Abstract: Natural language translation device contains a bus, an input interface connecting to the bus for receiving a source sentence in a first natural language to be translated to a target sentence in second natural language one word at a time in sequential order. A two-dimensional (2-D) symbol containing a super-character characterizing the i-th word of the target sentence based on the received source sentence is formed in accordance with a set of 2-D symbol creation rules. The i-th word of the target sentence is obtained by classifying the 2-D symbol via a deep learning model that contains multiple ordered convolution layers in a Cellular Neural Networks or Cellular Nonlinear Networks (CNN) based integrated circuit.

Abstract: Apparatus for recognition of handwritten Chinese characters contains a bus, an input means connecting to the bus for receiving input imagery data created from a handwritten Chinese character, a Cellular Neural Networks or Cellular Nonlinear Networks (CNN) based integrated circuit operatively connecting to the bus for extracting features out of the input imagery data using pre-trained filter coefficients of a plurality of order convolutional layers stored therein, a memory connecting the bus, the memory being configured for storing weight coefficients of fully-connected (FC) layers, a processing unit connecting to the bus for performing computations of FC layers to classify the extracted features from the CNN based integrated circuit to a particular Chinese character in a predefined Chinese character set, and a display unit connecting to the bus for displaying the particular Chinese character.

Abstract: Methods of recognizing motions of an object in a video clip or an image sequence are disclosed. A plurality of frames are selected out of a video clip or an image sequence of interest. A text category is associated with each frame by applying an image classification technique with a trained deep-learning model for a set of categories containing various poses of an object within each frame. A “super-character” is formed by embedding respective text categories of the frames as corresponding ideograms in a 2-D symbol having multiple ideograms contained therein. Particular motion of the object is recognized by obtaining the meaning of the “super-character” with image classification of the 2-D symbol via a trained convolutional neural networks model for various motions of the object derived from specific sequential combinations of text categories. Ideograms may contain imagery data instead of text categories, e.g., detailed images or reduced-size images.

Abstract: Systems and methods for communications between Internet of Things (IoT) devices using a multi-layer 2-D symbol that contains multiple ideograms are disclosed. A system for transmitting information within an IoT environment includes first and second IoT devices, and an optional IoT data center operatively connected to a communication network. First IoT device contains a first computing device for creating a 2-D symbol in response to a request or alert from a sensor or actuator operatively coupled with the first IoT device. Second IoT device contains a second computing device for learning the meaning of the “super-character” by using an image processing technique in the second IoT device to classify the 2-D symbol, which is transmitted optionally through the IoT data center from the first IoT device via the communication network. The optional IoT data center controls IoT devices and facilitates data transmission of the 2-D symbol amongst IoT devices.

Abstract: Multiple 3×3 convolutional filter kernels are used for approximating operations of fully-connected (FC) layers. Image classification task is entirely performed within a CNN based integrated circuit. Output at the end of ordered convolutional layers contains P feature maps with F×F pixels of data per feature map. 3×3 filter kernels comprises L layers with each organized in an array of R×Q of 3×3 filter kernels, Q and R are respective numbers of input and output feature maps of a particular layer of the L layers. Each input feature map of the particular layer comprises F×F pixels of data with one-pixel padding added around its perimeter. Each output feature map of the particular layer comprises (F?2)×(F?2) pixels of useful data. Output of the last layer of the L layers contains Z classes. L equals to (F?1)/2 if F is an odd number. P, F, Q, R and Z are positive integers.

Abstract: Methods of recognizing motions of an object in a video clip or an image sequence are disclosed. A plurality of frames are selected out of a video clip or an image sequence of interest. A text category is associated with each frame by applying an image classification technique with a trained deep-learning model for a set of categories containing various poses of an object within each frame. A “super-character” is formed by embedding respective text categories of the frames as corresponding ideograms in a 2-D symbol having multiple ideograms contained therein. Particular motion of the object is recognized by obtaining the meaning of the “super-character” with image classification of the 2-D symbol via a trained convolutional neural networks model for various motions of the object derived from specific sequential combinations of text categories. Ideograms may contain imagery data instead of text categories, e.g., detailed images or reduced-size images.

Abstract: Two-dimensional symbols with each containing multiple ideograms for facilitating machine learning are disclosed. Two-dimensional symbol comprises a matrix of N×N pixels of data representing a “super-character”. The matrix is divided into M×M sub-matrices with each of the sub-matrices containing (N/M)×(N/M) pixels. N and M are positive integers or whole numbers, and N is preferably a multiple of M. Each of the sub-matrices represents one ideogram defined in an ideogram collection set. “Super-character” represents at least one meaning each formed with a specific combination of a plurality of ideograms. Ideogram collection set includes, but is not limited to, pictograms, logosyllabic characters, Japanese characters, Korean characters, punctuation marks, numerals, special characters. Logosyllabic characters may contain one or more of Chinese characters, Japanese characters, Korean characters. Features of each ideogram can be represented by more than one layer of two-dimensional symbol.

Abstract: Two-dimensional symbol for facilitating machine learning of written Chinese language using logosyllabic characters is disclosed. The two-dimensional symbol comprises a matrix of N×N pixels of data containing a “super-character” that represents a specific form and meaning of written Chinese language. The matrix is divided into M×M sub-matrices with each sub-matrix containing (N/M)×(N/M) pixels. Each of sub-matrix represents one logosyllabric character defined in a standard set (e.g., GB18030). “Super-character” is recognized in a Cellular Neural Networks or Cellular Nonlinear Networks (CNN) based computing system via an image processing technique such as convolution neural networks algorithm. “Super-character” contains a minimum of two and a maximum of M×M characters for representing written Chinese language including, but not necessarily limited to, compounded phrases, idioms, proverbs, written passages, sentences, poems, paragraphs, articles (i.e., written works).

Abstract: A string of Latin-alphabet based language texts is received and formed a multi-layer 2-D symbol in a computing system. The received string contains at least one word with each word containing at least one letter of the Latin-alphabet based language. 2-D symbol comprises a matrix of N×N pixels of data representing a super-character. The matrix is divided into M×M sub-matrices. Each sub-matrix represents one ideogram formed from the at least one letter contained in a corresponding word in the received string. Ideogram has a square format with a dimension EL letters by EL letters (i.e., row and column). EL is determined from the total number of letters (LL) contained in the corresponding word. EL, LL, N and M are positive integers. Super-character represents a meaning formed from a specific combination of at least one ideogram. Meaning of the super-character is learned with image classification of the 2-D symbol.

Abstract: A string of natural language texts is received and formed a multi-layer 2-D symbol in a first computing system. The 2-D symbol comprises a matrix of N×N pixels of data representing a “super-character”. The matrix is divided into M×M sub-matrices with each sub-matrix containing (N/M)×(N/M) pixels. N and M are positive integers, and N is preferably a multiple of M. Each sub-matrix represents one ideogram defined in an ideogram collection set. “Super-character” represents a meaning formed from a specific combination of a plurality of ideograms. The meaning of the “super-character” is learned in a second computing system by using an image processing technique to classify the 2-D symbol, which is formed in the first computing system and transmitted to the second computing system. Image process technique includes predefining a set of categories and determining a probability for associating each of the predefined categories with the meaning of the “super-character”.

Abstract: A string of natural language texts is received and formed a multi-layer 2-D symbol in a computing system. The 2-D symbol comprises a matrix of N×N pixels of K-bit data representing a “super-character”. The matrix is divided into M×M sub-matrices with each sub-matrix containing (N/M)×(N/M) pixels. K, N and M are positive integers, and N is preferably a multiple of M. Each sub-matrix represents one ideogram defined in an ideogram collection set. “Super-character” represents a meaning formed from a specific combination of a plurality of ideograms. The meaning of the “super-character” is learned by classifying the 2-D symbol via a trained convolutional neural networks model having bi-valued 3×3 filter kernels in a Cellular Neural Networks or Cellular Nonlinear Networks (CNN) based integrated circuit.

Abstract: A deep learning object detection and recognition system contains a number of cellular neural networks (CNN) based integrated circuits (ICs) operatively coupling together via the network bus. The system is configured for detecting and then recognizing one or more objects out of a two-dimensional (2-D) imagery data. The 2-D imagery data is divided into N set of distinct sub-regions in accordance with respective N partition schemes. CNN based ICs are dynamically allocated for extracting features out of each sub-region for detecting and then recognizing an object potentially contained therein. Any two of the N sets of sub-regions overlap each other. N is a positive integer. Object detection is achieved with a two-category classification using a deep learning model based on approximated fully-connected layers, while object recognition is performed using a local database storing feature vectors of known objects.

Abstract: Image classification system contains a CNN based IC configured for extracting features out of input data by performing convolution operations using filter coefficients of ordered convolutional layers and a classifier IC configured for classifying the input data using reduced set of the extracted features based on a light-weight classifier. Light-weight classifier is derived by: training filter coefficients of the ordered convolutional layers using a dataset containing N labeled data, the trained filter coefficients are for the CNN based IC; outputting respective extracted features of the N labeled data after performing convolution operations of ordered convolutional layers using the trained filter coefficients, each labeled data contains X features; creating the reduced set of the extracted features by eliminating those of the X features that contain zeros in at least M of the N labeled data; and adjusting M until the light-weight classifier achieves satisfactory results using the reduced set.

Abstract: Method and systems of replacing operations of depthwise separable filters with first and second replacement convolutional layers are disclosed. Depthwise separable filters contains a combination of a depthwise convolutional layer followed by a pointwise convolutional layer with input of P feature maps and output of Q feature maps. The first replacement convolutional layer contains P×P of 3×3 filter kernels formed by placing each of the P×1 of 3×3 filter kernels of the depthwise convolutional layer on respective P diagonal locations, and zero-value 3×3 filter kernels zero-value 3×3 filter kernels in all off-diagonal locations. The second replacement convolutional layer contains Q×P of 3×3 filter kernels formed by placing Q×P of 1×1 filter coefficients of the pointwise convolutional layer in center position of the respective Q×P of 3×3 filter kernels, and numerical value zero in eight perimeter positions.

Abstract: Operations of a combination of first and second original convolutional layers followed by a short path are replaced by operations of a set of three particular convolutional layers. The first contains 2N×N filter kernels formed by placing said N×N filter kernels of the first original convolutional layer in left side and N×N filter kernels of an identity-value convolutional layer in right side. The second contains 2N×2N filter kernels formed by placing the N×N filter kernels of the second original convolutional layer in upper left corner, N×N filter kernels of an identity-value convolutional layer in lower right corner, and N×N filter kernels of two zero-value convolutional layers in either off-diagonal corner. The third contains N×2N of kernels formed by placing N×N filter kernels of a first identity-value convolutional layer and N×N filter kernels of a second identity-value convolutional layer in a vertical stack. Each filter kernel contains 3×3 filter coefficients.

Abstract: Methods and systems for extracting features directly from convolutional layers are disclosed. The last layer in the ordered convolutional layers contains reduced number of channels of features with respect to the immediately prior layer. Filter coefficients of the convolutional layers are trained for image classification task together with fully-connected networks. For image verification task, filter coefficients can be trained using Siamese networks. Training of the filter coefficients is performed in the sequential order of ordered convolutional layers. Once trained, the ordered convolutional layers with the last layer having reduced number of channels can be used directly for extracting features with acceptable accuracy in certain applications (e.g., face verification). Trained filter coefficients can optionally be converted to bi-valued filter coefficients, and then be loaded into a cellular neural networks (CNN) based digital integrated circuit.

Abstract: The invention discloses a treatment method of a chlorine-containing zinc oxide secondary material, which comprises the following steps: 1) leaching the chlorine-containing zinc oxide secondary material I through an acid solution; 2) selectively extracting zinc through di-(2-ethylhexyl)phosphoric acid (P204)-kerosene solvent; 3) implementing stripping-electrolysis zinc recovery; 4) repeating steps 1)-4); 5) taking out the raffinate obtained from the Step (4), mixing the residual taken out raffinate with chlorine-containing zinc oxide secondary material II when balance on chlorine ion input and taking out is achieved; carrying out liquid-solid separation; leaching the separated deposit through acid raffinate of the step 1); 6) after separated solution achieves preset conditions, purifying the chlorine-containing aqueous phase; 7) evaporating and concentrating to crystallize out KCl and NaCl products.

Abstract: CNN based integrated circuit is configured with a set of pre-trained filter coefficients or weights as a feature extractor of an input data. Multiple fully-connected networks (FCNs) are trained for use in a hierarchical category classification scheme. Each FCN is capable of classifying the input data via the extracted features in a specific level of the hierarchical category classification scheme. First, a root level FCN is used for classifying the input data among a set of top level categories. Then, a relevant next level FCN is used in conjunction with the same extracted features for further classifying the input data among a set of subcategories to the most probable category identified using the previous level FCN. Hierarchical category classification scheme continues for further detailed subcategories if desired.

Abstract: The invention discloses a treatment method of a chlorine-containing zinc oxide secondary material, which comprises the following steps: 1) leaching the chlorine-containing zinc oxide secondary material I through an acid solution; 2) selectively extracting zinc through P204-kerosene solvent; 3) implementing stripping-electrolysis zinc recovery; 4) repeating steps 1)-4); 5) taking out the raffinate obtained from the Step (4), mixing the residual taken out raffinate with chlorine-containing zinc oxide secondary material II when balance on chlorine ion input and taking out is achieved; carrying out liquid-solid separation; leaching the separated deposit through acid raffinate of the step 1); 6) after separated solution achieves preset conditions, purifying the chlorine-containing aqueous phase; 7) evaporating and concentrating to crystallize out KCl and NaCl products.