4.21 inch round lcd display manufacturer

In a span of six years, we have developed thousands of LCD and LCM products for telecom, consumer electronics,medical equipment and other applications

We follow a people-oriented, quality and customer-first approach in all of our dealings. That is why we maintain a cleanenvironment which is conducive for working and take the environment into account during the manufacturing process.Find out more about the benefits of working with us. Contact us today for any of your LCD / LCM projects and requirements.

US Micro Products manufactures a wide selection of TFT LCD (Active Matrix LCDs) displays to accommodate the needs of OEMs across many different industries, including medical, industrial, gaming, military and many more.

An array of available interfaces, brightness levels, and temperature ranges ensure that our TFT LCDs work well with your design and in the environment of your choice

If you can’t find what you are looking for, contact us at sales@usmicroproducts.com! We are a manufacturer of Custom Display Solutions and are experts in extreme applications.

This 4.2" circular TFT display module features high contrast ratio, high brightness, ultra wide viewing angles and extended operating temperature. Its round outline and wide operating temperature make it suitable for Automotive, Marine, Industrial and Medical and applications.

Although circular displays come at a premium price compared to the more traditional rectangular shapes, we can offer modules, with or without touchscreen with affordable NRE and tooling cost structure for industrial applications.

These displays do come with minimum MOQs, so even if your project does not meet these we can offer cost-effective customised coverlens solutions and enclosures which achieve acircular look and feel, just contact us to talk through your project and we can guide you through your options.

Tradechina.com offers 1638 circular lcd display products. About 36% % of these are lcd module, 6%% are oled/e-paper module, and 4%% are lcd touch screen.

“Just received the 15 samples this morning and have already incorporated one in a prototype and initial impressions are very favorable… Thank you and your team for the speedy turn-around, despite all the hurdles we threw at you.”

According to real LCD manufacturing conditions, the number of normal LCD panels exceeds greatly the number of defective LCD panels. Therefore, the normal PRs greatly outnumber the defective PRs. As a result, the collected data set for training would be imbalanced if a two-class classification approach is adopted, the SVM by Vapnik [4] for example, the class imbalance problem occurs.

In practice, in addition to the class imbalance problem, the LCD defect detection also suffers from another critical problem resulting from the absence of negative information. To facilitate the following problem description, the normal PR class and the defective PR class are defined as the positive class and negative class, respectively.

The main difference between a normal PR and a defective PR is that their appearances are apparently different, as can be observed from Figure 4. The color (or gray level) of a normal PR is nearly uniform, implying that the variation of the gray-level distribution of normal PRs is very small. On the contrary, the surfaces of defective PR not only contain various kinds of textures, but also vary greatly in color, implying that the variation of the true distribution for negative class in the data space is very large. Collecting a set of positive training data that can represent the true distribution of positive class is easy, because: (1) the variation of positive-class distribution is very small; and (2) most of the LCD panels are normal (the number of normal PRs is considerably large). Therefore, the positive class can be well-sampled during the data collection stage in real practice. However, representative defective PRs are difficult to obtain in practice for several reasons. For example, there are numerous types of defects in array process, more than 10 types at least. However, not all the defects would occur frequently. Some of the defects seldom appear, for example the defect caused by abnormal photo-resist coating (APRC). The defect “APRC” seldom occurs, because equipment/process engineers maintain the coating machines periodically. Even so, the coating machines might still break down occasionally. As a result, the number of available images containing the APRC defects is quite limited. But, the APRC defect has a large variation in color and texture. Unfortunately, limited APRC examples cannot stand for all kinds of APRC defects. Therefore, the collected negative training data are most likely under-sampled. Here, the “under-sampled” means that the collected negative training set cannot represent the true negative-class distribution in the data space, which is the problem of absence of negative information. Due to this problem, numerous false positive (i.e., missing defects) will be produced if a two-class classification approach (e.g., a binary SVM) is applied to the LCD defect detection, which has been evidenced by the results reported in [7]. Compared with two-class classification approach, novelty detection approach is a better choice.

Novelty detection is one-class classification [10,35], which is to solve the conventional two-class classification problems where one of the two classes is under-sampled, or only the data of one single class can be available for training [5,6,9–11,35–40]. As analyzed above, for the LCD defect detection application, the normal PRs can be well-sampled, while the defective PRs are in general undersampled. Therefore, the LCD defect detection can be treated as a typical novelty detection problem. Accordingly, one-class classification is a better solution.

To summarize, it can be seen that the LCD defect detection suffers from two problems simultaneously: one is the class imbalance problem, and the other is the problem of the absence of negative information. For the first problem, there have been many sophisticated solutions, including sampling, cost-sensitive learning, SVM-based, and one-class learning approaches. However, the only solution to the second problem is the novelty detection approach (i.e., one-class classification approach). Therefore, one-class classification would be a more appropriate approach to the LCD defect detection application.

There are several approaches for one-class classification, such as density approach (e.g., Gaussian mixture model [5]), boundary approach (e.g., SVDD [9] and one-class SVM [40]), neural network approach [6,36], and reconstruction-based approach (e.g., the kernel principal component analysis for novelty detection [35]). It has been proven in [9] that when a Gaussian kernel is used, the SVDD proposed by Tax and Duin [9] is identical to the one-class SVM proposed by Schölkopf et al. [40]. This paper focuses on the SVDD since it has been applied to the same application in the works of [7] and [10], and has shown to be effective in detecting defective PRs. However, as discussed in Section 1, generalization performance of SVDD is limited. Therefore, the intent of this paper is on proposing a method to improve generalization performance of SVDD, and applying the improved SVDD to the LCD defect detection treated as a novelty detection problem. The improved SVDD is called quasiconformal kernel SVDD (QK-SVDD). Note that the QK-SVDD and SVDD are not two independent classifiers. To obtain QK-SVDD, one has to train an SVDD first, which will be introduced in Section 2.4. In the following part of the paper, we first introduce the defect detection scheme, and then derive the proposed method in details.

Our new line of 10.1” TFT displays with IPS technology are now available! These 10.1” IPS displays offer three interface options to choose from including RGB, LVDS, and HDMI interface, each with two touchscreen options as capacitive or without a touchscreen.

The new line of 3.5” TFT displays with IPS technology is now available! Three touchscreen options are available: capacitive, resistive, or without a touchscreen.