Recent years have seen great strides being made in R&D of speech and language technologies. As these technologies continue to permeate our daily lives, they need to support diverse users and usage contexts, especially those with inputs that deviate from the mainstream.
In this blog, we investigate the model-driven deep learning for multiple input-multiple output (MIMO) detection. In particular, the MIMO detector is specially designed by unfolding an iterative algorithm and adding some trainable parameters.
A brief introduction to state estimation in multi-object system that arises from applications where the number of objects and their states are unknown and vary randomly with time. State space model (SSM) is a fundamental concept in system theory that permeated through many fields of study.
We design a data-driven wavelet transform, called the empirical wavelet transform, which permits to extract very accurate time-frequency information from signals, or features from images.
We develop algorithms to analyzing facial expression by learning from the data. Since local characters of muscle movements play an important role in distinguishing facial expression by machines, we explore the local characters of facial expressions by introducing the attention mechanism in both supervised and self-supervised supervised manners. Our methods is experimentally shown to be effective on facial expression recognition with occlusions and facial action unit detection.
Quantum search algorithms are capable of efficiently solving large-scale quantum computing and signal processing problems, but their operation is contaminated by the decoherence of quantum circuits. This may be mitigated by quantum codes. Secure QKD is already a commercial reality in 2021.
Accurate channel estimation is a major challenge in the next generation of wireless communication networks. To fully exploit setups with many antennas, estimation errors must be kept small. This can be achieved by exploiting the structure inherent in the channel vectors. For example, line-of-sight paths result in highly correlated channel coefficients.
Filtering is the fundamental operation upon which the field of signal processing is built. Loosely speaking, filtering is a mapping between signals, typically used to extract useful information (output signal) from data (input signal). Arguably, the most popular type of filter is the linear and shift-invariant (i.e. independent of the starting point of the signal) filter, which can be computed efficiently by leveraging the convolution operation.
The upcoming 5G network needs to achieve substantially larger link capacity and ultra-low latency to support emerging mobile applications. While conventional techniques have reached their limits, uplifting the carrier frequency to the millimeter-wave (mm-wave) band stands out as an effective approach to further boost the network capacity, as it provides orders of magnitude greater spectrum than current cellular bands.
With the explosive growth of data, it is a heavy burden for users to store the sheer amount of data locally. Therefore, more and more organizations and individuals would like to store their data in the cloud. However, the data stored in the cloud might be corrupted or lost due to the inevitable software bugs, hardware faults and human errors in the cloud.
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