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/OK3568_Linux_fs/kernel/Documentation/ABI/testing/
H A Dsysfs-bus-iio-adc-hi84356 Program sensor type for threshold detector inputs.
7 Could be either "GND-Open" or "Supply-Open" mode. Y is a
8 threshold detector input channel. Channels 0..7, 8..15, 16..23
16 Channel Y low voltage threshold. If sensor input voltage goes lower then
17 this value then the threshold falling event is pushed.
18 Depending on in_voltageY_sensing_mode the low voltage threshold
19 is separately set for "GND-Open" and "Supply-Open" modes.
20 Channels 0..31 have common low threshold values, but could have different
23 The low voltage threshold range is between 2..21V.
27 If falling threshold results hysteresis to odd value then rising
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/OK3568_Linux_fs/kernel/Documentation/trace/
H A Dhwlat_detector.rst2 Hardware Latency Detector
6 -------------
24 The hardware latency detector works by hogging one of the cpus for configurable
31 Note that the hwlat detector should *NEVER* be used in a production environment.
36 ------
40 redefine the threshold in microseconds (us) above which latency spikes will
50 - width - time period to sample with CPUs held (usecs)
52 - window - total period of sampling, width being inside (usecs)
55 for every 1,000,000 usecs (1s) the hwlat detector will spin for 500,000 usecs
57 change to a default of 10 usecs. If any latencies that exceed the threshold is
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/OK3568_Linux_fs/external/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/
H A Dobjdetect.hpp13 // Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
52 Haar Feature-based Cascade Classifier for Object Detection
53 ----------------------------------------------------------
55 The object detector described below has been initially proposed by Paul Viola @cite Viola01 and
58 First, a classifier (namely a *cascade of boosted classifiers working with haar-like features*) is
60 positive examples, that are scaled to the same size (say, 20x20), and negative examples - arbitrary
78 decision-tree classifiers with at least 2 leaves. Haar-like features are the input to the basic
80 Haar-like features:
93 To see the object detector at work, have a look at the facedetect demo:
100 addition to Haar-like features. .. [Viola01] Paul Viola and Michael J. Jones. Rapid Object Detection
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H A Dfeatures2d.hpp13 // Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
65 - An example explaining keypoint matching can be found at
67 - An example on descriptor matching evaluation can be found at
69 - An example on one to many image matching can be found at
78 - A complete Bag-Of-Words sample can be found at
80 - (Python) An example using the features2D framework to perform object categorization can be
154 @param mask Mask specifying where to look for keypoints (optional). It must be a 8-bit integer
155 matrix with non-zero values in the region of interest.
181 descriptor for keypoint j-th keypoint.
195 descriptor for keypoint j-th keypoint.
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H A Dimgproc.hpp13 // Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
51 This module includes image-processing functions.
56 Functions and classes described in this section are used to perform various linear or non-linear
62 will be of the same size as the input image. Normally, the functions support multi-channel arrays,
67 simple arithmetic functions, they need to extrapolate values of some non-existing pixels. For
69 processing the left-most pixels in each row, you need pixels to the left of them, that is, outside
70 of the image. You can let these pixels be the same as the left-most image pixels ("replicated
71 border" extrapolation method), or assume that all the non-existing pixels are zeros ("constant
78 --------------------------|----------------------
79 CV_8U | -1/CV_16S/CV_32F/CV_64F
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/OK3568_Linux_fs/kernel/Documentation/devicetree/bindings/iio/adc/
H A Dholt,hi8435.yaml1 # SPDX-License-Identifier: (GPL-2.0-only OR BSD-2-Clause)
3 ---
5 $schema: http://devicetree.org/meta-schemas/core.yaml#
7 title: Holt Integrated Circuits HI-8435 SPI threshold detector
10 - Vladimir Barinov <vladimir.barinov@cogentembedded.com>
13 Datasheet: http://www.holtic.com/documents/427-hi-8435_v-rev-lpdf.do
27 spi-max-frequency: true
29 "#io-channel-cells":
33 - compatible
34 - reg
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/OK3568_Linux_fs/kernel/Documentation/admin-guide/
H A Dlockup-watchdogs.rst2 Softlockup detector and hardlockup detector (aka nmi_watchdog)
14 "softlockup_panic" (see "Documentation/admin-guide/kernel-parameters.rst" for
26 (see "Documentation/admin-guide/kernel-parameters.rst" for details).
43 (compile-time initialized to 10 and configurable through sysctl of the
46 'hardlockup detector' (the handler for the NMI perf event) will
52 for 2*watchdog_thresh seconds (the softlockup threshold) the
53 'softlockup detector' (coded inside the hrtimer callback function)
60 detector kicks in.
64 event. The right value for a particular environment is a trade-off
77 to continue to run on the housekeeping (non-tickless) cores means
/OK3568_Linux_fs/external/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/
H A Dfeatures2d.hpp13 // Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
65 - An example explaining keypoint matching can be found at
67 - An example on descriptor matching evaluation can be found at
69 - An example on one to many image matching can be found at
78 - A complete Bag-Of-Words sample can be found at
80 - (Python) An example using the features2D framework to perform object categorization can be
154 @param mask Mask specifying where to look for keypoints (optional). It must be a 8-bit integer
155 matrix with non-zero values in the region of interest.
181 descriptor for keypoint j-th keypoint.
195 descriptor for keypoint j-th keypoint.
[all …]
H A Dimgproc.hpp13 // Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
51 This module includes image-processing functions.
56 Functions and classes described in this section are used to perform various linear or non-linear
62 will be of the same size as the input image. Normally, the functions support multi-channel arrays,
67 simple arithmetic functions, they need to extrapolate values of some non-existing pixels. For
69 processing the left-most pixels in each row, you need pixels to the left of them, that is, outside
70 of the image. You can let these pixels be the same as the left-most image pixels ("replicated
71 border" extrapolation method), or assume that all the non-existing pixels are zeros ("constant
78 --------------------------|----------------------
79 CV_8U | -1/CV_16S/CV_32F/CV_64F
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/OK3568_Linux_fs/external/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/
H A Dfeatures2d.hpp13 // Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
65 - An example explaining keypoint matching can be found at
67 - An example on descriptor matching evaluation can be found at
69 - An example on one to many image matching can be found at
78 - A complete Bag-Of-Words sample can be found at
80 - (Python) An example using the features2D framework to perform object categorization can be
154 @param mask Mask specifying where to look for keypoints (optional). It must be a 8-bit integer
155 matrix with non-zero values in the region of interest.
181 descriptor for keypoint j-th keypoint.
195 descriptor for keypoint j-th keypoint.
[all …]
H A Dimgproc.hpp13 // Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
51 This module includes image-processing functions.
56 Functions and classes described in this section are used to perform various linear or non-linear
62 will be of the same size as the input image. Normally, the functions support multi-channel arrays,
67 simple arithmetic functions, they need to extrapolate values of some non-existing pixels. For
69 processing the left-most pixels in each row, you need pixels to the left of them, that is, outside
70 of the image. You can let these pixels be the same as the left-most image pixels ("replicated
71 border" extrapolation method), or assume that all the non-existing pixels are zeros ("constant
78 --------------------------|----------------------
79 CV_8U | -1/CV_16S/CV_32F/CV_64F
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/OK3568_Linux_fs/kernel/kernel/
H A Dwatchdog.c1 // SPDX-License-Identifier: GPL-2.0
8 * detector, so thanks to Ingo for the initial implementation.
9 * Some chunks also taken from the old x86-specific nmi watchdog code, thanks
61 * Should we panic when a soft-lockup or hard-lockup occurs:
135 * own hardlockup detector.
160 * watchdog_nmi_stop - Stop the watchdog for reconfiguration
170 * watchdog_nmi_start - Start the watchdog after reconfiguration
176 * - watchdog_enabled
177 * - watchdog_thresh
178 * - watchdog_cpumask
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H A Dwatchdog_hld.c1 // SPDX-License-Identifier: GPL-2.0
8 * detector, so thanks to Ingo for the initial implementation.
9 * Some chunks also taken from the old x86-specific nmi watchdog code, thanks
56 * watchdog. That means the hrtimer should fire 2-3 times before in watchdog_update_hrtimer_threshold()
58 * unhalted CPU cycles, so if Turbo-Mode is enabled the CPU cycles in watchdog_update_hrtimer_threshold()
61 * frequency. Depending on the Turbo-Mode factor this might be fast in watchdog_update_hrtimer_threshold()
65 * The sample threshold is used to check in the NMI handler whether in watchdog_update_hrtimer_threshold()
69 * Set this to 4/5 of the actual watchdog threshold period so the in watchdog_update_hrtimer_threshold()
71 * watchdog threshold. in watchdog_update_hrtimer_threshold()
80 delta = now - __this_cpu_read(last_timestamp); in watchdog_check_timestamp()
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/OK3568_Linux_fs/kernel/kernel/trace/
H A Dtrace_hwlat.c1 // SPDX-License-Identifier: GPL-2.0
3 * trace_hwlat.c - A simple Hardware Latency detector.
20 * Although certain hardware-inducing latencies are necessary (for example,
22 * and remote management) they can wreak havoc upon any OS-level performance
23 * guarantees toward low-latency, especially when the OS is not even made
27 * sampling the built-in CPU timer, looking for discontiguous readings.
31 * environment requiring any kind of low-latency performance
34 * Copyright (C) 2008-2009 Jon Masters, Red Hat, Inc. <jcm@redhat.com>
35 * Copyright (C) 2013-2016 Steven Rostedt, Red Hat, Inc. <srostedt@redhat.com>
75 /* If the user changed threshold, remember it */
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/OK3568_Linux_fs/kernel/drivers/media/dvb-frontends/
H A Dz0194a.h1 /* SPDX-License-Identifier: GPL-2.0-only */
6 * see Documentation/driver-api/media/drivers/dvb-usb.rst for more information
56 0x15, 0xc9, /* lock detector threshold */
68 0x29, 0x1e, /* 1/2 threshold */
69 0x2a, 0x14, /* 2/3 threshold */
70 0x2b, 0x0f, /* 3/4 threshold */
71 0x2c, 0x09, /* 5/6 threshold */
72 0x2d, 0x05, /* 7/8 threshold */
H A Dbsru6.h1 /* SPDX-License-Identifier: GPL-2.0-or-later */
3 * bsru6.h - ALPS BSRU6 tuner support (moved from budget-ci.c)
27 0x15, 0xc9, // lock detector threshold
39 0x29, 0x1e, // 1/2 threshold
40 0x2a, 0x14, // 2/3 threshold
41 0x2b, 0x0f, // 3/4 threshold
42 0x2c, 0x09, // 5/6 threshold
43 0x2d, 0x05, // 7/8 threshold
89 struct dtv_frontend_properties *p = &fe->dtv_property_cache; in alps_bsru6_tuner_set_params()
93 struct i2c_adapter *i2c = fe->tuner_priv; in alps_bsru6_tuner_set_params()
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H A Dbsbe1.h1 /* SPDX-License-Identifier: GPL-2.0-or-later */
3 * bsbe1.h - ALPS BSBE1 tuner support
26 0x15, 0xc9, /* lock detector threshold */
57 struct dtv_frontend_properties *p = &fe->dtv_property_cache; in alps_bsbe1_tuner_set_params()
62 struct i2c_adapter *i2c = fe->tuner_priv; in alps_bsbe1_tuner_set_params()
64 if ((p->frequency < 950000) || (p->frequency > 2150000)) in alps_bsbe1_tuner_set_params()
65 return -EINVAL; in alps_bsbe1_tuner_set_params()
67 div = p->frequency / 1000; in alps_bsbe1_tuner_set_params()
73 if (fe->ops.i2c_gate_ctrl) in alps_bsbe1_tuner_set_params()
74 fe->ops.i2c_gate_ctrl(fe, 1); in alps_bsbe1_tuner_set_params()
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/OK3568_Linux_fs/kernel/drivers/net/wireless/ath/
H A Ddfs_pattern_detector.c25 * struct radar_types - contains array of patterns defined for one DFS domain
36 /* percentage on ppb threshold to trigger detection */
38 #define PPB_THRESH_RATE(PPB, RATE) ((PPB * RATE + 100 - RATE) / 100)
43 #define WIDTH_LOWER(X) ((X*(100-WIDTH_TOLERANCE)+50)/100)
49 (PRF2PRI(PMAX) - PRI_TOLERANCE), \
54 /* radar types as defined by ETSI EN-301-893 v1.5.1 */
74 PMIN - PRI_TOLERANCE, \
106 PMIN - PRI_TOLERANCE, \
135 * get_dfs_domain_radar_types() - get radar types for a given DFS domain
144 if (dfs_domains[i]->region == region) in get_dfs_domain_radar_types()
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/OK3568_Linux_fs/kernel/drivers/iio/adc/
H A Dhi8435.c1 // SPDX-License-Identifier: GPL-2.0-or-later
3 * Holt Integrated Circuits HI-8435 threshold detector driver
24 /* Register offsets for HI-8435 */
50 unsigned threshold_lo[2]; /* GND-Open and Supply-Open thresholds */
51 unsigned threshold_hi[2]; /* GND-Open and Supply-Open thresholds */
58 return spi_write_then_read(priv->spi, &reg, 1, val, 1); in hi8435_readb()
67 ret = spi_write_then_read(priv->spi, &reg, 1, &be_val, 2); in hi8435_readw()
79 ret = spi_write_then_read(priv->spi, &reg, 1, &be_val, 4); in hi8435_readl()
87 priv->reg_buffer[0] = reg | HI8435_WRITE_OPCODE; in hi8435_writeb()
88 priv->reg_buffer[1] = val; in hi8435_writeb()
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/OK3568_Linux_fs/kernel/drivers/iio/accel/
H A Dsca3000.c1 // SPDX-License-Identifier: GPL-2.0-only
3 * sca3000_core.c -- support VTI sca3000 series accelerometers via SPI
44 /* Temp read untested - the e05 doesn't have the sensor */
54 * is below a threshold for equivalent of 25cm drop
63 * (approx 1 - 25Hz) and then a programmable threshold used to trigger
82 /* Only available for SCA3000-D03 and SCA3000-D01 */
104 * Control which motion detector interrupts are on.
138 * Bypass - Bypass the low-pass filter in the signal channel so as to increase
141 * Narrow - Narrow low-pass filtering of the signal channel and half output
144 * Wide - Widen low-pass filtering of signal channel to increase bandwidth
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/OK3568_Linux_fs/kernel/sound/soc/codecs/
H A Dssm2518.c1 // SPDX-License-Identifier: GPL-2.0-only
6 * Author: Lars-Peter Clausen <lars@metafoo.de>
142 static const DECLARE_TLV_DB_MINMAX_MUTE(ssm2518_vol_tlv, -7125, 2400);
143 static const DECLARE_TLV_DB_SCALE(ssm2518_compressor_tlv, -3400, 200, 0);
144 static const DECLARE_TLV_DB_SCALE(ssm2518_expander_tlv, -8100, 300, 0);
145 static const DECLARE_TLV_DB_SCALE(ssm2518_noise_gate_tlv, -9600, 300, 0);
146 static const DECLARE_TLV_DB_SCALE(ssm2518_post_drc_tlv, -2400, 300, 0);
149 0, 7, TLV_DB_SCALE_ITEM(-2200, 200, 0),
150 7, 15, TLV_DB_SCALE_ITEM(-800, 100, 0),
187 SOC_SINGLE("Playback De-emphasis Switch", SSM2518_REG_MUTE_CTRL,
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/OK3568_Linux_fs/kernel/arch/powerpc/kernel/
H A Dwatchdog.c1 // SPDX-License-Identifier: GPL-2.0
34 * period, and updates a per-cpu timestamp and a "pending" cpumask. This is
38 * The local soft-NMI, and the SMP checker.
40 * The soft-NMI checker can detect lockups on the local CPU. When interrupts
41 * are disabled with local_irq_disable(), platforms that use soft-masking
47 * The soft-NMI checker will compare the heartbeat timestamp for this CPU
49 * watchdog threshold.
51 * The limitation of the soft-NMI watchdog is that it does not work when
63 * not been updated for a period exceeding the watchdog threshold, then it
97 hard_irq_disable(); /* Make it soft-NMI safe */ in wd_smp_lock()
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/OK3568_Linux_fs/external/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/imgproc/
H A Dtypes_c.h13 // Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
71 you want to smooth different pixels with different-size box kernels, you can use the integral
96 CV_SCHARR =-1,
341 // Edge-Aware Demosaicing
366 /** Sub-pixel interpolation methods */
520 /** initializes 8-element array for fast access to 3x3 neighborhood of a pixel */
522 ((deltas)[0] = (nch), (deltas)[1] = -(step) + (nch), \
523 (deltas)[2] = -(step), (deltas)[3] = -(step) - (nch), \
524 (deltas)[4] = -(nch), (deltas)[5] = (step) - (nch), \
537 …CV_CONTOURS_MATCH_I1 =1, //!< \f[I_1(A,B) = \sum _{i=1...7} \left | \frac{1}{m^A_i} - \frac{1…
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/OK3568_Linux_fs/external/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/imgproc/
H A Dtypes_c.h13 // Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
71 you want to smooth different pixels with different-size box kernels, you can use the integral
96 CV_SCHARR =-1,
341 // Edge-Aware Demosaicing
366 /** Sub-pixel interpolation methods */
520 /** initializes 8-element array for fast access to 3x3 neighborhood of a pixel */
522 ((deltas)[0] = (nch), (deltas)[1] = -(step) + (nch), \
523 (deltas)[2] = -(step), (deltas)[3] = -(step) - (nch), \
524 (deltas)[4] = -(nch), (deltas)[5] = (step) - (nch), \
537 …CV_CONTOURS_MATCH_I1 =1, //!< \f[I_1(A,B) = \sum _{i=1...7} \left | \frac{1}{m^A_i} - \frac{1…
[all …]
/OK3568_Linux_fs/external/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/imgproc/
H A Dtypes_c.h13 // Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
71 you want to smooth different pixels with different-size box kernels, you can use the integral
96 CV_SCHARR =-1,
341 // Edge-Aware Demosaicing
366 /** Sub-pixel interpolation methods */
520 /** initializes 8-element array for fast access to 3x3 neighborhood of a pixel */
522 ((deltas)[0] = (nch), (deltas)[1] = -(step) + (nch), \
523 (deltas)[2] = -(step), (deltas)[3] = -(step) - (nch), \
524 (deltas)[4] = -(nch), (deltas)[5] = (step) - (nch), \
537 …CV_CONTOURS_MATCH_I1 =1, //!< \f[I_1(A,B) = \sum _{i=1...7} \left | \frac{1}{m^A_i} - \frac{1…
[all …]

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