Sanpasaurus yaoiSanpasaurus is nonetheless diagnosable with respect to an autapomorphic feature of the holotypic dorsal vertebral series. Additional material thought to be collected from the type locality is tentatively referred to Sanpasaurus. If correctly attributed, a second autapomorphy is present in a referred humerus. The presence of a dorsoventrally compressed pedal ungual in Sanpasaurus is of particular interest, with taxa possessing this typically ‘vulcanodontid’ character exhibiting a much broader geographic distribution than previously thought. Furthermore, the association of this trait with other features of Sanpasaurus that are broadly characteristic of basal eusauropods underscores the mosaic nature of the early sauropod–eusauropod transition. Our revision of Sanpasaurus has palaeobiogeographic implications for Early Jurassic sauropods, with evidence that the group maintained a cosmopolitan Pangaean distribution.The Early Jurassic of China has long been recognized for its diverse array of sauropodomorph dinosaurs. However, the contribution of this record to our understanding of early sauropod evolution is complicated by a dearth of information on important transitional taxa. We present a revision of the poorly known taxon  Kunmingosaurus’ (Chuxiongosaurus (Yunnanosaurus. The Fengjiahe Formation (Yunnan Province), which is hypothesised to be a lateral equivalent of the Lower Lufeng Formation, has produced the putative basal sauropod Chinshakiangosaurus  still awaits a full description  from the Daanzhai Member of the Ziliujing Formation has often been noted as Early Jurassic in age, but potentially dates to the early Middle Jurassic  sauropod dinosaur  and below the ‘Zizhongosaurus’-bearing Daanzhai Member. Consequently, the Sanpasaurus assemblage has the potential to provide new insights into the sauropod fauna of the Chinese Early Jurassic either prior to, or penecontemporaneous with, the origin of Eusauropoda. Here we provide a detailed description of the identifiable material found within this assemblage, followed by an assessment of its monospecificity and potential taxonomic relationships.In 1944, C.C. Young described an assemblage of material collected from several quarries in the Maanshan (= Ma’anshan) Member of the Ziliujing Formation close to the town of Changshanling, near Weiyuan City in Sichuan Province. dinosaur . Althougdinosaur , SanpasaDINOSAURIA Owen, 1842SAURISCHIA Seeley, 1887SAUROPODOMORPHA Huene, 1932SAUROPODA Marsh, 1878Sanpasaurus yaoiYoung, 1944Holotype: IVPP V156A (IVPP V156 partim); Disarticulated middle-posterior dorsal vertebral series, consisting of three complete centra with partial neural arches.Referred material: IVPP V156B ; two centra from the dorsal vertebral series, lacking neural arches; two sacral centra from a small individual; an almost complete anterior-middle caudal vertebra; several distal caudal centra; numerous fragmentary rib shafts; proximal chevron; scapular remains from at least three different elements, potentially including the left and right elements of a single individual; a partial left forelimb consisting of the distal half of a humerus, complete ulna and radius, and the proximal half of a single metacarpal; a femoral head from a small individual; a small ?distal tibia; a proximal fibula; a non-first digit pedal ungual. . This, and the lack of clear evidence for association between the included elements, renders the taxon unstable, although at least some of the material appears to be taxonomically diagnostic. To protect the taxonomic stability of this species, we hereby restrict the holotype to three dorsal vertebrae, which bear clear autapomorphies that enable it to be diagnosed adequately. Henceforth, we designate the holotype as IVPP V156A. The other material included within IVPP V156 is regarded as potentially referable to the same taxon (see below), but to different individuals and is designated IVPP V156B. This action complies with Article 73.1.5 of the International Code of Zoological Nomenclature  in definDiagnosis:Sanpasaurus can be diagnosed by the following autapomorphy: middle-posterior dorsal neural arches with thin, dorsoventrally oriented ridges on the lateral surfaces of the arch, at approximately the anteroposterior mid-point, just above the neurocentral suture. Additionally, following the referral above, Sanpasaurus could be diagnosed by a second potential autapomorphy of the humerus: a distinct midline protuberance between the ulnar and radial condyles.Locality and horizon: The material was collected from the Maanshan Member of the Ziliujing Formation, Weiyuan region, Sichuan Province, People’s Republic of China in 1939 Locality c in age , and then in age . If the Previously referred material: In addition to IVPP V156, Sanpasaurus yaoi, and regarded two isolated vertebrae  from the Ziliujing Formation near to Chongqing as cf. Sanpasaurus yaoi. Sanpasaurus yaoi, although the stratigraphic unit of this locality is unknown. Lastly, Sanpasaurus; however, no further information has been published on this material. Based on a lack of overlapping diagnostic elements, none of these remains can be confidently referred to Sanpasaurus, and we regard them as indeterminate sauropods, restricting Sanpasaurus yaoi to IVPP V156.Sanpasaurus is composed of three dorsal vertebrae with partially preserved neural arches. The most complete is referred to as V156AI , it is clear that these specimens derive from at least the middle part of the dorsal series.Tazoudasaurus . The andasaurus , and 0.7rosaurus . By cont of ∼1.2 : Fig. 32The suture dividing the centrum from the neural arch is still clearly visible in all three specimens as a flat, non-interdigitated connection. Although the arch and centrum were clearly semi-fused at the time of death, the apparent lack of complete fusion potentially indicates that the relatively small size of the vertebrae is due to either juvenile or subadult status.Tazoudasaurus . This is a derived sauropodomorph feature and is observed in most basal sauropods . The neus sensu are onlyus sensu. A roundAs mentioned above, the base of the left prezygapophysis is preserved in V156AI, including what appears to be the posterior part of the flattened articular surface and the wall of the hypantrum. If this identification is correct, the prezygapophyseal articulation would have faced inwards at an angle of about 45° to the horizontal. The prezygapophyses appear to have been positioned very close to each other with respect to the midline. The beginning of a ridge extends backwards from the posterodorsal base of the prezygapophysis—towards either the diapophysis or the base of the neural spine , in the latter the spinoprezygapophyseal lamina (SPRL)). There is a vertical ridge along the midline of the posterior surface of the neural arch of V156AII, extending dorsally from the roof of the neural canal opening. This potentially represents either the intrapostzygapophyseal lamina (TPOL) or the broken ventral base of the hyposphene . V156AII and V156AIII also preserve the bases of the centropostzygapophyseal laminae (CPOLs). In V156AII these structures bracket either side of the TPOL and are directed steeply posteroventrally, forming the posterolateral margins of the neural arch. The right CPOL of V156AIII is more complete dorsally than in V156AII, and undergoes a marked anteroposterior compression at the level of the dorsal extent of the parapophysis. This narrow lamina forms the posterior wall of a deep, possibly natural, fossa that is walled medially by a thin ridge of bone similar to the median septum observed in V156AI.Sanpasaurus.In all specimens an unusual structure is present on the lateral surfaces of the neural arch. In V156AI and V156AII it consists of two short and low ridges, subparallel to each other, that extend vertically to produce a low scar or prominence. The dorsal termination of these ridges is roughly level with the ventral termination of the parapophyses, and the ridges themselves are approximately equidistant between the anterior and posterior margins of the neural arch. In V156AIII there is a single ridge that has a more posterodorsal inclination , which merges ultimately with the CPOL at roughly the level of the dorsal apex of the neural canal. No similar structures appear to be present in any other Early–Middle Jurassic sauropods, and we provisionally regard the presence of these ridges as an autapomorphy of In addition to the holotypic dorsal elements (see below) there are two isolated dorsal centra amongst the IVPP V156 assemblage. Both agree in general morphology: the anterior surfaces are nearly flat whereas the posterior surfaces are concave. Both appear to be slightly longer anteroposteriorly than dorsoventrally high or transversely wide . Their ventral surfaces are concave longitudinally due to the expansion of the anterior and posterior articular surfaces, but are mildly convex transversely. Neither of the dorsal centra possess a sharply-lipped lateral fossa (= pleurocoel). However, one of the centra, possibly from the anterior part of the dorsal series, possesses moderately deep lateral depressions, just posterior to the anterior surface . On accoA proximally and distally incomplete left thoracic rib is preserved in five pieces . The tubAlthough Pulanesaura  . The cenanesaura  and Tazodasaurus  are rougsensuThe position of the neural arch on the centrum exhibits a strong anterior bias, although it remains set back from the anterior margin by ∼1.5 cm. The bases of the transverse processes extend for a short distance onto the lateral surface of the centrum and are elliptical in cross-section. The prezygapophyses are narrowly spaced and steeply inclined, with the angle of the articular facets being just under 90° from the horizontal. Finely delimited SPRLs connect the posterior ends of the prezygapophyses with the anterior surface of the neural spine. The SPRLs are still observable at the dorsal termination of the broken neural spine. The fossa located at the base of the spine and bounded by these laminae  Haplocanthosaurus . The lahosaurus  and 9E. Shunosaurus, which are unbridged  are relrasaurus : Fig. 7.The humerus is broken at roughly mid-shaft, just below the level of the deltopectoral crest; however, when viewed laterally, a slight expansion at its proximal termination probably marks the distal-most extent of the deltopectoral crest. The shaft is subelliptical in cross-section with the long-axis of this section angled at roughly 45° with respect to the transverse axis of the distal end . The antMamenchisaurus and Spinophorosaurus  of basal sauropodomorph taxa . There irosaurus , althougondition , the disVulcanodon and more derived sauropods . Consistent with the morphology of other sauropods, the proximal end of the ulna is triradiate, with shorter and robust medial and lateral  processes , and a longer and thinner anterior process. The latter curves strongly laterally towards its termination in proximal view. The resulting concavity for the reception of the proximal radius is thus relatively deep, approaching the condition of Camarasaurus, for example  process, the element is mostly complete . The ulnauropods . Measure example . The artIn medial view, the shaft bows slightly anteriorly. The proximal part of the shaft is subtriangular in cross-section, with flat surfaces facing anteromedially, anterolaterally and posteriorly. At mid-shaft the ulna becomes more elliptical in cross-section, with the long-axis extending anteroposteriorly. The distal part expands lateromedially but does not expand much anteroposteriorly. The distal articular surface appears to be mildly convex and is highly rugose. There is no evidence of either a ridge or double ridge for ligamentous attachments to the radius on the distolateral corner of the shaft. However, there is a prominent bulge on the lateral surface towards the distal end, but it is not clear how much of this feature is real and how much has been caused by repairs to the shaft. The anterior surface of the distal shaft is planar whereas the other surfaces are gently convex.This is probably the corresponding antebrachial element to the left ulna. Although complete, the shaft is broken into three segments, joined together in a nail and socket arrangement . The impVulcanodon and other sauropods  .The proximolateral corner of the radius has suffered some slight erosion. The medial margin of the shaft is concave, but it is difficult to say to what degree this morphology is exaggerated due to the abovementioned breakage. In contrast, the radius of tion see : Fig. 6.l margin : Fig. 6 l margin : Fig. 9.Approximately one-third to half of the proximal end of the metacarpal is preserved . It is tThe femur is clearly from a smaller individual than the forelimb elements. Moreover, poorer preservation, coupled with a slightly darker colouring, suggests that the femur might come from a different locality than the forelimb elements. Although its incompleteness makes identification of the femur difficult, we interpret it as coming from the right side.Isanosaurus  . There rosaurus .We interpret this element as the distal end of a left tibia from a smaller sized animal than the forelimb elements. The distal end expands prominently transversely from a relatively narrow shaft that is subelliptical in cross-section . The antAntetonitrus . The laVulcanodon and Tazoudasaurus and represents a potential synapomorphy uniting the two within Vulcanodontidae sensuVulcanodon  exhibited by its dorsal margin. The dorsal surface is convex transversely and also slightly convex proximodistally. Near each margin is a prominent groove, each extending virtually the entire length of the claw as preserved. The margin with the slightly shallower groove is interpreted as the lateral because it is slightly concave in dorsal view, whereas the other is regarded as medial because it is slightly convex. This suggests that it is a left claw. It is worth noting, however, that if the unguals figured in Sanpasaurus confirms it as a provisionally valid taxon pending the discovery of further associated and/or referable material. Its validity stems from the two above-mentioned autapomorphic features (see Diagnosis) pertaining to the holotypic dorsal vertebral series and referred distal humerus. These features and other taxonomically significant characters are discussed in more detail below. Given that Sanpasaurus was originally interpreted as an ornithopod ornithischian  with respect to: (1) the anteroposterior restriction of their neural arch bases relative to the lengths of their respective centra ; (2) the lack of anterior centrodiapophyseal/centroparapophyseal laminae; and (3) the apparent absence of any pronounced excavations on the lateral surfaces of the neural arch. Consequently, on the basis of these features, the dorsal vertebrae of IVPP V156 can be considered to be unambiguously referable to Sauropoda.Although incomplete, it is clear that the dorsal neural arches were originally dorsoventrally tall relative to the height of their respective centra—as observed in Sauropoda . Within of China . Althouggosaurus , Huayanghe group . NonetheSanpasaurus was inconsistent with the more cross-sectionally “trapeziform” caudal vertebrae of ornithopod dinosaurs. To this we can add that the anterodorsally projecting prezygapophyses of the anterior caudal vertebra of Sanpasaurus contrast with the more anteriorly oriented prezygapophyses in Huayangosaurus  and perlosaurus . Althouglcanodon , and foldasaurus , it is nlcanodon . This isdosaurus . Moreovedosaurus . Given tSanpasaurus is difficult due to its incompleteness and the ambiguous associations of the type assemblage. It can be referred to Sauropoda based on a number of features that are derived within Sauropodomorpha  are the TPRLs, PRPLs and TPOLs. Unfortunately, the absence of cervical and dorsal vertebrae in the available material of Vulcanodon limits our understanding of the timing of acquisition of these features. Apatosaurus and Camarasaurus). Confusingly, the presence/absence of TPRLs in the middle-to-posterior dorsal vertebrae of all included taxa is listed as an inapplicable character  .Tazoudasaurus onwards . With rmbiguous : 647 stadasaurus ; Mamenchhisaurus ; Bellusalusaurus ), this flusaurus  these stSanpasaurus is the lateral excavation of the base of the neural arches, which is positioned directly ventral to where the diapophyses would have been located. Although there is no evidence of a Barapasaurus- or Patagosaurus-like cavity within the arch itself, which is linked to the external surface via a lateral foramen , all of the centra of Sanpasaurus are amphicoelous, and it is possible that the centrum with a shallow lateral fossa might represent an anterior dorsal vertebra. Furthermore, the lateral centrum surfaces in Barapasaurus, Cetiosaurus and Patagosaurus possess pronounced fossae, if not ‘true’ pluerocoels —a feature not seen in any of the dorsal centra within IVPP V156.A final point worth mentioning with respect to the dorsal vertebrae of  foramen , Upchurc foramen : 218 notiosaurus , there iVulcanodon and Tazoudasaurus. As mentioned above, the anterodistal margin of the humerus is ornamented with accessory projections of the distal condyles, a feature common (if variable in expression) to a number of sauropod genera , and thus might indicate a more complicated distribution for this feature).Although differing in forelimb morphology, the most striking similarity between sauropod . Confirmtosaurus : Fig. 12Tazoudasaurus, and the dorsoventrally compressed pedal ungual (see below for discussion regarding the ‘Vulcanodontidae’). An affinity with eusauropods is supported by the (probably) pneumatic excavations of the lateral surfaces of the dorsal vertebral neural arches, and the modifications to the distal condyles of the humerus. Based on these observations, we refer Sanpasaurus to Sauropoda incertae sedis, while highlighting the possibility that Sanpasaurus represents one of the most derived non-eusauropodan sauropods currently known  + Patagosaurus (Argentina) from the rest of Eusauropoda. This was not the first time that a form of early sauropod endemism has been hypothesized, with the grouping of Vulcanodon (Zimbabwe) and Tazoudasaurus (Morocco) into the subfamily ‘Vulcanodontidae’ suggestive of an African radiation of basal sauropods (The affinities discussed above for auropods . HoweverCetiosaurus (United Kingdom) outside of Eusauropoda. This is incompatible with almost all other recent analyses, which place Cetiosaurus well within Eusauropoda, and sometimes as the sister-taxon to Neosauropoda  composite mount from a large bone-bed, the monospecificity of which is yet to be fully demonstrated  ‘vulcanodontids’ extended beyond Africa; or (2) that dorsoventrally compressed pedal unguals characterized a wider range of basal sauropod taxa than currently recognized . Given that the limited information currently available for Sanpasaurus suggests a character suite broadly intermediary between basal sauropods and eusauropods, we argue that ‘vulcanodontid’ monophyly in a maximally inclusive sense is probably unlikely—an observation further supported by the depauperate taxonomic content of the proposed subfamily . Nonetheless, additional sampling of the Early Jurassic is required in order to establish a better sense of the phylogenetic distribution of these typically ‘vulcanodontid’ characters.Although support for a south Gondwanan basal eusauropod clade is weak, the evidence for a monophyletic radiation at the base of Sauropoda—the ‘Vulcanodontidae’—is somewhat stronger. This is due to a number of similarities between nosaurus , and to rosaurus , a closerosaurus . The posSanpasaurus tentatively suggests that early sauropod faunas were probably cosmopolitan throughout Pangaea in the Early Jurassic, with little evidence of geographically-bounded endemism. Although it remains possible that a grade of basal forms originated in Africa prior to its isolation by expansion of the Central Gondwanan Desert, uncertainties remain as to the degree to which aridity could restrict sauropod distributions, with the earliest representatives of the group possibly inhabiting semi-arid environments  are consistent with Shunosaurus, are necessary to more closely integrate these taxa into overviews of early eusauropod diversification.With respect to the above, and contrary to the scenario posited by Sanpasaurus has shown it to be a provisionally valid taxon pending additional sampling of Early–Middle Jurassic strata of China. The unique combination of plesiomorphic and apomophic characters observable in Sanpasaurus underscores the mosaic manner of trait-acquisition that likely characterized the basal sauropod–eusauropod transition. This is perhaps most evident with respect to the presence of dorsoventrally compressed pedal unguals in Sanpasaurus. Whereas the taxa possessing this feature can now be shown to have had a geographic distribution far beyond Africa, its association with eusauropod-like alterations of the dorsal vertebrae and distal humerus also provides additional support to previous assertions of ‘vulcanodontid’ paraphyly , will be necessary to corroborate the above observations and to better elucidate the contribution of the Chinese Early Jurassic fossil record to our understanding of basal sauropod evolution generally. However, the limited information available from Sanpasaurus provides evidence that at least some sauropod lineages had a global, or near-global, distribution during the Early Jurassic.Our reassessment of the basal sauropod ly e.g., . Althoug


Crystallization has been a bottleneck in the X-ray crystallography of proteins. Although many techniques have been developed to overcome this obstacle, the impurities caused by chemical reactions during crystallization have not been sufficiently considered. Oxidation of proteins, which can lead to poor reproducibility of the crystallization, is a prominent example. Protein oxidization in the crystallization droplet causes inter-molecular disulfide bridge formation, formation of oxidation film, and precipitation of proteins. These changes by oxidation are typically irreversible. The best approach for preventing protein oxidization during crystallization is anaerobic crystallization. Here we review the anaerobic crystallization of proteins, which was originally developed to trap a reaction intermediate of the enzyme in the crystal. We also summarize representative anaerobic crystallizations from our laboratory and the general setup of anaerobic crystallization. Since the tertiary structure of proteins  is a key to analyzing the molecular mechanisms underlying cellular biological processes, much research effort has been expended in analyzing tertiary structures. As a result, more than 130,000 coordinates have been deposited to date in the Protein Data Bank . One of the striking examples of using thermo-stabilization in protein crystallization is the introduction of a T4-lysozyme in a loop region of the G-protein-coupled receptors prior to their crystallization , causing heterogeneity of the protein in solution. This obstruction of crystallization by oxidation is a well-established phenomenon. The addition of a reducing reagent is a typical strategy to avoid the random formation of the intra/inter-molecular disulfide bridges. However, since it is rather difficult to completely avoid oxidation of the sample for a long period under aerobic conditions, the reproducibility of the experiment tends to be poor. Furthermore, oxidization of the reducing reagent results in heterogeneity of the crystallization solution, which would cause problems with the crystallization. It is therefore quite difficult to maintain all the conditions the same across many crystallization trials using a reducing reagent. To avoid heterogeneity in the crystallization solution, the use of anaerobic conditions is a potent method. For example, anaerobic crystallization can easily avoid the formation of an inter-molecular disulfide bridge and increase the reproducibility of crystallization, as observed in the crystallization of oxidized and reduced forms of BphA3  is introduced into the anaerobic chamber for 1 min every hour. Prior to starting the experiments, it is highly recommended that the O2 level be checked using an anaerobic indicator, such as an Oxoid anaerobic indicator , unless the chamber is equipped with an oxygen monitor.As described in the preceding section, anaerobic crystallization is an effective process for preserving the chemical purity of the protein sample. In this section, we describe a general setup for anaerobic crystallization. First, an anaerobic chamber is needed Fig. a, and se2 molecules from any plasticware, sample solution, or buffer solution used in the experiment. In particular, the removal of O2 molecules from any plasticware to be used in the anaerobic chamber must be done thoroughly and carefully. Since O2 molecules are hydrophobic in nature, they can be easily adsorbed onto the surface of plasticware. In our laboratory, plasticware intended for anaerobic use is usually kept in the anaerobic chamber for more than 2 weeks before use in order to allow the O2 molecules on the plastic surfaces to be exchanged with N2 , it becomes possible to remove the crystallization plate from the anaerobic chamber and keep it in an aerobic incubator. However, it is awkward and inconvenient to handle a crystallization plate sealed with vacuum grease. Therefore, it is preferable to perform the incubation and observation of the crystallization plates in the anaerobic chamber. Since a microscope can easily be equipped with a CCD camera, images of crystallization droplets can be observed through a monitor inside or outside the chamber. If the microscope is in the anaerobic chamber, it is possible to freeze the crystal inside the anaerobic chamber.Helicobacter pylori. The interaction between the tandem SH2 domain and the phosphorylated CagA peptide disturbs cellular signaling, causing tumorigenesis  domain of the tyrosine phosphatase SHP2 in complex with a phosphorylated peptide (Hayashi et al. Several trials of crystallization screening of the tandem SH2 domain with various phosphorylated peptides were performed under anaerobic conditions, and eventually micro-crystals of the tandem SH2 domain in complex with the phosphorylated EPIYA-C peptide were obtained. After optimization of the crystallization conditions, rod-shaped crystals were obtained in a reproducible manner under anaerobic conditions in the sitting drop setting Fig. b. IntereWhile the oxidation of proteins and crystallization reagents has been recognized for some time, there has been no systematic search for fundamental solutions to the problem. Here, we have reviewed the topic of anaerobic crystallization and described how it frequently improves the results and reproducibility of crystallization experiments. Since the oxidation of the protein in the crystallization droplet causes a reduction in the purity of the protein, avoiding oxidation under the anaerobic conditions is theoretically reasonable.


As the demand for indoor localization is increasing to support our daily life in large and complex indoor environments, sound-based localization technologies have attracted researchers’ attention because they have the advantages of being fully compatible with commercial off-the-shelf (COTS) smartphones, they have high positioning accuracy and low-cost infrastructure. However, the non-line-of-sight (NLOS) phenomenon poses a great challenge and has become the technology bottleneck for practical applications of acoustic smartphone indoor localization. Through identifying and discarding the NLOS measurements, the positioning performance can be improved by incorporating only the LOS measurements. In this paper, we focus on identifying NLOS components by characterizing the acoustic channels. Firstly, by analyzing indoor acoustic propagations, the changes of acoustic channel from the line-of-sight (LOS) condition to the NLOS condition are characterized as the difference of channel gain and channel delay between the two propagation scenarios. Then, an efficient approach to estimate relative channel gain and delay based on the cross-correlation method is proposed, which considers the mitigation of the Doppler Effect and reduction of the computational complexity. Nine novel features have been extracted, and a support vector machine (SVM) classifier with a radial-based function (RBF) kernel is used to realize NLOS identification. The experimental result with an overall 98.9% classification accuracy based on a data set with more than 10 thousand measurements shows that the proposed identification approach and features are effective in acoustic NLOS identification for acoustic indoor localization via a smartphone. In order to further evaluate the performance of the proposed SVM classifier, the performance of an SVM classifier is compared with that of traditional classifiers based on logistic regression (LR) and linear discriminant analysis (LDA). The results also show that a SVM with the RBF kernel function method outperforms others in acoustic NLOS identification. As smart mobile devices have been ubiquitously available for people to use in our daily life, a new demand for indoor navigation, precision marketing, public safety and emergency rescue has emerged, especially in large buildings such as underground parking, large-scale transportation terminals, and large shopping malls . LocatioIt is common that the line-of-sight (LOS) path, or direct path, is obstructed by human bodies, furniture, walls or corners, due to the arbitrariness of human movement. When LOS is not available, the received signals via NLOS will travel a longer distance than the LOS path. The estimation of the direction of arrival (DOA), time of arrival (TOA) and time difference of arrival (TDOA) would involve considerable errors. Through identifying and discarding the NLOS measurements, the positioning performance can be improved by incorporating only the LOS measurements ,12,13. TThe NLOS identification techniques for radio communications have been discussed extensively within cellular mobile networks and Ultra-Wideband (UWB) techniques, and many methods have been proposed ,15. ThesCompared with wireless localization, the main characteristics of acoustic smartphone indoor localization are the low update rate of user positioning  and the The methods based on channel characteristics are more suitable to address this problem. NLOS is induced by ambient environment, and the acoustic channel characteristics are also highly related to ambient environment, which makes using acoustic channel characteristics extracted from received signals a more direct way to realize NLOS identification. At the same time, the methods based on channel characteristics are a single-node approach which only uses the information of signals received from a single node. This could realize an independent and real-time acoustic NLOS identification of each ranging measurement between a transmitter and a receiver, and perfectly fit the acoustic indoor localization systems. However, many challenges still need to be overcome to realize acoustic NLOS identification via smartphones, including the following:(1) The distortion of acoustic signals received by smartphones. It is understood that the MEMS microphone and speaker equipped in COTS smartphones are used for communication and entertainment. Once these modules are used as sensors for ranging measurement, many defects will be exposed. Except the poor performance and non-consistency of MEMS microphones and speakers, the speed of the crystal oscillator in smartphones, which provides the clock of the audio sampling and broadcasting system, is usually unstable. This could induce severe signal distortions, as shown in (2) The Doppler Effect caused by human movement. The Doppler Effect is another great challenge to acoustic NLOS identification, because smartphones are usually carried by human beings. The arbitrary movement of a human being coupled with arm swing makes the smartphone an extremely complex manoeuvring movement with a high speed. It could introduce an obvious shift of phase even at a slow walking speed, due to the low speed of sound propagation. Thus, a channel parameter estimation algorithm with the Doppler Effect mitigation is crucial for acoustic NLOS identification.An acoustic NLOS identification approach based on acoustic channel characteristics is proposed for smartphone indoor localization in the real world. This approach is suitable for the acoustic localization systems based on DOA, TOA and TDOA strategies.An efficient approach to estimate relative channel gain and delay based on the cross-correlation method is proposed, in order to mitigate the influence of the Doppler Effect and reduce the computational complexity.The differences and characteristics of acoustic relative channel gain and delay under LOS and NLOS conditions are investigated through extensive measurements in office rooms and lobby environment using COTS smartphones. Novel features are extracted from these characteristics that capture the salient properties based on time delay characteristics, waveform characteristics, Rician K-factor and frequency characteristics of relative channel gain.An optimal kernel function for an SVM classifier to realize acoustic NLOS identification is evaluated and chosen under the accuracy criterion, based on a data set with more than 10 thousand measurements. The best feature set of the SVM classifier for acoustic NLOS identification is investigated and proposed.To the best of our knowledge, no prior works have considered and investigated LOS and NLOS identifications using the channel information from received acoustic signals in indoor environment. Therefore, aiming to address acoustic NLOS identification for smartphone indoor localization, we will systematically study this issue in this paper. The main contributions of this paper are as follows:The remainder of the paper is organized as follows. In Indoor environments are very complicated and different from each other. It is a dynamic environment due to the random walking of human beings and the displacement of small objects. In such a complicated environment, utilizing wave propagation theory, reverberation theory or a diffusion model to model indoor acoustic propagation is becoming difficult and complex. Geometrical room acoustics theory is a simplified model of indoor acoustic propagation . In thisd denote the parameters related to LOS, reflection and diffusion paths, respectively, and nth channel response with the path gain The length of the reflection path is definitely longer than the LOS path. With the increase of reflection time, Generally speaking, the energy of signals received from the LOS path and reflection path is larger than the signals received from the diffusion path, that is For a signal As shown in Then, the changes of channel characteristics from the LOS condition to the NLOS condition include (1) the total energy of received signals is decreased; (2) the path gain of reflection paths is decreased; (3) the path delays of reflection paths and diffusion paths are all increased; (4) the relative proportion of diffusion signals is increased. All these changes could be characterized as the differences of the channel gain and channel delay between the LOS and NLOS propagation scenarios.As mentioned above, the changes, when an NLOS condition occurs, could be characterized as the differences of channel gain and channel delay between the LOS and NLOS propagation scenarios. Based on these characteristics, the features can be studied and extracted for acoustic NLOS identification. The research of acoustic channel parameter estimation is mainly conducted in underwater communications and the method based on Fractional Fourier Transform (FrFT) is widely used . In ordew, and LUsing a speaker to broadcast an ideal modulated acoustic signal itten as (4)x(t)=Considering that the sound is a kind of low speed wave, the relative movement velocity between the transmitter and receiver caused by human beings is not a constant, and the parameter of environment also varies with time such as temperature, humidity and air pressure; the path gain Since smartphones are usually carried by human beings, the arbitrary movement of a human being coupled with arm swing makes the smartphone an extremely complex manoeuvring movement with a high speed. This could introduce an obvious shift of phase even at a slow moving speed, due to the low speed of sound propagation. However, we can still divide Equation  can be ri=1 denotes the first arrival path and i=m denotes the path that has the strongest signal energy. As the Doppler phase term gives an excess product term to One of the most efficient estimators of relative channel gain and delay is based on the cross-correlation method. For the received signal (1) If (2) If From Equation , by usinThe data set of acoustic signals used in this paper is obtained by a series of experiments in office rooms and a lobby, respectively. The measurements are based on a non-invasive LFM audio signal, the frequency band of which is between 16 kHz and 21 kHz. The audio signal is broadcast and received by COTS smartphones in order to decrease the cost of infrastructure and make the experiments more general. The primary purpose is to characterize the effects of obstructions. By using currently available smartphones, we can quickly build an experiment platform by installing a specially developed Android application. Six smartphones are used for signals acquisition, that is two new HUAWEI Honor 4  and four Google Nexus 4  which had been used for 2 years. The frequency response test results of those two kinds of smartphones are similar to the results reported in [The primary purpose of the experiment is to characterize the effects of obstructions in office rooms and the lobby. Several office rooms and one lobby constitute this experiment, as shown in Considering the actual NLOS condition, the obstructions include furniture, human body and corners. Even though we use the geometric room acoustic theory to describe room acoustic propagation for the sake of simplification, the diffraction phenomenon is actually existing. A brief depiction of this phenomenon is shown in Since the reflection and diffusion of indoor acoustic propagation is a directional distribution, the displacement of acoustic sources could significantly change the sound field distribution. In order to extensively study acoustic propagations, we should measure sound signals where the transmitters are placed at different positions. The height of the receivers is fixed at 0.8 m, which is lower than the possible height held by a human hand in the standing pose, because a lower height means a higher obstructed chance and it is beneficial for quick data collection. The height of transmitters includes 0.8 m, 1.5 m and 2.2 m, respectively. All the smartphones are placed on tripods, in order to conveniently adjust the height and positions.For the convenience of labeling the collected audio signals, the audio signals under LOS and NLOS conditions are collected separately. The process of the experiments is as following: (1) moving two acoustic sources to designated positions, and adjusting the height to 0.8 m; (2) dividing the measurement area into LOS and NLOS; (3) placing the four receivers at designated positions under the LOS condition; (4) moving the receivers to the next position with the displacement distances being limited at 0.2 m; (5) after measuring all the positions under the LOS condition, adjusting the height of sources to 1.5 m and 2.2 m, respectively, and repeating the processes (2)~(4) under the LOS condition; (6) moving two acoustic sources to the next designated position, and repeating the processes (1)~(5); (7) repeating the processes (1)~(6) for acoustic signals collection under the NLOS condition.During the data collection process under the LOS condition, no human behaviors are forbidden in the measurement area except walking through and construction activities. The common office ambience sound has no influence on the measurements, such as music, steps, human voice and etc., since it could be easily filtered out by an FIR (Finite Impulse Response) high-pass filter. However, the impulse noise generated by construction activities, such as the sounds of pneumatic hammers and air nailers, could introduce severe spectrogram pollution to received signals in the considered high frequency band. At the same time, when a human being walks through the measurement area, it is very hard to label the condition of current measurement. However, under the NLOS condition, to simulate the dynamic status in the actual scenario, the human walk is necessary in the measurement area. In addition to that, one receiver is carried by a person to move around in NLOS areas to collect the audio signals corrupted by the Doppler Effect. Through those processes, more than 1000 positions are measured in each room and lobby. The size of the data set used in this paper is more than 10 thousand measured positions.Utilizing the approach proposed in (1)  Time delay characteristicsThe mean excess delay dfittool in indoor environment. It is found that the two kinds of features can be approximately modeled by log-normal PDF (Probability Distribution Function) with different mean and variance.Generally, the values of (2)  Waveform characteristicsk and skewness s are two main waveform statistics to characterise the tailedness or normality and asymmetry of a distribution. The kurtosis and skewness can be given byr is the uniform sampling result of relative channel gain and delay, and the size of r is equal to r. From k and s under the LOS condition are larger than those under the NLOS condition. The distribution is shown in The kurtosis (3)  Rician K-factorThe Rician K-factor is the ratio of the LOS component to the diffusion component, and has been widely studied in link quality estimation of wireless communications since it is widely accepted that the unshadowed channel, LOS propagation path, is a Rician fading channel while the shadowed channel, NLOS path, is a Rayleigh fading channel ,28. Evenessed as (16)KR=1(4)  Frequency characteristics of relative channel gainjth interval and jth interval. During the practical calculation process, k and s. The feature, like the Rician K-factor, has no physical meaning in the frequency distribution of relative channel gain, since the time delay information is discarded. Thus, this kind of feature has not been studied in this paper.From the amplitude components of relative channel gain, we can clearly see the difference between LOS and NLOS conditions. By discarding the time delay information and compiling the statistics of the frequency of relative channel gain, we can obtain the frequency distribution, that is the histogram. Shown in k, skewness s, Rician K-factor For the indoor environment, most features also can be approximately modeled by the log-normal PDF, while the skewness, Rician K-factor and RMS frequency of relative channel gain under the LOS condition can be well modeled by the Rician PDF. At the same time, we can clearly observe that the PDFs of these features in indoor environment are quite distinct between the LOS condition and the NLOS condition. This implies that the nine features, which are the mean excess delay dfittool function, but the result is still not satisfactory. It still needs more statistical approaches and a larger size of data set. Therefore, in this paper, we propose the use of non-parametric machine learning techniques to realize acoustic NLOS identification, or LOS/NLOS classification. This is because they do not require a statistical distribution of features under LOS and NLOS conditions, and can perform this binary classification under a common framework.Acoustic NLOS identification is a binary classification problem. A joint likelihood ratio test could be used to test if a certain received signal is under the LOS or NLOS condition, through the extracted features . HoweverSupport vector machine (SVM) learning is a supervised learning technique used both for classification and regression problems , and hasw is the weight vector, b is a bias, and T is the transverse operator, C is a margin parameter which controls the trade-off between minimizing training errors and modelling complexity. Through S. Thus, according to the Lagrangian principle, its corresponding dual problem isQ is an m by m positive semi-definite matrix which is given byc are the positive kernel coefficients and d is the degree of polynomial kernel. Generally, we choose However, the training data collected in the real world usually cannot be separated without error or with small error. In 1995, Cortes and Vapnik introduced the principle of the kernel method to address the separability of features. The kernel function is used for implicitly mapping the input feature vector into an arbitrary high-dimensional feature space that can be linearly separable, because the probability that the feature space could be linearly separated becomes higher through nonlinearly mapping this low-dimensional feature space into a high-dimensional space. Then, in , the abo mapping . Then, bIn order to evaluate the performance of classifiers, a K-fold cross-validation process (K=10) is carried out to evaluate the performance of SVM classifiers with each kernel. Firstly, all the collected acoustic signals are mixed together as a whole data set and randomly divided into 10 non-overlapping subsets with the same data size. Secondly, any possible combination of nine subsets, that is The widely used evaluation criteria in binary classification include accuracy, error rate, sensitivity, specificity, precision, recall ratio, and F1-Measure . In thisectively .M is the size of the feature set. Due to the maximum feature set size in this paper being 9, that is M, the number of feature sets with different feature combinations is In order to choose a kernel function for the SVM classifier, the classification performance of four kinds of kernel functions are tested based on the data set with more than 10 thousand acoustic signals collected in indoor environment. The classifiers are tested in a different feature set In From tic and toc function of Maltab. Consequently, this classifier can be implemented in practical real-time applications. To optimize the From To further investigate the performance of the SVM classifier with RBF kernel function for acoustic NLOS identification, the performances of traditional classifiers based on logistic regression (LR)  and lineIn this paper, we focus on acoustic NLOS identification for smartphone indoor localization and propose an approach based on acoustic channel characteristics. Through analyzing indoor acoustic propagation, the changes of acoustic channel from the LOS condition to the NLOS condition are characterized as the difference of channel gain and delay between the two propagation scenarios. Then, in order to mitigate the Doppler Effect and reduce the computational complexity, an efficient approach to estimate relative channel gain and delay based on the cross-correlation method is proposed. Nine novel features have been extracted based on time delay characteristics, waveform characteristics, Rician K-factor and frequency characteristics of relative channel gain.To realize acoustic NLOS identification, an SVM classifier with four kinds of kernel functions has been proposed. By using the accuracy metric as an evaluation criterion, the evaluation result shows that the optimal kernel function is the RBF kernel. At the same time, the comparison results between the SVM and the traditional classifiers based on LR and LDA show that the SVM with the RBF kernel function method is the optimal classifier for acoustic NLOS identification. Meanwhile, we can conclude that (1) using acoustic channel characteristics for indoor localization is an efficient way to realize acoustic NLOS identification; (2) the features extracted from the received signals are available for NLOS identification and could achieve high accuracy and stability; (3) the channel parameter estimation approach proposed in this paper could effectively support the feature extraction.


Metaheuristics are widely used to solve large combinatorial optimization problems in bioinformatics because of the huge set of possible solutions. Two representative problems are gene selection for cancer classification and biclustering of gene expression data. In most cases, these metaheuristics, as well as other non-linear techniques, apply a fitness function to each possible solution with a size-limited population, and that step involves higher latencies than other parts of the algorithms, which is the reason why the execution time of the applications will mainly depend on the execution time of the fitness function. In addition, it is usual to find floating-point arithmetic formulations for the fitness functions. This way, a careful parallelization of these functions using the reconfigurable hardware technology will accelerate the computation, specially if they are applied in parallel to several solutions of the population.A fine-grained parallelization of two floating-point fitness functions of different complexities and features involved in biclustering of gene expression data and gene selection for cancer classification allowed for obtaining higher speedups and power-reduced computation with regard to usual microprocessors.The results show better performances using reconfigurable hardware technology instead of usual microprocessors, in computing time and power consumption terms, not only because of the parallelization of the arithmetic operations, but also thanks to the concurrent fitness evaluation for several individuals of the population in the metaheuristic. This is a good basis for building accelerated and low-energy solutions for intensive computing scenarios.The online version of this article (doi:10.1186/s12859-016-1200-9) contains supplementary material, which is available to authorized users. Bioinformatics is an area where we can find many large combinatorial optimization problems . The higThe core of this work deals with the hardware-level parallelization of the fitness functions used in two bioinformatics problems: gene selection for cancer classification and biclustering of gene expression data. The reason for designing fitness hardware accelerators is twofold. On the one hand, every fitness function is applied to each individual of a population in many bio-inspired metaheuristics; this fact allows us to parallelize the computation of the fitness evaluation phase if we place several copies of the same fitness hardware implementation. On the other hand, fitness functions are usually formulated by means of floating-point arithmetic equations that can involve many operation steps; this way, parallelization of some of these steps using repeated units of the same floating-point operator increases the performance of the design.Both reasons represent two levels of parallelism: in the bottom, a fine-grained parallelization of the fitness equation; in the top, a fast computation of the fitness evaluation phase applying replicated fitness units in parallel to several individuals of the population. We focused our research mainly on the fine-grained parallelization of the fitness formulation, although on-chip concurrent fitness evaluation has been explored as well. Figure The hardware implementation of fitness equations is made easier thanks to Hardware Description Languages (HDLs) and Field Programmable Gate Array (FPGA) devices . The FPGReconfigurable computing has been successfully applied to many bioinformatics problems, because they have a high parallelism degree. Knowing how to make the most of this parallelism, we can obtain speedups and energy savings needed for intensive computing or real-time applications. In this area, we can find FPGA implementations for DNA matching based on the BLAST algorithm , Bowtie This way, our main contribution in this paper is to demonstrate that the fine-grained parallelization of fitness functions based on floating-point arithmetic can surpass the performance given by CPUs, in time and power terms, when they are massively used by metaheuristics for solving large combinatorial optimization problems in bioinformatics. The conclusions of our work can be applied in general to similar cases, because of the representativeness of the fitness functions we have chosen. For this purpose, we have selected two specific fitness functions used in the above mentioned optimization problems by two reasons: on the one hand, there is not enough information about their implementation in FPGAs in the existing literature; on the other hand, they provide different computational workloads and parallelization levels because of their floating-point arithmetic formulations, being representative formulations of other similar functions widely used in bioinformatics.The hardware implementation of the fitness function can be used as a coprocessor of an embedded CPU running the metaheuristic in the same FPGA. Nevertheless, the need for scalability that large and real-world applications require, and the metaheuristic request of handling many individuals of the population in parallel, make it necessary to consider computing systems consisting of several FPGAs in multicore architectures. This coarse-grained parallelization belongs to the High-Performance Reconfigurable Computing (HPRC), a promising paradigm that exploits the possibilities of FPGAs , 14, altSummarizing, our proposal presents the performance from a computational perspective. Other performance features closer to the specific bioinformatics problems only can be tackled by the corresponding algorithmic methods and software packages, which are out of the scope of this work.As we pointed out in the previous section, bio-inspired and evolutionary optimization algorithms are very appropriate to be parallelized, not only by applying repeated fitness hardware units in parallel on several individuals of the population, but parallelizing other important parts. For example, the intrinsic parallelism in popular Genetic Algorithms (GAs)  allows bThe high performance cost of the fitness evaluation phase in relation to the overall computing time of the metaheuristic is a well-studied fact in the literature. Fitness evaluation can take up to 95 % of the total execution time in genetic programming  or 64 % The above considerations move us to implement the fitness functions in hardware to enhance the system performance. These functions have been accelerated by means of FPGA devices in genetic programs for financial markets , spatialThe first bioinformatics problem in our study is gene selection for classification of high dimensional Microarray data in cancer disease. This optimization problem has been studied using mainly GAs and Support Vector Machines (SVMs), where the GA is used to evolve gene subsets whose fitness is evaluated by a SVM classifier. In this line, there are approaches based on single objective  and multThe second optimization problem considered in our research deals with biclustering of gene expression data, which has been tackled by means of custom evolutionary algorithms , 32. We We have tackled the implementation of the two above mentioned bioinformatics problems following the same strategy: first, we design a fine-grain parallel circuit that implements the fitness function; then, we measure the speed-up with regard to current general-purpose processors for just one fitness evaluation; finally, we estimate the performance when several fitness circuits evaluate individuals in parallel, taking into account the area constraints for a single FPGA. This approach allows us to apply the fitness circuits as coprocessors of an embedded processor that drives different optimization algorithms. This methodology is similar to other studies, as , 35, wheThere are many other bioinformatics problems involving metaheuristics with fitness functions similar to these two cases, specifically with regard to the floating-point arithmetic , 37. ThiM) and samples (N) are very high, shaping a combinatorial optimization problem.The analysis of microarray-based gene expression allows us to compare between the gene expression levels of cancerous and normal cells, in order to select the genes under suspicion . These gx is the subset, A(x) is the leave-one-out-cross-validation accuracy provided by a classifier, R(x) is the number of selected genes in the subset, and w1 and w2 are weights for the accuracy level and the number of selected genes, respectively [A common approach to face this challenge consists in selecting a subset of suspicious genes for cancer classification. This is the basis of many metaheuristics where the individuals of the population are gene subsets. We have considered a fitness function given by , where xectively . This fiNF instances of the fitness circuit, NC instances of a floating-point comparator, and a controller that drives and parallelizes the operations involved in F. The value of NF depends on the FPGA area.The top-level circuit to test the fitness function , w2/M and M−R(x). This architecture needs three floating-point arithmetic operators  and an integer to float converter. The fitness controller supplies the operands to the arithmetic modules and receives the results. Once the calculation of F has been completed, the fitness controller gives it back to the controller.The fitness circuit implements the arithmetic operations involved in This problem deals with numerical matrices that represent information extracted from microarray data. These matrices can be built using clustering or biclustering methods . ClusterB of I rows  and J columns (number of genes), where the element bij is the expression level of the gen j under the experimental condition i.A general bicluster is represented by a matrix biJ of each row i , that provides lower values for better biclusters. The MSR value is calculated following these steps:This procedure is highly parallelizable. There are different ways to parallelize the calculation of MSR, according to the experimental constraints: the more resources we have, the more parallelization we can achieve. Since the parallelism comes basically from the use of replicated circuits of the floating-point arithmetic operators, the FPGA device can host different number of these units depending on two factors: the specific FPGA device (family and model) and the size of the bicluster. Due to this reason, we have considered two different parallelization models to compute MSR.MSR partially parallelized. This is a procedure useful for bigger matrices or FPGA devices with lower area, where we can only use a limited number of repeated circuits for the arithmetic operators. This procedure involves more sequential steps than in the case where we have as many multipliers as elements bij in the matrix. This way, the computation of MSR follows six sequential steps, each of them composed of parallel tasks, as Fig. bij of each row and column are added in parallel, obtaining at the same time the values of sum_biJi and sum_bIjj.The elements sum_bIJ of all the elements bij of the matrix  is obtained in parallel together with the values of biJi (obtained dividing sum_biJi by J) according to  followed by the matrix size. In addition, the number of fitness and comparator units is specified for the controller. For example, controller-f16x8-NF6-NC3 denotes the circuit implementation for a bicluster of 16 experimental conditions and 8 genes driven by the partially parallelized model using 6 parallel fitness units; in this case, the fitness circuit associated with this controller is identified as msr-f16x8.The controller and the fitness circuits have different implementations according to the parallelization model and the bicluster size. The implementation version is identified by one letter , the high-performance Virtex6 (xc6vlx550t) and the balanced Virtex5 (xc5vlx330). These devices may be characterized by four important features that describe the process technology , the number of logic cells (as indicator of the area available to host the circuits), the number of internal Digital Signal Processor (DSP) slices (related to the speed of the floating-point arithmetic operators) and the number of memory blocks (useful to handle the circuit data).We measured the performance of our three FPGA devices with the post-placement and routing simulation tool provided by the implementation environment. The validation of the results consisted in comparing the simulation times of the Virtex5 device with those measured with custom circuits on a prototyping board that hosted the xc5vlx330 device: Xilinx University Program Virtex5 Development Kit. Since both times were almost equal, we can approve the simulation results corresponding to the other FPGA devices.In order to establish a valid FPGA vs CPU comparison, and properly analyze the performances, we should consider the use of contemporary devices with similar technologies. This reason led us to use several processors of different CMOS technologies and clock frequencies, as we can see in Table We designed custom circuits to test the performance of the fitness function instead of using embedded processors because these ones take up an area that, otherwise, would be useful for hosting more parallel fitness circuits.Each synthesis was repeated several times following different strategies in order to obtain the highest clock frequency. On the one hand, we considered three optimization synthesis profiles: default, timing performance with physical synthesis, and timing performance without input/output blocks packing; other synthesis profiles were discarded because of their worse results. On the other hand, we have tested two possibilities when it comes to synthesizing the floating-point arithmetic operators by CORE Generator: using internal DSPs or logic blocks in the architecture optimization. If we consider DSPs, the performance can be better, but the limited number of DSPs forces us to consider digital logic if we want to have more parallel units, involving more area consumption; this tradeoff between number and performance of parallel operators must be evaluated in each case.NF value considered, the synthesis took from 1 hour to 2 days, also according to the processor used among those listed in Table This way, each design was synthesized up to 6 times (according to the 3 synthesis profiles and the 2 possibilities of using DSPs in the operator circuits), recording the best result among the obtained ones. For example, for the fitness function in the gene selection for cancer classification problem, we tested 6 cases ; therefore, 6 cases x 3 synthesis profiles x 2 operator optimizations = 36 synthesis experiments were performed. Depending on the Area occupation. Several indicators  allow us to calculate the number of circuits that we can replicate in the same FPGA device in order to work in parallel. Depending on the values returned by these indicators and the FPGA family and model, a different number of such circuits can be considered.NF parallel units of the fitness circuit, the time to process the different solutions is equal to that time.Timing performance. The value of the maximum frequency (MHz) (that corresponds to the minimum clock period in nanoseconds) allows us to determine the time to process the fitness function; if we consider Power consumption. Nowadays, it is very important to design energy-aware circuits in order to minimize operation costs when solving problems that involve massive computations along the time. The synthesis process tells us the power (watts) consumed by the fitness circuits.Each synthesis reports interesting data with regard to the scalability and performance of the fitness circuits: T [TCPU/TFPGA. Hence, a speedup greater than one means that FPGA is faster than CPU; otherwise the processor wins. It is important to realize that both values, TCPU and TFPGA, measure the same number of fitness evaluations; in the first case, using a loop of sequential computations, whereas the second case considers a parallel computation of NF fitness circuits.Among the many data returned by the synthesis processes, we analyze mainly the timing reports, since they provide the speedup of FPGA versus CPU . We understand by timing performance the reciprocal of the computing time T . To compNF. The FPGAs provide better speedups than CPUs (up to x9), even for the highest performance processor. We can observe that, the more parallel fitness units we consider, the better speedup we obtain, although this increase is not linear, because of the more dense top level circuits that slow down the clock frequency. In addition, Virtex5 provides better performance than Virtex6 because of the memory constraints to handle the synthesis of large designs (this constraint impedes to consider 256 fitness circuits for the Virtex6 device). Finally, since the Spartan6 device is a low-cost FPGA, it provides much lesser area than the other devices, making it impossible to host more than 32 parallel fitness units.According to this speedup definition, and taking into account the maximum number of parallel fitness circuits that can operate in parallel in the same FPGA, Fig. MSR fully parallelized model provides better performance than the MSR partially parallelized model for equal bicluster sizes, as the first one involves more parallel operations. Nevertheless, the highest number of replicated floating-point arithmetic operators runs out first the FPGA area available: this is the reason why we can not consider large matrix sizes in the fully parallelized model. On the other hand, when using the MSR partially parallelized model, since it parallelizes mainly by rows, we should compare matrix sizes with the same number of rows, for example f16x8 with f16x16. In this case, we find that the performance is better with fewer columns, as the lower number of floating-point arithmetic operators allows more area to host more fitness units working in parallel, which has more weight in the performance than the bicluster size.A similar analysis can be done seeing Fig. The speedups for the second bioinformatics problem (biclustering) are good in all the cases and higher than for the first problem (gene selection). We find the reason mainly in the parallelism degree of the fitness circuit design, rather than in the number of such circuits working in parallel. The bottom level of the fine-grained parallelization is the fitness circuit, which is composed of some basic floating point operators: adders, dividers, multipliers and integer to float converters. This way, the more floating point operators running in parallel, the better performance we expect. We find 4 operators in the fitness circuit for gene selection, whereas the fitness implementations for the different bicluster sizes and architectures go from 8 to 32 operators. The number of floating-point operators running in parallel has great influence on the final performance, even more than the number of replicated fitness circuits. In fact, the number of parallel units is higher in the first problem: the performance speedup for the gene selection test with 256 fitness units is x9, whereas 20 units in a f8x8 bicluster gives x14. The reason is simple: a greater number of parallel fitness units in the same FPGA device implies more circuit density in the top level architecture , which produces smaller clock frequencies with the corresponding time response decrease.The ratio of the area occupied by just one fitness circuit to the maximum number of such circuits that the FPGA can host can be seen in Fig. Finally, it is interesting to know the power consumption of the fitness circuits, since they have an important impact in the metaheuristics as we saw in the related work section. This impact involves high energy when the optimization problems demand intensive computations along the time.NF =8, we obtained a power consumptions of 3, 6.4 and 0.2 watts for Virtex5, Virtex6 and Spartan6 devices respectively, whereas the Core2-E6750 processor used around 40 watts for the same configuration. This means that the FPGA reduces the power consumption at least 84 % with regard to the CPU.The power consumption in the FPGA is calculated by the XPower Analyzer tool inside the Place&Route phase of the implementation, and the CPU energy is measured by the Powerstat tool under Linux, using the Advanced Configuration and Power Interface (ACPI) battery data of a laptop. Considering the gene selection for cancer classification problem with The interest of applying the reconfigurable computing technology based on FPGAs to implement the fitness function lies in the possibility of accelerating the evaluation phase in many metaheuristics. This phase evaluates a population of solutions to a combinatorial optimization problem in the bioinformatics domain. The design of a custom circuit that implements the fitness equation allows its replication in several processing units that work in parallel and, thus, accelerate the evaluation phase.Since many optimization problems in bioinformatics define fitness functions as floating-point arithmetic operations, we have tested two of them in order to check specific implementation features: area occupation, response time and energy, mainly. From these values we can obtain the number of replicated units working in parallel and the time for the evaluation phase. The results show that FPGAs provide better performances than CPUs, not only because of the parallelization of the arithmetic operations of the fitness, but also thanks to the concurrent fitness evaluation for several individuals of the population in the metaheuristic.Finally, the very low power consumption of the FPGA devices in comparison to CPUs proves that FPGA-based parallel computing environments are excellent low-cost computing solutions for intensive computing scenarios.As future research line, we will tackle the connection of these accelerated fitness functions with evolutionary frameworks for solving the combinatorial optimization problems. The main idea is to implement an EA in software, leaving the intensive fitness computation to the hardware.Build a hardware project under Xilinx ISE 14.6 environment, selecting the corresponding FPGA device.Design the code files corresponding to the bioinformatics problem: VHDL files. This is the core step of the work, meaning the greatest effort of the project.Generate the floating-point arithmetic operators from the CoreGen tool.Synthesize and implement the design, activating the corresponding option to obtain advanced reports.After the implementation phase, check the clock period required.Simulate the design using a VHDL testbench, adjusting the clock period to the reported before.Check the time response for the FPGA.Build a C code to run the fitness function in usual microprocessors, compile, run and check the time response.Compare the measured time against the obtained in the FPGA, and calculate the speedup.The methodology for designing and simulating the different circuits considers the software tools described before in Section “Design tools”. Assuming that these tools require depth knowledge in hardware description languages, as well as the corresponding software licenses from the vendors, the general methodology followed in this work is composed of nine steps:


Underwater Acoustic Sensor Network (UASN) comes with intrinsic constraints because it is deployed in the aquatic environment and uses the acoustic signals to communicate. The examples of those constraints are long propagation delay, very limited bandwidth, high energy cost for transmission, very high signal attenuation, costly deployment and battery replacement, and so forth. Therefore, the routing schemes for UASN must take into account those characteristics to achieve energy fairness, avoid energy holes, and improve the network lifetime. The depth based forwarding schemes in literature use node’s depth information to forward data towards the sink. They minimize the data packet duplication by employing the holding time strategy. However, to avoid void holes in the network, they use two hop node proximity information. In this paper, we propose the Energy and Depth variance-based Opportunistic Void avoidance (EDOVE) scheme to gain energy balancing and void avoidance in the network. EDOVE considers not only the depth parameter, but also the normalized residual energy of the one-hop nodes and the normalized depth variance of the second hop neighbors. Hence, it avoids the void regions as well as balances the network energy and increases the network lifetime. The simulation results show that the EDOVE gains more than 15% packet delivery ratio, propagates 50% less copies of data packet, consumes less energy, and has more lifetime than the state of the art forwarding schemes. Underwater acoustic sensor networks (UASNs) have gained a lot of attention from the research and industrial community due to their vast application areas. The applications of USANs range from marine ecosystem monitoring, seismic activity, offshore exploration monitoring and management, water pollution, to coastal area surveillance. All of those applications require continuous sensing of the aquatic parameters and relaying these sensed parameters towards the onshore station. Sensing and especially communication in the aquatic environment is a quite challenging compared to the terrestrial sensing networks and could not be realized without UASNs.S with transmission range i, j, and k with transmission ranges Main elements of the UASNs are the sensing nodes and the sink nodes. The sensing nodes or simply called sensors have the sensing and communication capabilities, they operate on the battery power, and usually, they rest at the bottom of the sea and/or anchored to the seabed. Normally, the anchored nodes in UASNs are the nodes that know their location information . Howeverreceiver and Sender node pair as well as the clear visibility. These two constraints are difficult to meet because of the water current and its turbidity. Hence, the optical communication fails to achieve the high data rate communication over the long range. Because of these challenges and limitations, the acoustic communication mode is widely used in the underwater networks. However, the data collection and dissemination solutions for the terrestrial networks can not directly be applied in the underwater network due to due to its inherent limitations.As stated earlier, the electromagnetic or radio and optical communications modes can also be used in the underwater environment; however, they are not suitable due to different aquatic characteristics. Electromagnetic signals at radio frequencies are preferable for the terrestrial communication because they consume low energy, offer high bandwidth, small propagation delay, and fairly low signal attenuation. Hence, the Sinks use the radio communication mode to connect the onshore stations. However, radio communication in the aquatic environment is not suitable because of the high attenuation that results in the severe absorption because of the water conductivity. In result, the radio communication has a limited small transmission range in the aquatic environment. Similarly, the optical communication requires a very precise line of sight between There are many routing protocols in the literature that tackle and consider the intrinsic acoustic parameters  to perform routing decisions. There are many ways the authors have classified the routing protocols in the literature, however, here we classify those protocols into two main categories, Location or localization-base and depth based routing protocols. In Location or localization based routing protocols, each node knows its own three-dimensional location, the location of the destination node(s) or sink(s), and sometimes the location of the neighboring nodes ,12,13,14Sender node. All the other nodes at the depths between the packet forwarder and the lowest depth node will simply discard or suppress the data packet transmission [receiver nodes. It assures that the potential forwarder must have the next hop node, not the void region. However, this type of schemes do not avoid the formation of the void region and do not provide any energy balancing in the network. Therefore, the routing schemes should avoid communication through void region and balance the energy by distributing the data broadcast load evenly on different nodes in the network. The lowest depth nodes with small residual energy must not be penalized. In this manner, we can delay the creation of the void region. However, if there is already a void region in the network, then the routing scheme must also divert the communication around the void area.The depth based routing schemes minimize the propagation delay by distributively selecting the next forwarder node among the neighbors that has the smallest depth than the packet forwarder or smission . This pasmission ,18,19,20receiver node in this context) and the sender data , forwarding or relaying node  as well as the neighbors of the receiver node. EDOVE prioritize the receiver node to become the potential forwarding candidate, if:it has maximum residual energy among its one hop neighbors.it has one or more than one neighbors with less depth.receiver node and their normalized depth variance should be larger.it has more number of neighbors with depth less than the By keeping all those features under consideration, we have proposed an Energy and Depth variance-based Opportunistic Void avoidance (EDOVE) scheme in this paper. EDOVE uses the depth difference information between the data packet receiving node  [receiver initiated (where the data packet receiver decides whether it forwards the packet or not) [As discussed in the previous text, the depth information is the prime metric that is used by the routing protocols. It is estimated locally from the reading of the pressure sensor. Data packets are forwarded from the higher depth nodes to the lower depth towards the surface where sink(s) that are floating on the water surface. In addition to the depth information, several other metrics have been adapted by the routing schemes to efficiently forward the data packets to sink(s). Depth based routing schemes are further categorized into two categories; er node) ,19 and t or not) ,16,18,20sender initiated protocols, the data packet Sender node selects the next hop relay node, and the information of the next forwarder is added in the data packet. Once the node receives that data packet, it easily determines from the header information of the data packet that node has to further relay the packet or not. On the other hand in the receiver initiated routing protocols, the data packet receiving nodes distributively contend to be the next forwarder. These nodes compute their respective holding times, and the potential forwarding candidate(s) has/have very small holding time compared to the other nodes. The node with smallest holding time forwards the data packet, and when the other nodes receive a copy of the same data packet while their holding time is still on, the simply discard the transmission [receiver initiated forwarding schemes suppress the data packet broadcast. Hence, the data packet broadcast suppression depends upon the variance of the holding times computed by the nodes. In this section, we merely focus on the receiver initiated depth based routing schemes because the proposed EDOVE belongs to that routing category.In the smission . In thisSender node S either sends or forwards the data packet. All nodes within the transmission range of S, S. According to DBR, node 3 is a preferable forwarder due to the lowest depth among all the other nodes. Conversely, the nodes 1 and 5 have higher depth than S. Hence, they will discard the packet. All potential forwarders in DBR compute their holding time and the smallest depth node has the very short holding time. DBR computes holding time using the depth difference between Sender and receiver nodes. Hence, the nodes with smaller depth and very large depth difference will always have the short holding time, such as node 3 in the scenario shown in In , authorsAuthors in , proposezero hop count value and any node that receives this beacon message just increments and records the received hop count value and then further disseminates the updated hop count in the same beacon message. In addition to the hop count metric, a node also stores the path quality towards the sink. Subsequently, each node periodically broadcasts its depth, path quality, and hop-count metrics during the beacon dissemination phase. Each node maintains the 1-hop neighbor table to make the forwarding decision. The data packet forwarding node first checks the lowest hop-count node neighbor to select it as a potential forwarder. In a tie situation, where two or more than two neighbors have the minimum lowest count, the decision is taken based on the path quality. If still that tie persists, then the next tie breaker metric is the depth of the node. The lowest depth node will get the chance to forward the data packet. The packets will be forwarded through the same set of nodes that have lowest hops, good path quality, and smaller depth. In result, it will penalize the low energy nodes and they will die out quickly. Moreover, the beaconing overhead to maintain the soft-state information is very high and that drastically consumes the network energy.Another depth, hop count, and link quality based routing scheme, called location-free link state routing (LLSR), has been proposed in . The proreceiver initiated void avoidance routing protocol, called inherently void avoidance routing (IVAR), has been proposed in [Another posed in . IVAR foposed in . Each dasender initiated opportunistic void avoidance routing (OVAR). These shortcomings include; the duplicate packet transmission that is resulted by the small difference in the holding times of the candidate forwarder and the hidden terminal problem of IVAR. OVAR adapts the same periodic beaconing method to maintain information of the 1-hop neighbors. This 1-hop adjacency information at each forwarder helps it to select the next candidate forwarders. The candidate nodes are selected in a proximity to avoid the hidden node problem and reduce duplicate packet transmission. In the above-discussed forwarding protocols that use beacons to update proximity graph, e.g., LLSR, OVAR and IVAR, neighborhood information freshness depends on the beacon interval. The lower the beacon interval, the higher the neighborhood information accuracy and better the forwarding decision. However, the low beacon interval has severe impact on the battery power. Recently, a receiver initiated depth based routing protocol, named weighting depth and forwarding area division DBR (WDFAD-DBR), has been proposed that avoids the void hole problem in UWSNs [The authors in  improvedin UWSNs . It achiS sends the data packet and nodes 1–5 are within transmission range of node S, S. As node 3 has no next hop forwarder, hence, it does not compute the holding time and simply discards the packet. However, nodes 2 and 4 compute their holding times using two hop depth difference; depth difference to S and maximum depth difference to their neighbors with the lower depths,  to compute the holding time because the estimation of the optimal value quite difficult in the unpredictable and error prone topology network, e.g., Sender node  that has smaller depth relative to the sender node and has more residual energy. To achieve this, we use depth difference between sender and its 1-hop neighbors (the first hop depth difference parameter) and it is prioritized using the normalized residual energy of the potential forwarder. The main objective of prioritizing the smaller depth potential forwarder with larger residual energy is to achieve energy efficiency, energy balancing, and disseminating data further in the network. In result, it extends the network lifetime and minimizes the data broadcast overhead.Select the forwarder from the 1-hop neighbors of the data second hop depth difference, and has more depth variance among its neighbors. The second hop depth parameter is scaled with the depth proximity variance of the neighbors of the potential forwarder node. Any potential forwarder that has the large depth proximity variance becomes the potential forwarder and it intends to avoid the packet collision and prevents the void region when it further forwards the packet.Next, choose the forwarder that has larger depth difference relative to its neighbors, which is the The adaptive weighting factors, based on 2 the residual energy and 3 the depth variance, with one and two hop depth difference parameters, significantly scale the holding time of the receivers to avoid multiple data packet transmissions.Inspired from the above discussion, we proposed a novel EDOVE for UASNs. EDOVE computes the holding time of candidate forwarders by keeping the following points under consideration.In the next section, we briefly discuss our proposed EDOVE scheme and next we discuss the simulation results, which shows that EDOVE improves energy efficiency, increases packet delivery ratio, and network lifetime.S and receiver i’s depth difference, j, H.In this section, we present the detailed discussion of our proposed EDOVE scheme. EDOVE avoids the void region by distributively selecting the forwarder with large residual energy and having multiple neighbors that are largely distributed depth-wise. The proposed scheme is based on and compared with the WDFAD-DBR, which avoids the void hole and uses the holding time H to compute the holding time at receiver i, S, and In addition to that, a maximum acoustic delay between all the receivers, H, the normalized residual energy of node i, S and receiver with large depth difference variance to minimize further data collisions. As a result, energy efficiency is achieved by avoiding the packet collision with void avoidance and increasing the network lifetime.Opposite to WDFAD-DBR, the proposed EDOVE computes S, receivers with residual energy S, and receiver with small hop neighbors. The sender S either generates or relays the data packet and all the receivers simply receive that packet and that packet has to be relayed through any of these receivers. However, these receivers are at the edge of the void zone and the forwarding algorithm must avoid selecting the relay that fails to further relay the packet due to the void zone. Furthermore, these receivers have different residual energy and the forwarding strategy has to select the receiver with large residual energy to balance network-wise energy consumption. Selection of one receiver among multiple potential ones is done through the holding time of the receiver i, Consider the underwater network scenario in i receives a data packet, it computes and starts the i receives another copy of the same packet, it suppresses the packet transmission. Nonetheless, i forwards the data packet if it does not receive any more copies of the data packet until the i. So, the duplicate packet broadcast along with the void hole can be avoided. However, the impact of this long holding time may result in the long end-to-end delay.When receiver Next, we discuss the proposed EDOVE and its holding time computation on how it suppresses the additional data packet and avoids the void area in the underwater network.Network size : is the total number of nodes The set of receiver nodes : consists the nodes that are within the transmission range of S and have depths less than the depth of S:j and S, and j and S, respectively. S and Next hop forwarders of node i, : are the nodes that lie within node i’s transmission range and having depths less than the depth of i:j and receiver node i, respectively. The next hop forwarder of receiver 4, Residual energy of node i, : is the current available battery level of node i.Normalized depth variance of node i, : is the normalized variance of the depth differences of all the nodes in EDOVE uses the notations in the holding time computation and the forwarding algorithm, and these notations are briefly described below with reference to the EDOVE network scenario shown in The above discussed notations are used by the EDOVE to compute holding time and the forwarding algorithm that are briefly described below.S sends or forwards the data packet and node i within its i first compares its depth to the depth of S. If i has the depth larger than that of S, i’s depth is less than the depth S and has one or more than one neighbors with smaller depth  and neighbor acknowledgment (NBR_ACK) packets, as in . This ini receives the data packet, it first checks whether the data packet is already in packet queue, Q, or not. If i did not already process the data packet, then it verifies the timer for that packet. Next, i compares its depth to the one in the data packet. If i is within the receivers of datai just updates the neighborhood table and drops the data packet. When i computes the data timer is running and i receives another copy of the packet, then it just discards the packet and updates the number of copies received count. Once the i checks whether it a single copy or multiple copies of the data packet. The node i drops the packet if it receives multiple copies. Otherwise, it forwards the data packet with the updated depth, energy, etc. parameters in the packet.It is very obvious from the Algorithm 1 that once a node Algorithm 1: Proposed EDOVE Scheme.The detailed performance comparison of the proposed EDOVE scheme in contrast to the conventional WDFAD-DBR scheme through simulations is presented in this section. During simulations, we considered the three-dimensional network area of 1 km length and width and 2 km of depth,  in the holding time computation of the WDFAD-DBR. To equality prioritize both the depth differences, we used As stated earlier that the source node is deployed at the seabed and it generates data packets destined for the sink node(s). The average data packet generation rate is The first performance metric that we investigate here is the number of copies forwarded in the network for all data packet transmissions initiated by the source node.S’s transmission range The next interesting trend that has been observed is that the number of forwarded copies of the data packets are inversely proportional to the transmission range in the fixed network size scenario. This phenomenon is also apparent due to the fact that the packet suppression is achieved at many nodes in the network when transmission range increases, refer m sinks and n data packets generated, the PDR is denoted as:It is the ratio of successfully received data packets by the sinks and the total number of generated data packets by the source node. Let, The main objective of any routing or forwarding scheme is to improve different performance metrics of the network and PDR is one of them. Therefore, we also analyzed and discussed the PDR performance metric of the EDOVE scheme. PDR performance of the EDOVE and WDFAD-DBR for varying network sizes and transmission ranges are shown in Next, in the fixed network size and variable transmission range scenario, as the transmission range increases node density increases as well, refer In addition to data broadcast overhead and PDR, we also analyzed the total energy consumption of the network as the simulation time progresses. The total energy consumption consists of transmission, reception and idle energy consumption of the network. This total consumed energy value is used in the normalized energy consumption that we discussed previously. This analysis shows the real behavior of the routing scheme proposed for the network. Next, the total energy consumption of the network for the fixed network size and different transmission ranges throughout the simulation course is shown in energy tax, In addition to the network energy consumption analysis, we also compared the proposed EDOVE with WDFAD-DBR in terms of the The energy tax analysis of the EDOVE for different network sizes and transmission ranges are shown A node is considered dead when its battery is depleted completely and it could not participate in the data forwarding process. The next detailed analysis related to the dead nodes is shown in Furthermore, we simulated the average number of dead nodes for the varying transmission ranges and different network sizes with a fixed transmission range of 800 m over the course of simulation, as shown in last data message from the source node. The operational time is the time instance during the simulation when any of the sink(s) received the Similarly, we recorded the average operational time for the varying transmission ranges and show them in It has been evident from all the previous results and discussion that EDOVE has high PDR, less data broadcast, consumes less energy, fewer number of dead nodes, and has large operational time. Next, we investigate the average number of hops the data packet is forwarded from the source to sink node(s). Comprehensively, the proposed EDOVE scheme improves the PDR, reduces the data packet broadcast, conserves more energy which in turn reduces the number of dead nodes, and has more operational time than the WDFAD-DBR. However, it has slightly longer end-to-end hop distance and slightly larger delay, which is the tradeoff of the proposed scheme or may be any scheme proposed for the UASNs.Sender node). In addition to that, it prioritizes the depth differences using the normalized residual energy of the forwarder and the normalized depth variance of the two-hop forwarders to compute the holding time. In this manner, it offers energy fairness, distributes data forwarding load over the fairly distributed density nodes and increases the network lifetime. Simulation results show that EDOVE propagates about An energy and depth variance based opportunistic void avoidance (EDOVE) scheme for underwater acoustic sensor networks has been proposed in this paper. EDOVE uses the depth difference information between data packet sender to its one-hop neighbor and one-hop neighbor to its forwarders (that are two-hop neighbors of the