Am J Clin Nutr-2008-Pearce-638-44

Effect of carbohydrate distribution on postprandial glucose peaks with the use of continuous glucose monitoring in type2diabetes1–3 Karma L Pearce,Manny Noakes,Jennifer Keogh,and Peter M Clifton

ABSTRACT

Background:Large postprandial glucose peaks are associated with increased risk of diabetic complications and cardiovascular disease. Objective:We investigated the effect of carbohydrate distribution on postprandial glucose peaks with continuous blood glucose mon-itoring(CGMS),when consuming a moderate carbohydrate diet in energy balance in subjects with type2diabetes.

Design:Twenty-three subjects with type2diabetes were randomly assigned to each of four3-d interventions in a crossover design with a4-d washout period.Identical foods were provided for each treat-ment with a ratio of total carbohydrate to protein to fat of40%:34%: 26%but differing in carbohydrate content at each meal:even distri-bution(CARB-E;?70g carbohydrate),breakfast(CARB-B),lunch (CARB-L),and dinner(CARB-D),each providing?125g carbohy-drate in the loaded meal in a9-MJ diet.Glucose concentrations were continuously measured with CGMS.Outcomes were assessed by postprandial peak glucose(G

max

),time spent 12mmol/L(T 12),

and total area under the glucose curve(AUC

20

).

Results:Daily G

max

differed between treatments(P?0.003)with CARB-L(14.2?1.0mmol/L),CARB-E(14.5?0.9mmol/L),and CARB-D(14.6?0.8mmol/L)being similar but lower than

CARB-B(16.5?0.8mmol/L).Meal G

max

was weakly related to carbohydrate amount and glycemic load(r?0.40–0.44).T 12 differed between treatments(P?0.014),and a treatment?fasting blood glucose(FBG)interaction(P?0.003)was observed with CARB-L(184?74min) CARB-B(190?49min) CARB-D

(234?87min) CARB-E(262?91min).Total AUC

20

was not significantly different between treatments.After adjustment for FBG,treatment became significant(P?0.006);CARB-L(10049?718mmol/L?20h) CARB-E(10493?706mmol/L?20h) CARB-B(10603?642mmol/L?20h) CARB-D(10717?638 mmol/L?20h).

Conclusion:CARB-E did not optimize blood glucose control as assessed by postprandial peaks,whereas CARB-L provided the most favorable postprandial profile.Am J Clin Nutr2008;87: 638–44.

KEY WORDS Type2diabetes,carbohydrate distribution, moderate carbohydrate diet,continuous glucose monitoring,energy balance,postprandial blood glucose

INTRODUCTION

More than140million people worldwide have diabetes,pre-dominately type2,with the prevalence of type2diabetes ex-pected to double by the year2030(1).In these persons,cardio-vascular disease(CVD)is the leading cause of morbidity and mortality,responsible for50–80%of deaths(2);estimates of the risk of CVD vary from2-fold(3)to30-fold(4)compared with persons without diabetes.

Although glycated hemoglobin(Hb A1C)is a standard assess-ment tool to assess glucose control in type2diabetes,postpran-dial glucose(PPG)peaks were implicated as a risk factor for microvascular and macrovascular complications(4).Endothelial dysfunction and activation of the coagulation cascade(5)are likely to be initial steps involved in producing carotid thickening (5)and atherosclerosis(6).

Although the European Diabetes Policy group has set maxi-mum PPG targets not to exceed either135mg/dL(7.5mmol/L) to reduce the arterial risk and160mg/dL(9.0mmol/L)to reduce microvascular risk(7),the American Diabetes Association (ADA)does not provide such targets,preferring to encourage persons with type2diabetes to maintain“blood glucose levels in the normal range or as close to normal as is safely possible”(8). The ADA does state that an understanding of the relation be-tween CVD events and treatments focused at explicitly lowering PPG is critical to reduce mortality as a consequence of CVD(9). Most studies confirm that the total carbohydrate intake from either a snack or a meal is a consistent predictor of PPG concen-trations(10).This has been observed in both single-meal(11)and mixed-meal studies(10).In2002,the ADA recommended that the carbohydrate and monosaturated fat together should provide 60–70%of energy intake(8),and moderate-carbohydrate diets are gaining popularity.Because of the difficulties in shifting large amounts of carbohydrate between meals,we used a moderate-carbohydrate,higher protein,energy-balanced diet (40%carbohydrate,34%protein,26%fat)to enable the greatest variation in carbohydrate distribution to be achieved.Studies that used moderate-carbohydrate interventions,comparing isoca-loric exchange of carbohydrate with protein,were shown to in-crease weight loss(12)and fat loss(12);to spare lean mass(13); 1From the Commonwealth Scientific and Industrial Research Organiza-tion(CSIRO),Health Sciences and Nutrition,Adelaide,South Australia, Australia(KLP,MN,JK,and PMC),and Department of Physiology,Uni-versity of Adelaide,Adelaide,South Australia,Australia(KLP).

2Supported by a PhD scholarship from the Commonwealth Scientific and Industrial Research Organization,Health Sciences and Nutrition(KLP),and from the Department of Physiology,University of Adelaide(KLP).

3Reprints not available.Address correspondence to KL Pearce,CSIRO Human Nutrition,PO Box10041,Adelaide BC,Australia.E-mail: karma.pearce@csiro.au.

Received August27,2007.

Accepted for publication October2,2007.

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to achieve better glycemic control (13)and insulin sensitivity (12);to reduce Hb A 1C values (14);and to improve the blood lipid profile (12).Dietary factors other than carbohydrate amount can affect blood glucose concentrations,eg,dietary fiber (15)and glycemic index (GI)(8).The consumption of protein (16)and fat (17),preprandial glucose concentrations (18),the degree of in-sulin resistance (19),and second meal effects (20)may also modify the effect of dietary carbohydrate on PPG concentrations.In addition,this study targeted persons with poorly controlled glycemia as a higher risk group for diabetic complications and effective clinical intervention.It was also anticipated that they would be more responsive to carbohydrate variability across the day.

Although persons with type 2diabetes are often advised to evenly distribute daily carbohydrate intake over meals and snacks to blunt PPG peaks (21),no studies support whether this approach provides optimal glucose control.The relation between meal frequency and energy distribution but not carbohydrate distribution per se was examined in several small studies (n ?6–120)(22–24).Beebe et al (24)showed that meal frequency did not alter fasting glucose or glucose tolerance,whereas Jenkins et al (22)and Bertelsten et al (23)have shown that an increase in meal frequency and a decrease in meal size lowered PPG con-centrations.No studies have examined the role of carbohydrate distribution at meals on daily PPG profiles.

Our aim was to comprehensively assess diurnal glucose pro-files in free-living persons with type 2diabetes,when carbohy-drate distribution at meals is variably distributed,but total car-bohydrate remains the same.Our use of a continuous blood glucose monitoring system (CGMS)enables a noninvasive ap-proach to measuring PPG responses.We used an isocaloric moderate-carbohydrate,higher protein,energy-balanced diet (40%carbohydrate,34%protein,26%fat)consumed as 3meals for each of 4intervention periods.We hypothesized that an even distribution of carbohydrates may be an optimum pattern com-pared with 3other carbohydrate distribution interventions for attenuating PPG excursions.

SUBJECTS AND METHODS

Subjects

Twenty-four white men (n ?8)and women (n ?16)with type 2diabetes,aged 30–75y,with Hb A 1C values ?6.5%were recruited by public advertisement.Subjects were excluded if they had a malignancy;a history of liver,kidney,or gastrointes-tinal disease;or were unable to comply with study requirements.All experimental procedures were approved by the human ethics committees of the Commonwealth Scientific Industrial Research Organisation and the University of Adelaide,and all subjects provided written informed consent.

Of the 24subjects,11managed their diabetes by diet,11required oral hypoglycemic medication [4with metformin (500mg/d to 3g/d)and gliclazide (1normal 160mg/d and 3slow release 30–60mg/d),1with metformin (2g/d)alone,1gliclazide 60mg/d alone,3with thiazolidinediones (rosiglitazone 8mg,pioglitazone 30–45mg)and metformin (1.5–2g/d),2with glimepiride (1–3mg)and metformin (500mg/d to 3g/d)],and 2required insulin (1with Humalog 30U and Protaphane 30U,the other with Protaphane 28U,gliclazide 90mg,and metformin 850

mg).Other medications included antidepressants,antihyperten-sives,and lipid-lowering medication.Subjects were asked to maintain their usual daily activities and a constant dose and timing of their medication for the duration of the study.Baseline characteristics are shown in Table 1.Measurements

CGMS is a well-recognized tool currently used by health pro-fessionals in type 1diabetes to identify timing and causes of hypoglycemia and hyperglycemic spikes with accuracy similar to that of self-monitoring of blood glucose (SMBG)(25).A Medtronic MiniMed CGMS (Northridge,CA)was used to obtain continuous glucose readings (26).Briefly,it consists of 4com-ponents:a sterile,single use glucose oxidase–based electrode sensor system inserted into interstitial fluid,a pager-sized elec-tronic monitor that records and stores data from the sensor,a cable that connects both the monitor and the sensor,and a com-munication station (Com-Station)that aids in the downloading of data to a personal computer (MEDTRONIC MINIMED software 3.0C program).A senserter,a spring-loaded device,was used to implant the sensor.The sensor obtained a glucose measurement of the extracellular glucose in the range of 2.2–22mmol/L (40–400mg/dL)every 10s,and the monitor stored a smoothed and filtered average of these values in its memory every 5min,yielding 288readings/d.This information was not revealed to the wearer.CGMS values 2.2mmol/L or 22mmol/L were recorded as 2.2or 22mmol/L.Fasting blood glucose (FBG)was recorded at 0530every morning during the 4d while wearing the CGMS monitor and averaged.

The mean of the daily differences (MODD)is a term used to evaluate overall interday glycemic variation when CGMS values were used to evaluate blood glucose concentrations (27).This is the mean of the absolute value of the difference for 2individual blood glucose values initialized from the time of eating,on 2different days,during the 20-h time period.

Resting blood pressure was measured by automated oscillom-etry (model 845XT/XT-IEC;Dinamap,Tampa,FL),with sub-jects in a seated position.Body height was measured to the nearest 0.1cm with the use of a stadiometer (SECA,Hamburg,Germany)with subjects barefoot in the free-standing position.Body weight was measured with subjects wearing light clothing

TABLE 1

Subject characteristics at baseline 1

Male (n ?7)

Female (n ?16)Total (n ?23)Age (y)62.6?9.360.3?10.561.0?10.0Weight (kg)93.6?23.094.9?25.994.5?24.5BMI (kg/m 2)31.5?7.736.1?9.334.7?9.0Hb A 1C (%)8.3?1.48.5?1.78.6?1.6FBG (mmol/L)7.1?1.38.4?3.57.5?2.2SBP (mm Hg)131.8?14.4139.0?11.0136.6?12.3DBP (mm Hg)

76.6?11.6

73.7?9.7

74.7?10.2

1

All values are x ??SD.Age,systolic blood pressure (SBP),diastolic blood pressure (DBP),activity level,and glycated hemoglobin (Hb A 1c )were assessed at screening (2wk before commencing the study).Weight,BMI,and fasting blood glucose (FBG)concentrations were obtained at the week 0visit.For conversion from mmol/L to mg/dL for blood glucose concentrations,multiply by 17.86.Statistics were performed using a one-factor ANOVA.There were no significant difference between sexes.

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with no shoes to the nearest 0.05kg,with the use of calibrated electronic digital scales (AMZ 14;Mercury,Tokyo,Japan).Daily activity levels were recorded,during each intervention,to 5%accuracy,with the use of a pedometer (model HJ-109,Omron Health Care,Tokyo,Japan).

A venous blood sample was collected in a EDTA-coated tube for the measurement of Hb A 1C with the use of HPLC ion ex-change chromatography on a Bio-Rad VARIANT II [Hercules,CA;method certified by the National Glycohemoglobin Stan-dardization Program and secured to the Diabetes Control and Complication Trial (28)at the Institute of Medical and Veteri-nary Science (Adelaide,Australia)].Diet

The study consisted of 4randomized diet treatments in which the ratio of carbohydrate to protein to fat was 40%:34%:26%(7%saturated fat,9%monounsaturated fat,9%polyunsaturated fat).Three-day physical activity diaries (29)were used in conjunction with the Schofield equation (30)to determine individualized energy requirements to maintain energy balance.All treatments contained identical foods and differed only in the way the foods were allocated at each meal (Table 2).For CARB-E,the carbo-hydrates were evenly distributed across the day (breakfast:70.7?2.3g;lunch:68.5?2.3g;dinner:68.2?2.2g);for CARB-B,the carbohydrates were loaded at breakfast (breakfast:128.4?4.2g;lunch:37.9?1.2g;dinner:38.3?1.3g);for CARB-L,the carbohydrates were loaded at lunch (breakfast:40.6?1.3g;lunch:125.4?4.1g;dinner:39.0?1.3g);and,for CARB-D,the carbohydrates were loaded at dinner (breakfast:44.1?1.4g;lunch:40.5?1.3g;dinner:122.7?0.4g).All foods were provided.Each 24-h treatment was repeated for 3consecutive days.The GI of the diet was calculated from international tables (31).GI data from persons with type 2diabetes were used when possible.The glycemic load (GL)of a typical serving of food was calculated as the product of the amount of available carbohydrate and the total GI of the food consumed at a given meal (32)(Table 2).

Experimental design

Subjects followed four,3-d treatment protocols in a random-ized crossover study design.Subjects attended the outpatients’clinic on a Monday afternoon.Subjects were weighed before the CGMS sensor was inserted subcutaneously into their abdominal wall or upper buttocks (a palm width below the waist)with the use of the senserter.The CGMS monitor was initialized and calibrated with the use of capillary SMBG measurements (Me-disense,Optimum;Abbott Laboratories,Abbott Park,IL)before leaving the clinic in accordance with the Medtronic Minimed operating instructions.The subjects consumed food of their choice for the remainder of the day (excluding alcohol)before fasting from 2000.The subjects calibrated the sensor 3additional times with the use of SMBG before retiring to bed.

During the following 3consecutive days,subjects were asked to consume only the provided foods and to calibrate the sensor 4times daily (before breakfast,lunch,and dinner and at retiring)with the use of SMBG.Kitchen scales were provided (DZC 5000A;Procon Technology,Brisbane,Australia).Subjects were free to consume breakfast at anytime they wanted,provided that meals were consumed 6h apart.Each day they recorded the number of steps taken,the time of eating episodes,SGBM re-sults,and other matters that could potentially influence glucose

values.Subjects also completed weighed food diaries that were analyzed with the use of FOODWORKS software (version 4;Xyris Software,Highgate Hill,Australia).The software is based on Australian Food Composition tables and food manufacturers’data.

On Friday morning subjects returned to the clinic to be weighed,to have the CGMS sensor removed,and to have the data downloaded to the computer.Subjects were also interviewed about dietary compliance,activity levels,and adverse events.The process was repeated for 4consecutive weeks.

Even though subjects were instructed to consume their meals 6h apart,the minimum length of time between meals was 5h.Accordingly,the 24-h CGMS trace was divided into 5-h intervals from the time of meal initiation for breakfast,lunch,and evening meals with a 5-h overnight slice beginning 5h after consuming the evening meal (fasting block),representing a total of 20h of blood glucose data during a 24-h period of monitoring.The 3d of monitoring produced 3?20h of blood glucose data used in the analysis.Outcome was assessed by postprandial peak glu-cose (G max ),time spent 12mmol/L (T 12),and total area under the glucose curve (AUC 20).Statistical analysis

All data are presented as means ?SEMs unless otherwise indicated.Statistical analysis was performed with the use of SPSS for WINDOWS 14.0software (SPSS Inc,Chicago,IL)with statistical significance set at an ?level of P 0.05.Dietary compliance data were analyzed with the use of repeated-measures analysis of variance with sex as a between-subject factor.The total 20-h glucose AUC responses were calculated with the use of zero as a baseline,with the trapezoidal rule (33).In the initial analysis of AUC 20,G max ,and T 12,treatment was assessed with the use of a repeated-measures analysis of variance with the use of sex as a between-subject factor.In secondary analysis,FBG was included as a covariate and oral hypoglycemic medication as a factor.

RESULTS

Subjects

With the exception of 1subject who failed to complete treat-ment CARB-L,24subjects completed at least 1d of the remain-ing treatments.Treatments CARB-E,CARB-B,CARB-L,and CARB-D were completed for 3full days by 21,22,19,and 24subjects,respectively.No adverse events were reported.

Overall the minimum mean compliance to the dietary protocol across all 4treatments as assessed by energy intake was 96.9%?0.9%,and carbohydrate intake was 98.9%?0.9%with no sig-nificant difference between treatments or sex (n ?23).The mean daily number of steps was 6117?469with no differences be-tween treatments.No weight change was observed between treatments (data not shown).Glycemic control

FBG concentrations did not differ significantly between days,by treatment,or by time (Figure 1).The MODD value was used to assess interday glycemic variation.The MODD value for treatment CARB-E varied between 1.3and 1.6mmol/L,repre-senting comparisons between days 1and 3,1and 2,and 2and 3.Similarly,the MODD value for treatments CARB-B,CARB-L,

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and CARB-D were 1.3–1.4mmol/L,0.0–1.2mmol/L,and 1.4–1.5mmol/L,respectively.Consequently,because no significant differences within treatment were observed (P 0.05),all avail-able data were used to produce a daily average.

The lowest daily G max values were achieved for CARB-L (14.2?1.0mmol/L)followed by CARB-E (14.5?0.9mmol/L)and CARB-D (14.6?0.8mmol/L)with the highest value for CARB-B (16.5?0.8mmol/L).A significant difference was observed between treatments overall (P ?0.003),with the dif-ference between CARB-B and CARB-E (P ?0.018),CARB-B

and CARB-L (P ?0.002),and CARB-B and CARB-D (P ?0.004)being significant.

In T 12,treatment became significant (P ?0.014)only after adjustment for FBG (P ?0.003)with the lowest values for CARB-L (184?74min)and CARB-B (190?49min)and the highest values for CARB-D (234?87min)and CARB-E (262?91min).With the glucose AUC 20data,treatment alone had no effect,but it became significant after adjusting for FBG (P ?0.006)(Figure 1E).AUC 20for CARB-L (10049?718mmol/L ?20h)was lowest with CARB-E (10493?706mmol/L ?

TABLE 2

Sample menu of foods and carbohydrate distribution during the day for an 8000-kJ diet 1

CARB-E

CARB-B

CARB-L CARB-D Value

Carbohydrate

Value Carbohydrate

Value Carbohydrate

Value Carbohydrate

Breakfast

Mixed-grain bread (g)

108461948243184318Polyunsaturated margarine (g)60408080Spreads (g)230230214060Fruit (g)3——1009————Ham (g)

————10001000Reduced-fat cheese (g)4303——300303Skim milk (g)

302——2001120011Total carbohydrate (g)52—94—30—32—Protein (g)21—22—38—35—Fat (g)10—9—12—12—GL 547—87—10—13—Lunch

Mixed-grain bread (g)

8637——151645018Polyunsaturated margarine (g)60——70120Spreads (g)2————281——Fruit (g)3

1001310012200258010Vegetables (g)6801801801801Ham (g)10001000————Tuna (g)

——————1400Reduced-fat cheese (g)4——303————Skim milk (g)

——23012————Total carbohydrate (g)51—28—91—29—Protein (g)25—35—16—36—Fat (g)10—9—10—15—GL 534—22—74—9—Dinner

Mixed-grain bread (g)

——————10846Polyunsaturated blended oil (g)801006080Spreads (g)2——————341Fruit (g)3

2002210013100921023Vegetable (g)7

2186218621862186Skinless chicken (g)2800280028001200Diet yogurt (g)20011200112001120011Skim milk (g)

20011——302302Total carbohydrate (g)50—30—28—89—Protein (g)81—74—75—53—Fat (g)24—26—22—19—GL 5

16

—

23

—

19

—

61

—

1

CARB-E,carbohydrate was evenly distributed across the day;CARB-B,carbohydrate was loaded at the breakfast meal;CARB-L,carbohydrate was loaded at the lunch meal;CARB-D,carbohydrate was loaded at the evening meal;GL,glycemic load.

2

Low-joule spreads (diet jam,vegemite).3

Apple,pear,or fruit salad.4

Fat 65%.5

Calculated as amount of carbohydrate (g)?glycemic index (32).Glycemic index calculated from tables (31).6

Lettuce and tomato.7

Carrots,beans,or broccoli.

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20h),CARB-B (10603?642mmol/L ?20h),and CARB-D (10717?638mmol/L ?20h)differing from CARB-L by 4%,5%,and 6%,respectively.No statistical difference was observed between the CARB-E,CARB-B,CARB-L,and CARB-D treat-ments when mode of diabetes control (diet,insulin,or other diabetes medication)was included as a between-subject factor for AUC (P ?0.497),G ma x (P ?0.693),or T 12(P ?0.068).Glycemic load

Because the daily AUC 20did not differ significantly across the treatments,it did not matter how the total daily GL was divided across meals.However,this was not the case with T 12and G max ,because both T 12and G max differed significantly across treatments.In the loaded meals,daily G max was equivalent to meal G max .Meal G max was greatest with CARB-B because this had the greatest meal GL,but this was not true when comparing meal GL to CARB-D and CARB-L.Daily T 12did not cor-relate with daily GL.For all 276meals the correlation between carbohydrate amount and G max was 0.40;ie,carbohydrate amount accounted for only 16%of the variance in G max ,whereas the use of GL did not significantly improve the correlation (r ?0.44).

DISCUSSION

The main findings of this study are that a more even distribu-tion of carbohydrates did not provide the most favorable total PPG profile.Lunchtime appeared to be the most favorable time to consume carbohydrates based on G max ,AUC 20,and T 12,but carbohydrate amount and GL at each meal was only weakly related to the G max of that meal,and they accounted for only 16–17%of the variance in G max .

Data from several large epidemiologic (5,34,35)and inter-vention (36)studies in persons with type 2diabetes have empha-sized the importance of mealtime hyperglycemia as the predom-inant factor associated with increased risk of cardiovascular morbidity and mortality.Although G max was higher with CARB-B consistent with the higher GL,the G max was lower for CARB-L compared with CARB-D despite a higher GL in the former.

When the T 12was examined,the lowest mean occurred with CARB-L followed closely by CARB-B,despite its higher peak values.When carbohydrates were loaded in the evening meal,CARB-D,a greater absolute amount of fat,protein,and meal volume at that meal might have led to a more delayed and sustained postprandial peak,the fat slowing the rate of gastric emptying (15),and the protein-induced insulin release may lower the peak (37).The lowest GL value for the loaded meal in CARB-D could not explain the highest T 12values for that arrangement.

The highest value for the T 12occurred with CARB-E;in this arrangement the carbohydrate was evenly distributed across the 3meals,providing 3opportunities for sustained PPG output.Because persons with diabetes have excessive basal glucose pro-duction in the presence of fasting hyperinsulinemia (38)and defective suppression of endogenous glucose production (39),repeated exposure to a carbohydrate load is likely to maintain undesirable but consistently higher concentrations of glucose.Increasing evidence suggests that the postprandial state and indeed the hyperglycemic spikes are a contributing factor to atherosclerosis and the onset of cardiovascular complications.In persons with type 2diabetes,the Diabetes Intervention Study showed that PPG concentrations after breakfast was found to predict myocardial infarction and mortality in patients

with

FIGURE 1.Diurnal glucose values.(A)Carbohydrate was evenly distributed across the day (n ?21),(B)carbohydrate was loaded at the breakfast meal (n ?22),(C)carbohydrate was loaded at the lunch meal (n ?17),(D)carbohydrate was loaded at the evening meal (n ?22).Each meal represents 5h of blood glucose monitoring.(E)CARB-E,carbohydrate was evenly distributed across the day;CARB-B,carbohydrate was loaded at the breakfast meal;CARB-L,carbohydrate was loaded at the lunch meal;CARB-D,carbohydrate was loaded at the evening meal (n ?23for all treatments).For conversion from mmol/L to mg/dL for blood glucose concentrations,multiply by 17.86.Note that each graph represents 720individual blood glucose measurements;hence,it is not practical to present mean ?SEM information.

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newly diagnosed disease (40),whereas the San Luigi Gonzaga Diabetes Study showed the postprandial state after lunch pre-dicted the occurrence of cardiovascular events in a 5-yr follow-up study (41).Because glycemia reached after a 2-h post-glucose load as measured by the oral glucose tolerance test was shown to correlate with the postprandial state after a mixed meal (42),a body of evidence from the DECODE study,the Chicago Heart Study,the Hoorn Study,and the Honolulu Heart study [reviewed in Ceriello (43)]support a relation between increased postprandial glycemia and cardiovascular risk.Hence,strategies to minimize PPG concentrations are vital to reduce diabetic com-plications.

Carbohydrate distribution had little influence on 20-h average glucose (AUC 20).However,when the data were examined fur-ther with the use of the average FBG as a covariate,CARB-L produced a more favorable profile,but the differences from the other treatments were small (4–6%).

Overall,CARB-L resulted in lower AUC 20,G max ,and T 12values;this represented the most favorable time to eat carbohy-drate.On the basis of weight stability,dietary compliance,low but stable activity levels,the provision of all food,and the sim-ilarity between each day of the same treatment,the glucose changes that resulted from consuming the prescribed treatments were considered to be mainly attributed to the distribution of carbohydrates and not other confounding factors (15).Other observations included a trend toward a higher FBG value com-pared with premeal blood glucose values,with lower premeal glucose values throughout the day,as observed by others (10),with values returning to approximately the same values the fol-lowing morning,which is in part explained by circadian rhythm (44)(Figure 1).

Although we expected to see some variability in interday gly-cemia because of subtle differences in timing of exercise,meals,and medication and poor health,the interday glycemic variation expressed as the MODD showed a maximum value of 1.6mmol/L across all 4treatments,essentially little difference in glycemic response across the 3treatment days.This variability is much smaller than the MODD value of 4.3mmol/L observed by others in subjects with type 2diabetes (27)and was due to the tight control and reproducibility of food consumption during the 3d in our study.

Limitations to the study include variability in the data high-lighted by individual differences in maximum glucose response times to a carbohydrate load;the differences in lag times ob-served were up to 105min.This led to discrepancies observed between the maximum glucose concentrations calculated for G max compared with the maximum glucose values resulting from AUC 20.A possible explanation for this may be the highly vari-able rate of gastric emptying observed in subjects with type 2diabetes (45),the different action of medication in 11subjects (sulfonylureas that stimulate the pancreas to produce more insu-lin and biguanides that reduce the amount of glucose produced by the liver)(46),and the amount of glucose absorbed from food along with an insulin-sensitizing effect on muscle tissue through nonoxidative pathways (47).

The strengths of this study include the use of CGMS as a noninvasive diurnal glucose monitoring tool in free-living per-sons.Without the use of the CGMS sensors,it would have been impossible to detect the dynamic changes in blood glucose con-centrations that cannot be detected with intermittent SMBG.The study diet composition of the main meals was similar to that of a

higher carbohydrate diet,which,in addition to 3main meals,incorporated higher carbohydrate snacks (48).The lower carbo-hydrate diet also enabled the greatest variation in carbohydrate distribution to be achieved.It was anticipated that persons with poorly controlled diabetes would be more responsive to carbo-hydrate variability across the day.Meal frequency and energy distribution were selected to reflect what many working persons with diabetes had reported in previous studies conducted by our group (data not published)and other groups (49).Exceptional compliance across all 4treatments we believe was in part due to the provision of all foods and the selection of commonly con-sumed items in the Australian diet.In addition,continuous glu-cose monitoring enabled a detailed glucose profile to be ob-tained.

In conclusion,our results show for this acute study in persons with poorly controlled diabetes that on a 40%carbohydrate di-etary pattern even carbohydrate distribution is not optimal for minimizing PPG peaks.Minimizing carbohydrate at breakfast and shifting it to the lunch meal may provide lower diurnal glucose excursions (AUC 20,T 12,and G max ).Importantly,we consider these studies in support of concept https://www.wendangku.net/doc/0b1060706.html,rger chronic studies involving subjects of different nationalities would be required to determine the applicability of this approach to in the management of type 2diabetes.

We thank the volunteers who made the study possible through their par-ticipation.We also thank Rosemary McArthur and Debbie Davies for their help in the nursing activities,Julia Weaver for assisting in the trial manage-ment,and Allen Gale for aiding in the recruitment.

The author’s responsibilities were as follows—KLP,MN,and PMC:conceived of and designed the study and contributed to data analysis and manuscript writing;KLP,MN,PMC,and JK:contributed to designing the study dietary protocol;KLP:implemented the study including the dietary protocol,collected the data,and wrote the manuscript.None of the authors had a personal or financial conflict of interest.

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基于matlab编程和simulink仿真的AM调制与解调

东北大学秦皇岛分校计算机与通信工程学院 综合课程设计 设计题目 专业名称通信工程 班级学号 学生姓名 指导教师 设计时间2013.12.30~2014.1.15

课程设计任务书 专业：通信工程学号：学生姓名（签名）： 设计题目：基于simulink和matlab编程的AM调制与解调 一、设计实验条件 AM调制与解调实验室 二、设计任务及要求 1.熟悉使用matlab和simulink软件环境及使用方法，包括函数、原理和方法的 应用； 2.熟悉AM信号的调制和解调方法； 3.调制出AM信号的时域波形图和频谱图； 4.定性的分析高斯白噪声对于信号波形的影响； 三、设计报告的内容 1.设计题目与设计任务 AM调制与解调电路的实现及调制性能分析 2.前言 利用matlab中的建模仿真工具Simulink对通信原理实验进行仿真，随着通信技术的发展日新月异，通信系统也日趋复杂，在通信通信系统的设计研发过程中，软件仿真已成为不可缺少的一部分，电子设计自动化EDA技术已成为电子设计的潮流。随着信息技术的不断发展电子EDA仿真技术也在突飞猛进之中，涌现出了许多功能强大的电子仿真软件，如Workbeench、Protel、Systemview、Matlab等。许多知名IT企业其实在产品开发阶段也是应用仿真软件进行开发，虚拟实验技术发展迅速，应用领域广泛，一些在现实世界无法开展的科研项目可借助于虚拟实验技术完成，例如交通网的智能控制，军事上新型武器开发等。 3.设计主体 3.1实验步骤： （1）产生AM调制信号； （2）对信号进行调制，产生调制信号； （3）绘制调制及解调时域图、频谱图； （4）改变采样频率后，绘制调制及解调信号的时域图、频谱图； （5）加上高斯噪声，绘制调制及解调的时域图和频谱图，分析噪声对调制信号和解调信号的影响。

DMP3200系列保护测控装置使用说明书

DMP3200系列保护测控装置使用说明书 南京磐能电力科技股份有限公司Nanjing PANENG Technology Development Co.,Ltd. 中国2南京

目录 简述： (1) 1 技术特点 (1) 1.1主要特点 (1) 1.2保护测控功能 (2) 1.3人机交互 (2) 1.4运行监视 (2) 1.5通信 (2) 1.6安装 (3) 1.7现场免维护 (3) 1.8独特性设计 (3) 2 系统结构图 (4) 3 主要技术参数 (4) 3.1额定参数 (4) 3.2技术性能 (5) 3.3定值误差 (5) 3.4整组动作时间及误差 (5) 3.5测量系统 (5) 3.6环境要求 (5) 3.7绝缘性能 (6) 3.8机械性能 (6) 3.9电磁兼容性能 (6) 4 硬件配置 (6) 4.1机箱结构 (6) 4.2电源模件 (7) 4.3交流模件 (7) 4.4 CPU模件 (7) 4.5操作板模件 (8)

4.6开入开出模件 (8) 4.7背板模件 (8) 4.8液晶显示模件 (8) 5 使用说明 (9) 5.1 装置介绍 (9) 5.2 菜单操作 (10) 5.3 实时信息功能说明 (17) 5.4 保护功能说明 (19) 5.5 远动功能说明 (19) 5.6 通信功能说明 (20) 5.6 口令功能说明 (20) 5.7 装置接地说明 (20) 6 用户安装调试说明 (21) 7 订货须知 (24) 8 储存及保修 (24) 8.1存储条件 (24) 8.2保修 (24) ii

DMP3200变电站综合自动化系统 简述： DMP3200系列成套微机保护测控装置是用高性能32位单片机，根据“一个设备（一个间隔）一个装置”的原则开发出来的保护测控一体化装置。装置密封性好、抗干扰、抗震动能力强，可满足35kV及以下电压等级各设备的保护和监控需求，为35kV及以下电压等级小型变电站、10kV开闭所、办公设施等电力客户提供最优性能价格比的成套保护测控装置。可安装在开关柜上，也可集中组屏。 1 技术特点 1.1主要特点 采用32位高端单片机、高精度AD的硬件平台，具有运算速度快，采样精度高的特点； 保护、监控一体化，使保护和监控之间做到“无缝连接”，集成化程度高，简化整个系统接线，方便了设计和现场施工，提高了整个系统的可靠性； 保护和监控具有各自独立的交流采样回路，既保证了检测精度，又保证了保护所要求的抗饱和性能； 完善的事件报告处理功能，可保存最新32次故障事件，最新32次预告事件，最新32次变位信息，最新32次操作记录报告，最新32次自检信息，5种报告的综合分析，便于复现故障的全部过程，便于监视对装置的各种操作； 各种保护跳闸逻辑出口可灵活组态配置，便于现场的调试； 各种保护投入的硬控制压板可灵活组态配置，便于现场的运行； 可以本地或远方整定定值，可以存储8套定值； 操作回路符合反措要求； 采用硬件实时时钟，掉电后仍连续计时； 1

AM系统仿真.

******************* 实践教学 ******************* 大学 计算机与通信学院 2014年秋季学期 通信原理课程设计 题目： AM调制系统仿真 专业班级：通信工程 姓名： 学号： 指导教师： 成绩： 摘要

这次的课程设计我们组主要运用MATLAB设计AM调制解调系统仿真。在这次课程设计中先根据AM调制与解调原理编写调制解调程序，然后设计FIR低通滤波器，合理设置参数并运行，并通过不断的修改优化得到需要信号，之后分别加入高斯白噪声，并分析对信号的影响，最后通过对解调信号的波形图、频谱图和功率谱的分析得出AM调制解调系统仿真是否成功。 关键词:AM；调制；解调；噪声；滤波 目录

前言 (1) 第一章基本原理 (2) 2.1 AM调制解调原理 (2) 2.2高斯白噪声原理 (4) 2.3 Matlab基本原理 (6) 第二章FTR滤波器的设计 (6) 2.1 FIR数字低通滤波器的设计 (6) 第三章基于Matlab的AM调制系统仿真 (8) 3.1 载波信号的仿真 (8) 3.2 AM调制信号的仿真 (9) 3.3 AM已调信号的信号仿真 (10) 3.4 AM解调信号的仿真 (11) 总结 (14) 致谢 (15) 参考文献 (16) 附录一 (17) 附录二 (20)

前言 调制就是使一个信号（如光、高频电磁振荡等）的某些参数（如振幅、频率等）按照另一个欲传输的信号（如声音、图像等）的特点变化的过程。用所要传播的语言或音乐信号去改变高频振荡的幅度，使高频振荡的幅度随语言或音乐信号的变化而变化，这个控制过程就称为调制。其中语言或音乐信号叫做调制信号，调制后的载波就载有调制信号所包含的信息，称为已调波。 解调是调制的逆过程，它的作用是从已调波信号中取出原来的调制信号。对于幅度调制来说，解调是从它的幅度变化提取调制信号的过程。对于频率调制来说，解调是从它的频率变化提取调制信号的过程。频率解调要比幅度解调复杂，用普通检波电路是无法解调出调制信号的，必须采用频率检波方式，如各类鉴频器电路。关于鉴频器电路可参阅有关资料，这里不再细述。 本课题利用MATLAB软件对AM信号调制解调系统进行模拟仿真，分别对余弦波进行调制，观察调制信号、已调信号和解调信号的波形和频谱分布。 调制与解调技术是通信电子线路课程中一个重要的环节，也是实现通信必不可少的一门技术，也是通信专业学生必须掌握的一门技术。课题在这里是把要处理的信号当做一种特殊的信号，即一种“复杂向量”来看待。也就是说，课题更多的还是体现了数字信号处理技术。 从课题的中心来看，课题“AM调制系统仿真”是希望将AM调制与解调技术应用于某一实际领域，这里就是指对信号进行调制。作为存储于计算机中的调制信号，其本身就是离散化了的向量，我们只需将这些离散的量提取出来，就可以对其进行处理了。这一过程的实现，用到了处理数字信号的强有力工具MATLAB。通过MATLAB里几个命令函数的调用，很轻易的在调制信号与载波信号的理论之间搭了一座桥。

PSL621U系列线路保护装置

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摘要 通信按照传统的理解就是信息的传输。在当今高度信息化的社会，信息和通信已经成为现代社会的“命脉”。信息作为一种资源，只有通过广泛的传播与交流，才能产生利用价值，促进社会成员之间的合作，推动社会生产力的发展，创造出巨大的经济效益。而通信作为传输信息的手段或方式，与传感技术、计算机技术相互融合，已成为21世纪国际社会和世界经济发展的强大动力。可以预见，未来的通信对人们的生活方式和社会的发展将会产生更加重大和意义深远的影响。 在此我们将分别介绍各种调制系统，并将重点放在发展迅猛的数字调制上。调制在通信系统中的作用至关重要。所谓调制就是把信号转换成适合在信道中传输的形式的一种过程。调制的方式有很多。根据调制信号时模拟信号还是数字信号，载波是连续波还是脉冲序列，相应的调制方式有模拟连续波调制、数字连续波调制、模拟脉冲调制和数字脉冲调制等。 关键字：模拟调制系统、调制解调、超外差、仿真

目录 引言 (4) 1. 通信系统简介 (5) 1. 1 通信的基本概念 (5) 1. 2 通信的发展史 (5) 1.3 通信系统的组成 (5) 1.4 通信系统的分类 (6) 2. AM调制原理 (6) 2. 1 基本概念 (6) 2．2 AM调制的SystemView仿真 (7) 2．3 仿真模型参数 (10) 2．3．1正弦波发生器 (11) 2.3.2运放 (11) 2.3.3噪声源 (11) 2.3.4低通滤波器 (11) 3. 结语 (12) 参考文献： (13)

PSU系列微机综合保护装置校验标准规范

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3.4继电保护和自动装置技术规程GB/T 14285—2006 3.5微机继电保护装置运行管理规程DL/T 587—1996 3.6继电保护及电网安全自动装置检验规程DLT995-2006 3.7电力系统继电保护及安全自动装置运行评价规程DL/T 623—1997 3.8火力发电厂、变电所二次接线设计技术规定NDGJ 8-89 四、试验设备及接线的基本要求 4.1试验仪器应检验合格，其精度不低于0.5级。 4.2试验回路接线原则，应使加入保护装置的电气量与实际情况相符。应具备对保护装置的整组试验的条件。 4.3试验设备：继电保护测试仪。 五、试验条件和要求注意事项 5.1交、直流试验电源质量和接线方式等要求参照《继电保护及电网安全自动装置检验规程》有关规定执行。 5.2试验时如无特殊说明，所加直流电源均为额定值。 5.3加入装置的的试验电压和电流均指从就地开关柜二次端子上加入。 5.4试验前应检查屏柜及装置接线端子是否有螺丝松动。 5.5试验中，一般不要插拨装置插件,不触摸插件电路,需插拨时,必须关闭电源。 5.6使用的试验仪器必须与屏柜可靠接地。 5.7为保证检验质量，对所有特性试验中的每一点，应重复试验三次，其中每次试验的数据与整定值的误差要求<5%，保护逻辑符合设计要求。 5.8采样误差小于5％。

信号的相位调制与解调概要

MATLAB仿真信号的相位调制与解调 专业：通信与信息系统 姓名：赵* 学号:********* 指导老师：****教授

摘要 Psk调制是通信系统中最为重要的环节之一，Psk调制技术的改进也是通信系统性能提高的重要途径。本文首先分析了数字调制系统的基本调制解调方法，然后，运用Matlab及附带的图形仿真工具——Simulink设计了这几种数字调制方法的仿真模型。通过仿真，观察了调制解调过程中各环节时域和频域的波形，并结合这几种调制方法的调制原理，跟踪分析了各个环节对调制性能的影响及仿真模型的可靠性。最后，在仿真的基础上分析比较了各种调制方法的性能，并通过比较仿真模型与理论计算的性能，证明了仿真模型的可行性。另外，本文还利用Matlab的图形用户界面（GUI）功能为仿真系统设计了一个便于操作的人机交互界面，使仿真系统更加完整，操作更加方便。 关键词：数字调制；分析与仿真；Matlab；Simulink；PSK；QPSK；

1.数字调制技术 (2) 2.PSK调制系统 (3) 2.1 QPSK调制部分，原理框图如图七所示 (6) 2.2 QPSK解调部分，原理框图如图八所示： (8) 3.用Simulink实现PSK调制 (9) 3.1 2PSK仿真 (9) 3.1.1调制 (9) 3.1.2 解调仿真 (12) 3.2 QPSK仿真 (13) 3.2.1 QPSK调制框图 (13) 参考文献 (18)

1.数字调制技术 通信按照传统的理解就是信息的传输与交换。在当今信息社会，通信则与遥感，计算技术紧密结合，成为整个社会的高级“神经中枢”。没有通信，人类社会是不可想象的。一般来说，社会生产力水平要求社会通信水平与之相适应。若通信水平跟不上，社会成员之间的合作程度就受到限制。可见，通信是十分重要的。 通信传输的消息是多种多样的，可以是符号的，文字的，数据和图像的等等。各种不同的消息可以分为两类：一类称为离散消息；另一类称为连续消息。离散消息的状态是可数的或离散的，比如符号，文字或数据等。离散消息也称数字消息。而连续消息则是其状态连续变化的消息，例如，连续变化的语音，图像等。连续消息也称模拟消息。因此按照信道中传输的是模拟信号还是数字信号可以将通信系统分为模拟通信系统和数字通信系统。 数字通信有以下突出的特点：第一，数字信号传输时，信道噪声或干扰所造成的差错，原则上是可以控制的。第二，当需要保密的时候，可以有效的对基带信号进行人为的“扰乱”，即加上密码。 数字通信系统可以用下图表示： →→→→→→→→信数信信数信 信源 道 字受道源字信 息编编调 解译译信 源 码码调码码者 制 道 器 器 器 器 器 器 图一 数字通信在近20年来得到了迅速的发展，其原因是： （1） 抗干扰能力强 （2） 便于进行各种数字信号处理 （3） 易于实现集成化 （4） 经济效益正赶上或超过模拟通信 （5） 传输与交换可结合起来，传输电话与传输数据也可结合起来，成为一个 统一整体，有利于实现综合业务通信网。

AM调制与解调

课程设计 班级： 姓名： 学号： 指导教师： 成绩： 电子与信息工程学院 信息与通信工程系

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目录 1 MC1496芯片设计 (2) 1.1MC1496内部结构及基本性能 (2) 2 信号调制的一般方法 (3) 2.1模拟调制 (3) 2.2数字调制 (3) 2.3脉冲调制 (3) 3 振幅调制 (4) 3.1基本原理 (4) 3.2AM调制与仿真实现 (4) 3.3DSB调制与仿真实现 (6) 4解调 (7) 4.1同步检波器原理框图 (7) 4.2同步检波解调电路图 (9) 4.3分析解调过程 (9) 4.4解调仿真结果 (10) 4.4.1 AM解调与仿真实现 (10) 4.4.2 DSB解调与仿真实现 (11) 5 小结与体会 (12) 6附录：总电路图 (12)

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指导教师：王宝珠日期：2014.12.22-1.4

目录 1 课程设计的目的、意义 (3) 2 课题任务 (3) 3 设计思路与方案 (4) 4 设计内容、步骤及要求 (4) 5 设计步骤及结果分析 (4) 5.1 必做部分 (6) 5.1.1 Matlab程序及运行结果 (6) 1.普通AM调制与解调 (6) 2.抑制双边带调制与解调 (10) 3.单边带调制与解调 (14) 5.1.2 Simulink仿真及运行结果 (16) 1.普通AM调制与解调 (16) 1.1 单音普通调制解调 (16) 1.2 复音普通调制解调 (18) 2.抑制双边带调制解调 (20) 2.1 单音双边带调制解调 (20) 2.2 复音抑制双边带调制解调 (21) 3.单边带调制解调 (22) 3.1 单音单边带调制解调 (22) 3.2 复音单边带调制解调 (24) 5.2 拓展部分 (26) 5.2.1 单音普通AM调制解调 (26) 5.2.2单音抑制双边带调制解调 (27) 5.2.3 单音单边带调制解调 (27) 6 总结 (29) 7 参考文献 (30) 8 意见、建议 (31)

摘要： 本课程设计主要利用MATLAB集成环境下的Simulink仿真平台及Labview虚拟仪器仿真研究AM 调制与解调模拟系统的理论设计和软件仿真方法。从而实现单音调制的普通调幅方式（AM）、抑制载波的双边带调制（DSB-SC）和单边带调制（SSB）的系统设计及仿真，并显示仿真结果，根据仿真显示结果分析所设计的系统性能。在课程设计中，幅度调制是用调制信号去控制高频载波的振幅，使其按调制信号的规律变化，其它参数不变。同时也是使高频载波的振幅载有传输信息的调制方式。 关键词：Simulink,GUI友好界面，调制与解调，Labview 1、本课题的目的与意义 1.1 目的： 本课程设计课题主要研究AM 调制与解调模拟系统的理论设计和软件仿真方法。通过完成本课题的设计，拟主要达到以下几个目的： 1．掌握模拟系统AM 调制与解调的原理。 2．掌握AM 调制与解调模拟系统的理论设计方法； 3．掌握应用MATLAB分析系统时域、频域特性的方法，进一步锻炼应用Matlab进行编程仿真的能力； 4．熟悉基于Simulink的动态建模和仿真的步骤和过程； 5．了解基于LabVIEW虚拟仪器的特点和使用方法，熟悉采用LabVIEW进行仿真的方法。 1.2 意义： 通过本次课程设计使我们了解了幅度调制与解调的基本原理。在进行了专业基础知识课程教学的基础上，设计分析一些简单的仿真系统，有助于加深对所学知识的巩固和理解。2、课题任务 设计AM调制与解调模拟系统，仿真实现相关功能。包括:可实现单音调制的普通调幅方式（AM）、抑制载波的双边带调制（DSB-SC）和单边带调制（SSB）的系统设计及仿真，要求给出系统的设计框图、源程序代码及仿真结果，并要求给出程序的具体解释说明，记录系统的各个输出点的波形和频谱图。具体内容为： 1）设计实现AM(包括普通AM、DSB-SC和SSB)调制与解调的模拟系统，给出系统的原理框图,对系统的主要参数进行设计说明。 2）采用Matlab语言设计相关程序,实现1）中所设计模拟系统的功能，要求采用两种方式进行仿真，即直接采用Matlab语言编程的静态仿真方式、采用Simulink进行动态建模和仿真的方式。要求采用两种以上调制信号源（如正弦波、三角波和方波）进行仿真，并记录系统的各个输出点的波形和频谱图。 3)设计图形用户界面。采用Matlab语言，利用GUI设计友好的图形用户界面，完成AM调制与解调的功能。 4)采用LabVIEW进行仿真设计,实现系统的功能,要求给出系统的前面板和框图，采用两种以上调制信号源（如正弦波、三角波和方波）进行仿真，并记录仿真结果。 5)要求分析上述三种实现方式（直接采用Matlab语言编程的静态仿真方式、采用Simulink 进行动态建模和仿真的方式和采用LabVIEW进行仿真设计）进行对比分析，并与理论设计结果进行比较分析。 6)对系统功能进行综合测试，整理数据，撰写设计报告。

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AM及SSB调制与解调详解

通信原理课程设计 设计题目：AM 及SSB 调制与解调及抗噪声性能分析 班级：学生姓名：学生学号：指导老师： 目录 一、引言 (3) 1.1概述 (3)

1.2课程设计的目的 (3) 1.3课程设计的要求 (3) 二、A M调制与解调及抗噪声性能分析 (4) 2.1AM 调制与解调 (4) 2.1.1AM 调制与解调原理 (4) 2.1.2调试过程 (6) 2.2相干解调的抗噪声性能分析 (9) 2.2.1 抗噪声性能分析原理 (9) 2.2.2调试过程 (10) 三、S SB调制与解调及抗噪声性能分析 (12) 3.1 SSB 调制与解调原理 (12) 3.2SSB 调制解调系统抗噪声性能分析 (13) 3.3调试过程 (15) 四、心得体会 (19) 五、参考文献 (19)

一、引言 1.1概述 《通信原理》是通信工程专业的一门极为重要的专业基础课，但内容抽象，基本概念较多，是一门难度较大的课程，通过MATLAB仿真能让我们更清晰地理解它的原理，因此信号的调制与解调在通信系统中具有重要的作用。本课程设计是AM及SSB 调制解调系统的设计与仿真，用于实现AM及 SSB 信号的调制解调过程，并显示仿真结果，根据仿真显示结果分析所设计的系统性能。在课程设计中，幅度调制是用调制信号去控制高频载波的振幅，使其按调制信号的规律变化，其他参数不变。同时也是使高频载波的振幅载有传输信息的调制方式。 1.2课程设计的目的 在此次课程设计中，我需要通过多方搜集资料与分析： (1)掌握模拟系统AM和SSB调制与解调的原理； (2)来理解并掌握AM和SSB调制解调的具体过程和它在MATLAB中的实现方法； (3)掌握应用MATLAB分析系统时域、频域特性的方法，进一步锻炼应用MATLAB进行编 程 仿真的能力。 通过这个课程设计，我将更清晰地了解AM和SSB的调制解调原理，同时加深对MATLAB 这 款《通信原理》辅助教学操作的熟练度。 1.3课程设计的要求 (1)熟悉MATLAB的使用方法，掌握AM信号的调制解调原理，以此为基础用MATLAB编程 实现信号的调制解调； (2)设计实现AM调制与解调的模拟系统，给出系统的原理框图，对系统的主要参数 进行设计说明； (3)采用MATLAB语言设计相关程序，实现系统的功能，要求采用一种方式进行仿真，即 直接采用MATLAB语言编程的静态方式。要求采用两种以上调制信号源进行仿真，并记录各个输出点的波形和频谱图； (4)对系统功能进行综合测试，整理数据，撰写课程设计论文。