Abstract: Provided are a granulated blast-furnace slag activator and a method of manufacturing the same. The granulated blast-furnace slag activator includes, in percent by weight, the following raw materials: 62% to 95% of gypsum and 5% to 38% of high belite sulfoaluminate cement clinker. Also provided is a method of manufacturing cement by mixing the granulated blast-furnace slag activator with granulated blast-furnace slag at a certain ratio.

Abstract: Provided are a granulated blast-furnace slag activator and a method of manufacturing the same. The granulated blast-furnace slag activator includes, in percent by weight, the following raw materials: 62% to 95% of gypsum and 5% to 38% of high belite sulfoaluminate cement clinker. Also provided is a method of manufacturing cement by mixing the granulated blast-furnace slag activator with granulated blast-furnace slag at a certain ratio.

Abstract: The invention discloses a rapid-hardening high-belite calcium sulfoaluminate cement clinker and relates generally to a rapid-hardening high-belite calcium sulfoaluminate cement clinker and methods to use and to manufacture the clinker. The clinker of the present invention comprises 20% to 35% by weight of C4A3S 3% to 9% by weight of C4AF, 37% to 47% by weight of C2S, 0.5% to 4.6% by weight of f-CaO and 14% to 26.3% by weight of CaSO4. The chemical compositions of the clinker are 12.9% to 16.1% by weight of SiO2, 12% to 19% by weight of Al2O3, 1% to 3% by weight of Fe2O3, 49% to 54% by weight of CaO and 12% to 18.43% by weight of SO3. It is manufactured by calcining, at a temperature of 1300° C.±50° C. in a rotary kiln, the raw meal, comprising 33% to 62% by weight of limestone, 10.5% to 28% by weight of fly ash, and 19% to 45% by weight of FGD gypsum.

Abstract: The invention discloses a rapid-hardening high-belite calcium sulfoaluminate cement clinker and relates generally to a rapid-hardening high-belite calcium sulfoaluminate cement clinker and methods to use and to manufacture the clinker. The clinker of the present invention comprises 20% to 35% by weight of C4A3S 3% to 9% by weight of C4AF, 37% to 47% by weight of C2S, 0.5% to 4.6% by weight of f-Ca° and 14% to 26.3% by weight of CaSO4. The chemical compositions of the clinker are 12.9% to 16.1% by weight of SiO2, 12% to 19% by weight of Al2O3, 1% to 3% by weight of Fe2O3, 49% to 54% by weight of CaO and 12% to 18.43% by weight of SO3. It is manufactured by calcining, at a temperature of 1300° C.±50° C. in a rotary kiln, the raw meal, comprising 33% to 62% by weight of limestone, 10.5% to 28% by weight of fly ash, and 19% to 45% by weight of FGD gypsum.

Abstract: A system and method for real-time multi-view (i.e. not just frontal view) face detection. The system and method uses a sequence of detectors of increasing complexity and face/non-face discriminating thresholds to quickly discard non-faces at the earliest stage possible, thus saving much computation compared to prior art systems. The detector-pyramid architecture for multi-view face detection uses a coarse-to-fine and simple-to-complex scheme. This architecture solves the problem of lengthy processing that precludes real-time face detection effectively and efficiently by discarding most of non-face sub-windows using the simplest possible features at the earliest possible stage. This leads to the first real-time multi-view face detection system which has the accuracy almost as good as the state-of-the-art system yet 270 times faster, allowing real-time performance.

Abstract: A novel statistical learning procedure that can be applied to many machine-learning applications is presented. Although this boosting learning procedure is described with respect to its applicability to face detection, it can be applied to speech recognition, text classification, image retrieval, document routing, online learning and medical diagnosis classification problems.

Abstract: A system and method for real-time multi-view (i.e. not just frontal view) face detection. The system and method uses a sequence of detectors of increasing complexity and face/non-face discriminating thresholds to quickly discard non-faces at the earliest stage possible, thus saving much computation compared to prior art systems. The detector-pyramid architecture for multi-view face detection uses a coarse-to-fine and simple-to-complex scheme. This architecture solves the problem of lengthy processing that precludes real-time face detection effectively and efficiently by discarding most of non-face sub-windows using the simplest possible features at the earliest possible stage. This leads to the first real-time multi-view face detection system which has the accuracy almost as good as the state-of-the-art system yet 270 times faster, allowing real-time performance.

Abstract: A system and method for real-time multi-view (i.e. not just frontal view) face detection. The system and method uses a sequence of detectors of increasing complexity and face/non-face discriminating thresholds to quickly discard non-faces at the earliest stage possible, thus saving much computation compared to prior art systems. The detector-pyramid architecture for multi-view face detection uses a coarse-to-fine and simple-to-complex scheme. This architecture solves the problem of lengthy processing that precludes real-time face detection effectively and efficiently by discarding most of non-face sub-windows using the simplest possible features at the earliest possible stage. This leads to the first real-time multi-view face detection system which has the accuracy almost as good as the state-of-the-art system yet 270 times faster, allowing real-time performance.

Abstract: A novel statistical learning procedure that can be applied to many machine-learning applications is presented. Although this boosting learning procedure is described with respect to its applicability to face detection, it can be applied to speech recognition, text classification, image retrieval, document routing, online learning and medical diagnosis classification problems.

Abstract: A novel statistical learning procedure that can be applied to many machine-learning applications is presented. Although this boosting learning procedure is described with respect to its applicability to face detection, it can be applied to speech recognition, text classification, image retrieval, document routing, online learning and medical diagnosis classification problems.

Abstract: A novel statistical learning procedure that can be applied to many machine-learning applications is presented. Although this boosting learning procedure is described with respect to its applicability to face detection, it can be applied to speech recognition, text classification, image retrieval, document routing, online learning and medical diagnosis classification problems.

Abstract: A novel statistical learning procedure that can be applied to many machine-learning applications is presented. Although this boosting learning procedure is described with respect to its applicability to face detection, it can be applied to speech recognition, text classification, image retrieval, document routing, online learning and medical diagnosis classification problems.

Abstract: A system and method for real-time multi-view (i.e. not just frontal view) face detection. The system and method uses a sequence of detectors of increasing complexity and face/non-face discriminating thresholds to quickly discard non-faces at the earliest stage possible, thus saving much computation compared to prior art systems. The detector-pyramid architecture for multi-view face detection uses a coarse-to-fine and simple-to-complex scheme. This architecture solves the problem of lengthy processing that precludes real-time face detection effectively and efficiently by discarding most of non-face sub-windows using the simplest possible features at the earliest possible stage. This leads to the first real-time multi-view face detection system which has the accuracy almost as good as the state-of-the-art system yet 270 times faster, allowing real-time performance.

Abstract: A novel statistical learning procedure that can be applied to many machine-learning applications is presented. Although this boosting learning procedure is described with respect to its applicability to face detection, it can be applied to speech recognition, text classification, image retrieval, document routing, online learning and medical diagnosis classification problems.