Analysis of business development of a technology commercial company

This thesis centers on analyzing SHEIN, one of the giant technology commercial companies all over the world. It is from China but only develops business abroad. SHEIN has got an extraordinary achievement in this decade and that is inextricably linked to its unique operating model which is composed of six important parts that are the business gene of SHEIN, the supply chain, the modern digital system, the marketing methods, the logistics and the industry environment. These six different parts are interlocked and closely linked to each other. Specially, different from other leading brands in the industry such as ZARA and Amazon, SHEIN has quickly captured the fast-fashion market of the younger generation with cheaper prices and faster logistics supply chain. So far, SHEIN has its own design line of products on the website and has also expanded other categories of products like household products. However, a large part of SHEIN’s success has been brought about by this era. With the development of the Internet economy, people from the younger generation have an increasing pursuit of fashion. The rapid rise of online marketing and social media make it easier to promote the brand reputation all over the world. In conclusion, it can be said that SHEIN is in line with the trend of this era and this era has achieved SHEIN’s business model. However, one corn has two sides, there is a voice of doubt here. How far can SHEIN go in the future? Whether it is to make quick money in a few years? It is undeniable that SHEIN does have a lot of problems, and although they have been well solved so far, if the solutions are sustainable in the long run or not and SHEIN would face them positively with their young and energetic company cultur

Collaborative Spectrum Sensing from Sparse Observations Using Matrix Completion for Cognitive Radio Networks

In cognitive radio, spectrum sensing is a key component to detect spectrum holes (i.e., channels not used by any primary users). Collaborative spectrum sensing among the cognitive radio nodes is expected to improve the ability of checking complete spectrum usage states. Unfortunately, due to power limitation and channel fading, available channel sensing information is far from being sufficient to tell the unoccupied channels directly. Aiming at breaking this bottleneck, we apply recent matrix completion techniques to greatly reduce the sensing information needed. We formulate the collaborative sensing problem as a matrix completion subproblem and a joint-sparsity reconstruction subproblem. Results of numerical simulations that validated the effectiveness and robustness of the proposed approach are presented. In particular, in noiseless cases, when number of primary user is small, exact detection was obtained with no more than 8% of the complete sensing information, whilst as number of primary user increases, to achieve a detection rate of 95.55%, the required information percentage was merely 16.8%

Collaborative Spectrum Sensing from Sparse Observations in Cognitive Radio Networks

Spectrum sensing, which aims at detecting spectrum holes, is the precondition for the implementation of cognitive radio (CR). Collaborative spectrum sensing among the cognitive radio nodes is expected to improve the ability of checking complete spectrum usage. Due to hardware limitations, each cognitive radio node can only sense a relatively narrow band of radio spectrum. Consequently, the available channel sensing information is far from being sufficient for precisely recognizing the wide range of unoccupied channels. Aiming at breaking this bottleneck, we propose to apply matrix completion and joint sparsity recovery to reduce sensing and transmitting requirements and improve sensing results. Specifically, equipped with a frequency selective filter, each cognitive radio node senses linear combinations of multiple channel information and reports them to the fusion center, where occupied channels are then decoded from the reports by using novel matrix completion and joint sparsity recovery algorithms. As a result, the number of reports sent from the CRs to the fusion center is significantly reduced. We propose two decoding approaches, one based on matrix completion and the other based on joint sparsity recovery, both of which allow exact recovery from incomplete reports. The numerical results validate the effectiveness and robustness of our approaches. In particular, in small-scale networks, the matrix completion approach achieves exact channel detection with a number of samples no more than 50% of the number of channels in the network, while joint sparsity recovery achieves similar performance in large-scale networks.Comment: 12 pages, 11 figure