A novel technology for encoding target data such as an image and an audio is provided. A code generation method for generating a code according to a content of target data using an information processing device is provided. The method includes a step of dividing the target data into a plurality of sampling ranges, a step of obtaining, for each of the sampling ranges, an average value of at least one data element among one or more types of data element included in each of the sampling ranges, each data element being represented by a numerical value, and a step of generating a reference code corresponding to the target data by concatenating, as character string data, the average values of the respective sampling ranges or numerals of a predetermined number of digits from a top digit of the average values.

A method for protecting data in a data memory against an undetected change, wherein a functional variable x is encoded via a value, an input constant, an input signature and a timestamp D into a coded variable, where the functional variable is normalized relative to a base to form the integer value from the functional variable.

Techniques and apparatus for verification of compressed data are described. In one embodiment, for example an apparatus to provide verification of compressed data may include at least one memory and logic, at least a portion of comprised in hardware coupled to the at least one memory, the logic to access compressed data, access compression information associated with the compressed data, decompress at least a portion of the compressed data to generate decompressed data, and verify the compressed data via a comparison of the decompressed data with the compression information. Other embodiments are described and claimed.

Technologies for error recovery in compressed data streams include a compute device configured to compress uncompressed data of an input stream to generate compressed data, perform a compression error check on the compressed data to verify integrity of the compressed data, and determine, as a result of the performed compression error check, whether the compressed data included a compression error. The compute device is further configured to transfer, in response to a determination that the performed compression error check indicated that the compressed data included the compression error, the uncompressed data into a destination buffer, and store an indication with the uncompressed data into the destination buffer, wherein the indication is usable to identify that the uncompressed data has been transferred into the destination buffer. Other embodiments are described herein.

According to an embodiment, a storage control device includes a controller, a compression condition determiner, a compressor, and an error correction encoder. The controller receives a write request for a data item and determines whether or not the wear degree of a target region in a storage device to which the data item is to be written is less than a threshold value. The compression condition determiner determines, based on the wear degree, an optimal compression condition out of compression conditions that include lossy compression. The compressor generates, based on the compression condition, compressed data. The error correction encoder subjects the data item to error correction and generates encoded data.

A lossless data compressor prevents normalization overruns on-the-fly as symbol occurrence counts are rounded to generate symbol frequencies, allowing an encoding table generator to generate encoding table entries without waiting for the symbol frequency table to finish filling. Rounding errors are accumulated as symbols are normalized and compensated for by reducing a symbol frequency when the symbol frequency is at least 2 and the accumulated errors have exceeded a threshold. The symbol frequency is also reduced when the number of remaining states in the encoding table is insufficient for a number of remaining unprocessed symbols and states for a current encoding table entry. Since error compensation occurs as symbols are being normalized, encoding table generation is not forced to wait for all symbols in the block to be processed, reducing latency. Three pipeline stages can operate on three input blocks: symbol counting, normalization/error compensation/encoding table generation, and data encoding.

Techniques and apparatus for verification of compressed data are described. In one embodiment, for example an apparatus to provide verification of compressed data may include at least one memory and logic, at least a portion of comprised in hardware coupled to the at least one memory, the logic to access compressed data, access compression information associated with the compressed data, decompress at least a portion of the compressed data to generate decompressed data, and verify the compressed data via a comparison of the decompressed data with the compression information. Other embodiments are described and claimed.

A method, a user equipment, UE, a network node, and a computer program product for classifying channel state information, CSI, compression quality in a wireless communication network are provided. The method is performed in a UE in the wireless communication network. The method includes obtaining CSI associated with one or more radio channels. Further, the method includes compressing the CSI into an encoded format representing a compressed CSI. The method further includes classifying a CSI compression quality related to reconstruction of the one or more radio channels of the compressed CSI using a classifier predicting a resulting performance loss associated with the reconstruction of the one or more radio channels. The classification of the CSI compression quality is based on a level of predicted performance loss.

The proposed approach deals with efficient transmission for distributed AI with a provision to switch among multiple bandwidths. During the distributed inference at edge devices, each device needs to load part of the AI model only once, but the input/output features communicated between them can be flexibly configured depending on the available transmission bandwidth by enabling/disabling connection between nodes in the Dynamic feature size Switch (DySw). When some nodes are connected or disconnected in order to achieve the desired compression factor, other parameters of the DNN remain the same. That is, the same DNN model is used for different compression factors, and no new DNN model needs to be downloaded to adapt to the compression factor or the network bandwidth.

Reconstructing compressed data to reduce compression loss. Data at a device is compressed using lossy compression and metadata values are added to the compressed data. A gateway receives a package including the compressed data and the metadata values. The data is decompressed and input to a machine learning model along with the metadata values. The machine learning model is trained to reduce the compression loss. The output of the model is an improved decompressed data. Actions may be performed based on the improved decompressed data.