X_message.topic = "" //clear topic string buffer Memset( x_message.payload, '\0', 150 ) // clear payload char buffer Sending queue data void IRAM_ATTR mqttCallback(char* topic, byte * payload, unsigned int length) } // void fparseMQTT( void *pvParameters ) } //if ( xQueueReceive(xQ_Message, &px_message, portMAX_DELAY) = pdTRUE ) X_eData.RM0 = String(px_message.payload).toFloat() If ( px_message.topic = topicRemainingMoisture_0 ) XSemaphoreTake( sema_mqttOK, portMAX_DELAY ) If ( xQueueReceive(xQ_Message, &px_message, portMAX_DELAY) = pdTRUE ) wait 'forever' or until a message arrives in the queue Receiving queue data void fparseMQTT( void *pvParameters ) Setting up the Queue in setup() xQ_Message = xQueueCreate( 1, sizeof(stu_message) ) QueueHandle_t xQ_Message // payload and topic queue of MQTT payload and topic Global decleration include "freertos/FreeRTOS.h" If you have an ESP32 or a ESP8266 with OS, here is how to setup a FIFO queue and use a Queue. } //if ( xQueueReceive(xQ_lrData, &lrData, portMAX_DELAY) = pdTRUE ) log_i( "DoTheBME280Thing high watermark % d", uxTaskGetStackHighWaterMark( NULL ) ) Log_i( "Correlation: %f Values: Y=%f and *X + %f ", lr.Correlation(), values, values ) // correlation is the strength and direction of the relationship Calculate( tsUnit ) ) //calculate next time unit? TsUnit *= tsAtom //setting time units to the same scale TsUnit = TimeStamps - TimeStamps //get the time stamp quantity Lr.Data( tsMean, DataPoints - dpMean ) // train lr TsAtom = 1.0f / (TimeStamps - TimeStamps) // no need to do this part of the calculation every for loop ++ timestamp mean is ts * (1 / ts_Firstcell - ts_Lastcell). insert new data points and time stamp (ts) at the end of the data arrays shift datapoints collected one place to the left Log_i( "lrCount %d TimeStamp %f lrData %f", lrCount, TimeStamps, DataPoints ) TimeStamps = (float)xTaskGetTickCount() / 1000.0f If ( xQueueReceive(xQ_lrData, &lrData, portMAX_DELAY) = pdTRUE ) The variable magicNumber is the buffer size. Here I am using 2 arrays as a FIFO buffer to collect dat to be used in a Linear Regression formula to predict future readings. First, let’s create our own Queue interface: public interface Queue RollingMovingAverage ma = new RollingMovingAverage ( initialMovAvgFrame ) ma. Internally, we can again use an array of a fixed length in combination with a rotating index. We can effectively use a ring.Īdding a new number to the queue and dropping the oldest one is the same as simply replacing the oldest element in this ring with a new one. Yesterday, it suddenly occurred to me that there is actually an even better alternative if the length of the queue is fixed (as in our example). Of course we lose the advantage of directly assessing an element in the queue by index, but for our purpose - calculating moving averages - this is something we do not want to do anyway. All we have to do is manipulating a few pointers. The advantage is obvious, no more copying or re-creating arrays in memory. Still this is not the best solution, because once the internal indices move outside the internal array’s boundaries a new copy of the internal array must be created.Ī typical alternative for implementing FIFO queues is thus using a linked list: Implementations based on classes such as or are already much better, because internally they rely on longer arrays and indices. This is of course very inefficient, because creation of an array in memory is relatively slow. In the above example, we would need to create a new array five times, once for every new Sum being calculated. But how exactly can a FIFO queue be implemented in a (non-functional) programming language such as Java? The first idea is typically to use an array-based implementation and to shift the position of elements in the array by repeatedly creating slightly shifted copies of the array. An appropriate data structure would be a first-in-first-out (FIFO) queue of numbers. ![]() We have to store these numbers somewhere. Still we have to keep track of what actually are the old and the new elements. ![]() In this way we can save a significant number of unnecessary computations. Instead, we can simply subtract the last element in the time frame and add the newest one to Sum. Of course it is possible to always reiterate over all numbers in the current time frame to do so, but this is unnecessarily slow. How would we efficiently implement this in Java code? The problem is that we need to calculate the Sum in the formula for every moving average. MA t = (Sum of all elements from t-3 to t) / 4 The formula for a moving average of length 4 thus is: Īssuming a moving average of length 4 results in the following array: Imagine for example calculating a moving average with a fixed size. When working with time series data, one often needs to calculate sums of consecutive numbers for a predetermined time frame. Implementing a Fixed-Length FIFO Queue in Java
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