifier.

For a binary pattern recognition problem in which the classes are linearly separable the SVM selects from among the infinite number of linear decision boundaries the one that minimizes the generalization error. If the two classes are not linearly separable, the SVM tries to find the hyperplane that maximizes the margin while, at the same time, minimizing the misclassification errors. In this research work a linear kernel has been utilized to train the SVM. In order to train the classifier, the separated training cocoons were labelled and indexed manually prior to being loaded into the VCM hopper. Once the cocoon was transferred from the VCM to FEM, the cocoon’s silhouette was acquired by camera and passed to the workstation. If the camera was rigidly fixed at distance of 18 cm from the flip plate, the area of an individual cocoons ranged between 500 and 550 pixels. If the area was greater than this interval, the system assumed that the VCM has transferred more than one cocoon to FEM, therefore the exceeding cocoons were ejected back to the hopper through the exit box. Once the FEM ensured the feeding of a single cocoon, a number of shape features were computed and extracted from the silhouette image as reported in Table 4. Table 4 Shape-related features extracted from the silhouette binary image. Computed using minimum bounding box (smallest rectangle containing every point in the shape) method.It is given by. The solidity of a convex shape is always 1 Convex area ( A C ) It is given by:Open in a separate window Once the features were computed, the flip plate rotated clockwise and weight of the cocoon (W) was obtained from the weight sensor placed below and serially transferred to work station. Later, using the air-blower mechanism explained in section, the cocoon moved to the next stage of the pipeline. The normalized IFV is labeled for supervised machine learning. The model is further evaluated using the testing data set.

Such matrices show the True Male, True Female, False Male, and False Female. With reference to the SVM training process, the classification results CMs for the CSR2 and Pure Mysore breeds cocoons are reported in Figure 13. Open in a separate window Figure 13 SVM training confusion matrices for CSR2 ( a ) and Pure Mysore cocoons ( b ). To validate the accuracy of the prototype, unknown cocoon samples are indexed and loaded into the VCM’s hopper. As the cocoon travel from VCM to FEM, its features are extracted and transferred to the workstation where the pre-trained SVM provides the predicted classification label. The label and the corresponding cocoon indices are stored as look up table within the workstation. When the cocoon moves along the HCM, the IR proximity sensors and with the microcontroller query the workstation to provide the classification label of the current cocoon. This label is used by the sensor to blow the cocoons to their respective trays as explained in Section 2. This process utilizes all the cocoons present in the testing dataset and the performance of the prototype with SVM model is evaluated similar to that of the training process. With reference to the SVM test process, the classification results CMs for the CSR2 and Pure Mysore breeds cocoons are reported in Figure 14. Open in a separate window Figure 14 SVM test confusion matrix for CSR2 cocoons ( a ) and Pure Mysore cocoons ( b ). Such performance metrics are reported in Table 5 with reference to both training and test phases for CSR2 and Pure Mysore cocoons respectively: Table 5 Performance metrics (PM) obtained for CSR2 and pure Mysore cocoons from SVM training and testing. The cocoons were indexed, and their classification labels recorded prior to the trial conduction. The prediction results from protype each cocoon for all the four trials are illustrated in Figure 15. The chart shows that 44 cocoons were correctly predicted in all the four trials.