Gurd’s and Wilson’s criteria (with permission from Dr. Nissar Shaikh) [6].
\r\n\t
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Dr. Tiefenbacher has published more than 200 papers on a diverse array of topics that examine perception and behaviors with regards to the application of pesticides, releases of toxic chemicals, environments of the U.S.-Mexico borderlands, wildlife hazards, and the geography of wine.",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"73876",title:"Dr.",name:"John P.",middleName:null,surname:"Tiefenbacher",slug:"john-p.-tiefenbacher",fullName:"John P. Tiefenbacher",profilePictureURL:"https://mts.intechopen.com/storage/users/73876/images/system/73876.jfif",biography:"Dr. John P. Tiefenbacher (Ph.D., Rutgers, 1992) is a professor of Geography at Texas State University. His research has focused on various aspects of hazards and environmental management. Dr. Tiefenbacher has published on a diverse array of topics that examine perception and behaviors with regards to the application of pesticides, releases of toxic chemicals, environments of the U.S.-Mexico borderlands, wildlife hazards, and the geography of wine. More recently his work pertains to spatial adaptation to climate change, spatial responses in wine growing regions to climate change, the geographies of viticulture and wine, artificial intelligence and machine learning to predict patterns of natural processes and hazards, historical ethnic enclaves in American cities and regions, and environmental adaptations of 19th century European immigrants to North America's landscapes.",institutionString:"Texas State University",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"1",totalChapterViews:"0",totalEditedBooks:"6",institution:{name:"Texas State University",institutionURL:null,country:{name:"United States of America"}}}],coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"12",title:"Environmental Sciences",slug:"environmental-sciences"}],chapters:[{id:"76073",title:"Integrating Ecological Site Descriptions with Soil Morphology to Optimize Forest Management: Three Missouri Case Studies",slug:"integrating-ecological-site-descriptions-with-soil-morphology-to-optimize-forest-management-three-mi",totalDownloads:25,totalCrossrefCites:0,authors:[{id:"185895",title:"Dr.",name:"Michael",surname:"Aide",slug:"michael-aide",fullName:"Michael Aide"},{id:"269286",title:"Dr.",name:"Christine",surname:"Aide",slug:"christine-aide",fullName:"Christine Aide"},{id:"269287",title:"Dr.",name:"Indi",surname:"Braden",slug:"indi-braden",fullName:"Indi Braden"}]},{id:"76399",title:"Hunting and Deforestation: A Threat to the Existence of the Niger Delta Red Colobus Monkey (Procolobus epieni)",slug:"hunting-and-deforestation-a-threat-to-the-existence-of-the-niger-delta-red-colobus-monkey-procolobus",totalDownloads:0,totalCrossrefCites:0,authors:[{id:"142349",title:"Dr.",name:"Gbolagade Akeem",surname:"Lameed",slug:"gbolagade-akeem-lameed",fullName:"Gbolagade Akeem Lameed"}]}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"194667",firstName:"Marijana",lastName:"Francetic",middleName:null,title:"Ms.",imageUrl:"https://mts.intechopen.com/storage/users/194667/images/4752_n.jpg",email:"marijana@intechopen.com",biography:"As an Author Service Manager my responsibilities include monitoring and facilitating all publishing activities for authors and editors. 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Fat embolism was described as early as 1862 by Zenker. Warthin [4] opined that fat embolism from traumatic lipemia was not rare and the most frequent cause of death following long bone fractures in the absence of infection.
More data on FES came forth in the early twenty‐first century from wounded soldiers involved in armed conflict across the world. A series of 1000 combats injured in the World War II reported the incidence of FES to be 0.8%. In Vietnam, cases of arterial hypoxemia among wounded soldiers were attributed to FES and also reported a few classic presentations of FES [3].
The pathophysiology of development of fat embolism syndrome is still unclear. However, two theories were hypothesized for its mechanism: mechanical and biochemical. Either the disrupted fat globules from bone marrow or adipose tissues enter into the bloodstreams (mechanical) or any sequel that leads to the production of toxic metabolites in the blood (biochemical) can give rise to a conundrum of clinical features that characterize the fat embolism syndrome. It is also likely that either the mechanisms exist in tandem or one gives rise to the other in the production of FES.
In the twentieth century, it was suggested that following a trauma, fat particles from the bone marrow and adipose tissues enter into the disrupted venules and travel to the pulmonary circulation or enter into the systemic circuit via arteriovenous shunts. The echocardiographic finding of echogenic material passing into the right heart during an orthopedic procedure contributed to this mechanical theory [5].
However, the mechanical theory does not explain the development of FES after a delay of 2–3 days postinjury. There are many biochemical mechanisms involved in the progression of fat embolism syndrome; the most widely accepted is the release of free fatty acids into the plasma following trauma, sepsis, and/or systemic inflammation. Acute phase reactants, like C‐reactive proteins, lead to lipid agglutination that tend to cause Acute respiratory distress syndrome (ARDS) in animal models, dysfunction of cardiac contractility and increase in plasma lipase concentration, which are the features of FES. These free fatty acids migrate to other organs, causing multiorgan failure [6]. This theory also helps in understanding the development of nontraumatic fat embolism syndrome.
Fat Embolism is common in trauma patients, particularly those with pelvic or long bone fractures [3]. Most literature reporting incidence of FES involves orthopedic or trauma patients with retrospective studies reporting an incidence of below 1%, while prospective studies have reported a much higher incidence of 11–19% [7]. Autopsy studies reported a much higher incidence of Fat embolism. One study demonstrated pulmonary fat emboli in 82% of trauma patients at autopsy [8]. Up to 67% of trauma patients without clinical features of fat embolism syndrome were shown to have circulating fat globules [3].
Fat emboli with a diameter of more than 20 µm have been shown to occur in up to 90% of patients with long bone fractures. Another study concluded that more than 90% of patients with long bone fractures had embolism with fat droplets more than 20 µm in diameter [9].
Gurd proposed that the clinical syndrome of fat embolism can be differentiated from a mere presence of fat emboli at autopsy in patients with no prior clinical features. Gurd suggested that a distinction can be made between the clinical syndrome of fat embolism and demonstration of fat embolism on autopsy with no prior clinical features of the syndrome [10].
Bulger et al. studied the incidence of FES at a level I trauma center over a 10‐year period, reporting an incidence of 0.9% among patients with long bone fractures [11]. More recent data from the National Hospital Discharge Survey in USA looking at 21,538,000 patients with long bones and pelvic fractures reported a diagnosis of FES in 0.12% of the patients [12].
The development of FES is frequently associated following an orthopedic trauma, with highest occurrence in closed and/or multiple long bone fractures, particularly of lower limb bones like femur. Aggressive nailing of the medullary canal poses increased risk of FES. Vigorous nailing of medullary cavity during intramedullary nailing and increase in gap between nail and cortical bone puts the patient at high risk of developing FES [6].
Furthermore, younger populations of 10–40 years and men more often than women are at high risk. Fat embolism has been reported in other nontraumatic conditions like pancreatitis, liposuction, bone marrow transplant, sickle cell disease, and liver disease. Nontraumatic causes of fat embolism syndrome include bone marrow transplant, pancreatitis, liposuction, alcoholic liver disease, and sickle cell crisis [13].
The phenomenon of fat embolism (fat droplets in circulation) is often undiagnosed in clinical practice. The clinical syndrome of FES tends to present with signs and symptoms similar to other critical illness and is mostly a diagnosis of exclusion. Fat embolism, which is a mere presence of fat emboli in circulation, may frequently go undiagnosed [10], while fat embolism syndrome presents with nonspecific clinical features common to other critical illnesses and is often a diagnosis of exclusion.
The fulminating form may present with sudden cardiovascular collapse and right ventricular failure subsequent to pulmonary and systemic fat embolization. More often, it is characterized by a more gradual onset of hypoxemia, neurological symptoms and a petechial rash about 12–36 hours after an injury [7].
Fat emboli could travel through the systemic vasculature resulting in a multiorgan disease involving lungs, brain, skin, retina, kidneys, liver, and heart.
The most common manifestations among patients with FES are of pulmonary system. Pumonary manifestations though of varying severity are the most common finding in patient with fat embolism syndrome. Bulger et al. reported hypoxemia in 96% of the cases, and 44% of the patients with FES required mechanical ventilation [11]. Patients may develop dyspnea and tachypnea and a more severe syndrome indistinguishable from Acute respiratory distress syndrome (ARDS) may develop.
Nonspecific neurological symptoms including lethargy, restlessness or a decrease in Glasgow Coma Scale (GCS) may suggest cerebral edema subsequent to FES [14]. Severe neurological deterioration with cerebral edema has been reported with FES [15].
Skin involvement characterized by a petechial rash manifests in up to 60% of patients and usually affects oral mucous membranes, neck and axilla skin folds and conjunctiva. Dermal manifestations with a petechial rash pathogonomic of FES usually involves the conjunctiva, oral mucous membranes and skin folds of the neck and axillae and occuring in up to 60% of patients with FES [7].
Universal agreement on a standard definition of FES is lacking. There is a lack of universally accepted definition of FES [3]. The major and minor criteria proposed by Gurd and Wilson (Table 1) in 1970 are still popular [10]. It required one major criterion plus four minor criteria’s in addition to fat macroglobulinemia for a diagnosis of FES.
|
Gurd’s and Wilson’s criteria (with permission from Dr. Nissar Shaikh) [6].
Other authors later adapted these criteria and proposed the combinations of major and minor features needed for a diagnosis. Schonfeld et al. proposed (Table 3) a quantitative measure to diagnose FES; a score of more than 5 is required to diagnose FES [3].
Scores | |
---|---|
Petechiae | 5 |
X‐Ray chest diffuse infiltrates | 4 |
Hypoxemia | 3 |
Fever | 1 |
Tachycardia | 1 |
Tachypnea | 1 |
Confusion | 1 |
Lindeque’s criteria (with permission from Dr. Nissar Shaikh) [6].
Lindeque proposed criteria for diagnosis of fat embolism syndrome based on respiratory changes alone [16]. A positive diagnosis of FES was proposed if atleast one of the criteria are met (Table 2).
Sustained pO2 < 8 kpa |
Sustained pCO2 > 7.3 kpa |
Sustained respiratory rate > 35 per min, in spite of sedation |
Increase work of breathing, dyspnea, tachycardia, anxiety |
Petechiae 5 |
Chest X-ray changes (diffuse alveolar infiltrates) 4 |
Hypoxaemia (Pao2 < 9.3 kPa) 3 |
Fever (>38°C) 1 |
Tachycardia (>120 beats min−1) 1 |
Tachypnoea (>30 bpm) 1 |
Cumulative score >5 required for diagnosis |
Schonfeld’s criteria (with permission from Dr. Nissar Shaikh) [6].
Diagnosis must be made on the basis of clinical findings but biochemical changes may be of value to support in diagnosis.
In the initial stages, a blood gas analysis is imperative, which will show hypoxia with paO2 of less than 60 mmHg and hypocapnia within the first 24–48 hours. Also, there will be an unexplained increase in the pulmonary shunt fraction and an alveolar to arterial oxygen tension difference. These are highly suggestive of a diagnosis of FES.
Nonspecific findings include anemia, thrombocytopenia, hypofibrinogenemia and high erythrocytes sedimentation rate. Cytological examination of urine, blood, sputum, and pulmonary capillary blood may detect fat globules in patients with FES; however, these tests are rarely done in the immediate period as they lack sensitivity and their absence does not rule out fat embolism [6].
Various nonspecific findings have been reported on chest X‐ray though none of them is diagnostic. Numerous radiological findings have been described but none is diagnostic of fat embolism syndrome. The chest X‐ray is often normal initially but in some patients bilateral fluffy shadows can be seen with worsening respiratory insufficiency (Figure 1).
AP radiograph of the chest showing bilateral basal air space‐filling lesions (consolidation in a patient of FES) (with permission from Dr. Nissar Shaikh) [
V/Q scans may demonstrate a mottled pattern of subsegmental perfusion defects with a normal ventilatory pattern.
Spiral CT scan of the chest may show focal areas of ground glass opacification with interlobular septal thickening. Normal findings or diffuse petechial hemorrhages of white matter may be seen on CT scan of brain. CT Head may be normal or reveal diffuse white‐matter petechial hemorrhages consistent with microvascular injury. This will also rule out other causes for deterioration in consciousness level (Figure 2).
CT image showing minimal hypodense changes in periventricular region, which are more evident in MRI DWI and T2WI as areas of high signals (with permission from Dr. Nissar Shaikh) [
It may reveal high‐intensity T2 signal which correlates with the degree of neurological impairment found clinically.
Treatment is largely supportive care in a unit equipped with intensive care capabilities. Maintaining adequate oxygenation, ventilation, and organ perfusion are the essential goals of treatment. Principles of treatment include maintenance of adequate oxygenation and ventilation, hemodynamics, and perfusion. Correction of hypoxemia to maintain normal oxygen tension may require simple measures like oxygen supplementation or mechanical ventilation and Positive end expiratory pressure (PEEP) depending on the clinical context. Shock in patients with FES can worsen the lung injury and hence restoration of intravascular volume with balanced salt solutions or albumin is often required. Albumin administration not only expands the intravascular volume but may also mitigate the extent of lung injury as a result of its binding with fatty acids. Vasopressors to maintain the hemodynamics may be required. It has been proposed that heparin by enhancing lipase activity may augment the clearance of lipids from blood circulation. Treatment modalities including corticosteroids and anticoagulation have unfortunately not been shown to improve the morbidity or mortality. Other medications including alcohol and dextran have also been shown to be ineffective [2, 6].
Fat embolism occur in around 90% of all trauma patient, but FES accounts for less than 5% of patients having long bone fracture [17, 18]. The unstable form of FES presents as acute respiratory failure, cor pulmonale, and/or embolic event, leading to death within a few hours of injury. This is seen more often among high‐risk patients and those with a background of multiple comorbidities.
It is hard to predict the extent of FES as it is often subclinical and the outcome of patients are generally favorable [6]. Mortality rate is less than 10% at present as there have been significant improvements in supportive care. Neurological deficits and pulmonary manifestations usually resolve completely over time [18].
Studies have shown early fixation of fractures involving long bones is important in decreasing the incidence of fat embolism syndrome and may prevent it [6, 19, 20]. Preventing significant increase in intraosseous pressure in orthopedic surgeries may reduce embolization of fat droplets and thereby reduce the incidence of FES. It has been suggested that plate fixation and external fixation results in less emboli and less severity of lung injury than surgical fixation with intramedullary nailing [6]. Prophylactic use of corticosteroids may have a beneficial effect in preventing fat embolism syndrome [21]. Wong et al. suggested monitoring with continuous pulse oximetry in patients with long bone fractures for early identification of desaturation [22]. This would allow early initiation of appropriate oxygen supplementation and other measures, possibly reducing the systemic complications of fat embolism syndrome [6].
In many of the industrial and laboratory systems, especially in control and monitoring tasks, hardware is used in a loop (Figure 1).
Real-time processing system hardware in the loop.
In the diagram shown above, information about the physical environment is obtained through sensors that respond to a physical stimulus (light, heat, pressure, magnetism, acceleration, stress) and that are designed so that the information acquired is transformed into an electrical signal proportional to the changes. Frequently, the electrical signal obtained from the sensor has noise or interference, so signal conditioning is necessary, which is achieved through some processing operations such as amplification, linearization, compensation, and filtering. Analog-to-digital converters (ADC) are used to sample and hold charge, thereby converting the analog circuit current/voltage into a digital value. Without encoding, sensors are useful in analog control systems, but for the use in digital control and monitoring systems, encoding is critical. Real-time embedded systems therefore require digital encoding of all sensor inputs, with the exception of subsystems, which are all analog [1].
One of the main tasks of embedded systems is the processing and interpretation of information that arrives from the outside. An embedded system is a combination of hardware and software that is specifically designed for a particular function. In most cases, an embedded system is used to replace an application specific electronics in consumer products. By doing so, most of the systems functionality is encapsulated in the firmware that runs the system, and it is possible to change and upgrade the system by changing the firmware, while keeping the hardware same [2]. When embedded systems are board-based, it is fairly straightforward to select the proper components, integrate them with software, and ship the product.
In the mid-1990s, the development of embedded systems evolved with the concept of ASIC technology, changing the philosophy of systems based on a chip-set to a concept System-on-a-Chip (SoC) based on embedded cores. The term SoC defines an integrated circuit (IC) designed by joining multiple independent VLSI models to provide full functionality for an application. Each model is predesigned with complex functions known as cores that serve to a variety of applications. Cores can use a library of components designed by intellectual property (IP) companies or by self in house. The chip used for the system may contain combinations of cores that are generally available in the form of a synthesizable high-level description language (HDL), as Verilog/VHDL, or optimized transistor-level design. Some examples of core-based SoC include high-end microprocessors, GPS positioning for autonomous vehicles, smartphone, and even PC-on-a-Chip [3].
Nowadays, embedded systems are made on SoC. The SoC can include several heterogeneous subsystems, including specific hardware components and sophisticated interconnects (Figure 2).
Embedded system architecture. In addition to hardware, a SoC includes classic application software- and hardware-dependent software that must be co-designed with hardware interfaces. The API hides hardware details such as interrupt controllers or memory and I/O systems [
Often in systems used in industrial and laboratory applications for control, monitoring, testing and measurement, and automation, data acquisition (DAQ) subsystem is the first stage. The main purpose of DAQ is to measure physical phenomena, converting the analog signal into a digital signal, and then send or save the data collected for further analysis. An important point to consider is the problems of output conversion into a digital format, as well as to high accuracy and speed conversion methods used. In addition, if the application requires simultaneously capturing several signals, the DAQ must be of the multichannel type and will need a central processor, which will control the channeling and organization of data acquisition for further displays or its use in control systems. The methods to be used in multichannel data acquisition depend on the control and measurement tasks and directly influence the structure and functionalities of the DAQ. In a modern system, the measurement and control sensors can be set up in different ways; the most used are:
Methods that use time-division channeling, which perform sensor multiplexing, that is, the time is shared by each sensor in the data acquisition.
Methods using space-division channeling, based on simultaneous data acquisition from all the sensors.
In both cases, access to information at any time depends on the control and measurement tasks used [5].
Commercial DAQ cards are differentiated by their viabilities such as sampling frequency, scale of acquired signal, power, and requirements but are generally high in cost, and they need a PC at the collection site. Embedded systems to data acquisition often require the participation of the embedded operating system. The modern on-board FPGA can not only overcome the deficiency of the microcontroller unit (MCU) or the digital signal processor (DSP) and meet the requirements of system for real-time and synchronization but also for embedded applications using SoC FPGA platforms with the high level coordination, versatility, and full-stacked operative system [6].
At present, the most used standard protocols in communication in wireless sensor networks (WSN) are IEEE 802.15.1 Bluetooth, IEEE 802.15.4, IEEE 802.15.4/a ZigBee, and IEEE 802.11 Wi-Fi. The following describes these protocols:
IEEE 802.15.1 protocol is an economical and secure wireless communication standard, used to exchange information between devices through a short-range radio frequency; it was invented in 1994 by a group of engineers of the Ericsson Company. The original idea of Bluetooth was to eliminate the need for a cable connection between devices by connecting them over short distances (up to 100 m). Bluetooth operates with industrial, scientific, and medical frequencies (ISM), from 2.4 to 2.4835 GHz starting at 2.402 GHz. Bluetooth devices can be configured to operate in two ways:
Basic and Enhanced Data Rates (BR/EDR) transmissions, where 79 radio frequency (RF) channels with 1 MHz spacing are used. This configuration uses frequency-hopping spread spectrum (FHSS) scheme, at a nominal rate of 1600 hop per second.
Low Energy (LE) mode, where only 40 RF channels with 2 MHz spacing are available and adaptive frequency hopping (AFH) is used (Figure 3) [7, 8].
Bluetooth frequency bands and RF channels. Each RF channel is ordered in channel number n as follows: f = 2402 + n MHz, where n = 0, …, 78 (BR/EDR) and f = 2402 + n*2 MHz, with n = 0, …, 39 (LE).
Since its appearance, Bluetooth protocol has continuously evolved, so there are several versions that are differentiated with a number. Bluetooth versions 1.0–3.0 are known as Bluetooth Classic category and originally supported a maximum data rate of 721 kbps. This is referred to as Basic Rate (BR). The Bluetooth 2.0 EDR specification added support for data rates up to 2.1 Mbps. This is referred to as Enhanced Data Rate (EDR). The Bluetooth 3.0 High Speed (HS) specification enhanced it even further to 24 Mbps. Bluetooth Low Energy (BLE) is a new category that include versions 4.0 and 5.0. Geared toward applications requiring low power consumption, BLE returns to a lower data throughput of 1 Mbps using the GFSK modulation scheme. The Bluetooth 4.0 specification did not add any additional data rates; it only reduced the current consumption to enable low-energy devices. In Bluetooth 5.0, in addition to low power consumption, four different data rates are offered to accommodate a variety of transmission ranges: 2 Mbps, 1 Mbps, 500 kbps, and 125 kbps. The lower data rate of 125 kbps was added to compensate for the increase in transmission range [9].
Bluetooth module generally consists of four components: radio transceiver, baseband/link controller, link manager, and a host controller interface (HCI) [8]. HCI is the interface to access the Bluetooth module setup from the host. Bluetooth communication is based on the following two network topologies:
Piconet: It consists of one master and up to seven slaves (Figure 4a).
Scatternet (combination or two or more piconets) (Figure 4b): It is formed when two or more piconets come together by sharing a device. Scatternets help to extend the number of Bluetooth devices that can communicate with each other. They allow more than seven devices to communicate with each other [10].
Bluetooth network topologies. (a) Piconet. (b) Scatternet.
ZigBee, also known as IEEE 802.15.4, was initially conceived in 1998, standardized in 2003, and finally revised in 2006; it is a low power standard for short-range communications between wireless devices. ZigBee is classified as a wireless personal area network (WPAN). ZigBee devices operate in one of three bands: 868 MHz (Europe), 915 MHz (North America), and 2.4 GHz (worldwide). The 2.4 GHz band is the most used by the ZigBee transceivers and uses offset quadrature phase-shift keying (OQPSK) modulation stream. This type of modulation, which is a derivation of traditional QPSK, is used for requiring less transmission power and achieving the same or better performance than similar ones. OQPSK modulation combined with the use of a 5 MHz channel bandwidth allows devices to reach a data rate of up to 250 kbits/s efficiently [11]. The IEEE 802.15.4 has three different operation modes (Figure 5):
Personal area network coordinator (ZigBee coordinator, ZC): It is the principal controller of the PAN. This device identifies the network, and in it the configurations that allow other devices to be associated are made. ZC function is to act as ZigBee Router (ZR) once the network is formed. ZC is a full-functional device (FFD) that implements the full protocol stack; it can operate with or without beacon mode. The beacon mode of operation is used when data packets must be sent within an allowable delay, such as in monitoring and control applications. The beaconless mode is suitable for applications where data is only sent when an event occurs, that is, there is no continuity in sending information such as motion detection. In a cluster-tree network, all ZRs will receive beacons from their parents and send their own beacons to synchronize the nodes that belong to their clusters.
Local Coordinator (ZigBee Router, ZR): This device must be associated with a ZC or with another ZR previously associated with a network, because it does not create its own network. ZR is a full-functional device (FFD) that implements the full protocol stack. This device participates in multi-hop routing of message in mesh and cluster-tree networks (in the latter case they are also called cluster heads (CHs)). ZR provides synchronization services through beacon transmission.
End device (ZigBee end device, ZED): It is a device that does not implement the previous functionalities and should associate with a ZC or ZR before interacting with the network. In ZigBee, it is just a sensor/actuator node; it can be a reduced function device (RFD) that implements a reduced subset of the protocol stack [12].
ZigBee network topologies. (a) Star topology contains a unique node that operates as ZC, which establishes the PAN identifier. The identifier should not be used by any other ZigBee network in the vicinity. Also in the star topology, the communication is centralized, so each device (FFD or RFD) joining the network and willing to communicate with other devices must send its data to the ZC, which sends it to the adequate destination. (b) Mesh topology includes a ZC that identifies the entire network. Communication in this topology is decentralized, so each node can communicate directly with any other node within its radio. (c) In cluster tree topology, there is a single routing path between any pair of nodes, and there is a distributed synchronization mechanism (IEEE 802.15.4 beacon-enabled mode). There is only one ZC that identifies the entire network and one ZR per cluster. Any of the FFDs can act as a ZR that provides synchronization services to other devices and ZRs [
It is important to consider some operational considerations that may be presented by topologies for traditional wireless sensor networks (WSN). If you choose to use the star topology, you should keep in mind (a) that the sensor node selected as ZC will quickly consume its battery and (b) that the coverage of an IEEE 802.15.4/ZigBee cluster is very limited when addressing a large-scale WSN, leading to a scalability problem. On the other hand, the mesh topology enables enhanced networking, but it induces additional complexity to provide end-to-end connectivity between all nodes in the network. Therefore, unlike the star topology, the mesh topology can be more energy efficient, since the communication process does not depend on a particular node [14].
Wi-Fi is the name given by the Wi-Fi Alliance [15] to the IEEE 802.11 suite of standards. 802.11 defined the initial standard for wireless local area networks (WLANs).
The evolution of Wi-Fi technology has focused on increasing speed, lower latency, and better user experiences in a multitude of environments and with a variety of device types. Wi-Fi Alliance has introduced generational names to devices and product descriptions. The latest generation of Wi-Fi devices, based on the 802.11ax standard, is known as Wi-Fi devices 6. If the device contains 802.11 ac, 5 GHz technology is known as Wi-Fi 5, or if the device uses technology 802.11n, 2.4 GHz is known as Wi-Fi 4 [16]. Generations of Wi-Fi prior to Wi-Fi 4 will not be assigned names. Most of devices available in the market today are identified as Wi-Fi 5.
Wi-Fi is a physical layer/link interface, as is Ethernet. A wireless station (STA) can be a personal computer (PC), a laptop, a personal digital assistant (PDA), or phone. When two or more STAs are connected wirelessly, they form a Basic Service Set (BSS) (Figure 6). This is the basic component of a Wi-Fi network [17].
BSS controlled by a single coordination function (CF). The CF determines when a STA transmits and when it receives.
Wi-Fi has two different operation modes: infrastructure mode and ad hoc mode. Each one uses the BSS, but they yield different network topologies.
Ad hoc mode: Wireless stations communicate directly with one another, with a peer-to-peer network model. A BSS operating in ad hoc mode is isolated, that is, there is no connection to other Wi-Fi or wired LAN networks. The utility of this network is in situations that demand a quick setup in places where there is no network infrastructure.
Infrastructure mode: This mode requires the BSS to contain a wireless access point (AP). An AP is an STA with additional functionality that allows extending access to wired networks for clients of a wireless network. Any wireless device that tries to join the BSS must first be associated with the AP. A distribution system (DS) is generated when an AP provides access to its associated STAs. The DS can allow communication between APs as shown in Figure 7.
All wireless communication to or from an associated STA goes through an AP. This type of setup is similar to the “star topology” used in wired networks.
The IEEE 802.11 standard does not define any specific implementations. Instead, nine services are specified that all implementations must provide; these are:
A STA that functions as an AP must implement the following services:
The elements used for the realization of the proposed system are shown in Figure 8. The platform is composed of four components: the FPGA board that includes A/D converter and three wireless interface Bluetooth, XBee (ZigBee protocol-based), and Wi-Fi module. The wireless modules provide the FPGA device the capacity to communicate with other system or the Internet.
Hardware components used for the real-time monitoring system.
The A/D converter chip used is the integrated circuit (IC) LTC2308, Linear Technology, whose characteristics are low noise and power consumption, up to 500 Kbps, 8-channel, 12-bit, and SPI/MICROWIRE compatible serial interface. The internal conversion clock allows the external serial output data clock (SCK) to operate at any frequency up to 40 MHz [18]. Figure 9 shows the block diagram of ADC.
(a) Block diagram LTC2308 device. Eight analog input and operation modes can be programmed by a 6-bit DIN word through SDI terminal. (b) Timing with a long pulse. The configuration signals are S/D can be single-ended/differential-bit; O/S can be odd/sing-bit; S1 and S0 addressing select bit; UNI can be unipolar/bipolar and SLP active sleep mode [
Cyclone V SoC device block diagram is composed of two distinct portions: A dual-core ARM cortex-A9 hard processor system (HPS) and an FPGA. The cortex-A9 processor has two 32-bit CPUs and associated subsystems on the Intel Cyclone V SoC chip, where hardware circuits can be implemented, which reduce the size of the board and increase the performance of the developed system [
The Digi XBee series modules implement the IEEE 802.15.4 radio and ZigBee networking protocol for its physical layer and MAC. Outdoor transmission distances to 0–90 meters depending on power output and environmental characteristics. XBee devices work in ISM 2.4 GHz frequency bands having a serial interface data rate from 1200 bps to 250Kbps. The following are the supported network topologies: point-to-point, point-to-multipoint, and peer-to-peer.
This module is a serial interface converter to Bluetooth adapter. HC-06 has a 2.4GHz digital wireless transceiver, low power consumption, an EDR module, the change range of modulation depth: 2Mbps–3Mbps, and standard HCI Port (UART or USB), and it can work at the low voltage (3.1–4.2 V). The module can be set by AT commands and have two modes, master and slave, but the mode cannot be switched during the process of communication. Serial baud rate is 1200–1,382,400 bps [21].
This module implements TCP/IP and full 802.11 b/g/n (support 2.4 GHz, up to 72.2 Mbps) WLAN MAC protocol. It can perform either as a stand-alone application or as the slave to a host MCU, so it supports Basic Service Set (BSS) STA and SoftAP operations under the distributed control function (DCF). ESP8266 includes a CPU Tensilica L106 32-bit processor, and it has peripheral interfaces: UART, SDIO, SPI, I2C, I2S, and IR. Power management is handled with minimum host interaction to minimize active duty period. ESP8266EX can be applied to any microcontroller design as a Wi-Fi adaptor through SPI/SDIO or UART interfaces [22].
The design of an FPGA-based remote monitoring system architectures is show in Figure 11. The resultant design is implemented in VHDL and block diagrams; it is validated in co-simulation environment, and finally, it is tested in a real-time application to monitoring an electric signal.
Block diagram of architectures implemented on FPGA. This module comprises five blocks: ADC controller, FIFO memory, Wi-fi, UART drivers, and a finite state machine (FSM).
There are three important features to consider before starting the development system: first, the nature of the feedback signal. If the sensor which measures the variable to be monitored has an analog nature, it is necessary to use an analog-to-digital converter (ADC) which has an output with a fixed bit width. Second: in order to avoid performing arithmetic operations between signals of different bit width, it is strongly recommended that the operations have the same bit width as the measured variable. Finally, the system output must be congruent with the bus width wireless interface.
The ADC LTC2308 operates on a 12-cycle operational frame, as shown in Figure 9b. ADC has four wires to control and communicate with the FPGA: SCLK, CS, DIN, and DOUT. The SCLK and CS signals are used to control the ADC. SCLK is the signal clock for the ADC. The CS signal serves as chip select for the ADC chip. The DIN and DOUT wires are used for transferring addresses and data between the two chips (ADC and FPGA). The FPGA uses the DIN connection to provide the address (3 bits in length) of the next channel requested for conversion. The DOUT connection is used by the ADC to send the digital value (12 bits long) of the converted signal to the FPGA. Both DIN and DOUT are sent in a serial manner at a rate of 1 bit per SCLK cycle [23].
In the case of our working example, SPI controller was developed to control the conversion process. A long CONVST pulse is used. Figure 9b shows time diagram to programming ADC. According to the diagram, “the conversions are initiated by a rising edge on the CONVST input. Once a conversion cycle has begun, it cannot be restarted. Between conversions, a 6-bit input word (DIN) at the SDI input configures the MUX and programs various modes of operation. As the DIN bits are shifted in, data from the previous conversion is shifted out on SDO. After the 6 bits of the DIN word have been shifted in, the ADC begins acquiring the analog input in preparation for the next conversion as the rest of the data is shifted out” [19]. Figure 12 shows the block diagram architecture corresponding to SPI controller.
SPI controller architecture. (a) 12 bits a/D conversion general architecture. (b) ADC_Core architecture. (c) ADC_Nano architecture generates signal control to ADC. The 4-bit counter counts 16 cycles in high for the acquisition of the signal and 16 cycles in low for the sending of the 12 output bits parallel to the configuration instruction for the next sample. The control ADC architecture is based on shift register.
A dual-clock First-In First-Out (FIFO) buffer was used to cross data between the two different clock domains: sampling frequency A/D converter (from 1 to 25 MHz) and transmission rate (from 9600 to 921,600 bps), Figure 13. In the systems’ clock frequency domain, the serialized outputs are continuously stored in 12 bits shift register, before they will be sent to FIFO buffer. The finite state machine (FSM) FIFO, in the system controller, wait until collected data of the last active channel will be sent through wireless module, before starting a new acquisition.
Dual-clock FIFO architecture. Two counters are used to addressing the data to read and write operations. RAM of 12-bit and 16 words is used to store data.
The following source code corresponds to the FIFO_LOGIC and RAM entities of the design.
Code 1. FIFO_LOGIC.vhd [24].
Code 2. RAM_16.vhd.
Serial communications depend on the two UART devices (the FPGA architecture and the wireless module) to be configured with compatible settings: baud rate, parity, control (start and stop bits), and data bits (Figure 14).
UART data packet has data format structure: Data bits, parity, and stop bits. In the graph, the data 0x9B (decimal number “155,” ASCII character “ø”) is transmitted through the wireless module with format: 8-N-1 [
In this system, a general port input/output (GPIO) is used to send serial data. Subsystem architecture (Figure 15) is used to set the baud rate in the output. UART interface will read out the data when it is filled in the FIFO and send to the host through the wireless link (Bluetooth or XBee modules), and finally the data can be displayed in the host with software application.
UART driver diagram. Serial transmission uses baud rate module (DIVISOR_8333). MAQUINAFSM together with MUXSALIDA sends data from FIFO to serial data in the transmission format. The parity is verified with PARIDAD.
Code 3. DIVISOR_8333.vhd.
Code 4. MUX_SALIDA.vhd.
Code 5. MAQUINA_FSM.vhd.
Code 6. PARIDAD.vhd.
The wireless modules are configured through AT commands. Command strings have the form ATxx (where xx is the name of a setting). The mode for both is slave to receive data from UART driver architecture. Bluetooth can be set to baud rate from 9600 to 921,600 bps. XBee can be set to baud rate from 9600 to 250,000 bps. Terminal software like Tera Term [26] can be used to have an initial configuration of the devices. Any USB to TTL converter, for example, PL2303HX device or similar, can be used.
In the case of Bluetooth, the module only needs to be connected to the Rx of module to Tx of USB-TTL converter and Tx of module. It is necessary to connect ground and Vcc. HC-06 module is permanently configured to be slaved, and it is always in AT mode when not paired to any other device. AT commands can be founded in datasheets [27].
XBee configuration needs a test utility (XCTU) to enable users to interact with radio frequency (RF) devices through a graphical interface. The application includes built-in tools that make it easy to set up, configure, and test RF devices. The software can be downloaded from the Internet [28].
ESP8266 Wi-Fi module is used to transmit the sensor data wirelessly to the Wi-Fi modem at the other end with Internet connection. ESP8266 can be initialized using a set of AT commands. Initialization process includes (a) verifying the communication between ESP8266 module and FPGA architecture (RST command) and (b) searching for a Wi-Fi network within its range and connecting to it, with the required credentials (CWJAP command). Sending process includes (a) setting the Wi-Fi module as a TCP/IP client (CIPSTART command); (b) transmitting data involves communication with cloud server using IP address (CIPSEND command). Address IP of the server is required to access the data from personal computing devices such laptop, tablet, and smartphone. Figure 16 shows the AT command sequence and a block of Wi-Fi architecture:
AT command sequence. (a) Flow diagram of WEB connection [
An analog signal is generated by the function generator to test the system, and the final data sent to the PC or WEB page is observed. Figures 17, 18 and 19 show the corresponding practical wave and storage wave.
Measurements of real signal sent to the host and WEB page. The signal has an offset = 3.98Vdc and 8.06Vpp and frequency of 60 Hz with harmonics of 3rd, 5th, 7th, and 9th. This signal is obtained from digital oscilloscope.
Data received from the remote DAQ system (Bluetooth or XBee module) using GUI development. Each cycle is represented by 133 samples (sampling frequency = 8 kHz). The UART baud rate is 115,200 bps.
Data sent to WEB page through Wi-fi module. The WEB page was made on a XAMPP package that includes apache WEB server, MySQL, and PHP [
The GUI (Figure 18) was made using Java Eclipse Oxygen [29] and serial communication libraries (jSerialComm). jSerialComm is a Java library designed to provide a platform-independent way to access standard serial ports without requiring external libraries, native code, or any other tools. It is meant as an alternative to Rx-Tx and the (deprecated) Java Communications API, with increased ease of use, an enhanced support for timeouts, and the ability to open multiple ports simultaneously [30].
This chapter described a data acquisition system based on FPGA. Several architectures to ADC controller, UART communication, FIFO memory, and Wi-Fi configuration process were made to develop the system. Experiments show that the system can convert the analog signals to digital signal and send to host computer to Java GUI or WEB page in real-time. The data can be acquired by using custom sampling frequency and baud rate. The entire system is designed to be simple, stable, and low cost.
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I am also a member of the team in charge for the supervision of Ph.D. students in the fields of development of silicon based planar waveguide sensor devices, study of inelastic electron tunnelling in planar tunnelling nanostructures for sensing applications and development of organotellurium(IV) compounds for semiconductor applications. I am a specialist in data analysis techniques and nanosurface structure. 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